{
// 获取包含Hugging Face文本的span元素
const spans = link.querySelectorAll('span.whitespace-nowrap, span.hidden.whitespace-nowrap');
spans.forEach(span => {
if (span.textContent && span.textContent.trim().match(/Hugging\s*Face/i)) {
span.textContent = 'AI快站';
}
});
});
// 替换logo图片的alt属性
document.querySelectorAll('img[alt*="Hugging"], img[alt*="Face"]').forEach(img => {
if (img.alt.match(/Hugging\s*Face/i)) {
img.alt = 'AI快站 logo';
}
});
}
// 替换导航栏中的链接
function replaceNavigationLinks() {
// 已替换标记，防止重复运行
if (window._navLinksReplaced) {
return;
}
// 已经替换过的链接集合，防止重复替换
const replacedLinks = new Set();
// 只在导航栏区域查找和替换链接
const headerArea = document.querySelector('header') || document.querySelector('nav');
if (!headerArea) {
return;
}
// 在导航区域内查找链接
const navLinks = headerArea.querySelectorAll('a');
navLinks.forEach(link => {
// 如果已经替换过，跳过
if (replacedLinks.has(link)) return;
const linkText = link.textContent.trim();
const linkHref = link.getAttribute('href') || '';
// 替换Spaces链接 - 仅替换一次
if (
(linkHref.includes('/spaces') || linkHref === '/spaces' ||
linkText === 'Spaces' || linkText.match(/^s*Spacess*$/i)) &&
linkText !== 'PDF TO Markdown' &&
linkText !== 'PDF TO Markdown'
) {
link.textContent = 'PDF TO Markdown';
link.href = 'https://fast360.xyz';
link.setAttribute('target', '_blank');
link.setAttribute('rel', 'noopener noreferrer');
replacedLinks.add(link);
}
// 删除Posts链接
else if (
(linkHref.includes('/posts') || linkHref === '/posts' ||
linkText === 'Posts' || linkText.match(/^s*Postss*$/i))
) {
if (link.parentNode) {
link.parentNode.removeChild(link);
}
replacedLinks.add(link);
}
// 替换Docs链接 - 仅替换一次
else if (
(linkHref.includes('/docs') || linkHref === '/docs' ||
linkText === 'Docs' || linkText.match(/^s*Docss*$/i)) &&
linkText !== 'Voice Cloning'
) {
link.textContent = 'Voice Cloning';
link.href = 'https://vibevoice.info/';
replacedLinks.add(link);
}
// 删除Enterprise链接
else if (
(linkHref.includes('/enterprise') || linkHref === '/enterprise' ||
linkText === 'Enterprise' || linkText.match(/^s*Enterprises*$/i))
) {
if (link.parentNode) {
link.parentNode.removeChild(link);
}
replacedLinks.add(link);
}
});
// 查找可能嵌套的Spaces和Posts文本
const textNodes = [];
function findTextNodes(element) {
if (element.nodeType === Node.TEXT_NODE) {
const text = element.textContent.trim();
if (text === 'Spaces' || text === 'Posts' || text === 'Enterprise') {
textNodes.push(element);
}
} else {
for (const child of element.childNodes) {
findTextNodes(child);
}
}
}
// 只在导航区域内查找文本节点
findTextNodes(headerArea);
// 替换找到的文本节点
textNodes.forEach(node => {
const text = node.textContent.trim();
if (text === 'Spaces') {
node.textContent = node.textContent.replace(/Spaces/g, 'PDF TO Markdown');
} else if (text === 'Posts') {
// 删除Posts文本节点
if (node.parentNode) {
node.parentNode.removeChild(node);
}
} else if (text === 'Enterprise') {
// 删除Enterprise文本节点
if (node.parentNode) {
node.parentNode.removeChild(node);
}
}
});
// 标记已替换完成
window._navLinksReplaced = true;
}
// 替换代码区域中的域名
function replaceCodeDomains() {
// 特别处理span.hljs-string和span.njs-string元素
document.querySelectorAll('span.hljs-string, span.njs-string, span[class*="hljs-string"], span[class*="njs-string"]').forEach(span => {
if (span.textContent && span.textContent.includes('huggingface.co')) {
span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 替换hljs-string类的span中的域名（移除多余的转义符号）
document.querySelectorAll('span.hljs-string, span[class*="hljs-string"]').forEach(span => {
if (span.textContent && span.textContent.includes('huggingface.co')) {
span.textContent = span.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 替换pre和code标签中包含git clone命令的域名
document.querySelectorAll('pre, code').forEach(element => {
if (element.textContent && element.textContent.includes('git clone')) {
const text = element.innerHTML;
if (text.includes('huggingface.co')) {
element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
// 处理特定的命令行示例
document.querySelectorAll('pre, code').forEach(element => {
const text = element.innerHTML;
if (text.includes('huggingface.co')) {
// 针对git clone命令的专门处理
if (text.includes('git clone') || text.includes('GIT_LFS_SKIP_SMUDGE=1')) {
element.innerHTML = text.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
// 特别处理模型下载页面上的代码片段
document.querySelectorAll('.flex.border-t, .svelte_hydrator, .inline-block').forEach(container => {
const content = container.innerHTML;
if (content && content.includes('huggingface.co')) {
container.innerHTML = content.replace(/huggingface.co/g, 'aifasthub.com');
}
});
// 特别处理模型仓库克隆对话框中的代码片段
try {
// 查找包含"Clone this model repository"标题的对话框
const cloneDialog = document.querySelector('.svelte_hydration_boundary, [data-target="MainHeader"]');
if (cloneDialog) {
// 查找对话框中所有的代码片段和命令示例
const codeElements = cloneDialog.querySelectorAll('pre, code, span');
codeElements.forEach(element => {
if (element.textContent && element.textContent.includes('huggingface.co')) {
if (element.innerHTML.includes('huggingface.co')) {
element.innerHTML = element.innerHTML.replace(/huggingface.co/g, 'aifasthub.com');
} else {
element.textContent = element.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
}
// 更精确地定位克隆命令中的域名
document.querySelectorAll('[data-target]').forEach(container => {
const codeBlocks = container.querySelectorAll('pre, code, span.hljs-string');
codeBlocks.forEach(block => {
if (block.textContent && block.textContent.includes('huggingface.co')) {
if (block.innerHTML.includes('huggingface.co')) {
block.innerHTML = block.innerHTML.replace(/huggingface.co/g, 'aifasthub.com');
} else {
block.textContent = block.textContent.replace(/huggingface.co/g, 'aifasthub.com');
}
}
});
});
} catch (e) {
// 错误处理但不打印日志
}
}
// 当DOM加载完成后执行替换
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', () => {
replaceHeaderBranding();
replaceNavigationLinks();
replaceCodeDomains();
// 只在必要时执行替换 - 3秒后再次检查
setTimeout(() => {
if (!window._navLinksReplaced) {
console.log('[Client] 3秒后重新检查导航链接');
replaceNavigationLinks();
}
}, 3000);
});
} else {
replaceHeaderBranding();
replaceNavigationLinks();
replaceCodeDomains();
// 只在必要时执行替换 - 3秒后再次检查
setTimeout(() => {
if (!window._navLinksReplaced) {
console.log('[Client] 3秒后重新检查导航链接');
replaceNavigationLinks();
}
}, 3000);
}
// 增加一个MutationObserver来处理可能的动态元素加载
const observer = new MutationObserver(mutations => {
// 检查是否导航区域有变化
const hasNavChanges = mutations.some(mutation => {
// 检查是否存在header或nav元素变化
return Array.from(mutation.addedNodes).some(node => {
if (node.nodeType === Node.ELEMENT_NODE) {
// 检查是否是导航元素或其子元素
if (node.tagName === 'HEADER' || node.tagName === 'NAV' ||
node.querySelector('header, nav')) {
return true;
}
// 检查是否在导航元素内部
let parent = node.parentElement;
while (parent) {
if (parent.tagName === 'HEADER' || parent.tagName === 'NAV') {
return true;
}
parent = parent.parentElement;
}
}
return false;
});
});
// 只在导航区域有变化时执行替换
if (hasNavChanges) {
// 重置替换状态，允许再次替换
window._navLinksReplaced = false;
replaceHeaderBranding();
replaceNavigationLinks();
}
});
// 开始观察document.body的变化，包括子节点
if (document.body) {
observer.observe(document.body, { childList: true, subtree: true });
} else {
document.addEventListener('DOMContentLoaded', () => {
observer.observe(document.body, { childList: true, subtree: true });
});
}
})();
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'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
SCREAMING_SNAKE_CASE = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self : str , UpperCAmelCase : str , UpperCAmelCase : bool , UpperCAmelCase : str = None , UpperCAmelCase : list = None ) -> Optional[int]:
'''simple docstring'''
lowercase : Optional[Any] =None
lowercase : Any =os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
lowercase : List[str] =os.path.abspath('''examples''' )
for item in os.listdir(UpperCAmelCase ):
if item not in EXCLUDE_EXAMPLES:
lowercase : Union[str, Any] =os.path.join(UpperCAmelCase , UpperCAmelCase )
if os.path.isfile(UpperCAmelCase ) and ".py" in item_path:
with self.subTest(
tested_script=UpperCAmelCase , feature_script=UpperCAmelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
lowercase : List[Any] =compare_against_test(
os.path.join(UpperCAmelCase , UpperCAmelCase ) , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
lowercase : Optional[int] ='''\n'''.join(UpperCAmelCase )
if special_strings is not None:
for string in special_strings:
lowercase : Optional[Any] =diff.replace(UpperCAmelCase , '''''' )
self.assertEqual(UpperCAmelCase , '''''' )
def A__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
self.one_complete_example('''complete_nlp_example.py''' , UpperCAmelCase )
self.one_complete_example('''complete_nlp_example.py''' , UpperCAmelCase )
def A__ ( self : str ) -> Any:
'''simple docstring'''
lowercase : str =os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
lowercase : str =[
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
self.one_complete_example('''complete_cv_example.py''' , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = False
@classmethod
def A__ ( cls : Optional[Any] ) -> List[Any]:
'''simple docstring'''
super().setUpClass()
lowercase : List[str] =tempfile.mkdtemp()
lowercase : Union[str, Any] =os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
lowercase : Any =['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def A__ ( cls : Optional[Any] ) -> Tuple:
'''simple docstring'''
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def A__ ( self : List[str] ) -> Tuple:
'''simple docstring'''
lowercase : Dict =f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def A__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Dict =f'\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n '.split()
lowercase : str =run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def A__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
lowercase : Optional[int] =f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}\n '.split()
lowercase : Any =run_command(self._launch_args + testargs , return_stdout=UpperCAmelCase )
self.assertNotIn('''epoch 0:''' , UpperCAmelCase )
self.assertIn('''epoch 1:''' , UpperCAmelCase )
def A__ ( self : str ) -> List[Any]:
'''simple docstring'''
lowercase : List[str] =f'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}\n '.split()
lowercase : Tuple =run_command(self._launch_args + testargs , return_stdout=UpperCAmelCase )
if torch.cuda.is_available():
lowercase : List[Any] =torch.cuda.device_count()
else:
lowercase : Dict =1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , UpperCAmelCase )
self.assertIn('''epoch 1:''' , UpperCAmelCase )
else:
self.assertIn('''epoch 0:''' , UpperCAmelCase )
self.assertIn('''epoch 1:''' , UpperCAmelCase )
@slow
def A__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
lowercase : List[Any] ='''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
lowercase : List[str] =run_command(self._launch_args + testargs , return_stdout=UpperCAmelCase )
lowercase : List[Any] =re.findall('''({.+})''' , UpperCAmelCase )
lowercase : Tuple =[r for r in results if '''accuracy''' in r][-1]
lowercase : Union[str, Any] =ast.literal_eval(UpperCAmelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def A__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Any =['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def A__ ( self : int ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
lowercase : Tuple =f'\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase , '''tracking''' ) ) )
def A__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowercase : Optional[Any] =['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def A__ ( self : Dict ) -> Tuple:
'''simple docstring'''
lowercase : List[str] =['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 94 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( __A : dict , __A : str , __A : set , __A : set , __A : dict , __A : dict , __A : PriorityQueue , __A : dict , __A : float | int , ) -> float | int:
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
lowercase : str =cst_fwd.get(__A , np.inf )
lowercase : str =cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
lowercase : int =new_cost_f
lowercase : Optional[int] =v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
lowercase : List[Any] =cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowercase_ ( __A : str , __A : str , __A : dict , __A : dict ) -> int:
"""simple docstring"""
lowercase : Optional[Any] =-1
lowercase : Tuple =set()
lowercase : str =set()
lowercase : List[str] ={source: 0}
lowercase : List[str] ={destination: 0}
lowercase : Tuple ={source: None}
lowercase : List[Any] ={destination: None}
lowercase : PriorityQueue[Any] =PriorityQueue()
lowercase : PriorityQueue[Any] =PriorityQueue()
lowercase : Any =np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
lowercase , lowercase : int =queue_forward.get()
visited_forward.add(__A )
lowercase , lowercase : int =queue_backward.get()
visited_backward.add(__A )
lowercase : Dict =pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
lowercase : List[Any] =pass_and_relaxation(
__A , __A , __A , __A , __A , __A , __A , __A , __A , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
lowercase : Union[str, Any] =shortest_distance
return shortest_path_distance
SCREAMING_SNAKE_CASE = {
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE = {
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 94 | 1 |
"""simple docstring"""
import re
def lowerCamelCase_ ( UpperCAmelCase_ ) ->bool:
"""simple docstring"""
__UpperCAmelCase : str = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(_lowerCamelCase , _lowerCamelCase ) )
if __name__ == "__main__":
lowercase__ :List[str] = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 706 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowercase__ :int = TypeVar('T')
class snake_case ( Generic[T] ):
'''simple docstring'''
def __init__( self : Optional[Any] , __lowercase : list[T] , __lowercase : Callable[[T, T], T] ):
'''simple docstring'''
__UpperCAmelCase : Any | T = None
__UpperCAmelCase : int = len(__lowercase )
__UpperCAmelCase : list[T] = [any_type for _ in range(self.N )] + arr
__UpperCAmelCase : List[Any] = fnc
self.build()
def A_ ( self : str ):
'''simple docstring'''
for p in range(self.N - 1 , 0 , -1 ):
__UpperCAmelCase : Any = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def A_ ( self : Union[str, Any] , __lowercase : int , __lowercase : T ):
'''simple docstring'''
p += self.N
__UpperCAmelCase : Tuple = v
while p > 1:
__UpperCAmelCase : Union[str, Any] = p // 2
__UpperCAmelCase : Any = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def A_ ( self : str , __lowercase : int , __lowercase : int ): # noqa: E741
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : int = l + self.N, r + self.N
__UpperCAmelCase : T | None = None
while l <= r:
if l % 2 == 1:
__UpperCAmelCase : Any = self.st[l] if res is None else self.fn(__lowercase , self.st[l] )
if r % 2 == 0:
__UpperCAmelCase : Any = self.st[r] if res is None else self.fn(__lowercase , self.st[r] )
__UpperCAmelCase , __UpperCAmelCase : Any = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
lowercase__ :Any = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
lowercase__ :List[Any] = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
lowercase__ :List[str] = SegmentTree(test_array, min)
lowercase__ :List[str] = SegmentTree(test_array, max)
lowercase__ :Tuple = SegmentTree(test_array, lambda a, b: a + b)
def lowerCamelCase_ ( ) ->None:
"""simple docstring"""
for i in range(len(UpperCAmelCase_ ) ):
for j in range(UpperCAmelCase_ , len(UpperCAmelCase_ ) ):
__UpperCAmelCase : Optional[Any] = reduce(UpperCAmelCase_ , test_array[i : j + 1] )
__UpperCAmelCase : Optional[int] = reduce(UpperCAmelCase_ , test_array[i : j + 1] )
__UpperCAmelCase : Union[str, Any] = reduce(lambda UpperCAmelCase_ , UpperCAmelCase_ : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(UpperCAmelCase_ , UpperCAmelCase_ )
assert max_range == max_segment_tree.query(UpperCAmelCase_ , UpperCAmelCase_ )
assert sum_range == sum_segment_tree.query(UpperCAmelCase_ , UpperCAmelCase_ )
test_all_segments()
for index, value in test_updates.items():
lowercase__ :Optional[Any] = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 374 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
def __init__( self : Any , UpperCAmelCase : Any , UpperCAmelCase : int=13 , UpperCAmelCase : Optional[int]=32 , UpperCAmelCase : List[str]=2 , UpperCAmelCase : Any=3 , UpperCAmelCase : List[str]=16 , UpperCAmelCase : int=[1, 2, 1] , UpperCAmelCase : List[str]=[2, 2, 4] , UpperCAmelCase : List[str]=2 , UpperCAmelCase : Optional[Any]=2.0 , UpperCAmelCase : Tuple=True , UpperCAmelCase : int=0.0 , UpperCAmelCase : Any=0.0 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : int=False , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : Any=0.02 , UpperCAmelCase : Union[str, Any]=1E-5 , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : int=None , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : str=10 , UpperCAmelCase : Union[str, Any]=8 , ):
SCREAMING_SNAKE_CASE_ :int = parent
SCREAMING_SNAKE_CASE_ :Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE_ :Dict = image_size
SCREAMING_SNAKE_CASE_ :List[str] = patch_size
SCREAMING_SNAKE_CASE_ :Optional[Any] = num_channels
SCREAMING_SNAKE_CASE_ :Dict = embed_dim
SCREAMING_SNAKE_CASE_ :Dict = depths
SCREAMING_SNAKE_CASE_ :Dict = num_heads
SCREAMING_SNAKE_CASE_ :Tuple = window_size
SCREAMING_SNAKE_CASE_ :Optional[int] = mlp_ratio
SCREAMING_SNAKE_CASE_ :Dict = qkv_bias
SCREAMING_SNAKE_CASE_ :Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ :str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ :Tuple = drop_path_rate
SCREAMING_SNAKE_CASE_ :Optional[int] = hidden_act
SCREAMING_SNAKE_CASE_ :str = use_absolute_embeddings
SCREAMING_SNAKE_CASE_ :List[str] = patch_norm
SCREAMING_SNAKE_CASE_ :List[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE_ :Optional[int] = initializer_range
SCREAMING_SNAKE_CASE_ :List[Any] = is_training
SCREAMING_SNAKE_CASE_ :Tuple = scope
SCREAMING_SNAKE_CASE_ :Any = use_labels
SCREAMING_SNAKE_CASE_ :Union[str, Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE_ :int = encoder_stride
def _snake_case ( self : Optional[Any]):
SCREAMING_SNAKE_CASE_ :str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
SCREAMING_SNAKE_CASE_ :Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ :Any = ids_tensor([self.batch_size] , self.type_sequence_label_size)
SCREAMING_SNAKE_CASE_ :List[Any] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self : Tuple):
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _snake_case ( self : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str):
SCREAMING_SNAKE_CASE_ :int = SwinvaModel(config=UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
SCREAMING_SNAKE_CASE_ :int = model(UpperCAmelCase)
SCREAMING_SNAKE_CASE_ :List[str] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
SCREAMING_SNAKE_CASE_ :List[str] = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim))
def _snake_case ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = SwinvaForMaskedImageModeling(config=UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
SCREAMING_SNAKE_CASE_ :Union[str, Any] = model(UpperCAmelCase)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
SCREAMING_SNAKE_CASE_ :str = 1
SCREAMING_SNAKE_CASE_ :Union[str, Any] = SwinvaForMaskedImageModeling(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
SCREAMING_SNAKE_CASE_ :Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
SCREAMING_SNAKE_CASE_ :Optional[int] = model(UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size))
def _snake_case ( self : Dict , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : Any):
SCREAMING_SNAKE_CASE_ :Tuple = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ :List[str] = SwinvaForImageClassification(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
SCREAMING_SNAKE_CASE_ :str = model(UpperCAmelCase , labels=UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def _snake_case ( self : int):
SCREAMING_SNAKE_CASE_ :Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Union[str, Any] = config_and_inputs
SCREAMING_SNAKE_CASE_ :List[str] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( lowercase , lowercase , unittest.TestCase ):
lowerCamelCase_ : Any = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
lowerCamelCase_ : Dict = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : Tuple = False
lowerCamelCase_ : List[Any] = False
lowerCamelCase_ : Dict = False
lowerCamelCase_ : Union[str, Any] = False
def _snake_case ( self : Union[str, Any]):
SCREAMING_SNAKE_CASE_ :Any = SwinvaModelTester(self)
SCREAMING_SNAKE_CASE_ :Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , embed_dim=37)
def _snake_case ( self : int):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _snake_case ( self : List[str]):
SCREAMING_SNAKE_CASE_ :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase)
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
def _snake_case ( self : Union[str, Any]):
pass
@unittest.skip(reason="Swinv2 does not use inputs_embeds")
def _snake_case ( self : Union[str, Any]):
pass
def _snake_case ( self : int):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :Optional[Any] = model_class(UpperCAmelCase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
SCREAMING_SNAKE_CASE_ :Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase , nn.Linear))
def _snake_case ( self : str):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :int = model_class(UpperCAmelCase)
SCREAMING_SNAKE_CASE_ :Tuple = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE_ :Any = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE_ :Any = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase)
def _snake_case ( self : List[str]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ :Optional[Any] = True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :str = True
SCREAMING_SNAKE_CASE_ :Optional[Any] = False
SCREAMING_SNAKE_CASE_ :int = True
SCREAMING_SNAKE_CASE_ :Tuple = model_class(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :Dict = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase))
SCREAMING_SNAKE_CASE_ :Tuple = outputs.attentions
SCREAMING_SNAKE_CASE_ :Optional[int] = len(self.model_tester.depths)
self.assertEqual(len(UpperCAmelCase) , UpperCAmelCase)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE_ :List[Any] = True
SCREAMING_SNAKE_CASE_ :Dict = config.window_size**2
SCREAMING_SNAKE_CASE_ :Optional[int] = model_class(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :List[Any] = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase))
SCREAMING_SNAKE_CASE_ :List[str] = outputs.attentions
self.assertEqual(len(UpperCAmelCase) , UpperCAmelCase)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
SCREAMING_SNAKE_CASE_ :Optional[Any] = len(UpperCAmelCase)
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE_ :int = True
SCREAMING_SNAKE_CASE_ :int = True
SCREAMING_SNAKE_CASE_ :Tuple = model_class(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :List[Any] = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase))
if hasattr(self.model_tester , "num_hidden_states_types"):
SCREAMING_SNAKE_CASE_ :List[Any] = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
SCREAMING_SNAKE_CASE_ :int = 2
self.assertEqual(out_len + added_hidden_states , len(UpperCAmelCase))
SCREAMING_SNAKE_CASE_ :Dict = outputs.attentions
self.assertEqual(len(UpperCAmelCase) , UpperCAmelCase)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def _snake_case ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : int , UpperCAmelCase : int):
SCREAMING_SNAKE_CASE_ :Optional[int] = model_class(UpperCAmelCase)
model.to(UpperCAmelCase)
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :str = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase))
SCREAMING_SNAKE_CASE_ :int = outputs.hidden_states
SCREAMING_SNAKE_CASE_ :List[str] = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths) + 1)
self.assertEqual(len(UpperCAmelCase) , UpperCAmelCase)
# Swinv2 has a different seq_length
SCREAMING_SNAKE_CASE_ :Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
SCREAMING_SNAKE_CASE_ :str = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
SCREAMING_SNAKE_CASE_ :str = outputs.reshaped_hidden_states
self.assertEqual(len(UpperCAmelCase) , UpperCAmelCase)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Dict = reshaped_hidden_states[0].shape
SCREAMING_SNAKE_CASE_ :Union[str, Any] = (
reshaped_hidden_states[0].view(UpperCAmelCase , UpperCAmelCase , height * width).permute(0 , 2 , 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]) , [num_patches, self.model_tester.embed_dim] , )
def _snake_case ( self : Optional[int]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ :Tuple = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :Optional[Any] = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE_ :List[str] = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase)
def _snake_case ( self : List[Any]):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ :List[str] = 3
SCREAMING_SNAKE_CASE_ :str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
SCREAMING_SNAKE_CASE_ :int = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
SCREAMING_SNAKE_CASE_ :str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
SCREAMING_SNAKE_CASE_ :List[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :Dict = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE_ :List[Any] = True
self.check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , (padded_height, padded_width))
def _snake_case ( self : Union[str, Any]):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase)
def _snake_case ( self : int):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)
@slow
def _snake_case ( self : Dict):
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ :Optional[int] = SwinvaModel.from_pretrained(UpperCAmelCase)
self.assertIsNotNone(UpperCAmelCase)
def _snake_case ( self : Tuple):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ :Optional[Any] = _config_zero_init(UpperCAmelCase)
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :List[str] = model_class(config=UpperCAmelCase)
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def _snake_case ( self : Any):
return (
AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
if is_vision_available()
else None
)
@slow
def _snake_case ( self : Tuple):
SCREAMING_SNAKE_CASE_ :Optional[int] = SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256").to(
UpperCAmelCase)
SCREAMING_SNAKE_CASE_ :Tuple = self.default_image_processor
SCREAMING_SNAKE_CASE_ :Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
SCREAMING_SNAKE_CASE_ :Optional[int] = image_processor(images=UpperCAmelCase , return_tensors="pt").to(UpperCAmelCase)
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :Tuple = model(**UpperCAmelCase)
# verify the logits
SCREAMING_SNAKE_CASE_ :int = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , UpperCAmelCase)
SCREAMING_SNAKE_CASE_ :Union[str, Any] = torch.tensor([-0.3947, -0.4306, 0.0026]).to(UpperCAmelCase)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase , atol=1E-4))
| 631 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowercase ( a , a ):
'''simple docstring'''
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
SCREAMING_SNAKE_CASE_ :List[Any] = flax_key_tuple[:-1] + ("weight",)
SCREAMING_SNAKE_CASE_ :Any = torch.permute(a , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
# linear layer
SCREAMING_SNAKE_CASE_ :str = flax_key_tuple[:-1] + ("weight",)
SCREAMING_SNAKE_CASE_ :Optional[int] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def lowercase ( a , a , a ):
'''simple docstring'''
if "metadata" in layer:
SCREAMING_SNAKE_CASE_ :Dict = layer.split("metadata" )
SCREAMING_SNAKE_CASE_ :Optional[int] = "".join(split_layer[0] )[:-1]
SCREAMING_SNAKE_CASE_ :str = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
SCREAMING_SNAKE_CASE_ :str = layer.split("kvstore" )
SCREAMING_SNAKE_CASE_ :str = "".join(split_layer[0] )[:-1]
SCREAMING_SNAKE_CASE_ :str = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = layer.split("/" )
SCREAMING_SNAKE_CASE_ :Optional[int] = "/".join(split_layer[:-1] )
SCREAMING_SNAKE_CASE_ :int = (split_layer[-1],)
if "kvstore/path" in layer:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
elif "kvstore/driver" in layer:
SCREAMING_SNAKE_CASE_ :Tuple = "file"
else:
SCREAMING_SNAKE_CASE_ :str = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = rename_keys(a )
SCREAMING_SNAKE_CASE_ :Union[str, Any] = {}
for k, v in current_block.items():
SCREAMING_SNAKE_CASE_ :List[str] = v
SCREAMING_SNAKE_CASE_ :Optional[Any] = new_current_block
torch.save(a , a )
def lowercase ( a , a , a , a , a = WEIGHTS_NAME ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Optional[int] = convert_file_size_to_int(a )
SCREAMING_SNAKE_CASE_ :int = []
SCREAMING_SNAKE_CASE_ :str = {}
SCREAMING_SNAKE_CASE_ :List[str] = 0
SCREAMING_SNAKE_CASE_ :Optional[int] = 0
os.makedirs(a , exist_ok=a )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
SCREAMING_SNAKE_CASE_ :int = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
SCREAMING_SNAKE_CASE_ :Any = flatten_dict(a , sep="/" )
SCREAMING_SNAKE_CASE_ :Optional[Any] = {}
for layer in checkpoint_info.keys():
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :List[Any] = get_key_and_tensorstore_dict(
a , a , a )
if curr_real_layer_name in all_layers:
SCREAMING_SNAKE_CASE_ :str = content
else:
SCREAMING_SNAKE_CASE_ :Optional[Any] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
SCREAMING_SNAKE_CASE_ :Any = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
SCREAMING_SNAKE_CASE_ :List[Any] = torch.tensor(a )
SCREAMING_SNAKE_CASE_ :str = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Optional[Any] = rename_base_flax_keys(tuple(key.split("/" ) ) , a )
SCREAMING_SNAKE_CASE_ :Any = "/".join(a )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
SCREAMING_SNAKE_CASE_ :str = os.path.join(
a , weights_name.replace(".bin" , F"-{len(a )+1:05d}-of-???.bin" ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
del current_block
SCREAMING_SNAKE_CASE_ :Tuple = {}
SCREAMING_SNAKE_CASE_ :Dict = 0
SCREAMING_SNAKE_CASE_ :Optional[int] = raw_weights.to(getattr(a , a ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
SCREAMING_SNAKE_CASE_ :Dict = os.path.join(a , weights_name.replace(".bin" , F"-{len(a )+1:05d}-of-???.bin" ) )
rename_and_save_block(a , a )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(a ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE_ :Optional[int] = {}
SCREAMING_SNAKE_CASE_ :int = {}
for idx, shard in enumerate(a ):
SCREAMING_SNAKE_CASE_ :Optional[Any] = weights_name.replace(
".bin" , F"-{idx+1:05d}-of-{len(a ):05d}.bin" ) # len(sharded_state_dicts):05d}
SCREAMING_SNAKE_CASE_ :Any = os.path.join(a , weights_name.replace(".bin" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(a , os.path.join(a , a ) )
SCREAMING_SNAKE_CASE_ :List[Any] = shard
for key in shard:
SCREAMING_SNAKE_CASE_ :str = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE_ :List[str] = {"total_size": total_size}
SCREAMING_SNAKE_CASE_ :Optional[int] = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(a , a ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ :Optional[int] = json.dumps(a , indent=2 , sort_keys=a ) + "\n"
f.write(a )
return metadata, index
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowercase ( ):
'''simple docstring'''
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
SCREAMING_SNAKE_CASE_ :Dict = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
SCREAMING_SNAKE_CASE_ :str = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
SCREAMING_SNAKE_CASE_ :List[Any] = TaTokenizer.from_pretrained("t5-small" )
SCREAMING_SNAKE_CASE_ :Optional[int] = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
SCREAMING_SNAKE_CASE_ :List[Any] = tokenizer(a , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE_ :List[str] = model.generate(a , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 631 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : int , a_ : Tuple , a_ : Union[str, Any]=7 , a_ : int=3 , a_ : Optional[Any]=30 , a_ : List[Any]=400 , a_ : Tuple=True , a_ : Dict=None , a_ : Any=True , a_ : Union[str, Any]=[0.5, 0.5, 0.5] , a_ : Any=[0.5, 0.5, 0.5] , a_ : Dict=True , a_ : int=1 / 255 , a_ : Tuple=True , )-> Tuple:
"""simple docstring"""
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE__ : Optional[Any] = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
SCREAMING_SNAKE_CASE__ : Optional[int] = parent
SCREAMING_SNAKE_CASE__ : List[str] = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : int = do_resize
SCREAMING_SNAKE_CASE__ : List[str] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Tuple = image_mean
SCREAMING_SNAKE_CASE__ : Optional[int] = image_std
SCREAMING_SNAKE_CASE__ : Optional[int] = do_rescale
SCREAMING_SNAKE_CASE__ : Optional[int] = rescale_factor
SCREAMING_SNAKE_CASE__ : str = do_pad
def __lowercase( self : str )-> Dict:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowercase( self : Any , a_ : Union[str, Any] , a_ : List[Any]=False )-> List[Any]:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_inputs[0]
if isinstance(a_ , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(self.size['shortest_edge'] * h / w )
SCREAMING_SNAKE_CASE__ : Tuple = self.size['shortest_edge']
elif w > h:
SCREAMING_SNAKE_CASE__ : Any = self.size['shortest_edge']
SCREAMING_SNAKE_CASE__ : Any = int(self.size['shortest_edge'] * w / h )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.size['shortest_edge']
SCREAMING_SNAKE_CASE__ : str = self.size['shortest_edge']
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ : Any = max(a_ , key=lambda a_ : item[0] )[0]
SCREAMING_SNAKE_CASE__ : Tuple = max(a_ , key=lambda a_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = ConditionalDetrImageProcessor if is_vision_available() else None
def __lowercase( self : Dict )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ConditionalDetrImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : str )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'image_mean' ) )
self.assertTrue(hasattr(a_ , 'image_std' ) )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_resize' ) )
self.assertTrue(hasattr(a_ , 'size' ) )
def __lowercase( self : int )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1333} )
self.assertEqual(image_processor.do_pad , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=a_ )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , a_ )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : List[Any] )-> str:
"""simple docstring"""
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processor_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor_tester.get_expected_values(a_ , batched=a_ )
SCREAMING_SNAKE_CASE__ : Dict = image_processing(a_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processor_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processing(a_ , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor_tester.get_expected_values(a_ , batched=a_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase( self : List[Any] )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.get_expected_values(a_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processing(a_ , return_tensors='pt' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.image_processor_tester.get_expected_values(a_ , batched=a_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __lowercase( self : List[str] )-> Tuple:
"""simple docstring"""
# prepare image and target
SCREAMING_SNAKE_CASE__ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[int] = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ : str = {'image_id': 3_9769, 'annotations': target}
# encode them
SCREAMING_SNAKE_CASE__ : Optional[Any] = ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
SCREAMING_SNAKE_CASE__ : List[Any] = image_processing(images=a_ , annotations=a_ , return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , a_ , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , a_ ) )
# verify boxes
SCREAMING_SNAKE_CASE__ : str = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , a_ )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , a_ , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ : str = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , a_ ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , a_ ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , a_ ) )
# verify orig_size
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , a_ ) )
# verify size
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , a_ ) )
@slow
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE__ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
SCREAMING_SNAKE_CASE__ : Dict = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ : Dict = {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
SCREAMING_SNAKE_CASE__ : List[str] = ConditionalDetrImageProcessor(format='coco_panoptic' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processing(images=a_ , annotations=a_ , masks_path=a_ , return_tensors='pt' )
# verify pixel values
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['pixel_values'].shape , a_ )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , a_ , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ : int = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , a_ ) )
# verify boxes
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , a_ , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , a_ ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , a_ ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , a_ ) )
# verify masks
SCREAMING_SNAKE_CASE__ : Tuple = 82_2873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , a_ )
# verify orig_size
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , a_ ) )
# verify size
SCREAMING_SNAKE_CASE__ : str = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , a_ ) )
| 636 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 1 |
'''simple docstring'''
from math import isqrt
def __UpperCamelCase( _A : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(_A ) + 1 ) )
def __UpperCamelCase( _A : int = 10**6 ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = 0
UpperCAmelCase__ : List[Any] = 1
UpperCAmelCase__ : List[str] = 7
while prime_candidate < max_prime:
primes_count += is_prime(_A )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 614 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase( _A : list[int] , _A : int , _A : int , _A : int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = array[indexa], array[indexa]
def __UpperCamelCase( _A : list[int] , _A : int , _A : int , _A : int ):
'''simple docstring'''
if length > 1:
UpperCAmelCase__ : List[Any] = int(length / 2 )
for i in range(_A , low + middle ):
comp_and_swap(_A , _A , i + middle , _A )
bitonic_merge(_A , _A , _A , _A )
bitonic_merge(_A , low + middle , _A , _A )
def __UpperCamelCase( _A : list[int] , _A : int , _A : int , _A : int ):
'''simple docstring'''
if length > 1:
UpperCAmelCase__ : Optional[int] = int(length / 2 )
bitonic_sort(_A , _A , _A , 1 )
bitonic_sort(_A , low + middle , _A , 0 )
bitonic_merge(_A , _A , _A , _A )
if __name__ == "__main__":
UpperCamelCase__ : Dict = input('Enter numbers separated by a comma:\n').strip()
UpperCamelCase__ : Tuple = [int(item.strip()) for item in user_input.split(',')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('\nSorted array in ascending order is: ', end='')
print(*unsorted, sep=', ')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('Sorted array in descending order is: ', end='')
print(*unsorted, sep=', ')
| 614 | 1 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A__ : Dict= logging.getLogger(__name__)
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ = np.argmax(SCREAMING_SNAKE_CASE , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , encoding='utf_8' ) as f:
UpperCamelCase__ = csv.reader(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = []
next(SCREAMING_SNAKE_CASE ) # skip the first line
for line in tqdm(SCREAMING_SNAKE_CASE ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ = []
for dataset in encoded_datasets:
UpperCamelCase__ = len(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
UpperCamelCase__ = np.zeros((n_batch, 2) , dtype=np.intaa )
UpperCamelCase__ = np.full((n_batch, 2, input_len) , fill_value=-1_00 , dtype=np.intaa )
UpperCamelCase__ = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase__ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
UpperCamelCase__ = with_conta
UpperCamelCase__ = with_conta
UpperCamelCase__ = len(SCREAMING_SNAKE_CASE ) - 1
UpperCamelCase__ = len(SCREAMING_SNAKE_CASE ) - 1
UpperCamelCase__ = with_conta
UpperCamelCase__ = with_conta
UpperCamelCase__ = mc_label
UpperCamelCase__ = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase_( ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=SCREAMING_SNAKE_CASE , default='openai-gpt' , help='pretrained model name' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=SCREAMING_SNAKE_CASE , default='' )
parser.add_argument('--eval_dataset' , type=SCREAMING_SNAKE_CASE , default='' )
parser.add_argument('--seed' , type=SCREAMING_SNAKE_CASE , default=42 )
parser.add_argument('--num_train_epochs' , type=SCREAMING_SNAKE_CASE , default=3 )
parser.add_argument('--train_batch_size' , type=SCREAMING_SNAKE_CASE , default=8 )
parser.add_argument('--eval_batch_size' , type=SCREAMING_SNAKE_CASE , default=16 )
parser.add_argument('--adam_epsilon' , default=1E-8 , type=SCREAMING_SNAKE_CASE , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , type=SCREAMING_SNAKE_CASE , default=1 )
parser.add_argument(
'--max_steps' , default=-1 , type=SCREAMING_SNAKE_CASE , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=SCREAMING_SNAKE_CASE , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=SCREAMING_SNAKE_CASE , default=6.25E-5 )
parser.add_argument('--warmup_steps' , default=0 , type=SCREAMING_SNAKE_CASE , help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule' , type=SCREAMING_SNAKE_CASE , default='warmup_linear' )
parser.add_argument('--weight_decay' , type=SCREAMING_SNAKE_CASE , default=0.01 )
parser.add_argument('--lm_coef' , type=SCREAMING_SNAKE_CASE , default=0.9 )
parser.add_argument('--n_valid' , type=SCREAMING_SNAKE_CASE , default=3_74 )
parser.add_argument('--server_ip' , type=SCREAMING_SNAKE_CASE , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=SCREAMING_SNAKE_CASE , default='' , help='Can be used for distant debugging.' )
UpperCamelCase__ = parser.parse_args()
print(SCREAMING_SNAKE_CASE )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=SCREAMING_SNAKE_CASE )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
UpperCamelCase__ = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
UpperCamelCase__ = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
UpperCamelCase__ = ['_start_', '_delimiter_', '_classify_']
UpperCamelCase__ = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE ) )
model.to(SCREAMING_SNAKE_CASE )
# Load and encode the datasets
def tokenize_and_encode(SCREAMING_SNAKE_CASE ):
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE ) )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return obj
return [tokenize_and_encode(SCREAMING_SNAKE_CASE ) for o in obj]
logger.info('Encoding dataset...' )
UpperCamelCase__ = load_rocstories_dataset(args.train_dataset )
UpperCamelCase__ = load_rocstories_dataset(args.eval_dataset )
UpperCamelCase__ = (train_dataset, eval_dataset)
UpperCamelCase__ = tokenize_and_encode(SCREAMING_SNAKE_CASE )
# Compute the max input length for the Transformer
UpperCamelCase__ = model.config.n_positions // 2 - 2
UpperCamelCase__ = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
UpperCamelCase__ = min(SCREAMING_SNAKE_CASE , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
UpperCamelCase__ = pre_process_datasets(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ = tensor_datasets[0], tensor_datasets[1]
UpperCamelCase__ = TensorDataset(*SCREAMING_SNAKE_CASE )
UpperCamelCase__ = RandomSampler(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = DataLoader(SCREAMING_SNAKE_CASE , sampler=SCREAMING_SNAKE_CASE , batch_size=args.train_batch_size )
UpperCamelCase__ = TensorDataset(*SCREAMING_SNAKE_CASE )
UpperCamelCase__ = SequentialSampler(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = DataLoader(SCREAMING_SNAKE_CASE , sampler=SCREAMING_SNAKE_CASE , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
UpperCamelCase__ = args.max_steps
UpperCamelCase__ = args.max_steps // (len(SCREAMING_SNAKE_CASE ) // args.gradient_accumulation_steps) + 1
else:
UpperCamelCase__ = len(SCREAMING_SNAKE_CASE ) // args.gradient_accumulation_steps * args.num_train_epochs
UpperCamelCase__ = list(model.named_parameters() )
UpperCamelCase__ = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
UpperCamelCase__ = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
UpperCamelCase__ = AdamW(SCREAMING_SNAKE_CASE , lr=args.learning_rate , eps=args.adam_epsilon )
UpperCamelCase__ = get_linear_schedule_with_warmup(
SCREAMING_SNAKE_CASE , num_warmup_steps=args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE )
if args.do_train:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='Epoch' ):
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = tqdm(SCREAMING_SNAKE_CASE , desc='Training' )
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
UpperCamelCase__ = tuple(t.to(SCREAMING_SNAKE_CASE ) for t in batch )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = batch
UpperCamelCase__ = model(SCREAMING_SNAKE_CASE , mc_token_ids=SCREAMING_SNAKE_CASE , lm_labels=SCREAMING_SNAKE_CASE , mc_labels=SCREAMING_SNAKE_CASE )
UpperCamelCase__ = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
UpperCamelCase__ = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
UpperCamelCase__ = 'Training loss: {:.2e} lr: {:.2e}'.format(SCREAMING_SNAKE_CASE , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
UpperCamelCase__ = model.module if hasattr(SCREAMING_SNAKE_CASE , 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
UpperCamelCase__ = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE )
UpperCamelCase__ = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE )
torch.save(model_to_save.state_dict() , SCREAMING_SNAKE_CASE )
model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
UpperCamelCase__ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
UpperCamelCase__ = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(SCREAMING_SNAKE_CASE )
if args.do_eval:
model.eval()
UpperCamelCase__ , UpperCamelCase__ = 0, 0
UpperCamelCase__ , UpperCamelCase__ = 0, 0
for batch in tqdm(SCREAMING_SNAKE_CASE , desc='Evaluating' ):
UpperCamelCase__ = tuple(t.to(SCREAMING_SNAKE_CASE ) for t in batch )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = batch
with torch.no_grad():
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = model(
SCREAMING_SNAKE_CASE , mc_token_ids=SCREAMING_SNAKE_CASE , lm_labels=SCREAMING_SNAKE_CASE , mc_labels=SCREAMING_SNAKE_CASE )
UpperCamelCase__ = mc_logits.detach().cpu().numpy()
UpperCamelCase__ = mc_labels.to('cpu' ).numpy()
UpperCamelCase__ = accuracy(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
UpperCamelCase__ = eval_loss / nb_eval_steps
UpperCamelCase__ = eval_accuracy / nb_eval_examples
UpperCamelCase__ = tr_loss / nb_tr_steps if args.do_train else None
UpperCamelCase__ = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
UpperCamelCase__ = os.path.join(args.output_dir , 'eval_results.txt' )
with open(SCREAMING_SNAKE_CASE , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , SCREAMING_SNAKE_CASE , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 709 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str]= logging.get_logger(__name__)
class __lowerCamelCase ( _a ):
a : Optional[int] ="""timm_backbone"""
def __init__( self , snake_case_=None , snake_case_=3 , snake_case_=True , snake_case_=True , snake_case_=None , **snake_case_ , ) -> Dict:
super().__init__(**snake_case_ )
UpperCamelCase__ = backbone
UpperCamelCase__ = num_channels
UpperCamelCase__ = features_only
UpperCamelCase__ = use_pretrained_backbone
UpperCamelCase__ = True
UpperCamelCase__ = out_indices if out_indices is not None else (-1,)
| 20 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
def wrapper(*lowercase_ , **lowercase_ ):
A__ = timeit.default_timer()
A__ = func(*lowercase_ , **lowercase_ )
A__ = timeit.default_timer() - starttime
return delta
A__ = func.__name__
return wrapper
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=100 , lowercase_=None ) -> Dict:
"""simple docstring"""
A__ = []
A__ = seq_shapes or {}
for i in range(lowercase_ ):
A__ = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(lowercase_ , _ArrayXD ):
A__ = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(lowercase_ , datasets.Value ):
if v.dtype == "string":
A__ = 'The small grey turtle was surprisingly fast when challenged.'
else:
A__ = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(lowercase_ , datasets.Sequence ):
while isinstance(lowercase_ , datasets.Sequence ):
A__ = v.feature
A__ = seq_shapes[k]
A__ = np.random.rand(*lowercase_ ).astype(v.dtype )
A__ = data
dummy_data.append((i, example) )
return dummy_data
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_=100 , lowercase_=None ) -> Any:
"""simple docstring"""
A__ = generate_examples(lowercase_ , num_examples=lowercase_ , seq_shapes=lowercase_ )
with ArrowWriter(features=lowercase_ , path=lowercase_ ) as writer:
for key, record in dummy_data:
A__ = features.encode_example(lowercase_ )
writer.write(lowercase_ )
A__ = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.""" )
A__ = datasets.Dataset.from_file(filename=lowercase_ , info=datasets.DatasetInfo(features=lowercase_ ) )
return dataset
| 87 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def lowercase__ ( lowerCamelCase ):
# initialize config
if "resnet-50" in model_name:
_SCREAMING_SNAKE_CASE : Optional[int] = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
_SCREAMING_SNAKE_CASE : Optional[Any] = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
_SCREAMING_SNAKE_CASE : Optional[Any] = DetrConfig(use_timm_backbone=lowerCamelCase, backbone_config=lowerCamelCase )
# set label attributes
_SCREAMING_SNAKE_CASE : Optional[int] = 'panoptic' in model_name
if is_panoptic:
_SCREAMING_SNAKE_CASE : List[str] = 250
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = 91
_SCREAMING_SNAKE_CASE : Union[str, Any] = 'huggingface/label-files'
_SCREAMING_SNAKE_CASE : Dict = 'coco-detection-id2label.json'
_SCREAMING_SNAKE_CASE : List[str] = json.load(open(hf_hub_download(lowerCamelCase, lowerCamelCase, repo_type='dataset' ), 'r' ) )
_SCREAMING_SNAKE_CASE : Dict = {int(lowerCamelCase ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE : List[Any] = idalabel
_SCREAMING_SNAKE_CASE : Tuple = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def lowercase__ ( lowerCamelCase ):
# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE : Optional[int] = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
f"""encoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
f"""decoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
) )
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
) )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def lowercase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = state_dict.pop(lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = val
def lowercase__ ( lowerCamelCase, lowerCamelCase=False ):
_SCREAMING_SNAKE_CASE : Optional[int] = ''
if is_panoptic:
_SCREAMING_SNAKE_CASE : List[Any] = 'detr.'
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_SCREAMING_SNAKE_CASE : Optional[Any] = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
_SCREAMING_SNAKE_CASE : Dict = state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE : Any = in_proj_weight[:256, :]
_SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_bias[:256]
_SCREAMING_SNAKE_CASE : Tuple = in_proj_weight[256:512, :]
_SCREAMING_SNAKE_CASE : Tuple = in_proj_bias[256:512]
_SCREAMING_SNAKE_CASE : Optional[int] = in_proj_weight[-256:, :]
_SCREAMING_SNAKE_CASE : Optional[int] = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_SCREAMING_SNAKE_CASE : Optional[Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
_SCREAMING_SNAKE_CASE : int = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE : Tuple = in_proj_weight[:256, :]
_SCREAMING_SNAKE_CASE : str = in_proj_bias[:256]
_SCREAMING_SNAKE_CASE : str = in_proj_weight[256:512, :]
_SCREAMING_SNAKE_CASE : int = in_proj_bias[256:512]
_SCREAMING_SNAKE_CASE : List[Any] = in_proj_weight[-256:, :]
_SCREAMING_SNAKE_CASE : List[str] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_SCREAMING_SNAKE_CASE : str = state_dict.pop(
f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
_SCREAMING_SNAKE_CASE : List[Any] = state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_SCREAMING_SNAKE_CASE : Union[str, Any] = in_proj_weight_cross_attn[:256, :]
_SCREAMING_SNAKE_CASE : Optional[int] = in_proj_bias_cross_attn[:256]
_SCREAMING_SNAKE_CASE : Union[str, Any] = in_proj_weight_cross_attn[256:512, :]
_SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_bias_cross_attn[256:512]
_SCREAMING_SNAKE_CASE : int = in_proj_weight_cross_attn[-256:, :]
_SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_bias_cross_attn[-256:]
def lowercase__ ( ):
_SCREAMING_SNAKE_CASE : List[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_SCREAMING_SNAKE_CASE : Dict = Image.open(requests.get(lowerCamelCase, stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def lowercase__ ( lowerCamelCase, lowerCamelCase=None, lowerCamelCase=False ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = get_detr_config(lowerCamelCase )
# load original model from torch hub
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'detr-resnet-50': 'detr_resnet50',
'detr-resnet-101': 'detr_resnet101',
}
logger.info(f"""Converting model {model_name}...""" )
_SCREAMING_SNAKE_CASE : int = torch.hub.load('facebookresearch/detr', model_name_to_original_name[model_name], pretrained=lowerCamelCase ).eval()
_SCREAMING_SNAKE_CASE : Optional[Any] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(lowerCamelCase ):
if is_panoptic:
_SCREAMING_SNAKE_CASE : int = 'detr.' + src
rename_key(lowerCamelCase, lowerCamelCase, lowerCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase, is_panoptic=lowerCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_SCREAMING_SNAKE_CASE : str = 'detr.model.' if is_panoptic else 'model.'
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
_SCREAMING_SNAKE_CASE : Optional[int] = state_dict.pop(lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_SCREAMING_SNAKE_CASE : Tuple = state_dict.pop(lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
_SCREAMING_SNAKE_CASE : Optional[int] = state_dict.pop(lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
_SCREAMING_SNAKE_CASE : List[str] = state_dict.pop(lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = val
# finally, create HuggingFace model and load state dict
_SCREAMING_SNAKE_CASE : Dict = DetrForSegmentation(lowerCamelCase ) if is_panoptic else DetrForObjectDetection(lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
# verify our conversion on an image
_SCREAMING_SNAKE_CASE : Optional[Any] = 'coco_panoptic' if is_panoptic else 'coco_detection'
_SCREAMING_SNAKE_CASE : int = DetrImageProcessor(format=lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = processor(images=prepare_img(), return_tensors='pt' )
_SCREAMING_SNAKE_CASE : Tuple = encoding['pixel_values']
_SCREAMING_SNAKE_CASE : List[str] = detr(lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = model(lowerCamelCase )
assert torch.allclose(outputs.logits, original_outputs['pred_logits'], atol=1E-3 )
assert torch.allclose(outputs.pred_boxes, original_outputs['pred_boxes'], atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs['pred_masks'], atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(f"""nielsr/{model_name}""" )
processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='detr-resnet-50',
type=str,
choices=['detr-resnet-50', 'detr-resnet-101'],
help='Name of the DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
lowerCAmelCase__ = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 621 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
a : List[str] = logging.get_logger(__name__)
class a_ ( _UpperCAmelCase ):
a : int = ['pixel_values']
def __init__( self : List[Any] , __UpperCamelCase : bool = True , __UpperCamelCase : int = 32 , __UpperCamelCase : Optional[Any]=PILImageResampling.BILINEAR , __UpperCamelCase : bool = True , **__UpperCamelCase : Any , ) ->None:
'''simple docstring'''
_UpperCAmelCase = do_resize
_UpperCAmelCase = do_rescale
_UpperCAmelCase = size_divisor
_UpperCAmelCase = resample
super().__init__(**__UpperCamelCase )
def _snake_case ( self : Optional[Any] , __UpperCamelCase : np.ndarray , __UpperCamelCase : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[ChannelDimension] = None , **__UpperCamelCase : Any ) ->np.ndarray:
'''simple docstring'''
_UpperCAmelCase ,_UpperCAmelCase = get_image_size(__UpperCamelCase )
# Rounds the height and width down to the closest multiple of size_divisor
_UpperCAmelCase = height // size_divisor * size_divisor
_UpperCAmelCase = width // size_divisor * size_divisor
_UpperCAmelCase = resize(__UpperCamelCase , (new_h, new_w) , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
return image
def _snake_case ( self : str , __UpperCamelCase : np.ndarray , __UpperCamelCase : float , __UpperCamelCase : Optional[ChannelDimension] = None , **__UpperCamelCase : Optional[Any] ) ->np.ndarray:
'''simple docstring'''
return rescale(image=__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def _snake_case ( self : int , __UpperCamelCase : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , __UpperCamelCase : Optional[bool] = None , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Dict=None , __UpperCamelCase : Optional[bool] = None , __UpperCamelCase : Optional[Union[TensorType, str]] = None , __UpperCamelCase : ChannelDimension = ChannelDimension.FIRST , **__UpperCamelCase : Optional[int] , ) ->BatchFeature:
'''simple docstring'''
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = size_divisor if size_divisor is not None else self.size_divisor
_UpperCAmelCase = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_UpperCAmelCase = make_list_of_images(__UpperCamelCase )
if not valid_images(__UpperCamelCase ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(__UpperCamelCase ) for img in images]
if do_resize:
_UpperCAmelCase = [self.resize(__UpperCamelCase , size_divisor=__UpperCamelCase , resample=__UpperCamelCase ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(__UpperCamelCase , scale=1 / 2_55 ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase ) for image in images]
_UpperCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 19 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : List[str] = [
'''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PegasusXForConditionalGeneration''',
'''PegasusXModel''',
'''PegasusXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
a : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 19 | 1 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : torch.FloatTensor
lowerCamelCase : Optional[torch.FloatTensor] = None
def UpperCAmelCase__ ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Union[str, Any]=0.999 , UpperCAmelCase_ : Tuple="cosine" , ) -> Optional[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCAmelCase_ : Optional[int] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCAmelCase_ : Tuple ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
__lowerCamelCase : List[str] = []
for i in range(UpperCAmelCase_ ):
__lowerCamelCase : int = i / num_diffusion_timesteps
__lowerCamelCase : List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_ ) / alpha_bar_fn(UpperCAmelCase_ ) , UpperCAmelCase_ ) )
return torch.tensor(UpperCAmelCase_ , dtype=torch.floataa )
class UpperCAmelCase_ (_UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
@register_to_config
def __init__( self , SCREAMING_SNAKE_CASE_ = 10_00 , SCREAMING_SNAKE_CASE_ = "fixed_small_log" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1.0 , SCREAMING_SNAKE_CASE_ = "epsilon" , SCREAMING_SNAKE_CASE_ = "squaredcos_cap_v2" , ) -> List[str]:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )
__lowerCamelCase : Optional[Any] = betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = 1.0 - self.betas
__lowerCamelCase : Union[str, Any] = torch.cumprod(self.alphas , dim=0 )
__lowerCamelCase : Optional[Any] = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__lowerCamelCase : Optional[Any] = 1.0
# setable values
__lowerCamelCase : Optional[Any] = None
__lowerCamelCase : Optional[int] = torch.from_numpy(np.arange(0 , SCREAMING_SNAKE_CASE_ )[::-1].copy() )
__lowerCamelCase : Optional[int] = variance_type
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> torch.FloatTensor:
return sample
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Optional[int]:
__lowerCamelCase : Optional[int] = num_inference_steps
__lowerCamelCase : Any = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__lowerCamelCase : str = (np.arange(0 , SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__lowerCamelCase : List[str] = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) -> Dict:
if prev_timestep is None:
__lowerCamelCase : Dict = t - 1
__lowerCamelCase : int = self.alphas_cumprod[t]
__lowerCamelCase : str = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__lowerCamelCase : Optional[int] = 1 - alpha_prod_t
__lowerCamelCase : Dict = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__lowerCamelCase : List[str] = self.betas[t]
else:
__lowerCamelCase : List[str] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__lowerCamelCase : List[str] = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__lowerCamelCase : str = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__lowerCamelCase : Optional[Any] = torch.log(torch.clamp(SCREAMING_SNAKE_CASE_ , min=1E-20 ) )
__lowerCamelCase : List[Any] = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__lowerCamelCase : str = variance.log()
__lowerCamelCase : Dict = beta.log()
__lowerCamelCase : List[str] = (predicted_variance + 1) / 2
__lowerCamelCase : str = frac * max_log + (1 - frac) * min_log
return variance
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
__lowerCamelCase : List[str] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__lowerCamelCase , __lowerCamelCase : List[Any] = torch.split(SCREAMING_SNAKE_CASE_ , sample.shape[1] , dim=1 )
else:
__lowerCamelCase : Optional[int] = None
# 1. compute alphas, betas
if prev_timestep is None:
__lowerCamelCase : str = t - 1
__lowerCamelCase : int = self.alphas_cumprod[t]
__lowerCamelCase : Optional[int] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__lowerCamelCase : int = 1 - alpha_prod_t
__lowerCamelCase : List[Any] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__lowerCamelCase : Dict = self.betas[t]
__lowerCamelCase : List[Any] = self.alphas[t]
else:
__lowerCamelCase : Optional[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
__lowerCamelCase : Dict = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__lowerCamelCase : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__lowerCamelCase : Optional[Any] = model_output
else:
raise ValueError(
f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
' for the UnCLIPScheduler.' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__lowerCamelCase : Dict = torch.clamp(
SCREAMING_SNAKE_CASE_ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__lowerCamelCase : Any = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__lowerCamelCase : Optional[int] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__lowerCamelCase : Union[str, Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__lowerCamelCase : Dict = 0
if t > 0:
__lowerCamelCase : Union[str, Any] = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=SCREAMING_SNAKE_CASE_ , device=model_output.device )
__lowerCamelCase : Union[str, Any] = self._get_variance(
SCREAMING_SNAKE_CASE_ , predicted_variance=SCREAMING_SNAKE_CASE_ , prev_timestep=SCREAMING_SNAKE_CASE_ , )
if self.variance_type == "fixed_small_log":
__lowerCamelCase : Optional[Any] = variance
elif self.variance_type == "learned_range":
__lowerCamelCase : Tuple = (0.5 * variance).exp()
else:
raise ValueError(
f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
' for the UnCLIPScheduler.' )
__lowerCamelCase : Tuple = variance * variance_noise
__lowerCamelCase : List[str] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ , pred_original_sample=SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
__lowerCamelCase : str = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__lowerCamelCase : Any = timesteps.to(original_samples.device )
__lowerCamelCase : Tuple = alphas_cumprod[timesteps] ** 0.5
__lowerCamelCase : List[Any] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__lowerCamelCase : Union[str, Any] = sqrt_alpha_prod.unsqueeze(-1 )
__lowerCamelCase : Optional[Any] = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCamelCase : Tuple = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__lowerCamelCase : str = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__lowerCamelCase : str = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 13 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : str = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = '''camembert'''
def __init__( self ,lowerCamelCase_=30522 ,lowerCamelCase_=768 ,lowerCamelCase_=12 ,lowerCamelCase_=12 ,lowerCamelCase_=3072 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=512 ,lowerCamelCase_=2 ,lowerCamelCase_=0.02 ,lowerCamelCase_=1e-12 ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,lowerCamelCase_=2 ,lowerCamelCase_="absolute" ,lowerCamelCase_=True ,lowerCamelCase_=None ,**lowerCamelCase_ ,) -> str:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase_ ,bos_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,**lowerCamelCase_ )
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : List[Any] = hidden_size
UpperCAmelCase__ : Tuple = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : int = hidden_act
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : List[Any] = hidden_dropout_prob
UpperCAmelCase__ : Tuple = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = type_vocab_size
UpperCAmelCase__ : int = initializer_range
UpperCAmelCase__ : Optional[Any] = layer_norm_eps
UpperCAmelCase__ : Optional[Any] = position_embedding_type
UpperCAmelCase__ : str = use_cache
UpperCAmelCase__ : List[Any] = classifier_dropout
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase__ : Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase__ : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 614 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = {
"google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class __magic_name__ ( A__ ):
lowercase : Tuple ='''umt5'''
lowercase : Any =['''past_key_values''']
def __init__( self : Dict , UpperCamelCase__ : int=25_01_12 , UpperCamelCase__ : Optional[int]=5_12 , UpperCamelCase__ : str=64 , UpperCamelCase__ : str=10_24 , UpperCamelCase__ : int=8 , UpperCamelCase__ : int=None , UpperCamelCase__ : List[Any]=6 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : str=1_28 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Tuple=1e-6 , UpperCamelCase__ : int=1.0 , UpperCamelCase__ : Optional[int]="gated-gelu" , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Tuple="T5Tokenizer" , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Tuple=0 , UpperCamelCase__ : Optional[int]=1 , UpperCamelCase__ : List[Any]=0 , **UpperCamelCase__ : Union[str, Any] , ) -> int:
'''simple docstring'''
super().__init__(
is_encoder_decoder=UpperCamelCase__ , tokenizer_class=UpperCamelCase__ , tie_word_embeddings=UpperCamelCase__ , pad_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , decoder_start_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase = vocab_size
UpperCAmelCase = d_model
UpperCAmelCase = d_kv
UpperCAmelCase = d_ff
UpperCAmelCase = num_layers
UpperCAmelCase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
UpperCAmelCase = num_heads
UpperCAmelCase = relative_attention_num_buckets
UpperCAmelCase = relative_attention_max_distance
UpperCAmelCase = dropout_rate
UpperCAmelCase = layer_norm_epsilon
UpperCAmelCase = initializer_factor
UpperCAmelCase = feed_forward_proj
UpperCAmelCase = use_cache
UpperCAmelCase = self.feed_forward_proj.split("-" )
UpperCAmelCase = act_info[-1]
UpperCAmelCase = act_info[0] == "gated"
if len(UpperCamelCase__ ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__ ) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
"'gated-gelu' or 'relu'" )
if feed_forward_proj == "gated-gelu":
UpperCAmelCase = "gelu_new"
@property
def SCREAMING_SNAKE_CASE_ ( self : str ) -> int:
'''simple docstring'''
return self.d_model
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.num_heads
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.num_layers
class __magic_name__ ( A__ ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
UpperCAmelCase = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
UpperCAmelCase = "past_encoder_sequence + sequence"
UpperCAmelCase = {0: "batch"}
UpperCAmelCase = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
UpperCAmelCase = {0: "batch", 1: "decoder_sequence"}
UpperCAmelCase = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase__ , direction="inputs" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return 13
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> float:
'''simple docstring'''
return 5e-4
| 708 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __magic_name__ ( A__, unittest.TestCase ):
lowercase : Optional[Any] =CpmAntTokenizer
lowercase : Dict =False
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
super().setUp()
UpperCAmelCase = [
"<d>",
"</d>",
"<s>",
"</s>",
"</_>",
"<unk>",
"<pad>",
"</n>",
"我",
"是",
"C",
"P",
"M",
"A",
"n",
"t",
]
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
@tooslow
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b" )
UpperCAmelCase = "今天天气真好！"
UpperCAmelCase = ["今天", "天气", "真", "好", "！"]
UpperCAmelCase = tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = "今天天气真好！"
UpperCAmelCase = [tokenizer.bos_token] + tokens
UpperCAmelCase = [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ )
UpperCAmelCase = tokenizer.decode(UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
| 457 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCAmelCase_ ( __a , __a ) -> Dict:
"""simple docstring"""
lowerCamelCase__: int =Mock()
lowerCamelCase__: Tuple =conn, Mock()
lowerCamelCase__: Optional[int] =iter([1, None] )
lowerCamelCase__: str =lambda __a : next(_lowerCAmelCase )
# ===== invoke =====
send_file(filename="mytext.txt" , testing=_lowerCAmelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 59 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[Any] = self._trie
for char in text:
if char not in trie:
A : str = {}
A : str = trie[char]
A : Optional[int] = True
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Dict = self._trie
for char in prefix:
if char in trie:
A : Optional[Any] = trie[char]
else:
return []
return self._elements(lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : int = []
for c, v in d.items():
A : List[Any] = [""" """] if c == END else [(c + s) for s in self._elements(lowerCamelCase__ )]
result.extend(lowerCamelCase__ )
return tuple(lowerCamelCase__ )
SCREAMING_SNAKE_CASE_:Any = Trie()
SCREAMING_SNAKE_CASE_:Tuple = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple:
"""simple docstring"""
A : List[str] = trie.find_word(_lowerCAmelCase )
return tuple(string + word for word in suffixes )
def __UpperCamelCase ( ) -> None:
"""simple docstring"""
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 662 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _snake_case ( lowerCamelCase__ ):
_A = 'wavlm'
def __init__( self ,UpperCamelCase=32 ,UpperCamelCase=768 ,UpperCamelCase=12 ,UpperCamelCase=12 ,UpperCamelCase=3_072 ,UpperCamelCase="gelu" ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=0.0 ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=0.02 ,UpperCamelCase=1E-5 ,UpperCamelCase="group" ,UpperCamelCase="gelu" ,UpperCamelCase=(512, 512, 512, 512, 512, 512, 512) ,UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,UpperCamelCase=False ,UpperCamelCase=128 ,UpperCamelCase=16 ,UpperCamelCase=320 ,UpperCamelCase=800 ,UpperCamelCase=False ,UpperCamelCase=True ,UpperCamelCase=0.05 ,UpperCamelCase=10 ,UpperCamelCase=2 ,UpperCamelCase=0.0 ,UpperCamelCase=10 ,UpperCamelCase=320 ,UpperCamelCase=2 ,UpperCamelCase=0.1 ,UpperCamelCase=100 ,UpperCamelCase=256 ,UpperCamelCase=256 ,UpperCamelCase=0.1 ,UpperCamelCase="mean" ,UpperCamelCase=False ,UpperCamelCase=False ,UpperCamelCase=256 ,UpperCamelCase=(512, 512, 512, 512, 1_500) ,UpperCamelCase=(5, 3, 3, 1, 1) ,UpperCamelCase=(1, 2, 3, 1, 1) ,UpperCamelCase=512 ,UpperCamelCase=80 ,UpperCamelCase=0 ,UpperCamelCase=1 ,UpperCamelCase=2 ,UpperCamelCase=False ,UpperCamelCase=3 ,UpperCamelCase=2 ,UpperCamelCase=3 ,UpperCamelCase=None ,**UpperCamelCase ,) -> Tuple:
super().__init__(**__lowerCamelCase ,pad_token_id=__lowerCamelCase ,bos_token_id=__lowerCamelCase ,eos_token_id=__lowerCamelCase )
snake_case__ :Tuple = hidden_size
snake_case__ :Dict = feat_extract_norm
snake_case__ :List[Any] = feat_extract_activation
snake_case__ :Union[str, Any] = list(__lowerCamelCase )
snake_case__ :Union[str, Any] = list(__lowerCamelCase )
snake_case__ :Tuple = list(__lowerCamelCase )
snake_case__ :int = conv_bias
snake_case__ :Optional[int] = num_buckets
snake_case__ :Dict = max_bucket_distance
snake_case__ :Optional[Any] = num_conv_pos_embeddings
snake_case__ :Dict = num_conv_pos_embedding_groups
snake_case__ :Union[str, Any] = len(self.conv_dim )
snake_case__ :List[str] = num_hidden_layers
snake_case__ :Union[str, Any] = intermediate_size
snake_case__ :Optional[Any] = hidden_act
snake_case__ :List[Any] = num_attention_heads
snake_case__ :Tuple = hidden_dropout
snake_case__ :List[Any] = attention_dropout
snake_case__ :List[str] = activation_dropout
snake_case__ :Any = feat_proj_dropout
snake_case__ :int = final_dropout
snake_case__ :Optional[Any] = layerdrop
snake_case__ :Dict = layer_norm_eps
snake_case__ :Optional[int] = initializer_range
snake_case__ :int = num_ctc_classes
snake_case__ :Tuple = vocab_size
snake_case__ :Any = do_stable_layer_norm
snake_case__ :List[str] = use_weighted_layer_sum
snake_case__ :Any = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case__ :Union[str, Any] = apply_spec_augment
snake_case__ :List[Any] = mask_time_prob
snake_case__ :int = mask_time_length
snake_case__ :Union[str, Any] = mask_time_min_masks
snake_case__ :List[Any] = mask_feature_prob
snake_case__ :List[Any] = mask_feature_length
# parameters for pretraining with codevector quantized representations
snake_case__ :List[str] = num_codevectors_per_group
snake_case__ :str = num_codevector_groups
snake_case__ :Dict = contrastive_logits_temperature
snake_case__ :Any = num_negatives
snake_case__ :List[str] = codevector_dim
snake_case__ :str = proj_codevector_dim
snake_case__ :Union[str, Any] = diversity_loss_weight
# ctc loss
snake_case__ :Any = ctc_loss_reduction
snake_case__ :Dict = ctc_zero_infinity
# adapter
snake_case__ :int = add_adapter
snake_case__ :Dict = adapter_kernel_size
snake_case__ :Dict = adapter_stride
snake_case__ :Dict = num_adapter_layers
snake_case__ :str = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case__ :Tuple = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case__ :Union[str, Any] = list(__lowerCamelCase )
snake_case__ :int = list(__lowerCamelCase )
snake_case__ :List[Any] = list(__lowerCamelCase )
snake_case__ :int = xvector_output_dim
@property
def lowerCAmelCase_ ( self ) -> List[str]:
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 701 |
import pytest
__UpperCAmelCase : int = "__dummy_dataset1__"
__UpperCAmelCase : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
'''simple docstring'''
snake_case__ :Optional[Any] = dataset_loading_script_name
snake_case__ :Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
snake_case__ :List[Any] = script_dir / F'{script_name}.py'
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 57 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case__ : int = logging.get_logger(__name__)
snake_case__ : List[str] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
snake_case__ : Optional[Any] = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
snake_case__ : Tuple = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
snake_case__ : Dict = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
snake_case__ : Any = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
snake_case__ : Optional[int] = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
snake_case__ : Union[str, Any] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
snake_case__ : Optional[int] = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
snake_case__ : str = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
snake_case__ : Dict = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
snake_case__ : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
snake_case__ : Any = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
snake_case__ : Optional[int] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
snake_case__ : List[str] = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
snake_case__ : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
snake_case__ : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
snake_case__ : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
snake_case__ : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
snake_case__ : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
snake_case__ : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
snake_case__ : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
snake_case__ : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
snake_case__ : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
snake_case__ : Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
snake_case__ : List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
snake_case__ : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_MAPPING
snake_case__ : Optional[int] = auto_class_update(FlaxAutoModel)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_PRETRAINING_MAPPING
snake_case__ : Optional[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
snake_case__ : Tuple = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_MASKED_LM_MAPPING
snake_case__ : List[Any] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case__ : int = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
snake_case__ : Optional[int] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
snake_case__ : int = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
snake_case__ : List[Any] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
snake_case__ : Any = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
snake_case__ : Union[str, Any] = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
snake_case__ : Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
snake_case__ : int = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class _a ( _BaseAutoModelClass ):
"""simple docstring"""
A_ = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
snake_case__ : Optional[int] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 23 |
from __future__ import annotations
__A : str = list[tuple[int, int]]
__A : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__A : List[str] = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : float , __lowerCamelCase : Node | None , ):
SCREAMING_SNAKE_CASE = pos_x
SCREAMING_SNAKE_CASE = pos_y
SCREAMING_SNAKE_CASE = (pos_y, pos_x)
SCREAMING_SNAKE_CASE = goal_x
SCREAMING_SNAKE_CASE = goal_y
SCREAMING_SNAKE_CASE = g_cost
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = self.calculate_heuristic()
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = abs(self.pos_x - self.goal_x )
SCREAMING_SNAKE_CASE = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self : Union[str, Any] , __lowerCamelCase : List[Any] ):
return self.f_cost < other.f_cost
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[int] , __lowerCamelCase : tuple[int, int] , __lowerCamelCase : tuple[int, int] ):
SCREAMING_SNAKE_CASE = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __lowerCamelCase )
SCREAMING_SNAKE_CASE = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , __lowerCamelCase )
SCREAMING_SNAKE_CASE = [self.start]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = False
def _snake_case ( self : Optional[Any] ):
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
SCREAMING_SNAKE_CASE = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
SCREAMING_SNAKE_CASE = True
return self.retrace_path(__lowerCamelCase )
self.closed_nodes.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = self.get_successors(__lowerCamelCase )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__lowerCamelCase )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE = self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__lowerCamelCase )
else:
self.open_nodes.append(__lowerCamelCase )
if not self.reached:
return [self.start.pos]
return None
def _snake_case ( self : List[Any] , __lowerCamelCase : Node ):
SCREAMING_SNAKE_CASE = []
for action in delta:
SCREAMING_SNAKE_CASE = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__lowerCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__lowerCamelCase , __lowerCamelCase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __lowerCamelCase , ) )
return successors
def _snake_case ( self : str , __lowerCamelCase : Node | None ):
SCREAMING_SNAKE_CASE = node
SCREAMING_SNAKE_CASE = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
__A : Optional[Any] = (0, 0)
__A : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('------')
__A : List[str] = GreedyBestFirst(init, goal)
__A : Tuple = greedy_bf.search()
if path:
for pos_x, pos_y in path:
__A : Optional[Any] = 2
for elem in grid:
print(elem)
| 16 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCAmelCase__ ( a__ , a__=None ) ->str:
'''simple docstring'''
_UpperCamelCase = None
if token is not None:
_UpperCamelCase = {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}
_UpperCamelCase = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
_UpperCamelCase = requests.get(a__ , headers=a__ ).json()
_UpperCamelCase = {}
try:
job_links.update({job["name"]: job["html_url"] for job in result["jobs"]} )
_UpperCamelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(a__ ):
_UpperCamelCase = requests.get(url + f'&page={i + 2}' , headers=a__ ).json()
job_links.update({job["name"]: job["html_url"] for job in result["jobs"]} )
return job_links
except Exception:
print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def lowerCAmelCase__ ( a__ , a__=None ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if token is not None:
_UpperCamelCase = {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}
_UpperCamelCase = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
_UpperCamelCase = requests.get(a__ , headers=a__ ).json()
_UpperCamelCase = {}
try:
artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]} )
_UpperCamelCase = math.ceil((result["total_count"] - 100) / 100 )
for i in range(a__ ):
_UpperCamelCase = requests.get(url + f'&page={i + 2}' , headers=a__ ).json()
artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]} )
return artifacts
except Exception:
print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ ) ->Dict:
'''simple docstring'''
_UpperCamelCase = None
if token is not None:
_UpperCamelCase = {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}
_UpperCamelCase = requests.get(a__ , headers=a__ , allow_redirects=a__ )
_UpperCamelCase = result.headers["Location"]
_UpperCamelCase = requests.get(a__ , allow_redirects=a__ )
_UpperCamelCase = os.path.join(a__ , f'{artifact_name}.zip' )
with open(a__ , "wb" ) as fp:
fp.write(response.content )
def lowerCAmelCase__ ( a__ , a__=None ) ->str:
'''simple docstring'''
_UpperCamelCase = []
_UpperCamelCase = []
_UpperCamelCase = None
with zipfile.ZipFile(a__ ) as z:
for filename in z.namelist():
if not os.path.isdir(a__ ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(a__ ) as f:
for line in f:
_UpperCamelCase = line.decode("UTF-8" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
_UpperCamelCase = line[: line.index(": " )]
_UpperCamelCase = line[line.index(": " ) + len(": " ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("FAILED " ):
# `test` is the test method that failed
_UpperCamelCase = line[len("FAILED " ) :]
failed_tests.append(a__ )
elif filename == "job_name.txt":
_UpperCamelCase = line
if len(a__ ) != len(a__ ):
raise ValueError(
f'`errors` and `failed_tests` should have the same number of elements. Got {len(a__ )} for `errors` '
f'and {len(a__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
" problem." )
_UpperCamelCase = None
if job_name and job_links:
_UpperCamelCase = job_links.get(a__ , a__ )
# A list with elements of the form (line of error, error, failed test)
_UpperCamelCase = [x + [y] + [job_link] for x, y in zip(a__ , a__ )]
return result
def lowerCAmelCase__ ( a__ , a__=None ) ->Dict:
'''simple docstring'''
_UpperCamelCase = []
_UpperCamelCase = [os.path.join(a__ , a__ ) for p in os.listdir(a__ ) if p.endswith(".zip" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(a__ , job_links=a__ ) )
return errors
def lowerCAmelCase__ ( a__ , a__=None ) ->Tuple:
'''simple docstring'''
_UpperCamelCase = Counter()
counter.update([x[1] for x in logs] )
_UpperCamelCase = counter.most_common()
_UpperCamelCase = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
_UpperCamelCase = {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}
_UpperCamelCase = dict(sorted(r.items() , key=lambda a__ : item[1]["count"] , reverse=a__ ) )
return r
def lowerCAmelCase__ ( a__ ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase = test.split("::" )[0]
if test.startswith("tests/models/" ):
_UpperCamelCase = test.split("/" )[2]
else:
_UpperCamelCase = None
return test
def lowerCAmelCase__ ( a__ , a__=None ) ->int:
'''simple docstring'''
_UpperCamelCase = [(x[0], x[1], get_model(x[2] )) for x in logs]
_UpperCamelCase = [x for x in logs if x[2] is not None]
_UpperCamelCase = {x[2] for x in logs}
_UpperCamelCase = {}
for test in tests:
_UpperCamelCase = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
_UpperCamelCase = counter.most_common()
_UpperCamelCase = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
_UpperCamelCase = sum(error_counts.values() )
if n_errors > 0:
_UpperCamelCase = {"count": n_errors, "errors": error_counts}
_UpperCamelCase = dict(sorted(r.items() , key=lambda a__ : item[1]["count"] , reverse=a__ ) )
return r
def lowerCAmelCase__ ( a__ ) ->Dict:
'''simple docstring'''
_UpperCamelCase = "| no. | error | status |"
_UpperCamelCase = "|-:|:-|:-|"
_UpperCamelCase = [header, sep]
for error in reduced_by_error:
_UpperCamelCase = reduced_by_error[error]["count"]
_UpperCamelCase = f'| {count} | {error[:100]} | |'
lines.append(a__ )
return "\n".join(a__ )
def lowerCAmelCase__ ( a__ ) ->Tuple:
'''simple docstring'''
_UpperCamelCase = "| model | no. of errors | major error | count |"
_UpperCamelCase = "|-:|-:|-:|-:|"
_UpperCamelCase = [header, sep]
for model in reduced_by_model:
_UpperCamelCase = reduced_by_model[model]["count"]
_UpperCamelCase , _UpperCamelCase = list(reduced_by_model[model]["errors"].items() )[0]
_UpperCamelCase = f'| {model} | {count} | {error[:60]} | {_count} |'
lines.append(a__ )
return "\n".join(a__ )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
parser.add_argument(
'''--output_dir''',
type=str,
required=True,
help='''Where to store the downloaded artifacts and other result files.''',
)
parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
lowerCamelCase__ = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
lowerCamelCase__ = get_job_links(args.workflow_run_id, token=args.token)
lowerCamelCase__ = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
lowerCamelCase__ = k.find(''' / ''')
lowerCamelCase__ = k[index + len(''' / ''') :]
lowerCamelCase__ = v
with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
lowerCamelCase__ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
lowerCamelCase__ = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
lowerCamelCase__ = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
lowerCamelCase__ = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
lowerCamelCase__ = reduce_by_error(errors)
lowerCamelCase__ = reduce_by_model(errors)
lowerCamelCase__ = make_github_table(reduced_by_error)
lowerCamelCase__ = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
fp.write(sa)
| 82 |
lowerCamelCase__ = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( a__ ) ->int:
'''simple docstring'''
_UpperCamelCase = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
_UpperCamelCase = Stack()
_UpperCamelCase = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(a__ ) )
elif i in operators:
# RULE 2
operator_stack.push(a__ )
elif i == ")":
# RULE 4
_UpperCamelCase = operator_stack.peek()
operator_stack.pop()
_UpperCamelCase = operand_stack.peek()
operand_stack.pop()
_UpperCamelCase = operand_stack.peek()
operand_stack.pop()
_UpperCamelCase = operators[opr](a__ , a__ )
operand_stack.push(a__ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
lowerCamelCase__ = '''(5 + ((4 * 2) * (2 + 3)))'''
# answer = 45
print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 82 | 1 |
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
a__ = """SpeechT5FeatureExtractor"""
a__ = """SpeechT5Tokenizer"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
super().__init__(a__ , a__ )
def __call__( self : Tuple , *UpperCamelCase__ : Any , **UpperCamelCase__ : int ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = kwargs.pop("""audio""" , a__ )
__magic_name__ = kwargs.pop("""text""" , a__ )
__magic_name__ = kwargs.pop("""text_target""" , a__ )
__magic_name__ = kwargs.pop("""audio_target""" , a__ )
__magic_name__ = kwargs.pop("""sampling_rate""" , a__ )
if audio is not None and text is not None:
raise ValueError(
"""Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?""" )
if audio_target is not None and text_target is not None:
raise ValueError(
"""Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?""" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"""You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.""" )
if audio is not None:
__magic_name__ = self.feature_extractor(a__ , *a__ , sampling_rate=a__ , **a__ )
elif text is not None:
__magic_name__ = self.tokenizer(a__ , **a__ )
else:
__magic_name__ = None
if audio_target is not None:
__magic_name__ = self.feature_extractor(audio_target=a__ , *a__ , sampling_rate=a__ , **a__ )
__magic_name__ = targets["input_values"]
elif text_target is not None:
__magic_name__ = self.tokenizer(a__ , **a__ )
__magic_name__ = targets["input_ids"]
else:
__magic_name__ = None
if inputs is None:
return targets
if targets is not None:
__magic_name__ = labels
__magic_name__ = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
__magic_name__ = decoder_attention_mask
return inputs
def _lowercase ( self : Dict , *UpperCamelCase__ : int , **UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
__magic_name__ = kwargs.pop("""input_values""" , a__ )
__magic_name__ = kwargs.pop("""input_ids""" , a__ )
__magic_name__ = kwargs.pop("""labels""" , a__ )
if input_values is not None and input_ids is not None:
raise ValueError("""Cannot process both `input_values` and `input_ids` inputs.""" )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"""You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.""" )
if input_values is not None:
__magic_name__ = self.feature_extractor.pad(a__ , *a__ , **a__ )
elif input_ids is not None:
__magic_name__ = self.tokenizer.pad(a__ , **a__ )
else:
__magic_name__ = None
if labels is not None:
if "input_ids" in labels or (isinstance(a__ , a__ ) and "input_ids" in labels[0]):
__magic_name__ = self.tokenizer.pad(a__ , **a__ )
__magic_name__ = targets["input_ids"]
else:
__magic_name__ = self.feature_extractor.feature_size
__magic_name__ = self.feature_extractor.num_mel_bins
__magic_name__ = self.feature_extractor.pad(a__ , *a__ , **a__ )
__magic_name__ = feature_size_hack
__magic_name__ = targets["input_values"]
else:
__magic_name__ = None
if inputs is None:
return targets
if targets is not None:
__magic_name__ = labels
__magic_name__ = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
__magic_name__ = decoder_attention_mask
return inputs
def _lowercase ( self : Dict , *UpperCamelCase__ : int , **UpperCamelCase__ : Any ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a__ , **a__ )
def _lowercase ( self : Any , *UpperCamelCase__ : Any , **UpperCamelCase__ : Optional[int] ) -> str:
"""simple docstring"""
return self.tokenizer.decode(*a__ , **a__ )
| 529 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class lowerCAmelCase :
@staticmethod
def _A ( *a__ : str , **a__ : List[str] ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
A_ : List[Any] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _A ( self : List[Any] , a__ : int , a__ : Dict , a__ : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = DepthEstimationPipeline(model=a__ , image_processor=a__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _A ( self : int , a__ : Optional[int] , a__ : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Any = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )} , a__ )
import datasets
lowerCAmelCase__ : List[str] = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
lowerCAmelCase__ : List[str] = depth_estimator(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
] )
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
] , a__ , )
@require_tf
@unittest.skip("Depth estimation is not implemented in TF" )
def _A ( self : Optional[Any] ):
'''simple docstring'''
pass
@slow
@require_torch
def _A ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : str = "Intel/dpt-large"
lowerCAmelCase__ : Dict = pipeline("depth-estimation" , model=a__ )
lowerCAmelCase__ : Any = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
lowerCAmelCase__ : Any = hashimage(outputs["depth"] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() ) , 2.662 )
@require_torch
def _A ( self : Any ):
'''simple docstring'''
self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT" )
| 378 | 0 |
from math import factorial, radians
def A__ ( __A , __A = 18 , __A = 10 ):
'''simple docstring'''
_lowerCamelCase : List[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
_lowerCamelCase : List[Any] = radians(__A )
_lowerCamelCase : Tuple = angle_in_radians
_lowerCamelCase : Any = 3
_lowerCamelCase : Any = -1
for _ in range(__A ):
result += (b * (angle_in_radians**a)) / factorial(__A )
_lowerCamelCase : List[Any] = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(__A , __A )
if __name__ == "__main__":
__import__("doctest").testmod()
| 15 |
from __future__ import annotations
class __snake_case :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = key
def _SCREAMING_SNAKE_CASE ( self : Any , _UpperCamelCase : str , _UpperCamelCase : int) ->list[str]:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : Union[str, Any] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(_UpperCamelCase) ^ key) for ch in content]
def _SCREAMING_SNAKE_CASE ( self : str , _UpperCamelCase : str , _UpperCamelCase : int) ->list[str]:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : Optional[int] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(_UpperCamelCase) ^ key) for ch in content]
def _SCREAMING_SNAKE_CASE ( self : List[str] , _UpperCamelCase : str , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : int = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
_lowerCamelCase : Any = """"""
for ch in content:
ans += chr(ord(_UpperCamelCase) ^ key)
return ans
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , _UpperCamelCase : str , _UpperCamelCase : int = 0) ->str:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
_lowerCamelCase : int = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
_lowerCamelCase : Optional[Any] = """"""
for ch in content:
ans += chr(ord(_UpperCamelCase) ^ key)
return ans
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : int = 0) ->bool:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
try:
with open(_UpperCamelCase) as fin, open("""encrypt.out""" , """w+""") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(_UpperCamelCase , _UpperCamelCase))
except OSError:
return False
return True
def _SCREAMING_SNAKE_CASE ( self : List[Any] , _UpperCamelCase : str , _UpperCamelCase : int) ->bool:
"""simple docstring"""
assert isinstance(_UpperCamelCase , _UpperCamelCase) and isinstance(_UpperCamelCase , _UpperCamelCase)
try:
with open(_UpperCamelCase) as fin, open("""decrypt.out""" , """w+""") as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(_UpperCamelCase , _UpperCamelCase))
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 15 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging
if is_vision_available():
import PIL
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['pixel_values']
def __init__( self : List[str] , a_ : bool = True , a_ : Dict[str, int] = None , a_ : PILImageResampling = PILImageResampling.BILINEAR , a_ : bool = True , a_ : Union[int, float] = 1 / 255 , a_ : bool = True , a_ : Dict[str, int] = None , a_ : bool = True , **a_ : str , )-> None:
"""simple docstring"""
super().__init__(**a_ )
SCREAMING_SNAKE_CASE__ : Any = size if size is not None else {'shortest_edge': 224}
SCREAMING_SNAKE_CASE__ : int = get_size_dict(a_ , default_to_square=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = crop_size if crop_size is not None else {'height': 256, 'width': 256}
SCREAMING_SNAKE_CASE__ : str = get_size_dict(a_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ : Optional[int] = do_resize
SCREAMING_SNAKE_CASE__ : str = size
SCREAMING_SNAKE_CASE__ : str = resample
SCREAMING_SNAKE_CASE__ : List[str] = do_rescale
SCREAMING_SNAKE_CASE__ : Tuple = rescale_factor
SCREAMING_SNAKE_CASE__ : List[str] = do_center_crop
SCREAMING_SNAKE_CASE__ : Tuple = crop_size
SCREAMING_SNAKE_CASE__ : Optional[int] = do_flip_channel_order
def __lowercase( self : str , a_ : np.ndarray , a_ : Dict[str, int] , a_ : PILImageResampling = PIL.Image.BILINEAR , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : List[Any] , )-> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_size_dict(a_ , default_to_square=a_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = get_resize_output_image_size(a_ , size=size['shortest_edge'] , default_to_square=a_ )
return resize(a_ , size=a_ , resample=a_ , data_format=a_ , **a_ )
def __lowercase( self : Any , a_ : np.ndarray , a_ : Dict[str, int] , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : Dict , )-> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_size_dict(a_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(a_ , size=(size['height'], size['width']) , data_format=a_ , **a_ )
def __lowercase( self : Union[str, Any] , a_ : np.ndarray , a_ : Union[int, float] , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : Dict , )-> Dict:
"""simple docstring"""
return rescale(a_ , scale=a_ , data_format=a_ , **a_ )
def __lowercase( self : Optional[Any] , a_ : np.ndarray , a_ : Optional[Union[str, ChannelDimension]] = None )-> np.ndarray:
"""simple docstring"""
return flip_channel_order(a_ , data_format=a_ )
def __lowercase( self : int , a_ : ImageInput , a_ : bool = None , a_ : Dict[str, int] = None , a_ : PILImageResampling = None , a_ : bool = None , a_ : float = None , a_ : bool = None , a_ : Dict[str, int] = None , a_ : bool = None , a_ : Optional[Union[str, TensorType]] = None , a_ : ChannelDimension = ChannelDimension.FIRST , **a_ : List[Any] , )-> PIL.Image.Image:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ : List[str] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ : str = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ : Any = (
do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
)
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ : Dict = get_size_dict(a_ , default_to_square=a_ )
SCREAMING_SNAKE_CASE__ : Dict = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ : List[str] = get_size_dict(a_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = make_list_of_images(a_ )
if not valid_images(a_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : List[str] = [to_numpy_array(a_ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.resize(image=a_ , size=a_ , resample=a_ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE__ : int = [self.center_crop(image=a_ , size=a_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ : Tuple = [self.rescale(image=a_ , scale=a_ ) for image in images]
# the pretrained checkpoints assume images are BGR, not RGB
if do_flip_channel_order:
SCREAMING_SNAKE_CASE__ : List[Any] = [self.flip_channel_order(image=a_ ) for image in images]
SCREAMING_SNAKE_CASE__ : int = [to_channel_dimension_format(a_ , a_ ) for image in images]
SCREAMING_SNAKE_CASE__ : List[Any] = {'pixel_values': images}
return BatchFeature(data=a_ , tensor_type=a_ )
def __lowercase( self : Optional[Any] , a_ : Any , a_ : List[Tuple] = None )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(a_ ) != len(a_ ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(a_ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = target_sizes.numpy()
SCREAMING_SNAKE_CASE__ : Any = []
for idx in range(len(a_ ) ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=a_ )
SCREAMING_SNAKE_CASE__ : Any = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(a_ )
else:
SCREAMING_SNAKE_CASE__ : str = logits.argmax(dim=1 )
SCREAMING_SNAKE_CASE__ : int = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 85 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float ) -> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 549 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( UpperCAmelCase ):
"""simple docstring"""
@staticmethod
@abstractmethod
def snake_case__ ( snake_case ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def snake_case__ ( self ):
'''simple docstring'''
raise NotImplementedError()
| 185 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 185 | 1 |
import re
def _a ( lowerCAmelCase )-> list:
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def _a ( lowerCAmelCase )-> str:
SCREAMING_SNAKE_CASE_ = split_input(str_ )
return "".join(
[''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def _a ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )-> str:
try:
SCREAMING_SNAKE_CASE_ = split_input(lowerCAmelCase )
if upper:
SCREAMING_SNAKE_CASE_ = ''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
SCREAMING_SNAKE_CASE_ = ''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def _a ( lowerCAmelCase )-> str:
return to_simple_case(lowerCAmelCase )
def _a ( lowerCAmelCase )-> str:
try:
SCREAMING_SNAKE_CASE_ = to_simple_case(lowerCAmelCase )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def _a ( lowerCAmelCase , lowerCAmelCase )-> str:
return to_complex_case(lowerCAmelCase , lowerCAmelCase , '_' )
def _a ( lowerCAmelCase , lowerCAmelCase )-> str:
return to_complex_case(lowerCAmelCase , lowerCAmelCase , '-' )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 360 |
from math import isqrt
def _a ( lowerCAmelCase )-> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase ) + 1 ) )
def _a ( lowerCAmelCase = 10**6 )-> int:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 7
while prime_candidate < max_prime:
primes_count += is_prime(lowerCAmelCase )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 360 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = """"""
for i in table:
res += inp[i - 1]
return res
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return data[1:] + data[0]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = """"""
for i in range(len(lowerCamelCase__ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = int("""0b""" + data[0] + data[-1] , 2 )
lowerCAmelCase__ = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = message[:4]
lowerCAmelCase__ = message[4:]
lowerCAmelCase__ = apply_table(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = xor(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = apply_sbox(lowerCamelCase__ , temp[:4] ) # noqa: E741
lowerCAmelCase__ = apply_sbox(lowerCamelCase__ , temp[4:] )
lowerCAmelCase__ = """0""" * (2 - len(lowerCamelCase__ )) + l # noqa: E741
lowerCAmelCase__ = """0""" * (2 - len(lowerCamelCase__ )) + r
lowerCAmelCase__ = apply_table(l + r , lowerCamelCase__ )
lowerCAmelCase__ = xor(lowerCamelCase__ , lowerCamelCase__ )
return temp + right
if __name__ == "__main__":
__lowerCAmelCase : List[str] = input("Enter 10 bit key: ")
__lowerCAmelCase : Optional[int] = input("Enter 8 bit message: ")
__lowerCAmelCase : str = [6, 3, 7, 4, 8, 5, 10, 9]
__lowerCAmelCase : Union[str, Any] = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__lowerCAmelCase : Tuple = [2, 4, 3, 1]
__lowerCAmelCase : Dict = [2, 6, 3, 1, 4, 8, 5, 7]
__lowerCAmelCase : str = [4, 1, 3, 5, 7, 2, 8, 6]
__lowerCAmelCase : List[str] = [4, 1, 2, 3, 2, 3, 4, 1]
__lowerCAmelCase : Union[str, Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__lowerCAmelCase : str = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__lowerCAmelCase : List[Any] = apply_table(key, paa_table)
__lowerCAmelCase : Dict = temp[:5]
__lowerCAmelCase : List[Any] = temp[5:]
__lowerCAmelCase : int = left_shift(left)
__lowerCAmelCase : Optional[Any] = left_shift(right)
__lowerCAmelCase : List[Any] = apply_table(left + right, pa_table)
__lowerCAmelCase : Tuple = left_shift(left)
__lowerCAmelCase : Dict = left_shift(right)
__lowerCAmelCase : Any = left_shift(left)
__lowerCAmelCase : Optional[Any] = left_shift(right)
__lowerCAmelCase : int = apply_table(left + right, pa_table)
# encryption
__lowerCAmelCase : Union[str, Any] = apply_table(message, IP)
__lowerCAmelCase : Dict = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[Any] = temp[4:] + temp[:4]
__lowerCAmelCase : List[str] = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : Any = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__lowerCAmelCase : Union[str, Any] = apply_table(CT, IP)
__lowerCAmelCase : int = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[str] = temp[4:] + temp[:4]
__lowerCAmelCase : List[str] = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[str] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 704 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : Dict = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : List[str] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 674 | 0 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(A__ )
class __UpperCamelCase ( A__ ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
self.check_model_type(_UpperCamelCase )
def UpperCamelCase( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase ):
_UpperCAmelCase , _UpperCAmelCase = {}, {}
if padding is not None:
_UpperCAmelCase = padding
if truncation is not None:
_UpperCAmelCase = truncation
if top_k is not None:
_UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ):
if isinstance(_UpperCamelCase , (Image.Image, str) ) and isinstance(_UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase = {'''image''': image, '''question''': question}
else:
_UpperCAmelCase = image
_UpperCAmelCase = super().__call__(_UpperCamelCase , **_UpperCamelCase )
return results
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=False , _UpperCamelCase=False ):
_UpperCAmelCase = load_image(inputs['''image'''] )
_UpperCAmelCase = self.tokenizer(
inputs['''question'''] , return_tensors=self.framework , padding=_UpperCamelCase , truncation=_UpperCamelCase )
_UpperCAmelCase = self.image_processor(images=_UpperCamelCase , return_tensors=self.framework )
model_inputs.update(_UpperCamelCase )
return model_inputs
def UpperCamelCase( self , _UpperCamelCase ):
_UpperCAmelCase = self.model(**_UpperCamelCase )
return model_outputs
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=5 ):
if top_k > self.model.config.num_labels:
_UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
_UpperCAmelCase = model_outputs.logits.sigmoid()[0]
_UpperCAmelCase , _UpperCAmelCase = probs.topk(_UpperCamelCase )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
_UpperCAmelCase = scores.tolist()
_UpperCAmelCase = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase , _UpperCamelCase )]
| 32 |
from ..utils import DummyObject, requires_backends
class __UpperCamelCase ( metaclass=A__ ):
__A : str = ["""torch""", """scipy"""]
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def UpperCamelCase( cls , *_UpperCamelCase , **_UpperCamelCase ):
requires_backends(cls , ['''torch''', '''scipy'''] )
| 32 | 1 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {'''vocab_file''': '''vocab.json'''}
snake_case = {
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
}
}
snake_case = {'''mgp-str''': 2_7}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : Dict = VOCAB_FILES_NAMES
A__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Optional[Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[int]="[GO]" , __lowerCamelCase : Union[str, Any]="[GO]" , __lowerCamelCase : int="[s]" , __lowerCamelCase : str="[GO]" , **__lowerCamelCase : str ):
"""simple docstring"""
super().__init__(
unk_token=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , pad_token=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding='''utf-8''' ) as vocab_handle:
_snake_case = json.load(__lowerCamelCase )
_snake_case = {v: k for k, v in self.vocab.items()}
@property
def __UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return len(self.vocab )
def __UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
return dict(self.vocab , **self.added_tokens_encoder )
def __UpperCAmelCase ( self : Dict , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
_snake_case = []
for s in text:
char_tokens.extend(__lowerCamelCase )
return char_tokens
def __UpperCAmelCase ( self : int , __lowerCamelCase : Dict ):
"""simple docstring"""
return self.vocab.get(__lowerCamelCase , self.vocab.get(self.unk_token ) )
def __UpperCAmelCase ( self : Any , __lowerCamelCase : Dict ):
"""simple docstring"""
return self.decoder.get(__lowerCamelCase )
def __UpperCAmelCase ( self : Any , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(__lowerCamelCase ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(__lowerCamelCase ) )
return
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
with open(__lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + '''\n''' )
return (vocab_file,)
| 404 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class UpperCAmelCase :
def __init__( self : Any , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Tuple , __lowerCamelCase : Any=None , __lowerCamelCase : List[Any]=None ):
"""simple docstring"""
_snake_case = start
_snake_case = end
_snake_case = val
_snake_case = (start + end) // 2
_snake_case = left
_snake_case = right
def __repr__( self : List[str] ):
"""simple docstring"""
return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""
class UpperCAmelCase :
def __init__( self : Dict , __lowerCamelCase : Sequence , __lowerCamelCase : Tuple ):
"""simple docstring"""
_snake_case = collection
_snake_case = function
if self.collection:
_snake_case = self._build_tree(0 , len(__lowerCamelCase ) - 1 )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : str , __lowerCamelCase : Dict ):
"""simple docstring"""
self._update_tree(self.root , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any] ):
"""simple docstring"""
return self._query_range(self.root , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : int ):
"""simple docstring"""
if start == end:
return SegmentTreeNode(__lowerCamelCase , __lowerCamelCase , self.collection[start] )
_snake_case = (start + end) // 2
_snake_case = self._build_tree(__lowerCamelCase , __lowerCamelCase )
_snake_case = self._build_tree(mid + 1 , __lowerCamelCase )
return SegmentTreeNode(__lowerCamelCase , __lowerCamelCase , self.fn(left.val , right.val ) , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int ):
"""simple docstring"""
if node.start == i and node.end == i:
_snake_case = val
return
if i <= node.mid:
self._update_tree(node.left , __lowerCamelCase , __lowerCamelCase )
else:
self._update_tree(node.right , __lowerCamelCase , __lowerCamelCase )
_snake_case = self.fn(node.left.val , node.right.val )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] ):
"""simple docstring"""
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , __lowerCamelCase , __lowerCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , __lowerCamelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , __lowerCamelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
if self.root is not None:
_snake_case = Queue()
queue.put(self.root )
while not queue.empty():
_snake_case = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 5_0)
snake_case = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 404 | 1 |
def A__ ( SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = 2
while True:
_UpperCAmelCase = 0
_UpperCAmelCase = fa + fa
_UpperCAmelCase , _UpperCAmelCase = fa, f
index += 1
for _ in str(SCREAMING_SNAKE_CASE_ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 32 |
from math import sqrt
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A__ ( SCREAMING_SNAKE_CASE_ : int = 1_00_01 ) -> int:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 1
while count != nth and number < 3:
number += 1
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
while count != nth:
number += 2
if is_prime(SCREAMING_SNAKE_CASE_ ):
count += 1
return number
if __name__ == "__main__":
print(f'''{solution() = }''')
| 32 | 1 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> int:
if not nums:
return 0
UpperCamelCase__ :Dict = nums[0]
UpperCamelCase__ :Dict = 0
for num in nums[1:]:
UpperCamelCase__ :Optional[Any] = (
max_excluding + num,
max(lowercase__ , lowercase__ ),
)
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709 |
def A ( lowercase__ : List[str]=2_8123 ) -> Union[str, Any]:
UpperCamelCase__ :Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
UpperCamelCase__ :Optional[int] = set()
UpperCamelCase__ :Optional[Any] = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(lowercase__ )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 383 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
"""processing_mctct""": ["""MCTCTProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase : Union[str, Any] = [
"""MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MCTCTForCTC""",
"""MCTCTModel""",
"""MCTCTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowercase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 302 |
from cva import destroyAllWindows, imread, imshow, waitKey
def A_ ( A__ ) -> Tuple:
# getting number of pixels in the image
a__ , a__ : Any = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(A__ ):
for j in range(A__ ):
a__ : str = [255, 255, 255] - img[i][j]
return img
if __name__ == "__main__":
# read original image
lowercase : Dict = imread("""image_data/lena.jpg""", 1)
# convert to its negative
lowercase : Tuple = convert_to_negative(img)
# show result image
imshow("""negative of original image""", img)
waitKey(0)
destroyAllWindows()
| 302 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlenderbotConfig''',
'''BlenderbotOnnxConfig''',
],
'''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''BlenderbotTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlenderbotForCausalLM''',
'''BlenderbotForConditionalGeneration''',
'''BlenderbotModel''',
'''BlenderbotPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''TFBlenderbotForConditionalGeneration''',
'''TFBlenderbotModel''',
'''TFBlenderbotPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = [
'''FlaxBlenderbotForConditionalGeneration''',
'''FlaxBlenderbotModel''',
'''FlaxBlenderbotPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 719 |
from __future__ import annotations
import math
def __UpperCAmelCase ( __A , __A ) -> float:
'''simple docstring'''
UpperCAmelCase__ = u
for i in range(1 , __A ):
UpperCAmelCase__ = temp * (u - i)
return temp
def __UpperCAmelCase ( ) -> None:
'''simple docstring'''
UpperCAmelCase__ = int(input("enter the numbers of values: " ) )
UpperCAmelCase__ = []
for _ in range(__A ):
y.append([] )
for i in range(__A ):
for j in range(__A ):
y[i].append(__A )
UpperCAmelCase__ = 0
print("enter the values of parameters in a list: " )
UpperCAmelCase__ = list(map(__A , input().split() ) )
print("enter the values of corresponding parameters: " )
for i in range(__A ):
UpperCAmelCase__ = float(input() )
UpperCAmelCase__ = int(input("enter the value to interpolate: " ) )
UpperCAmelCase__ = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , __A ):
for j in range(n - i ):
UpperCAmelCase__ = y[j + 1][i - 1] - y[j][i - 1]
UpperCAmelCase__ = y[0][0]
for i in range(1 , __A ):
summ += (ucal(__A , __A ) * y[0][i]) / math.factorial(__A )
print(F"""the value at {value} is {summ}""" )
if __name__ == "__main__":
main()
| 277 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCamelCase : int =logging.get_logger(__name__)
_UpperCamelCase : List[Any] ={"""tokenizer_file""": """tokenizer.json"""}
_UpperCamelCase : List[str] ={
"""tokenizer_file""": {
"""bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
"""bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
"""bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
"""bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
"""bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
"""bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
"""bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
},
}
class _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE_ = None
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case="<unk>" , _snake_case="<s>" , _snake_case="</s>" , _snake_case="<pad>" , _snake_case=False , _snake_case=False , **_snake_case , ):
"""simple docstring"""
super().__init__(
lowercase__ , lowercase__ , tokenizer_file=lowercase__ , unk_token=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , pad_token=lowercase__ , add_prefix_space=lowercase__ , clean_up_tokenization_spaces=lowercase__ , **lowercase__ , )
__lowerCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowercase__ ) != add_prefix_space:
__lowerCamelCase = getattr(lowercase__ , pre_tok_state.pop('''type''' ) )
__lowerCamelCase = add_prefix_space
__lowerCamelCase = pre_tok_class(**lowercase__ )
__lowerCamelCase = add_prefix_space
def _lowerCamelCase ( self , *_snake_case , **_snake_case ):
"""simple docstring"""
__lowerCamelCase = kwargs.get('''is_split_into_words''' , lowercase__ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
''' pretokenized inputs.''' )
return super()._batch_encode_plus(*lowercase__ , **lowercase__ )
def _lowerCamelCase ( self , *_snake_case , **_snake_case ):
"""simple docstring"""
__lowerCamelCase = kwargs.get('''is_split_into_words''' , lowercase__ )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
''' pretokenized inputs.''' )
return super()._encode_plus(*lowercase__ , **lowercase__ )
def _lowerCamelCase ( self , _snake_case , _snake_case = None ):
"""simple docstring"""
__lowerCamelCase = self._tokenizer.model.save(lowercase__ , name=lowercase__ )
return tuple(lowercase__ )
def _lowerCamelCase ( self , _snake_case ):
"""simple docstring"""
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase__ , add_special_tokens=lowercase__ ) + [self.eos_token_id] )
if len(lowercase__ ) > self.model_max_length:
__lowerCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 316 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowerCAmelCase_ : Dict = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , lowercase__ : Tuple , lowercase__ : Tuple=7 , lowercase__ : Any=3 , lowercase__ : Optional[Any]=18 , lowercase__ : int=30 , lowercase__ : Dict=400 , lowercase__ : List[Any]=None , lowercase__ : List[str]=True , lowercase__ : Optional[Any]=True , lowercase__ : Tuple=None , ) ->List[str]:
'''simple docstring'''
_UpperCamelCase : Dict = size if size is not None else {"height": 20, "width": 20}
_UpperCamelCase : Optional[int] = parent
_UpperCamelCase : str = batch_size
_UpperCamelCase : Dict = num_channels
_UpperCamelCase : Union[str, Any] = image_size
_UpperCamelCase : Tuple = min_resolution
_UpperCamelCase : Tuple = max_resolution
_UpperCamelCase : List[Any] = size
_UpperCamelCase : Dict = do_normalize
_UpperCamelCase : Tuple = do_convert_rgb
_UpperCamelCase : str = [512, 1_024, 2_048, 4_096]
_UpperCamelCase : Optional[int] = patch_size if patch_size is not None else {"height": 16, "width": 16}
def snake_case__ ( self : int ) ->Any:
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def snake_case__ ( self : Optional[int] ) ->Tuple:
'''simple docstring'''
_UpperCamelCase : str = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
_UpperCamelCase : Optional[int] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw ).convert("RGB" )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = PixaStructImageProcessor if is_vision_available() else None
def snake_case__ ( self : Union[str, Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCamelCase : Any = PixaStructImageProcessingTester(self )
@property
def snake_case__ ( self : Any ) ->Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self : int ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowercase__ , "do_convert_rgb" ) )
def snake_case__ ( self : int ) ->Tuple:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.image_processor_tester.prepare_dummy_image()
_UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
_UpperCamelCase : int = 2_048
_UpperCamelCase : List[Any] = image_processor(lowercase__ , return_tensors="pt" , max_patches=lowercase__ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0_6_0_6 ) , atol=1e-3 , rtol=1e-3 ) )
def snake_case__ ( self : Optional[Any] ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_UpperCamelCase : Tuple = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCamelCase : Tuple = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCamelCase : List[str] = image_processor(
lowercase__ , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def snake_case__ ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_UpperCamelCase : Any = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
_UpperCamelCase : List[str] = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(lowercase__ ):
_UpperCamelCase : Any = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
_UpperCamelCase : List[Any] = "Hello"
_UpperCamelCase : List[str] = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ , header_text=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCamelCase : Tuple = image_processor(
lowercase__ , return_tensors="pt" , max_patches=lowercase__ , header_text=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def snake_case__ ( self : List[str] ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCamelCase : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ , numpify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , np.ndarray )
_UpperCamelCase : Union[str, Any] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCamelCase : Dict = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCamelCase : Union[str, Any] = image_processor(
lowercase__ , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def snake_case__ ( self : List[str] ) ->Dict:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCamelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ , torchify=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , torch.Tensor )
# Test not batched input
_UpperCamelCase : Optional[int] = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCamelCase : Tuple = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCamelCase : Any = image_processor(
lowercase__ , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''' , )
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = PixaStructImageProcessor if is_vision_available() else None
def snake_case__ ( self : Optional[Any] ) ->List[Any]:
'''simple docstring'''
_UpperCamelCase : int = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCamelCase : Optional[int] = 3
@property
def snake_case__ ( self : Tuple ) ->List[str]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self : Optional[int] ) ->Tuple:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , "do_normalize" ) )
self.assertTrue(hasattr(lowercase__ , "do_convert_rgb" ) )
def snake_case__ ( self : List[Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
_UpperCamelCase : str = (
(self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCamelCase : str = image_processor(
image_inputs[0] , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCamelCase : Tuple = image_processor(
lowercase__ , return_tensors="pt" , max_patches=lowercase__ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 435 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def __lowerCamelCase ( __lowerCAmelCase : Dict ) -> Any:
# initialize config
if "resnet-50" in model_name:
snake_case = ResNetConfig.from_pretrained("""microsoft/resnet-50""" )
elif "resnet-101" in model_name:
snake_case = ResNetConfig.from_pretrained("""microsoft/resnet-101""" )
else:
raise ValueError("""Model name should include either resnet50 or resnet101""" )
snake_case = DetrConfig(use_timm_backbone=__SCREAMING_SNAKE_CASE , backbone_config=__SCREAMING_SNAKE_CASE )
# set label attributes
snake_case = """panoptic""" in model_name
if is_panoptic:
snake_case = 2_50
else:
snake_case = 91
snake_case = """huggingface/label-files"""
snake_case = """coco-detection-id2label.json"""
snake_case = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
snake_case = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def __lowerCamelCase ( __lowerCAmelCase : Dict ) -> Tuple:
# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case = []
# stem
# fmt: off
rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""") )
rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""") )
rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""") )
rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""") )
rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
] )
return rename_keys
def __lowerCamelCase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] ) -> Optional[Any]:
snake_case = state_dict.pop(__SCREAMING_SNAKE_CASE )
snake_case = val
def __lowerCamelCase ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any]=False ) -> Optional[Any]:
snake_case = """"""
if is_panoptic:
snake_case = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[:2_56, :]
snake_case = in_proj_bias[:2_56]
snake_case = in_proj_weight[2_56:5_12, :]
snake_case = in_proj_bias[2_56:5_12]
snake_case = in_proj_weight[-2_56:, :]
snake_case = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
snake_case = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case = in_proj_weight[:2_56, :]
snake_case = in_proj_bias[:2_56]
snake_case = in_proj_weight[2_56:5_12, :]
snake_case = in_proj_bias[2_56:5_12]
snake_case = in_proj_weight[-2_56:, :]
snake_case = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
snake_case = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
snake_case = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
snake_case = in_proj_weight_cross_attn[:2_56, :]
snake_case = in_proj_bias_cross_attn[:2_56]
snake_case = in_proj_weight_cross_attn[2_56:5_12, :]
snake_case = in_proj_bias_cross_attn[2_56:5_12]
snake_case = in_proj_weight_cross_attn[-2_56:, :]
snake_case = in_proj_bias_cross_attn[-2_56:]
def __lowerCamelCase ( ) -> str:
snake_case = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( __lowerCAmelCase : List[str] , __lowerCAmelCase : int=None , __lowerCAmelCase : List[str]=False ) -> str:
snake_case , snake_case = get_detr_config(__SCREAMING_SNAKE_CASE )
# load original model from torch hub
snake_case = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(F'''Converting model {model_name}...''' )
snake_case = torch.hub.load("""facebookresearch/detr""" , model_name_to_original_name[model_name] , pretrained=__SCREAMING_SNAKE_CASE ).eval()
snake_case = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(__SCREAMING_SNAKE_CASE ):
if is_panoptic:
snake_case = """detr.""" + src
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# query, key and value matrices need special treatment
read_in_q_k_v(__SCREAMING_SNAKE_CASE , is_panoptic=__SCREAMING_SNAKE_CASE )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
snake_case = state_dict.pop(__SCREAMING_SNAKE_CASE )
snake_case = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case = state_dict.pop(__SCREAMING_SNAKE_CASE )
snake_case = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
snake_case = state_dict.pop(__SCREAMING_SNAKE_CASE )
snake_case = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
snake_case = state_dict.pop(__SCREAMING_SNAKE_CASE )
snake_case = val
# finally, create HuggingFace model and load state dict
snake_case = DetrForSegmentation(__SCREAMING_SNAKE_CASE ) if is_panoptic else DetrForObjectDetection(__SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
model.eval()
# verify our conversion on an image
snake_case = """coco_panoptic""" if is_panoptic else """coco_detection"""
snake_case = DetrImageProcessor(format=__SCREAMING_SNAKE_CASE )
snake_case = processor(images=prepare_img() , return_tensors="""pt""" )
snake_case = encoding["""pixel_values"""]
snake_case = detr(__SCREAMING_SNAKE_CASE )
snake_case = model(__SCREAMING_SNAKE_CASE )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("""Uploading PyTorch model and image processor to the hub...""" )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="detr-resnet-50",
type=str,
choices=["detr-resnet-50", "detr-resnet-101"],
help="Name of the DETR model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 701 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
snake_case_ = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def lowerCAmelCase ( self : Tuple , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : List[Any] )-> Tuple:
snake_case = AudioClassificationPipeline(model=__snake_case , feature_extractor=__snake_case )
# test with a raw waveform
snake_case = np.zeros((3_40_00,) )
snake_case = np.zeros((1_40_00,) )
return audio_classifier, [audioa, audio]
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str , __snake_case : str )-> Any:
snake_case , snake_case = examples
snake_case = audio_classifier(__snake_case )
# by default a model is initialized with num_labels=2
self.assertEqual(
__snake_case , [
{"""score""": ANY(__snake_case ), """label""": ANY(__snake_case )},
{"""score""": ANY(__snake_case ), """label""": ANY(__snake_case )},
] , )
snake_case = audio_classifier(__snake_case , top_k=1 )
self.assertEqual(
__snake_case , [
{"""score""": ANY(__snake_case ), """label""": ANY(__snake_case )},
] , )
self.run_torchaudio(__snake_case )
@require_torchaudio
def lowerCAmelCase ( self : Optional[Any] , __snake_case : Optional[Any] )-> List[Any]:
import datasets
# test with a local file
snake_case = datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
snake_case = dataset[0]["""audio"""]["""array"""]
snake_case = audio_classifier(__snake_case )
self.assertEqual(
__snake_case , [
{"""score""": ANY(__snake_case ), """label""": ANY(__snake_case )},
{"""score""": ANY(__snake_case ), """label""": ANY(__snake_case )},
] , )
@require_torch
def lowerCAmelCase ( self : Tuple )-> Any:
snake_case = """anton-l/wav2vec2-random-tiny-classifier"""
snake_case = pipeline("""audio-classification""" , model=__snake_case )
snake_case = np.ones((80_00,) )
snake_case = audio_classifier(__snake_case , top_k=4 )
snake_case = [
{"""score""": 0.08_42, """label""": """no"""},
{"""score""": 0.08_38, """label""": """up"""},
{"""score""": 0.08_37, """label""": """go"""},
{"""score""": 0.08_34, """label""": """right"""},
]
snake_case = [
{"""score""": 0.08_45, """label""": """stop"""},
{"""score""": 0.08_44, """label""": """on"""},
{"""score""": 0.08_41, """label""": """right"""},
{"""score""": 0.08_34, """label""": """left"""},
]
self.assertIn(nested_simplify(__snake_case , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
snake_case = {"""array""": np.ones((80_00,) ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
snake_case = audio_classifier(__snake_case , top_k=4 )
self.assertIn(nested_simplify(__snake_case , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def lowerCAmelCase ( self : Union[str, Any] )-> Any:
import datasets
snake_case = """superb/wav2vec2-base-superb-ks"""
snake_case = pipeline("""audio-classification""" , model=__snake_case )
snake_case = datasets.load_dataset("""anton-l/superb_dummy""" , """ks""" , split="""test""" )
snake_case = np.array(dataset[3]["""speech"""] , dtype=np.floataa )
snake_case = audio_classifier(__snake_case , top_k=4 )
self.assertEqual(
nested_simplify(__snake_case , decimals=3 ) , [
{"""score""": 0.9_81, """label""": """go"""},
{"""score""": 0.0_07, """label""": """up"""},
{"""score""": 0.0_06, """label""": """_unknown_"""},
{"""score""": 0.0_01, """label""": """down"""},
] , )
@require_tf
@unittest.skip("""Audio classification is not implemented for TF""" )
def lowerCAmelCase ( self : Tuple )-> int:
pass
| 517 | 0 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config_file""": """tokenizer_config.json""",
"""merges_file""": """merges.txt""",
}
__snake_case = {
"""vocab_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
),
},
"""tokenizer_config_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
),
},
"""merges_file""": {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
),
},
}
__snake_case = """</w>"""
__snake_case = """@@ """
def __lowerCAmelCase ( lowercase : Tuple ) -> List[str]:
"""simple docstring"""
snake_case : Union[str, Any] = set()
snake_case : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case : Tuple = char
return pairs
# Speech2Text2 has no max input length
__snake_case = {"""facebook/s2t-wav2vec2-large-en-de""": 1024}
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
__UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Tuple = ['''input_ids''', '''attention_mask''']
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<s>" , UpperCamelCase__="<pad>" , UpperCamelCase__="</s>" , UpperCamelCase__="<unk>" , UpperCamelCase__=False , UpperCamelCase__=None , **UpperCamelCase__ , ) -> List[Any]:
'''simple docstring'''
super().__init__(
unk_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , do_lower_case=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case : int = do_lower_case
with open(UpperCamelCase__ , encoding="utf-8" ) as vocab_handle:
snake_case : int = json.load(UpperCamelCase__ )
snake_case : List[str] = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.' )
snake_case : Any = None
snake_case : Tuple = None
else:
with open(UpperCamelCase__ , encoding="utf-8" ) as merges_handle:
snake_case : Union[str, Any] = merges_handle.read().split("\n" )[:-1]
snake_case : List[str] = [tuple(merge.split()[:2] ) for merge in merges]
snake_case : List[str] = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
snake_case : Dict = {}
@property
def lowerCamelCase ( self ) -> int:
'''simple docstring'''
return len(self.decoder )
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
snake_case : List[Any] = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
snake_case : List[str] = get_pairs(UpperCamelCase__ )
if not pairs:
return token
while True:
snake_case : Dict = min(UpperCamelCase__ , key=lambda UpperCamelCase__ : self.bpe_ranks.get(UpperCamelCase__ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case ,snake_case : str = bigram
snake_case : Tuple = []
snake_case : Optional[int] = 0
while i < len(UpperCamelCase__ ):
try:
snake_case : Tuple = word.index(UpperCamelCase__ , UpperCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case : str = j
if word[i] == first and i < len(UpperCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case : Any = tuple(UpperCamelCase__ )
snake_case : List[Any] = new_word
if len(UpperCamelCase__ ) == 1:
break
else:
snake_case : Optional[int] = get_pairs(UpperCamelCase__ )
snake_case : Optional[int] = " ".join(UpperCamelCase__ )
if word == "\n " + BPE_TOKEN_MERGES:
snake_case : List[Any] = "\n" + BPE_TOKEN_MERGES
if word.endswith(UpperCamelCase__ ):
snake_case : str = word.replace(UpperCamelCase__ , "" )
snake_case : Dict = word.replace(" " , UpperCamelCase__ )
snake_case : List[str] = word
return word
def lowerCamelCase ( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
snake_case : int = text.lower()
snake_case : Union[str, Any] = text.split()
snake_case : List[Any] = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(UpperCamelCase__ ).split(" " ) ) )
return split_tokens
def lowerCamelCase ( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
return self.encoder.get(UpperCamelCase__ , self.encoder.get(self.unk_token ) )
def lowerCamelCase ( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
snake_case : Union[str, Any] = self.decoder.get(UpperCamelCase__ , self.unk_token )
return result
def lowerCamelCase ( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
snake_case : Tuple = " ".join(UpperCamelCase__ )
# make sure @@ tokens are concatenated
snake_case : Optional[Any] = "".join(string.split(UpperCamelCase__ ) )
return string
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
snake_case : Optional[Any] = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case : Optional[Any] = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCamelCase__ , ensure_ascii=UpperCamelCase__ ) + "\n" )
snake_case : int = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda UpperCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
" Please check that the tokenizer is not corrupted!" )
snake_case : Optional[int] = token_index
writer.write(" ".join(UpperCamelCase__ ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 178 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__snake_case = logging.get_logger(__name__)
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__=None , UpperCamelCase__=None ) -> Optional[Any]:
'''simple docstring'''
if not conversation_id:
snake_case : Union[str, Any] = uuid.uuida()
if past_user_inputs is None:
snake_case : Optional[Any] = []
if generated_responses is None:
snake_case : Optional[Any] = []
snake_case : uuid.UUID = conversation_id
snake_case : List[str] = past_user_inputs
snake_case : List[str] = generated_responses
snake_case : Optional[str] = text
def __eq__( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Dict:
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
F'with: "{text}".' )
snake_case : int = text
else:
logger.warning(
F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' )
else:
snake_case : Any = text
def lowerCamelCase ( self ) -> List[str]:
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
snake_case : Dict = None
def lowerCamelCase ( self , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
self.generated_responses.append(UpperCamelCase__ )
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[Any] = F'Conversation id: {self.uuid} \n'
for is_user, text in self.iter_texts():
snake_case : List[str] = "user" if is_user else "bot"
output += F'{name} >> {text} \n'
return output
@add_end_docstrings(
snake_case_ , R'''
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
''' , )
class _lowerCAmelCase ( snake_case_ ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
'''simple docstring'''
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
if self.tokenizer.pad_token_id is None:
snake_case : str = self.tokenizer.eos_token
def lowerCamelCase ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Tuple = {}
snake_case : Optional[int] = {}
snake_case : Optional[Any] = {}
if min_length_for_response is not None:
snake_case : int = min_length_for_response
if minimum_tokens is not None:
snake_case : Dict = minimum_tokens
if "max_length" in generate_kwargs:
snake_case : Any = generate_kwargs["max_length"]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
snake_case : Optional[Any] = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(UpperCamelCase__ )
return preprocess_params, forward_params, postprocess_params
def __call__( self , UpperCamelCase__ , UpperCamelCase__=0 , **UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
snake_case : int = super().__call__(UpperCamelCase__ , num_workers=UpperCamelCase__ , **UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and len(UpperCamelCase__ ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=32 ) -> Dict[str, Any]:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("ConversationalPipeline, expects Conversation as inputs" )
if conversation.new_user_input is None:
raise ValueError(
F'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
"Add user inputs with the conversation's `add_user_input` method" )
if hasattr(self.tokenizer , "_build_conversation_input_ids" ):
snake_case : Optional[Any] = self.tokenizer._build_conversation_input_ids(UpperCamelCase__ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
snake_case : Optional[Any] = self._legacy_parse_and_tokenize(UpperCamelCase__ )
if self.framework == "pt":
snake_case : Union[str, Any] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
snake_case : Optional[int] = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=10 , **UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Dict = generate_kwargs.get("max_length" , self.model.config.max_length )
snake_case : Optional[Any] = model_inputs["input_ids"].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
snake_case : List[str] = max_length - minimum_tokens
snake_case : Dict = model_inputs["input_ids"][:, -trim:]
if "attention_mask" in model_inputs:
snake_case : List[Any] = model_inputs["attention_mask"][:, -trim:]
snake_case : Union[str, Any] = model_inputs.pop("conversation" )
snake_case : Union[str, Any] = max_length
snake_case : Any = self.model.generate(**UpperCamelCase__ , **UpperCamelCase__ )
if self.model.config.is_encoder_decoder:
snake_case : Optional[int] = 1
else:
snake_case : int = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__=True ) -> Tuple:
'''simple docstring'''
snake_case : Union[str, Any] = model_outputs["output_ids"]
snake_case : str = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ , )
snake_case : List[Any] = model_outputs["conversation"]
conversation.mark_processed()
conversation.append_response(UpperCamelCase__ )
return conversation
def lowerCamelCase ( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
snake_case : str = self.tokenizer.eos_token_id
snake_case : str = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) )
if len(UpperCamelCase__ ) > self.tokenizer.model_max_length:
snake_case : str = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 178 | 1 |
"""simple docstring"""
import operator as op
def _SCREAMING_SNAKE_CASE ( _lowercase : Union[str, Any] ) ->Any:
'''simple docstring'''
a : str = []
a : List[str] = lambda _lowercase , _lowercase : int(x / y ) # noqa: E731 integer division operation
a : Union[str, Any] = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ) , "Action".center(12 ) , "Stack" , sep=" | " )
print("-" * (30 + len(_lowercase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(_lowercase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("push(" + x + ")").ljust(12 ) , ",".join(_lowercase ) , sep=" | " )
else:
a : Optional[Any] = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + b + ")").ljust(12 ) , ",".join(_lowercase ) , sep=" | " )
a : List[str] = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ) , ("pop(" + a + ")").ljust(12 ) , ",".join(_lowercase ) , sep=" | " )
stack.append(
str(opr[x](int(_lowercase ) , int(_lowercase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("push(" + a + x + b + ")").ljust(12 ) , ",".join(_lowercase ) , sep=" | " , )
return int(stack[0] )
if __name__ == "__main__":
a : Optional[Any] = input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
print('''\n\tResult = ''', solve(Postfix))
| 31 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
a : Optional[int] = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def _SCREAMING_SNAKE_CASE ( _lowercase : Any=None ) ->Optional[Any]:
'''simple docstring'''
if subparsers is not None:
a : int = subparsers.add_parser("tpu-config" , description=_description )
else:
a : List[Any] = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
a : Dict = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=_lowercase , default=_lowercase , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=_lowercase , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=_lowercase , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
a : Any = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=_lowercase , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=_lowercase )
return parser
def _SCREAMING_SNAKE_CASE ( _lowercase : Any ) ->Tuple:
'''simple docstring'''
a : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_lowercase ):
a : Optional[Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
a : int = defaults.command_file
if not args.command and defaults.commands is not None:
a : Union[str, Any] = defaults.commands
if not args.tpu_name:
a : int = defaults.tpu_name
if not args.tpu_zone:
a : Union[str, Any] = defaults.tpu_zone
if args.accelerate_version == "dev":
a : int = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
a : Optional[Any] = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , _lowercase ):
a : Optional[Any] = F"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
a : int = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _lowercase ):
a : Union[str, Any] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
a : Tuple = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [F"""pip install {args.accelerate_version}"""]
new_cmd += args.command
a : List[Any] = "; ".join(_lowercase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
a : str = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"""Running {' '.join(_lowercase )}""" )
return
subprocess.run(_lowercase )
print("Successfully setup pod." )
def _SCREAMING_SNAKE_CASE ( ) ->Tuple:
'''simple docstring'''
a : List[Any] = tpu_command_parser()
a : Optional[int] = parser.parse_args()
tpu_command_launcher(_lowercase )
| 31 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {
"""configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""AlbertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""AlbertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AlbertForMaskedLM""",
"""AlbertForMultipleChoice""",
"""AlbertForPreTraining""",
"""AlbertForQuestionAnswering""",
"""AlbertForSequenceClassification""",
"""AlbertForTokenClassification""",
"""AlbertModel""",
"""AlbertPreTrainedModel""",
"""load_tf_weights_in_albert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAlbertForMaskedLM""",
"""TFAlbertForMultipleChoice""",
"""TFAlbertForPreTraining""",
"""TFAlbertForQuestionAnswering""",
"""TFAlbertForSequenceClassification""",
"""TFAlbertForTokenClassification""",
"""TFAlbertMainLayer""",
"""TFAlbertModel""",
"""TFAlbertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""FlaxAlbertForMaskedLM""",
"""FlaxAlbertForMultipleChoice""",
"""FlaxAlbertForPreTraining""",
"""FlaxAlbertForQuestionAnswering""",
"""FlaxAlbertForSequenceClassification""",
"""FlaxAlbertForTokenClassification""",
"""FlaxAlbertModel""",
"""FlaxAlbertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 67 |
'''simple docstring'''
from collections import defaultdict
def UpperCAmelCase ( A : int ):
SCREAMING_SNAKE_CASE : List[Any] = 1
SCREAMING_SNAKE_CASE : Dict = True
for v in tree[start]:
if v not in visited:
ret += dfs(A )
if ret % 2 == 0:
cuts.append(A )
return ret
def UpperCAmelCase ( ):
dfs(1 )
if __name__ == "__main__":
lowerCAmelCase_ , lowerCAmelCase_ : Dict = 10, 9
lowerCAmelCase_ : Dict = defaultdict(list)
lowerCAmelCase_ : dict[int, bool] = {}
lowerCAmelCase_ : list[int] = []
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 527 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowercase__ : Optional[Any] = str(bin(_lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowercase__ : Optional[Any] = str(bin(_lowerCAmelCase ) )[2:]
if shift_amount >= len(_lowerCAmelCase ):
return "0b0"
lowercase__ : int = binary_number[: len(_lowerCAmelCase ) - shift_amount]
return "0b" + shifted_binary_number
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
lowercase__ : Tuple = '0' + str(bin(_lowerCAmelCase ) ).strip('-' )[2:]
else: # Get binary (2's complement) representation of negative number
lowercase__ : Tuple = len(bin(_lowerCAmelCase )[3:] ) # Find 2's complement of number
lowercase__ : str = bin(abs(_lowerCAmelCase ) - (1 << binary_number_length) )[3:]
lowercase__ : Union[str, Any] = (
'1' + '0' * (binary_number_length - len(_lowerCAmelCase )) + binary_number
)
if shift_amount >= len(_lowerCAmelCase ):
return "0b" + binary_number[0] * len(_lowerCAmelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(_lowerCAmelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 645 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_UpperCamelCase : List[str] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
lowercase__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
lowercase__ : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
lowercase__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
lowercase__ : Optional[Any] = value
elif weight_type == "weight_g":
lowercase__ : Dict = value
elif weight_type == "weight_v":
lowercase__ : List[str] = value
elif weight_type == "bias":
lowercase__ : Optional[Any] = value
else:
lowercase__ : List[str] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
lowercase__ : Tuple = []
lowercase__ : List[str] = fairseq_model.state_dict()
lowercase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
lowercase__ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == 'group' , )
lowercase__ : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
lowercase__ : List[Any] = 'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
lowercase__ : int = True
if "*" in mapped_key:
lowercase__ : Optional[int] = name.split(_lowerCAmelCase )[0].split('.' )[-2]
lowercase__ : List[str] = mapped_key.replace('*' , _lowerCAmelCase )
if "weight_g" in name:
lowercase__ : List[Any] = 'weight_g'
elif "weight_v" in name:
lowercase__ : int = 'weight_v'
elif "bias" in name:
lowercase__ : Dict = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase__ : Union[str, Any] = 'weight'
else:
lowercase__ : int = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : int = full_name.split('conv_layers.' )[-1]
lowercase__ : int = name.split('.' )
lowercase__ : int = int(items[0] )
lowercase__ : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowercase__ : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowercase__ : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
lowercase__ : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
lowercase__ : int = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def a_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : str=None , _lowerCAmelCase : Tuple=True ):
'''simple docstring'''
if config_path is not None:
lowercase__ : Any = UniSpeechSatConfig.from_pretrained(_lowerCAmelCase )
else:
lowercase__ : Any = UniSpeechSatConfig()
lowercase__ : Union[str, Any] = ''
if is_finetuned:
lowercase__ : Optional[Any] = UniSpeechSatForCTC(_lowerCAmelCase )
else:
lowercase__ : List[Any] = UniSpeechSatForPreTraining(_lowerCAmelCase )
lowercase__ , lowercase__ , lowercase__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
lowercase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
_UpperCamelCase : str = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 645 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase__ : str = 5_00_00
UpperCAmelCase__ : int = 50_00
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = os.path.split(__file__)
UpperCAmelCase__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def A ( snake_case__ : str , snake_case__ : Dict ) -> Tuple:
'''simple docstring'''
for i in range(snake_case__ ):
__snake_case = dataset[i]
@get_duration
def A ( snake_case__ : str , snake_case__ : Optional[int] , snake_case__ : int ) -> List[Any]:
'''simple docstring'''
for i in range(0 , len(snake_case__ ) , snake_case__ ):
__snake_case = dataset[i : i + batch_size]
@get_duration
def A ( snake_case__ : Dict , snake_case__ : str , snake_case__ : int ) -> List[str]:
'''simple docstring'''
with dataset.formatted_as(type=snake_case__ ):
for i in range(snake_case__ ):
__snake_case = dataset[i]
@get_duration
def A ( snake_case__ : str , snake_case__ : Tuple , snake_case__ : str , snake_case__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
with dataset.formatted_as(type=snake_case__ ):
for i in range(0 , snake_case__ , snake_case__ ):
__snake_case = dataset[i : i + batch_size]
def A ( ) -> Optional[int]:
'''simple docstring'''
__snake_case = {"""num examples""": SPEED_TEST_N_EXAMPLES}
__snake_case = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
(read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
]
__snake_case = [
(read, {"""length""": SMALL_TEST}),
(read, {"""length""": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
(read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
(read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
(read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
__snake_case = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
__snake_case = generate_example_dataset(
os.path.join(snake_case__ , 'dataset.arrow' ) , snake_case__ , num_examples=snake_case__ , seq_shapes={'list': (100,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(snake_case__ ) )
__snake_case = func(snake_case__ , **snake_case__ )
print('shuffling dataset' )
__snake_case = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(snake_case__ ) )
__snake_case = func(
snake_case__ , **snake_case__ )
with open(snake_case__ , 'wb' ) as f:
f.write(json.dumps(snake_case__ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 313 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
_UpperCAmelCase = get_logger(__name__)
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase=0 ):
os.makedirs(lowercase , exist_ok=lowercase )
with FSDP.state_dict_type(
lowercase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
SCREAMING_SNAKE_CASE_: List[Any] =model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
SCREAMING_SNAKE_CASE_: str =f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
SCREAMING_SNAKE_CASE_: List[Any] =os.path.join(lowercase , lowercase )
if accelerator.process_index == 0:
logger.info(f'''Saving model to {output_model_file}''' )
torch.save(lowercase , lowercase )
logger.info(f'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
SCREAMING_SNAKE_CASE_: Dict =(
f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
SCREAMING_SNAKE_CASE_: int =os.path.join(lowercase , lowercase )
logger.info(f'''Saving model to {output_model_file}''' )
torch.save(lowercase , lowercase )
logger.info(f'''Model saved to {output_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
SCREAMING_SNAKE_CASE_: Dict =os.path.join(lowercase , f'''{MODEL_NAME}_{model_index}''' )
os.makedirs(lowercase , exist_ok=lowercase )
logger.info(f'''Saving model to {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_: Dict ={"""model""": state_dict}
dist_cp.save_state_dict(
state_dict=lowercase , storage_writer=dist_cp.FileSystemWriter(lowercase ) , planner=DefaultSavePlanner() , )
logger.info(f'''Model saved to {ckpt_dir}''' )
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
lowercase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(lowercase ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"""Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
"""initializing FSDP object""" )
return
SCREAMING_SNAKE_CASE_: List[Any] =f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
SCREAMING_SNAKE_CASE_: int =os.path.join(lowercase , lowercase )
logger.info(f'''Loading model from {input_model_file}''' )
SCREAMING_SNAKE_CASE_: List[Any] =torch.load(lowercase )
logger.info(f'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
SCREAMING_SNAKE_CASE_: Dict =(
f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
if model_index == 0
else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
)
SCREAMING_SNAKE_CASE_: Dict =os.path.join(lowercase , lowercase )
logger.info(f'''Loading model from {input_model_file}''' )
SCREAMING_SNAKE_CASE_: int =torch.load(lowercase )
logger.info(f'''Model loaded from {input_model_file}''' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
SCREAMING_SNAKE_CASE_: Optional[Any] =(
os.path.join(lowercase , f'''{MODEL_NAME}_{model_index}''' )
if f'''{MODEL_NAME}''' not in input_dir
else input_dir
)
logger.info(f'''Loading model from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_: List[Any] ={"""model""": model.state_dict()}
dist_cp.load_state_dict(
state_dict=lowercase , storage_reader=dist_cp.FileSystemReader(lowercase ) , planner=DefaultLoadPlanner() , )
SCREAMING_SNAKE_CASE_: Optional[Any] =state_dict["""model"""]
logger.info(f'''Model loaded from {ckpt_dir}''' )
model.load_state_dict(lowercase )
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=0 ):
os.makedirs(lowercase , exist_ok=lowercase )
with FSDP.state_dict_type(
lowercase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
SCREAMING_SNAKE_CASE_: Optional[int] =FSDP.optim_state_dict(lowercase , lowercase )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
SCREAMING_SNAKE_CASE_: Optional[int] =(
f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
SCREAMING_SNAKE_CASE_: Tuple =os.path.join(lowercase , lowercase )
logger.info(f'''Saving Optimizer state to {output_optimizer_file}''' )
torch.save(lowercase , lowercase )
logger.info(f'''Optimizer state saved in {output_optimizer_file}''' )
else:
SCREAMING_SNAKE_CASE_: Dict =os.path.join(lowercase , f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
os.makedirs(lowercase , exist_ok=lowercase )
logger.info(f'''Saving Optimizer state to {ckpt_dir}''' )
dist_cp.save_state_dict(
state_dict={"""optimizer""": optim_state} , storage_writer=dist_cp.FileSystemWriter(lowercase ) , planner=DefaultSavePlanner() , )
logger.info(f'''Optimizer state saved in {ckpt_dir}''' )
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
lowercase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
SCREAMING_SNAKE_CASE_: int =None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
SCREAMING_SNAKE_CASE_: Tuple =(
f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
)
SCREAMING_SNAKE_CASE_: Union[str, Any] =os.path.join(lowercase , lowercase )
logger.info(f'''Loading Optimizer state from {input_optimizer_file}''' )
SCREAMING_SNAKE_CASE_: Optional[Any] =torch.load(lowercase )
logger.info(f'''Optimizer state loaded from {input_optimizer_file}''' )
else:
SCREAMING_SNAKE_CASE_: str =(
os.path.join(lowercase , f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
if f'''{OPTIMIZER_NAME}''' not in input_dir
else input_dir
)
logger.info(f'''Loading Optimizer from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_: Any =load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="""optimizer""" , storage_reader=dist_cp.FileSystemReader(lowercase ) , )
SCREAMING_SNAKE_CASE_: Any =optim_state["""optimizer"""]
logger.info(f'''Optimizer loaded from {ckpt_dir}''' )
SCREAMING_SNAKE_CASE_: Tuple =FSDP.optim_state_dict_to_load(lowercase , lowercase , lowercase )
optimizer.load_state_dict(lowercase )
| 409 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['image_processor', 'tokenizer']
UpperCamelCase = 'CLIPImageProcessor'
UpperCamelCase = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : Union[str, Any] , A_ : str=None , A_ : Union[str, Any]=None , **A_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase__ , )
lowerCamelCase_ = kwargs.pop('feature_extractor' )
lowerCamelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCAmelCase__ , UpperCAmelCase__ )
def __call__( self : Any , A_ : Dict=None , A_ : Union[str, Any]=None , A_ : int=None , **A_ : Union[str, Any] ) -> int:
"""simple docstring"""
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
lowerCamelCase_ = self.tokenizer(UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
if images is not None:
lowerCamelCase_ = self.image_processor(UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
if text is not None and images is not None:
lowerCamelCase_ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase__ ) , tensor_type=UpperCAmelCase__ )
def a__ ( self : List[str] , *A_ : Dict , **A_ : int ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
def a__ ( self : Optional[int] , *A_ : Optional[Any] , **A_ : str ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
@property
def a__ ( self : Tuple ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.tokenizer.model_input_names
lowerCamelCase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def a__ ( self : Tuple ) -> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase__ , )
return self.image_processor_class
@property
def a__ ( self : Any ) -> str:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase__ , )
return self.image_processor
| 718 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A:
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = None
UpperCamelCase = None
lowerCamelCase : str = namedtuple("CoinsDistribResult", "moves excess")
def _SCREAMING_SNAKE_CASE ( lowercase : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(lowercase : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(lowercase : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(lowercase ) != count_coins(lowercase ):
raise ValueError('The nodes number should be same as the number of coins' )
# Main calculation
def get_distrib(lowercase : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowerCamelCase_ , lowerCamelCase_ = get_distrib(node.left )
lowerCamelCase_ , lowerCamelCase_ = get_distrib(node.right )
lowerCamelCase_ = 1 - left_distrib_excess
lowerCamelCase_ = 1 - right_distrib_excess
lowerCamelCase_ = (
left_distrib_moves
+ right_distrib_moves
+ abs(lowercase )
+ abs(lowercase )
)
lowerCamelCase_ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(lowercase , lowercase )
return get_distrib(lowercase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 651 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
}
}
__lowerCamelCase = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 128,
'''task_specific_params.summarization.min_length''': 12,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 142,
'''task_specific_params.summarization_cnn.min_length''': 56,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 62,
'''task_specific_params.summarization_xsum.min_length''': 11,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(__UpperCAmelCase ) , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase ) , x.transpose() ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase ) , transpose(__UpperCAmelCase ).numpy() ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase , axes=(1, 2, 0) ) , transpose(__UpperCAmelCase , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase ) , transpose(__UpperCAmelCase ).numpy() ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase , axes=(1, 2, 0) ) , transpose(__UpperCAmelCase , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase ) , np.asarray(transpose(__UpperCAmelCase ) ) ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(transpose(__UpperCAmelCase , axes=(1, 2, 0) ) , np.asarray(transpose(__UpperCAmelCase , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (4, 3) ) , np.reshape(__UpperCAmelCase , (4, 3) ) ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (12, 5) ) , np.reshape(__UpperCAmelCase , (12, 5) ) ) )
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (4, 3) ) , reshape(__UpperCAmelCase , (4, 3) ).numpy() ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (12, 5) ) , reshape(__UpperCAmelCase , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (4, 3) ) , reshape(__UpperCAmelCase , (4, 3) ).numpy() ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (12, 5) ) , reshape(__UpperCAmelCase , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (4, 3) ) , np.asarray(reshape(__UpperCAmelCase , (4, 3) ) ) ) )
__lowerCamelCase = np.random.randn(3 , 4 , 5 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(reshape(__UpperCAmelCase , (12, 5) ) , np.asarray(reshape(__UpperCAmelCase , (12, 5) ) ) ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase ) , np.squeeze(__UpperCAmelCase ) ) )
__lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase , axis=2 ) , np.squeeze(__UpperCAmelCase , axis=2 ) ) )
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(1 , 3 , 4 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase ) , squeeze(__UpperCAmelCase ).numpy() ) )
__lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase , axis=2 ) , squeeze(__UpperCAmelCase , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(1 , 3 , 4 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase ) , squeeze(__UpperCAmelCase ).numpy() ) )
__lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase , axis=2 ) , squeeze(__UpperCAmelCase , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(1 , 3 , 4 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase ) , np.asarray(squeeze(__UpperCAmelCase ) ) ) )
__lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(squeeze(__UpperCAmelCase , axis=2 ) , np.asarray(squeeze(__UpperCAmelCase , axis=2 ) ) ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase , axis=1 ) , np.expand_dims(__UpperCAmelCase , axis=1 ) ) )
@require_torch
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = torch.tensor(__UpperCAmelCase )
self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase , axis=1 ) , expand_dims(__UpperCAmelCase , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = tf.constant(__UpperCAmelCase )
self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase , axis=1 ) , expand_dims(__UpperCAmelCase , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = np.random.randn(3 , 4 )
__lowerCamelCase = jnp.array(__UpperCAmelCase )
self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase , axis=1 ) , np.asarray(expand_dims(__UpperCAmelCase , axis=1 ) ) ) )
| 175 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=64 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=1 , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = seq_length
__lowerCamelCase = is_training
__lowerCamelCase = use_input_mask
__lowerCamelCase = use_token_type_ids
__lowerCamelCase = use_labels
__lowerCamelCase = vocab_size
__lowerCamelCase = hidden_size
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = num_attention_heads
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = hidden_dropout_prob
__lowerCamelCase = attention_probs_dropout_prob
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = type_vocab_size
__lowerCamelCase = type_sequence_label_size
__lowerCamelCase = initializer_range
__lowerCamelCase = num_labels
__lowerCamelCase = num_choices
__lowerCamelCase = scope
__lowerCamelCase = q_groups
__lowerCamelCase = k_groups
__lowerCamelCase = v_groups
__lowerCamelCase = post_attention_groups
__lowerCamelCase = intermediate_groups
__lowerCamelCase = output_groups
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase = None
if self.use_input_mask:
__lowerCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
'''simple docstring'''
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = SqueezeBertModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , __UpperCAmelCase )
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = SqueezeBertForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = SqueezeBertForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = SqueezeBertForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_labels
__lowerCamelCase = SqueezeBertForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.num_choices
__lowerCamelCase = SqueezeBertForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
((__lowerCamelCase) ,(__lowerCamelCase) ,(__lowerCamelCase) ,(__lowerCamelCase) ,(__lowerCamelCase) ,(__lowerCamelCase)) = config_and_inputs
__lowerCamelCase = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCAmelCase__ = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SqueezeBertModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=__UpperCAmelCase , dim=37 )
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = SqueezeBertModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''' )
__lowerCamelCase = torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]] )
__lowerCamelCase = model(__UpperCAmelCase )[0]
__lowerCamelCase = torch.Size((1, 3) )
self.assertEqual(output.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([[0.6_401, -0.0_349, -0.6_041]] )
self.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4 ) )
| 175 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : float , UpperCamelCase : float ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : int ) -> int:
"""simple docstring"""
a_ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : int ) -> int:
"""simple docstring"""
a_ = 0
while number > 0:
a_ = number % 10
sum_of_digits += last_digit
a_ = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : int = 100 ) -> int:
"""simple docstring"""
a_ = factorial(UpperCamelCase )
a_ = split_and_add(UpperCamelCase )
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 403 | 0 |
'''simple docstring'''
import socket
def _lowercase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : List[str] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__UpperCAmelCase : Dict = socket.gethostname()
__UpperCAmelCase : Optional[Any] = 1_2312
sock.connect((host, port) )
sock.send(b"Hello server!" )
with open("Received_file" , "wb" ) as out_file:
print("File opened" )
print("Receiving data..." )
while True:
__UpperCAmelCase : List[Any] = sock.recv(1024 )
if not data:
break
out_file.write(lowerCamelCase__ )
print("Successfully received the file" )
sock.close()
print("Connection closed" )
if __name__ == "__main__":
main()
| 168 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : List[str] = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
class __A (__magic_name__ ):
snake_case :List[str] = "van"
def __init__( self , UpperCamelCase_=2_24 , UpperCamelCase_=3 , UpperCamelCase_=[7, 3, 3, 3] , UpperCamelCase_=[4, 2, 2, 2] , UpperCamelCase_=[64, 1_28, 3_20, 5_12] , UpperCamelCase_=[3, 3, 12, 3] , UpperCamelCase_=[8, 8, 4, 4] , UpperCamelCase_="gelu" , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-6 , UpperCamelCase_=1E-2 , UpperCamelCase_=0.0 , UpperCamelCase_=0.0 , **UpperCamelCase_ , ):
super().__init__(**UpperCamelCase_ )
__UpperCAmelCase : List[Any] = image_size
__UpperCAmelCase : Dict = num_channels
__UpperCAmelCase : Optional[Any] = patch_sizes
__UpperCAmelCase : Tuple = strides
__UpperCAmelCase : Any = hidden_sizes
__UpperCAmelCase : str = depths
__UpperCAmelCase : Optional[Any] = mlp_ratios
__UpperCAmelCase : Union[str, Any] = hidden_act
__UpperCAmelCase : int = initializer_range
__UpperCAmelCase : Dict = layer_norm_eps
__UpperCAmelCase : int = layer_scale_init_value
__UpperCAmelCase : Optional[int] = drop_path_rate
__UpperCAmelCase : str = dropout_rate
| 168 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def A_ ( self : Optional[int] , __a : Optional[int]=None , __a : Optional[int]=None , __a : Union[str, Any]=None , **__a : int ) -> Dict:
'''simple docstring'''
if tokenize_kwargs is None:
__snake_case : List[str] = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
__snake_case : List[Any] = truncation
__snake_case : Tuple = tokenize_kwargs
__snake_case : Optional[int] = {}
if return_tensors is not None:
__snake_case : str = return_tensors
return preprocess_params, {}, postprocess_params
def A_ ( self : Tuple , __a : Optional[int] , **__a : int ) -> Dict[str, GenericTensor]:
'''simple docstring'''
__snake_case : int = self.framework
__snake_case : List[str] = self.tokenizer(__a , return_tensors=__a , **__a )
return model_inputs
def A_ ( self : List[Any] , __a : Tuple ) -> Tuple:
'''simple docstring'''
__snake_case : List[Any] = self.model(**__a )
return model_outputs
def A_ ( self : List[str] , __a : Tuple , __a : Union[str, Any]=False ) -> Dict:
'''simple docstring'''
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self : Optional[int] , *__a : str , **__a : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return super().__call__(*__a , **__a )
| 124 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
A__ : Union[str, Any] = logging.getLogger(__name__)
A__ : List[str] = tf.data.AUTOTUNE
def a_ ( ) -> Tuple:
__snake_case : str = argparse.ArgumentParser(description='Train a masked language model on TPU.' )
parser.add_argument(
'--pretrained_model_config' ,type=_UpperCAmelCase ,default='roberta-base' ,help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!' ,)
parser.add_argument(
'--tokenizer' ,type=_UpperCAmelCase ,default='unigram-tokenizer-wikitext' ,help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.' ,)
parser.add_argument(
'--per_replica_batch_size' ,type=_UpperCAmelCase ,default=8 ,help='Batch size per TPU core.' ,)
parser.add_argument(
'--no_tpu' ,action='store_true' ,help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.' ,)
parser.add_argument(
'--tpu_name' ,type=_UpperCAmelCase ,help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.' ,default='local' ,)
parser.add_argument(
'--tpu_zone' ,type=_UpperCAmelCase ,help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.' ,)
parser.add_argument(
'--gcp_project' ,type=_UpperCAmelCase ,help='Google cloud project name. Only used for non-Colab TPU nodes.' )
parser.add_argument(
'--bfloat16' ,action='store_true' ,help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.' ,)
parser.add_argument(
'--train_dataset' ,type=_UpperCAmelCase ,help='Path to training dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.' ,)
parser.add_argument(
'--shuffle_buffer_size' ,type=_UpperCAmelCase ,default=2**18 ,help='Size of the shuffle buffer (in samples)' ,)
parser.add_argument(
'--eval_dataset' ,type=_UpperCAmelCase ,help='Path to evaluation dataset to load. If the path begins with `gs://`'
' then the dataset will be loaded from a Google Cloud Storage bucket.' ,)
parser.add_argument(
'--num_epochs' ,type=_UpperCAmelCase ,default=1 ,help='Number of epochs to train for.' ,)
parser.add_argument(
'--learning_rate' ,type=_UpperCAmelCase ,default=1E-4 ,help='Learning rate to use for training.' ,)
parser.add_argument(
'--weight_decay_rate' ,type=_UpperCAmelCase ,default=1E-3 ,help='Weight decay rate to use for training.' ,)
parser.add_argument(
'--max_length' ,type=_UpperCAmelCase ,default=5_12 ,help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py' ,)
parser.add_argument(
'--mlm_probability' ,type=_UpperCAmelCase ,default=0.1_5 ,help='Fraction of tokens to mask during training.' ,)
parser.add_argument('--output_dir' ,type=_UpperCAmelCase ,required=_UpperCAmelCase ,help='Path to save model checkpoints to.' )
parser.add_argument('--hub_model_id' ,type=_UpperCAmelCase ,help='Model ID to upload to on the Hugging Face Hub.' )
__snake_case : Dict = parser.parse_args()
return args
def a_ ( _UpperCAmelCase : int ) -> str:
try:
if args.tpu_name:
__snake_case : List[str] = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name ,zone=args.tpu_zone ,project=args.gcp_project )
else:
__snake_case : Any = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
'--gcp_project. When running on a TPU VM, use --tpu_name local.' )
tf.config.experimental_connect_to_cluster(_UpperCAmelCase )
tf.tpu.experimental.initialize_tpu_system(_UpperCAmelCase )
return tpu
def a_ ( _UpperCAmelCase : int ) -> Optional[int]:
__snake_case : Union[str, Any] = 0
for file in file_list:
__snake_case : Optional[int] = file.split('/' )[-1]
__snake_case : Optional[Any] = re.search(r'-\d+-(\d+)\.tfrecord' ,_UpperCAmelCase ).group(1 )
__snake_case : Any = int(_UpperCAmelCase )
num_samples += sample_count
return num_samples
def a_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : int ,_UpperCAmelCase : Tuple=None ) -> Dict:
__snake_case : int = count_samples(_UpperCAmelCase )
__snake_case : Optional[Any] = tf.data.Dataset.from_tensor_slices(_UpperCAmelCase )
if shuffle:
__snake_case : Optional[Any] = dataset.shuffle(len(_UpperCAmelCase ) )
__snake_case : Dict = tf.data.TFRecordDataset(_UpperCAmelCase ,num_parallel_reads=_UpperCAmelCase )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
__snake_case : Any = dataset.apply(tf.data.experimental.assert_cardinality(_UpperCAmelCase ) )
__snake_case : Dict = dataset.map(_UpperCAmelCase ,num_parallel_calls=_UpperCAmelCase )
if shuffle:
assert shuffle_buffer_size is not None
__snake_case : Optional[Any] = dataset.shuffle(args.shuffle_buffer_size )
__snake_case : Any = dataset.batch(_UpperCAmelCase ,drop_remainder=_UpperCAmelCase )
__snake_case : int = dataset.map(_UpperCAmelCase ,num_parallel_calls=_UpperCAmelCase )
__snake_case : Optional[int] = dataset.prefetch(_UpperCAmelCase )
return dataset
def a_ ( _UpperCAmelCase : int ) -> List[Any]:
if not args.no_tpu:
__snake_case : Tuple = initialize_tpu(_UpperCAmelCase )
__snake_case : Optional[Any] = tf.distribute.TPUStrategy(_UpperCAmelCase )
else:
__snake_case : Optional[int] = tf.distribute.OneDeviceStrategy(device='/gpu:0' )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16' )
__snake_case : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer )
__snake_case : int = AutoConfig.from_pretrained(args.pretrained_model_config )
__snake_case : List[Any] = tokenizer.vocab_size
__snake_case : Any = tf.io.gfile.glob(os.path.join(args.train_dataset ,'*.tfrecord' ) )
if not training_records:
raise ValueError(f'''No .tfrecord files found in {args.train_dataset}.''' )
__snake_case : List[str] = tf.io.gfile.glob(os.path.join(args.eval_dataset ,'*.tfrecord' ) )
if not eval_records:
raise ValueError(f'''No .tfrecord files found in {args.eval_dataset}.''' )
__snake_case : Optional[int] = count_samples(_UpperCAmelCase )
__snake_case : Optional[Any] = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
__snake_case : Dict = steps_per_epoch * args.num_epochs
with strategy.scope():
__snake_case : int = TFAutoModelForMaskedLM.from_config(_UpperCAmelCase )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
__snake_case , __snake_case : int = create_optimizer(
num_train_steps=_UpperCAmelCase ,num_warmup_steps=total_train_steps // 20 ,init_lr=args.learning_rate ,weight_decay_rate=args.weight_decay_rate ,)
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=_UpperCAmelCase ,metrics=['accuracy'] )
def decode_fn(_UpperCAmelCase : Optional[Any] ):
__snake_case : Optional[Any] = {
'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa ,shape=(args.max_length,) ),
'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa ,shape=(args.max_length,) ),
}
return tf.io.parse_single_example(_UpperCAmelCase ,_UpperCAmelCase )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
__snake_case : Optional[Any] = DataCollatorForLanguageModeling(
tokenizer=_UpperCAmelCase ,mlm_probability=args.mlm_probability ,mlm=_UpperCAmelCase ,return_tensors='tf' )
def mask_with_collator(_UpperCAmelCase : List[str] ):
# TF really needs an isin() function
__snake_case : int = (
~tf.cast(batch['attention_mask'] ,tf.bool )
| (batch['input_ids'] == tokenizer.cls_token_id)
| (batch['input_ids'] == tokenizer.sep_token_id)
)
__snake_case , __snake_case : str = data_collator.tf_mask_tokens(
batch['input_ids'] ,vocab_size=len(_UpperCAmelCase ) ,mask_token_id=tokenizer.mask_token_id ,special_tokens_mask=_UpperCAmelCase ,)
return batch
__snake_case : int = args.per_replica_batch_size * strategy.num_replicas_in_sync
__snake_case : Optional[int] = prepare_dataset(
_UpperCAmelCase ,decode_fn=_UpperCAmelCase ,mask_fn=_UpperCAmelCase ,batch_size=_UpperCAmelCase ,shuffle=_UpperCAmelCase ,shuffle_buffer_size=args.shuffle_buffer_size ,)
__snake_case : List[Any] = prepare_dataset(
_UpperCAmelCase ,decode_fn=_UpperCAmelCase ,mask_fn=_UpperCAmelCase ,batch_size=_UpperCAmelCase ,shuffle=_UpperCAmelCase ,)
__snake_case : Tuple = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir ,hub_model_id=args.hub_model_id ,tokenizer=_UpperCAmelCase ) )
model.fit(
_UpperCAmelCase ,validation_data=_UpperCAmelCase ,epochs=args.num_epochs ,callbacks=_UpperCAmelCase ,)
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
A__ : List[str] = parse_args()
main(args)
| 124 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a__ : List[str] = ''
a__ : Optional[int] = ''
a__ : int = ''
a__ : List[Any] = 1 # (0 is vertical, 1 is horizontal)
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
A_ = get_dataset(_UpperCAmelCase , _UpperCAmelCase )
print('''Processing...''' )
A_ = update_image_and_anno(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
for index, image in enumerate(_UpperCAmelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
A_ = random_chars(32 )
A_ = paths[index].split(os.sep )[-1].rsplit('''.''' , 1 )[0]
A_ = f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
cva.imwrite(f'/{file_root}.jpg' , _UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'Success {index+1}/{len(_UpperCAmelCase )} with {file_name}' )
A_ = []
for anno in new_annos[index]:
A_ = f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
annos_list.append(_UpperCAmelCase )
with open(f'/{file_root}.txt' , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def UpperCAmelCase_ ( _UpperCAmelCase :Tuple , _UpperCAmelCase :Any ) -> tuple[list, list]:
'''simple docstring'''
A_ = []
A_ = []
for label_file in glob.glob(os.path.join(_UpperCAmelCase , '''*.txt''' ) ):
A_ = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(_UpperCAmelCase ) as in_file:
A_ = in_file.readlines()
A_ = os.path.join(_UpperCAmelCase , f'{label_name}.jpg' )
A_ = []
for obj_list in obj_lists:
A_ = obj_list.rstrip('''\n''' ).split(''' ''' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(_UpperCAmelCase )
labels.append(_UpperCAmelCase )
return img_paths, labels
def UpperCAmelCase_ ( _UpperCAmelCase :Optional[Any] , _UpperCAmelCase :Union[str, Any] , _UpperCAmelCase :str = 1 ) -> tuple[list, list, list]:
'''simple docstring'''
A_ = []
A_ = []
A_ = []
for idx in range(len(_UpperCAmelCase ) ):
A_ = []
A_ = img_list[idx]
path_list.append(_UpperCAmelCase )
A_ = anno_list[idx]
A_ = cva.imread(_UpperCAmelCase )
if flip_type == 1:
A_ = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
A_ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
A_ = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
A_ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(_UpperCAmelCase )
new_imgs_list.append(_UpperCAmelCase )
return new_imgs_list, new_annos_lists, path_list
def UpperCAmelCase_ ( _UpperCAmelCase :int = 32 ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
A_ = ascii_lowercase + digits
return "".join(random.choice(_UpperCAmelCase ) for _ in range(_UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 188 |
from __future__ import annotations
def snake_case( __magic_name__ , __magic_name__ ) -> list[list[int]]:
'''simple docstring'''
lowercase : list[list[int]] = []
lowercase : list[int] = []
lowercase : List[str] = 0
lowercase : Any = sum(__magic_name__ )
create_state_space_tree(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
return result
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> None:
'''simple docstring'''
if sum(__magic_name__ ) > max_sum or (remaining_nums_sum + sum(__magic_name__ )) < max_sum:
return
if sum(__magic_name__ ) == max_sum:
result.append(__magic_name__ )
return
for index in range(__magic_name__ , len(__magic_name__ ) ):
create_state_space_tree(
__magic_name__ , __magic_name__ , index + 1 , [*path, nums[index]] , __magic_name__ , remaining_nums_sum - nums[index] , )
lowerCAmelCase_ = [3, 34, 4, 12, 5, 2]
lowerCAmelCase_ = 9
lowerCAmelCase_ = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 217 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
def __A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase_ = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
UpperCAmelCase_ = dict(zip(lowerCAmelCase , range(len(lowerCAmelCase ) ) ) )
UpperCAmelCase_ = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
UpperCAmelCase_ = {"unk_token": "<unk>"}
UpperCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase ) )
UpperCAmelCase_ = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
UpperCAmelCase_ = os.path.join(self.tmpdirname , lowerCAmelCase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(lowerCAmelCase , lowerCAmelCase )
def __A ( self : Optional[Any] , **lowerCAmelCase : Any ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def __A ( self : Tuple , **lowerCAmelCase : List[Any] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def __A ( self : Union[str, Any] , **lowerCAmelCase : int ):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def __A ( self : str ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase_ = [Image.fromarray(np.moveaxis(lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = self.get_rust_tokenizer()
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase_ = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCAmelCase )
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase_ = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , lowerCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCAmelCase )
self.assertIsInstance(processor_fast.image_processor , lowerCAmelCase )
def __A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
UpperCAmelCase_ = self.get_image_processor(do_normalize=lowerCAmelCase , padding_value=1.0 )
UpperCAmelCase_ = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase )
def __A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = image_processor(lowerCAmelCase , return_tensors="np" )
UpperCAmelCase_ = processor(images=lowerCAmelCase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
UpperCAmelCase_ = "lower newer"
UpperCAmelCase_ = processor(text=lowerCAmelCase )
UpperCAmelCase_ = tokenizer(lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Any ):
'''simple docstring'''
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
UpperCAmelCase_ = "lower newer"
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = processor(text=lowerCAmelCase , images=lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase ):
processor()
def __A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
UpperCAmelCase_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase_ = processor.batch_decode(lowerCAmelCase )
UpperCAmelCase_ = tokenizer.batch_decode(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def __A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ = self.get_image_processor()
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = CLIPProcessor(tokenizer=lowerCAmelCase , image_processor=lowerCAmelCase )
UpperCAmelCase_ = "lower newer"
UpperCAmelCase_ = self.prepare_image_inputs()
UpperCAmelCase_ = processor(text=lowerCAmelCase , images=lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 268 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a: List[str] = logging.get_logger(__name__)
_a: Any = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = 'vit_msn'
def __init__( self : Union[str, Any] , lowerCAmelCase : Optional[int]=768 , lowerCAmelCase : Dict=12 , lowerCAmelCase : str=12 , lowerCAmelCase : List[str]=3_072 , lowerCAmelCase : Tuple="gelu" , lowerCAmelCase : int=0.0 , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Union[str, Any]=0.02 , lowerCAmelCase : str=1e-06 , lowerCAmelCase : Dict=224 , lowerCAmelCase : str=16 , lowerCAmelCase : List[Any]=3 , lowerCAmelCase : Union[str, Any]=True , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
super().__init__(**lowerCAmelCase )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = qkv_bias
| 268 | 1 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = 100 ):
_snake_case = 1
_snake_case = 2
for i in range(2 , max_n + 1 ):
_snake_case = pre_numerator
_snake_case = 2 * i // 3 if i % 3 == 0 else 1
_snake_case = cur_numerator
_snake_case = e_cont * pre_numerator + temp
return sum_digits(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 585 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__lowerCAmelCase = logging.get_logger(__name__)
# General docstring
__lowerCAmelCase = 'MobileNetV1Config'
# Base docstring
__lowerCAmelCase = 'google/mobilenet_v1_1.0_224'
__lowerCAmelCase = [1, 1_024, 7, 7]
# Image classification docstring
__lowerCAmelCase = 'google/mobilenet_v1_1.0_224'
__lowerCAmelCase = 'tabby, tabby cat'
__lowerCAmelCase = [
'google/mobilenet_v1_1.0_224',
'google/mobilenet_v1_0.75_192',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
_snake_case = {}
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = model.mobilenet_va
else:
_snake_case = model
_snake_case = """MobilenetV1/Conv2d_0/"""
_snake_case = backbone.conv_stem.convolution.weight
_snake_case = backbone.conv_stem.normalization.bias
_snake_case = backbone.conv_stem.normalization.weight
_snake_case = backbone.conv_stem.normalization.running_mean
_snake_case = backbone.conv_stem.normalization.running_var
for i in range(13 ):
_snake_case = i + 1
_snake_case = i * 2
_snake_case = backbone.layer[pt_index]
_snake_case = f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
_snake_case = pointer.convolution.weight
_snake_case = pointer.normalization.bias
_snake_case = pointer.normalization.weight
_snake_case = pointer.normalization.running_mean
_snake_case = pointer.normalization.running_var
_snake_case = backbone.layer[pt_index + 1]
_snake_case = f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
_snake_case = pointer.convolution.weight
_snake_case = pointer.normalization.bias
_snake_case = pointer.normalization.weight
_snake_case = pointer.normalization.running_mean
_snake_case = pointer.normalization.running_var
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = """MobilenetV1/Logits/Conv2d_1c_1x1/"""
_snake_case = model.classifier.weight
_snake_case = model.classifier.bias
return tf_to_pt_map
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"""Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
"""https://www.tensorflow.org/install/ for installation instructions.""" )
raise
# Load weights from TF model
_snake_case = tf.train.list_variables(_SCREAMING_SNAKE_CASE )
_snake_case = {}
for name, shape in init_vars:
logger.info(f"""Loading TF weight {name} with shape {shape}""" )
_snake_case = tf.train.load_variable(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_snake_case = array
# Build TF to PyTorch weights loading map
_snake_case = _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for name, pointer in tf_to_pt_map.items():
logger.info(f"""Importing {name}""" )
if name not in tf_weights:
logger.info(f"""{name} not in tf pre-trained weights, skipping""" )
continue
_snake_case = tf_weights[name]
if "depthwise_weights" in name:
logger.info("""Transposing depthwise""" )
_snake_case = np.transpose(_SCREAMING_SNAKE_CASE , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("""Transposing""" )
if len(pointer.shape ) == 2: # copying into linear layer
_snake_case = array.squeeze().transpose()
else:
_snake_case = np.transpose(_SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(f"""Initialize PyTorch weight {name} {array.shape}""" )
_snake_case = torch.from_numpy(_SCREAMING_SNAKE_CASE )
tf_weights.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + """/RMSProp""" , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + """/RMSProp_1""" , _SCREAMING_SNAKE_CASE )
tf_weights.pop(name + """/ExponentialMovingAverage""" , _SCREAMING_SNAKE_CASE )
logger.info(f"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}""" )
return model
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case, _snake_case = features.shape[-2:]
_snake_case, _snake_case = conv_layer.stride
_snake_case, _snake_case = conv_layer.kernel_size
if in_height % stride_height == 0:
_snake_case = max(kernel_height - stride_height , 0 )
else:
_snake_case = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
_snake_case = max(kernel_width - stride_width , 0 )
else:
_snake_case = max(kernel_width - (in_width % stride_width) , 0 )
_snake_case = pad_along_width // 2
_snake_case = pad_along_width - pad_left
_snake_case = pad_along_height // 2
_snake_case = pad_along_height - pad_top
_snake_case = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """constant""" , 0.0 )
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1 , UpperCAmelCase = 1 , UpperCAmelCase = False , UpperCAmelCase = True , UpperCAmelCase = True , ) -> None:
super().__init__()
_snake_case = config
if in_channels % groups != 0:
raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
_snake_case = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
_snake_case = nn.Convad(
in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , kernel_size=UpperCAmelCase , stride=UpperCAmelCase , padding=UpperCAmelCase , groups=UpperCAmelCase , bias=UpperCAmelCase , padding_mode="""zeros""" , )
if use_normalization:
_snake_case = nn.BatchNormad(
num_features=UpperCAmelCase , eps=config.layer_norm_eps , momentum=0.9997 , affine=UpperCAmelCase , track_running_stats=UpperCAmelCase , )
else:
_snake_case = None
if use_activation:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = ACTaFN[use_activation]
elif isinstance(config.hidden_act , UpperCAmelCase ):
_snake_case = ACTaFN[config.hidden_act]
else:
_snake_case = config.hidden_act
else:
_snake_case = None
def lowercase (self , UpperCAmelCase ) -> torch.Tensor:
if self.config.tf_padding:
_snake_case = apply_tf_padding(UpperCAmelCase , self.convolution )
_snake_case = self.convolution(UpperCAmelCase )
if self.normalization is not None:
_snake_case = self.normalization(UpperCAmelCase )
if self.activation is not None:
_snake_case = self.activation(UpperCAmelCase )
return features
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = MobileNetVaConfig
lowerCAmelCase_ = load_tf_weights_in_mobilenet_va
lowerCAmelCase_ = "mobilenet_v1"
lowerCAmelCase_ = "pixel_values"
lowerCAmelCase_ = False
def lowercase (self , UpperCAmelCase ) -> None:
if isinstance(UpperCAmelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(UpperCAmelCase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__lowerCAmelCase = r'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
__lowerCAmelCase = r'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , __snake_case , )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase = True ) -> Dict:
super().__init__(UpperCAmelCase )
_snake_case = config
_snake_case = 32
_snake_case = max(int(depth * config.depth_multiplier ) , config.min_depth )
_snake_case = MobileNetVaConvLayer(
UpperCAmelCase , in_channels=config.num_channels , out_channels=UpperCAmelCase , kernel_size=3 , stride=2 , )
_snake_case = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
_snake_case = nn.ModuleList()
for i in range(13 ):
_snake_case = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
_snake_case = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , kernel_size=3 , stride=strides[i] , groups=UpperCAmelCase , ) )
self.layer.append(
MobileNetVaConvLayer(
UpperCAmelCase , in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , kernel_size=1 , ) )
_snake_case = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def lowercase (self , UpperCAmelCase ) -> Dict:
raise NotImplementedError
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase (self , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("""You have to specify pixel_values""" )
_snake_case = self.conv_stem(UpperCAmelCase )
_snake_case = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
_snake_case = layer_module(UpperCAmelCase )
if output_hidden_states:
_snake_case = all_hidden_states + (hidden_states,)
_snake_case = hidden_states
if self.pooler is not None:
_snake_case = torch.flatten(self.pooler(UpperCAmelCase ) , start_dim=1 )
else:
_snake_case = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase , pooler_output=UpperCAmelCase , hidden_states=UpperCAmelCase , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __snake_case , )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> None:
super().__init__(UpperCAmelCase )
_snake_case = config.num_labels
_snake_case = MobileNetVaModel(UpperCAmelCase )
_snake_case = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
_snake_case = nn.Dropout(config.classifier_dropout_prob , inplace=UpperCAmelCase )
_snake_case = nn.Linear(UpperCAmelCase , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase (self , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.mobilenet_va(UpperCAmelCase , output_hidden_states=UpperCAmelCase , return_dict=UpperCAmelCase )
_snake_case = outputs.pooler_output if return_dict else outputs[1]
_snake_case = self.classifier(self.dropout(UpperCAmelCase ) )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = """single_label_classification"""
else:
_snake_case = """multi_label_classification"""
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(UpperCAmelCase , UpperCAmelCase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=UpperCAmelCase , logits=UpperCAmelCase , hidden_states=outputs.hidden_states , )
| 585 | 1 |
import random
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = num - 1
__lowerCAmelCase = 0
while s % 2 == 0:
__lowerCAmelCase = s // 2
t += 1
for _ in range(5 ):
__lowerCAmelCase = random.randrange(2 , num - 1 )
__lowerCAmelCase = pow(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if v != 1:
__lowerCAmelCase = 0
while v != (num - 1):
if i == t - 1:
return False
else:
__lowerCAmelCase = i + 1
__lowerCAmelCase = (v**2) % num
return True
def __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
if num < 2:
return False
__lowerCAmelCase = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(UpperCAmelCase__ )
def __lowercase ( UpperCAmelCase__ = 1_024 ):
"""simple docstring"""
while True:
__lowerCAmelCase = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(UpperCAmelCase__ ):
return num
if __name__ == "__main__":
lowerCamelCase = generate_large_prime()
print(('''Prime number:''', num))
print(('''is_prime_low_num:''', is_prime_low_num(num)))
| 102 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase = data_utils.TransfoXLTokenizer
lowerCamelCase = data_utils.TransfoXLCorpus
lowerCamelCase = data_utils
lowerCamelCase = data_utils
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(UpperCAmelCase__ , 'rb' ) as fp:
__lowerCAmelCase = pickle.load(UpperCAmelCase__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
__lowerCAmelCase = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F"""Save vocabulary to {pytorch_vocab_dump_path}""" )
__lowerCAmelCase = corpus.vocab.__dict__
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
__lowerCAmelCase = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , UpperCAmelCase__ )
__lowerCAmelCase = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F"""Save dataset to {pytorch_dataset_dump_path}""" )
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
__lowerCAmelCase = os.path.abspath(UpperCAmelCase__ )
__lowerCAmelCase = os.path.abspath(UpperCAmelCase__ )
print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
# Initialise PyTorch model
if transfo_xl_config_file == "":
__lowerCAmelCase = TransfoXLConfig()
else:
__lowerCAmelCase = TransfoXLConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
__lowerCAmelCase = TransfoXLLMHeadModel(UpperCAmelCase__ )
__lowerCAmelCase = load_tf_weights_in_transfo_xl(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
__lowerCAmelCase = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
__lowerCAmelCase = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ )
print(F"""Save PyTorch model to {os.path.abspath(UpperCAmelCase__ )}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
print(F"""Save configuration file to {os.path.abspath(UpperCAmelCase__ )}""" )
with open(UpperCAmelCase__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
lowerCamelCase = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 102 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} )
SCREAMING_SNAKE_CASE = Features({'text': Value('string' )} )
SCREAMING_SNAKE_CASE = Features({} )
SCREAMING_SNAKE_CASE = "text"
@property
def _a (self ):
"""simple docstring"""
return {self.text_column: "text"}
| 182 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Dict = AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" , return_dict=_lowerCamelCase ).to(_lowerCamelCase )
UpperCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
UpperCAmelCase__ : Any = tokenizer("""Hello there""" , return_tensors="""pt""" ).input_ids
UpperCAmelCase__ : str = tokenizer("""Hi I am""" , return_tensors="""pt""" ).input_ids
UpperCAmelCase__ : Tuple = model(input_ids.to(_lowerCamelCase ) , labels=labels.to(_lowerCamelCase ) ).loss
UpperCAmelCase__ : Union[str, Any] = -(labels.shape[-1] * loss.item())
UpperCAmelCase__ : List[str] = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 182 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
)
UpperCamelCase = None
UpperCamelCase = {
'7B': 1_1008,
'13B': 1_3824,
'30B': 1_7920,
'65B': 2_2016,
'70B': 2_8672,
}
UpperCamelCase = {
'7B': 1,
'7Bf': 1,
'13B': 2,
'13Bf': 2,
'30B': 4,
'65B': 8,
'70B': 8,
'70Bf': 8,
}
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : List[Any]=256 ):
"""simple docstring"""
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def _A ( lowerCAmelCase_ : Any ):
"""simple docstring"""
with open(lowerCAmelCase_ , "r" ) as f:
return json.load(lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
with open(lowerCAmelCase_ , "w" ) as f:
json.dump(lowerCAmelCase_ , lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str]=True ):
"""simple docstring"""
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "tmp" )
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
lowerCAmelCase__ = read_json(os.path.join(lowerCAmelCase_ , "params.json" ) )
lowerCAmelCase__ = NUM_SHARDS[model_size]
lowerCAmelCase__ = params['''n_layers''']
lowerCAmelCase__ = params['''n_heads''']
lowerCAmelCase__ = n_heads // num_shards
lowerCAmelCase__ = params['''dim''']
lowerCAmelCase__ = dim // n_heads
lowerCAmelCase__ = 1_0000.0
lowerCAmelCase__ = 1.0 / (base ** (torch.arange(0 , lowerCAmelCase_ , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
lowerCAmelCase__ = params['''n_kv_heads'''] # for GQA / MQA
lowerCAmelCase__ = n_heads_per_shard // num_key_value_heads
lowerCAmelCase__ = dim // num_key_value_heads
else: # compatibility with other checkpoints
lowerCAmelCase__ = n_heads
lowerCAmelCase__ = n_heads_per_shard
lowerCAmelCase__ = dim
# permute for sliced rotary
def permute(lowerCAmelCase_ : str , lowerCAmelCase_ : int=n_heads , lowerCAmelCase_ : str=dim , lowerCAmelCase_ : List[str]=dim ):
return w.view(lowerCAmelCase_ , dima // n_heads // 2 , 2 , lowerCAmelCase_ ).transpose(1 , 2 ).reshape(lowerCAmelCase_ , lowerCAmelCase_ )
print(F'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
lowerCAmelCase__ = torch.load(os.path.join(lowerCAmelCase_ , "consolidated.00.pth" ) , map_location="cpu" )
else:
# Sharded
lowerCAmelCase__ = [
torch.load(os.path.join(lowerCAmelCase_ , F'consolidated.{i:02d}.pth' ) , map_location="cpu" )
for i in range(lowerCAmelCase_ )
]
lowerCAmelCase__ = 0
lowerCAmelCase__ = {'''weight_map''': {}}
for layer_i in range(lowerCAmelCase_ ):
lowerCAmelCase__ = F'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
lowerCAmelCase__ = {
F'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[F'layers.{layer_i}.attention.wq.weight'] ),
F'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[F'layers.{layer_i}.attention.wk.weight'] ),
F'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[F'layers.{layer_i}.attention.wv.weight'],
F'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[F'layers.{layer_i}.attention.wo.weight'],
F'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w1.weight'],
F'model.layers.{layer_i}.mlp.down_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w2.weight'],
F'model.layers.{layer_i}.mlp.up_proj.weight': loaded[F'layers.{layer_i}.feed_forward.w3.weight'],
F'model.layers.{layer_i}.input_layernorm.weight': loaded[F'layers.{layer_i}.attention_norm.weight'],
F'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[F'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
lowerCAmelCase__ = {
F'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
F'layers.{layer_i}.attention_norm.weight'
].clone(),
F'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
F'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
lowerCAmelCase__ = permute(
torch.cat(
[
loaded[i][F'layers.{layer_i}.attention.wq.weight'].view(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for i in range(lowerCAmelCase_ )
] , dim=0 , ).reshape(lowerCAmelCase_ , lowerCAmelCase_ ) )
lowerCAmelCase__ = permute(
torch.cat(
[
loaded[i][F'layers.{layer_i}.attention.wk.weight'].view(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for i in range(lowerCAmelCase_ )
] , dim=0 , ).reshape(lowerCAmelCase_ , lowerCAmelCase_ ) , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
lowerCAmelCase__ = torch.cat(
[
loaded[i][F'layers.{layer_i}.attention.wv.weight'].view(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
for i in range(lowerCAmelCase_ )
] , dim=0 , ).reshape(lowerCAmelCase_ , lowerCAmelCase_ )
lowerCAmelCase__ = torch.cat(
[loaded[i][F'layers.{layer_i}.attention.wo.weight'] for i in range(lowerCAmelCase_ )] , dim=1 )
lowerCAmelCase__ = torch.cat(
[loaded[i][F'layers.{layer_i}.feed_forward.w1.weight'] for i in range(lowerCAmelCase_ )] , dim=0 )
lowerCAmelCase__ = torch.cat(
[loaded[i][F'layers.{layer_i}.feed_forward.w2.weight'] for i in range(lowerCAmelCase_ )] , dim=1 )
lowerCAmelCase__ = torch.cat(
[loaded[i][F'layers.{layer_i}.feed_forward.w3.weight'] for i in range(lowerCAmelCase_ )] , dim=0 )
lowerCAmelCase__ = inv_freq
for k, v in state_dict.items():
lowerCAmelCase__ = filename
param_count += v.numel()
torch.save(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) )
lowerCAmelCase__ = F'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
lowerCAmelCase__ = {
'''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
'''model.norm.weight''': loaded['''norm.weight'''],
'''lm_head.weight''': loaded['''output.weight'''],
}
else:
lowerCAmelCase__ = {
'''model.norm.weight''': loaded[0]['''norm.weight'''],
'''model.embed_tokens.weight''': torch.cat(
[loaded[i]["tok_embeddings.weight"] for i in range(lowerCAmelCase_ )] , dim=1 ),
'''lm_head.weight''': torch.cat([loaded[i]["output.weight"] for i in range(lowerCAmelCase_ )] , dim=0 ),
}
for k, v in state_dict.items():
lowerCAmelCase__ = filename
param_count += v.numel()
torch.save(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) )
# Write configs
lowerCAmelCase__ = {'''total_size''': param_count * 2}
write_json(lowerCAmelCase_ , os.path.join(lowerCAmelCase_ , "pytorch_model.bin.index.json" ) )
lowerCAmelCase__ = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
lowerCAmelCase__ = params['''multiple_of'''] if '''multiple_of''' in params else 256
lowerCAmelCase__ = LlamaConfig(
hidden_size=lowerCAmelCase_ , intermediate_size=compute_intermediate_size(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) , num_attention_heads=params["n_heads"] , num_hidden_layers=params["n_layers"] , rms_norm_eps=params["norm_eps"] , num_key_value_heads=lowerCAmelCase_ , )
config.save_pretrained(lowerCAmelCase_ )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("Loading the checkpoint in a Llama model." )
lowerCAmelCase__ = LlamaForCausalLM.from_pretrained(lowerCAmelCase_ , torch_dtype=torch.floataa , low_cpu_mem_usage=lowerCAmelCase_ )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("Saving in the Transformers format." )
model.save_pretrained(lowerCAmelCase_ , safe_serialization=lowerCAmelCase_ )
shutil.rmtree(lowerCAmelCase_ )
def _A ( lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
lowerCAmelCase__ = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(F'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
lowerCAmelCase__ = tokenizer_class(lowerCAmelCase_ )
tokenizer.save_pretrained(lowerCAmelCase_ )
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--input_dir" , help="Location of LLaMA weights, which contains tokenizer.model and model folders" , )
parser.add_argument(
"--model_size" , choices=["7B", "7Bf", "13B", "13Bf", "30B", "65B", "70B", "70Bf", "tokenizer_only"] , )
parser.add_argument(
"--output_dir" , help="Location to write HF model and tokenizer" , )
parser.add_argument("--safe_serialization" , type=lowerCAmelCase_ , help="Whether or not to save using `safetensors`." )
lowerCAmelCase__ = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
lowerCAmelCase__ = os.path.join(args.input_dir , "tokenizer.model" )
write_tokenizer(args.output_dir , lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 703 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = [randint(-1000 , 1000 ) for i in range(10 )]
lowerCAmelCase__ = randint(-5000 , 5000 )
return (arr, r)
UpperCamelCase = make_dataset()
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : int ):
"""simple docstring"""
for triplet in permutations(lowerCAmelCase_ , 3 ):
if sum(lowerCAmelCase_ ) == target:
return tuple(sorted(lowerCAmelCase_ ) )
return (0, 0, 0)
def _A ( lowerCAmelCase_ : list[int] , lowerCAmelCase_ : int ):
"""simple docstring"""
arr.sort()
lowerCAmelCase__ = len(lowerCAmelCase_ )
for i in range(n - 1 ):
lowerCAmelCase__ , lowerCAmelCase__ = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def _A ( ):
"""simple docstring"""
lowerCAmelCase__ = "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
lowerCAmelCase__ = "\ntriplet_sum1(*dataset)\n"
lowerCAmelCase__ = "\ntriplet_sum2(*dataset)\n"
lowerCAmelCase__ = repeat(setup=lowerCAmelCase_ , stmt=lowerCAmelCase_ , repeat=5 , number=1_0000 )
lowerCAmelCase__ = repeat(setup=lowerCAmelCase_ , stmt=lowerCAmelCase_ , repeat=5 , number=1_0000 )
return (min(lowerCAmelCase_ ), min(lowerCAmelCase_ ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCamelCase = solution_times()
print(F"""The time for naive implementation is {times[0]}.""")
print(F"""The time for optimized implementation is {times[1]}.""")
| 125 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_owlvit": [
"OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"OwlViTConfig",
"OwlViTOnnxConfig",
"OwlViTTextConfig",
"OwlViTVisionConfig",
],
"processing_owlvit": ["OwlViTProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["OwlViTFeatureExtractor"]
SCREAMING_SNAKE_CASE__ = ["OwlViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"OwlViTModel",
"OwlViTPreTrainedModel",
"OwlViTTextModel",
"OwlViTVisionModel",
"OwlViTForObjectDetection",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 631 |
def lowercase ( a , a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = [False] * len(a )
SCREAMING_SNAKE_CASE_ :List[Any] = []
queue.append(a )
SCREAMING_SNAKE_CASE_ :int = True
while queue:
SCREAMING_SNAKE_CASE_ :int = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(a )
SCREAMING_SNAKE_CASE_ :Tuple = True
SCREAMING_SNAKE_CASE_ :Optional[int] = u
return visited[t]
def lowercase ( a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Any = [-1] * (len(a ))
SCREAMING_SNAKE_CASE_ :Tuple = 0
while bfs(a , a , a , a ):
SCREAMING_SNAKE_CASE_ :List[Any] = float("Inf" )
SCREAMING_SNAKE_CASE_ :str = sink
while s != source:
# Find the minimum value in select path
SCREAMING_SNAKE_CASE_ :str = min(a , graph[parent[s]][s] )
SCREAMING_SNAKE_CASE_ :Optional[Any] = parent[s]
max_flow += path_flow
SCREAMING_SNAKE_CASE_ :Dict = sink
while v != source:
SCREAMING_SNAKE_CASE_ :int = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
SCREAMING_SNAKE_CASE_ :Any = parent[v]
return max_flow
SCREAMING_SNAKE_CASE__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 5
print(ford_fulkerson(graph, source, sink))
| 631 | 1 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
snake_case_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--user", type=str, default="ubuntu")
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--key_path", type=str, default=None)
parser.add_argument("--instance", type=str, default="V100:1")
parser.add_argument("--provider", type=str, default="cheapest")
parser.add_argument("--use_spot", type=bool, default=False)
parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
snake_case_ , snake_case_ : Tuple = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError("Cannot specify both BYO and on-demand cluster args")
snake_case_ : List[str] = rh.cluster(
name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
)
else:
snake_case_ : Tuple = rh.cluster(
name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
snake_case_ : int = args.example.rsplit("/", 1)[0]
# Set up remote environment
cluster.install_packages(["pip:./"]) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 169 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def A (__A : Tuple , __A : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
UpperCAmelCase_ = requests.get(__A , headers=__A ).json()
UpperCAmelCase_ = {}
try:
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
UpperCAmelCase_ = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(__A ):
UpperCAmelCase_ = requests.get(url + F"""&page={i + 2}""" , headers=__A ).json()
job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return job_links
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def A (__A : Union[str, Any] , __A : Optional[Any]=None ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
UpperCAmelCase_ = requests.get(__A , headers=__A ).json()
UpperCAmelCase_ = {}
try:
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
UpperCAmelCase_ = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(__A ):
UpperCAmelCase_ = requests.get(url + F"""&page={i + 2}""" , headers=__A ).json()
artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']} )
return artifacts
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
def A (__A : Any , __A : Tuple , __A : Optional[int] , __A : List[str] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}
UpperCAmelCase_ = requests.get(__A , headers=__A , allow_redirects=__A )
UpperCAmelCase_ = result.headers['''Location''']
UpperCAmelCase_ = requests.get(__A , allow_redirects=__A )
UpperCAmelCase_ = os.path.join(__A , F"""{artifact_name}.zip""" )
with open(__A , '''wb''' ) as fp:
fp.write(response.content )
def A (__A : Union[str, Any] , __A : Optional[int]=None ) -> int:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = None
with zipfile.ZipFile(__A ) as z:
for filename in z.namelist():
if not os.path.isdir(__A ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(__A ) as f:
for line in f:
UpperCAmelCase_ = line.decode('''UTF-8''' ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
UpperCAmelCase_ = line[: line.index(''': ''' )]
UpperCAmelCase_ = line[line.index(''': ''' ) + len(''': ''' ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith('''FAILED ''' ):
# `test` is the test method that failed
UpperCAmelCase_ = line[len('''FAILED ''' ) :]
failed_tests.append(__A )
elif filename == "job_name.txt":
UpperCAmelCase_ = line
if len(__A ) != len(__A ):
raise ValueError(
F"""`errors` and `failed_tests` should have the same number of elements. Got {len(__A )} for `errors` """
F"""and {len(__A )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
''' problem.''' )
UpperCAmelCase_ = None
if job_name and job_links:
UpperCAmelCase_ = job_links.get(__A , __A )
# A list with elements of the form (line of error, error, failed test)
UpperCAmelCase_ = [x + [y] + [job_link] for x, y in zip(__A , __A )]
return result
def A (__A : List[str] , __A : Any=None ) -> int:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = [os.path.join(__A , __A ) for p in os.listdir(__A ) if p.endswith('''.zip''' )]
for p in paths:
errors.extend(get_errors_from_single_artifact(__A , job_links=__A ) )
return errors
def A (__A : Tuple , __A : Dict=None ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = Counter()
counter.update([x[1] for x in logs] )
UpperCAmelCase_ = counter.most_common()
UpperCAmelCase_ = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
UpperCAmelCase_ = {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}
UpperCAmelCase_ = dict(sorted(r.items() , key=lambda __A : item[1]["count"] , reverse=__A ) )
return r
def A (__A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = test.split('''::''' )[0]
if test.startswith('''tests/models/''' ):
UpperCAmelCase_ = test.split('''/''' )[2]
else:
UpperCAmelCase_ = None
return test
def A (__A : str , __A : int=None ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = [(x[0], x[1], get_model(x[2] )) for x in logs]
UpperCAmelCase_ = [x for x in logs if x[2] is not None]
UpperCAmelCase_ = {x[2] for x in logs}
UpperCAmelCase_ = {}
for test in tests:
UpperCAmelCase_ = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
UpperCAmelCase_ = counter.most_common()
UpperCAmelCase_ = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
UpperCAmelCase_ = sum(error_counts.values() )
if n_errors > 0:
UpperCAmelCase_ = {'''count''': n_errors, '''errors''': error_counts}
UpperCAmelCase_ = dict(sorted(r.items() , key=lambda __A : item[1]["count"] , reverse=__A ) )
return r
def A (__A : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = '''| no. | error | status |'''
UpperCAmelCase_ = '''|-:|:-|:-|'''
UpperCAmelCase_ = [header, sep]
for error in reduced_by_error:
UpperCAmelCase_ = reduced_by_error[error]['''count''']
UpperCAmelCase_ = F"""| {count} | {error[:100]} | |"""
lines.append(__A )
return "\n".join(__A )
def A (__A : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = '''| model | no. of errors | major error | count |'''
UpperCAmelCase_ = '''|-:|-:|-:|-:|'''
UpperCAmelCase_ = [header, sep]
for model in reduced_by_model:
UpperCAmelCase_ = reduced_by_model[model]['''count''']
UpperCAmelCase_ , UpperCAmelCase_ = list(reduced_by_model[model]['''errors'''].items() )[0]
UpperCAmelCase_ = F"""| {model} | {count} | {error[:60]} | {_count} |"""
lines.append(__A )
return "\n".join(__A )
if __name__ == "__main__":
snake_case_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
snake_case_ : Union[str, Any] = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
snake_case_ : Dict = get_job_links(args.workflow_run_id, token=args.token)
snake_case_ : Dict = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
snake_case_ : List[Any] = k.find(" / ")
snake_case_ : List[str] = k[index + len(" / ") :]
snake_case_ : Optional[int] = v
with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
snake_case_ : Optional[int] = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
snake_case_ : Optional[Any] = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
snake_case_ : str = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
snake_case_ : Dict = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
snake_case_ : str = reduce_by_error(errors)
snake_case_ : Optional[Any] = reduce_by_model(errors)
snake_case_ : int = make_github_table(reduced_by_error)
snake_case_ : Optional[int] = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
fp.write(sa)
| 169 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Optional[Any] = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',
'merges_file': 'merges.txt',
}
lowercase : Any = {
'vocab_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
),
},
'tokenizer_config_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
),
},
'merges_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
),
},
}
lowercase : Optional[Any] = '</w>'
lowercase : str = '@@ '
def __a ( A__ ) -> int:
lowerCAmelCase = set()
lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCAmelCase = char
return pairs
# Speech2Text2 has no max input length
lowercase : Tuple = {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int="<s>" , SCREAMING_SNAKE_CASE : Dict="<pad>" , SCREAMING_SNAKE_CASE : List[str]="</s>" , SCREAMING_SNAKE_CASE : str="<unk>" , SCREAMING_SNAKE_CASE : Optional[int]=False , SCREAMING_SNAKE_CASE : List[Any]=None , **SCREAMING_SNAKE_CASE : Any , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
unk_token=SCREAMING_SNAKE_CASE , bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , do_lower_case=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
lowerCAmelCase = do_lower_case
with open(SCREAMING_SNAKE_CASE , encoding="utf-8" ) as vocab_handle:
lowerCAmelCase = json.load(SCREAMING_SNAKE_CASE )
lowerCAmelCase = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding." )
lowerCAmelCase = None
lowerCAmelCase = None
else:
with open(SCREAMING_SNAKE_CASE , encoding="utf-8" ) as merges_handle:
lowerCAmelCase = merges_handle.read().split("\n" )[:-1]
lowerCAmelCase = [tuple(merge.split()[:2] ) for merge in merges]
lowerCAmelCase = dict(zip(SCREAMING_SNAKE_CASE , range(len(SCREAMING_SNAKE_CASE ) ) ) )
lowerCAmelCase = {}
@property
def __A ( self : int ) -> int:
"""simple docstring"""
return len(self.decoder )
def __A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def __A ( self : str , SCREAMING_SNAKE_CASE : Dict ) -> Tuple:
"""simple docstring"""
lowerCAmelCase = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
lowerCAmelCase = get_pairs(SCREAMING_SNAKE_CASE )
if not pairs:
return token
while True:
lowerCAmelCase = min(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE : self.bpe_ranks.get(SCREAMING_SNAKE_CASE , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase , lowerCAmelCase = bigram
lowerCAmelCase = []
lowerCAmelCase = 0
while i < len(SCREAMING_SNAKE_CASE ):
try:
lowerCAmelCase = word.index(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase = j
if word[i] == first and i < len(SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCAmelCase = tuple(SCREAMING_SNAKE_CASE )
lowerCAmelCase = new_word
if len(SCREAMING_SNAKE_CASE ) == 1:
break
else:
lowerCAmelCase = get_pairs(SCREAMING_SNAKE_CASE )
lowerCAmelCase = " ".join(SCREAMING_SNAKE_CASE )
if word == "\n " + BPE_TOKEN_MERGES:
lowerCAmelCase = "\n" + BPE_TOKEN_MERGES
if word.endswith(SCREAMING_SNAKE_CASE ):
lowerCAmelCase = word.replace(SCREAMING_SNAKE_CASE , "" )
lowerCAmelCase = word.replace(" " , SCREAMING_SNAKE_CASE )
lowerCAmelCase = word
return word
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : List[Any] ) -> Tuple:
"""simple docstring"""
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
lowerCAmelCase = text.lower()
lowerCAmelCase = text.split()
lowerCAmelCase = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE ).split(" " ) ) )
return split_tokens
def __A ( self : Any , SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
return self.encoder.get(SCREAMING_SNAKE_CASE , self.encoder.get(self.unk_token ) )
def __A ( self : Any , SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
lowerCAmelCase = self.decoder.get(SCREAMING_SNAKE_CASE , self.unk_token )
return result
def __A ( self : List[str] , SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
lowerCAmelCase = " ".join(SCREAMING_SNAKE_CASE )
# make sure @@ tokens are concatenated
lowerCAmelCase = "".join(string.split(SCREAMING_SNAKE_CASE ) )
return string
def __A ( self : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowerCAmelCase = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE , ensure_ascii=SCREAMING_SNAKE_CASE ) + "\n" )
lowerCAmelCase = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
lowerCAmelCase = token_index
writer.write(" ".join(SCREAMING_SNAKE_CASE ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 649 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Optional[Any] = {
'nielsr/canine-s': 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
lowercase : Dict = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowercase : List[str] = 0
lowercase : List[str] = 0Xe_000
lowercase : Optional[int] = 0Xe_001
lowercase : Union[str, Any] = 0Xe_002
lowercase : List[str] = 0Xe_003
lowercase : str = 0Xe_004
# Maps special codepoints to human-readable names.
lowercase : Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
lowercase : Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , SCREAMING_SNAKE_CASE : Optional[int]=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : int=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : Tuple=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : Tuple=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : Union[str, Any]=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : int=chr(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE : Union[str, Any]=False , SCREAMING_SNAKE_CASE : List[Any]=2_0_4_8 , **SCREAMING_SNAKE_CASE : Optional[Any] , ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else bos_token
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else eos_token
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else sep_token
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else cls_token
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE ) if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , sep_token=SCREAMING_SNAKE_CASE , cls_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , mask_token=SCREAMING_SNAKE_CASE , add_prefix_space=SCREAMING_SNAKE_CASE , model_max_length=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
# Creates a mapping for looking up the IDs of special symbols.
lowerCAmelCase = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
lowerCAmelCase = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
lowerCAmelCase = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
lowerCAmelCase = UNICODE_VOCAB_SIZE
lowerCAmelCase = len(self._special_codepoints )
@property
def __A ( self : List[Any] ) -> int:
"""simple docstring"""
return self._unicode_vocab_size
def __A ( self : str , SCREAMING_SNAKE_CASE : str ) -> List[str]:
"""simple docstring"""
return list(SCREAMING_SNAKE_CASE )
def __A ( self : Dict , SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
try:
return ord(SCREAMING_SNAKE_CASE )
except TypeError:
raise ValueError(f"invalid token: '{token}'" )
def __A ( self : str , SCREAMING_SNAKE_CASE : int ) -> str:
"""simple docstring"""
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(SCREAMING_SNAKE_CASE )
except TypeError:
raise ValueError(f"invalid id: {index}" )
def __A ( self : Any , SCREAMING_SNAKE_CASE : str ) -> Optional[int]:
"""simple docstring"""
return "".join(SCREAMING_SNAKE_CASE )
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
lowerCAmelCase = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def __A ( self : List[str] , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None , SCREAMING_SNAKE_CASE : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE , token_ids_a=SCREAMING_SNAKE_CASE , already_has_special_tokens=SCREAMING_SNAKE_CASE )
lowerCAmelCase = [1] + ([0] * len(SCREAMING_SNAKE_CASE )) + [1]
if token_ids_a is not None:
result += ([0] * len(SCREAMING_SNAKE_CASE )) + [1]
return result
def __A ( self : Dict , SCREAMING_SNAKE_CASE : List[int] , SCREAMING_SNAKE_CASE : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
lowerCAmelCase = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def __A ( self : Dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[str] = None ) -> str:
"""simple docstring"""
return ()
| 649 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def __lowerCAmelCase ( ) -> List[Any]:
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def __lowerCAmelCase ( ) -> Optional[Any]:
lowerCAmelCase__ : str = '''mock-s3-bucket'''
lowerCAmelCase__ : int = F"""s3://{mock_bucket}"""
lowerCAmelCase__ : Dict = extract_path_from_uri(UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
lowerCAmelCase__ : str = '''./local/path'''
lowerCAmelCase__ : str = extract_path_from_uri(UpperCamelCase )
assert dataset_path == new_dataset_path
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Any = is_remote_filesystem(UpperCamelCase )
assert is_remote is True
lowerCAmelCase__ : List[Any] = fsspec.filesystem('''file''' )
lowerCAmelCase__ : Union[str, Any] = is_remote_filesystem(UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , UpperCamelCase )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Optional[int] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
lowerCAmelCase__ : Any = input_paths[compression_fs_class.protocol]
if input_path is None:
lowerCAmelCase__ : int = F"""for '{compression_fs_class.protocol}' compression protocol, """
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(UpperCamelCase )
lowerCAmelCase__ : Tuple = fsspec.filesystem(compression_fs_class.protocol , fo=UpperCamelCase )
assert isinstance(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = os.path.basename(UpperCamelCase )
lowerCAmelCase__ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : str = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
lowerCAmelCase__ : List[str] = compressed_file_paths[protocol]
lowerCAmelCase__ : Tuple = '''dataset.jsonl'''
lowerCAmelCase__ : Dict = F"""{protocol}://{member_file_path}::{compressed_file_path}"""
lowerCAmelCase__ : Tuple = fsspec.get_fs_token_paths(UpperCamelCase )
assert fs.isfile(UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCAmelCase__ : Optional[int] = hf_api.dataset_info(UpperCamelCase , token=UpperCamelCase )
lowerCAmelCase__ : List[Any] = HfFileSystem(repo_info=UpperCamelCase , token=UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def __lowerCAmelCase ( ) -> List[str]:
lowerCAmelCase__ : List[str] = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(UpperCamelCase , UpperCamelCase , clobber=UpperCamelCase )
with pytest.warns(UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
)
| 711 |
def __lowerCAmelCase ( UpperCamelCase ) -> bool:
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(UpperCamelCase ) == 0:
raise ValueError('''Input list must be a non empty list''' )
if len(UpperCamelCase ) == 1:
return True
lowerCAmelCase__ : Tuple = series[1] - series[0]
for index in range(len(UpperCamelCase ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def __lowerCAmelCase ( UpperCamelCase ) -> float:
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(UpperCamelCase ) == 0:
raise ValueError('''Input list must be a non empty list''' )
lowerCAmelCase__ : int = 0
for val in series:
answer += val
return answer / len(UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 470 | 0 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCAmelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[str] = True , UpperCAmelCase__ : Tuple = math.inf , UpperCAmelCase__ : Union[str, Any] = -math.inf , UpperCAmelCase__ : List[str] = math.inf , UpperCAmelCase__ : Optional[Any] = -math.inf , UpperCAmelCase__ : List[Any] = False , UpperCAmelCase__ : Tuple = 1_0_0 , UpperCAmelCase__ : Union[str, Any] = 0.0_1 , UpperCAmelCase__ : List[str] = 1 , ) -> Any:
lowerCamelCase_ = False
lowerCamelCase_ = search_prob
lowerCamelCase_ = start_temperate
lowerCamelCase_ = []
lowerCamelCase_ = 0
lowerCamelCase_ = None
while not search_end:
lowerCamelCase_ = current_state.score()
if best_state is None or current_score > best_state.score():
lowerCamelCase_ = current_state
scores.append(snake_case__ )
iterations += 1
lowerCamelCase_ = None
lowerCamelCase_ = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
lowerCamelCase_ = random.randint(0 , len(snake_case__ ) - 1 ) # picking a random neighbor
lowerCamelCase_ = neighbors.pop(snake_case__ )
lowerCamelCase_ = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
lowerCamelCase_ = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
lowerCamelCase_ = picked_neighbor
else:
lowerCamelCase_ = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
lowerCamelCase_ = picked_neighbor
lowerCamelCase_ = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
lowerCamelCase_ = True
else:
lowerCamelCase_ = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(snake_case__ ) , snake_case__ )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def __lowerCAmelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] ) -> List[Any]:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowercase = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowercase = simulated_annealing(
prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
'''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
lowercase = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowercase = simulated_annealing(
prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
'''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def __lowerCAmelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : str ) -> Tuple:
return (3 * x**2) - (6 * y)
lowercase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase = simulated_annealing(prob, find_max=False, visualization=True)
print(
'''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F"""{local_min.score()}"""
)
lowercase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase = simulated_annealing(prob, find_max=True, visualization=True)
print(
'''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F"""{local_min.score()}"""
)
| 272 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = 42
class __snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
@register_to_config
def __init__( self ,a_ = 16 ,a_ = 88 ,a_ = None ,a_ = None ,a_ = 1 ,a_ = 0.0 ,a_ = 32 ,a_ = None ,a_ = False ,a_ = None ,a_ = "geglu" ,a_ = True ,a_ = True ,):
"""simple docstring"""
super().__init__()
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = attention_head_dim
lowerCAmelCase__ = num_attention_heads * attention_head_dim
lowerCAmelCase__ = in_channels
lowerCAmelCase__ = torch.nn.GroupNorm(num_groups=a_ ,num_channels=a_ ,eps=1e-6 ,affine=a_ )
lowerCAmelCase__ = nn.Linear(a_ ,a_ )
# 3. Define transformers blocks
lowerCAmelCase__ = nn.ModuleList(
[
BasicTransformerBlock(
a_ ,a_ ,a_ ,dropout=a_ ,cross_attention_dim=a_ ,activation_fn=a_ ,attention_bias=a_ ,double_self_attention=a_ ,norm_elementwise_affine=a_ ,)
for d in range(a_ )
] )
lowerCAmelCase__ = nn.Linear(a_ ,a_ )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_=None ,a_=None ,a_=None ,a_=1 ,a_=None ,a_ = True ,):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = hidden_states.shape
lowerCAmelCase__ = batch_frames // num_frames
lowerCAmelCase__ = hidden_states
lowerCAmelCase__ = hidden_states[None, :].reshape(a_ ,a_ ,a_ ,a_ ,a_ )
lowerCAmelCase__ = hidden_states.permute(0 ,2 ,1 ,3 ,4 )
lowerCAmelCase__ = self.norm(a_ )
lowerCAmelCase__ = hidden_states.permute(0 ,3 ,4 ,2 ,1 ).reshape(batch_size * height * width ,a_ ,a_ )
lowerCAmelCase__ = self.proj_in(a_ )
# 2. Blocks
for block in self.transformer_blocks:
lowerCAmelCase__ = block(
a_ ,encoder_hidden_states=a_ ,timestep=a_ ,cross_attention_kwargs=a_ ,class_labels=a_ ,)
# 3. Output
lowerCAmelCase__ = self.proj_out(a_ )
lowerCAmelCase__ = (
hidden_states[None, None, :]
.reshape(a_ ,a_ ,a_ ,a_ ,a_ )
.permute(0 ,3 ,4 ,1 ,2 )
.contiguous()
)
lowerCAmelCase__ = hidden_states.reshape(a_ ,a_ ,a_ ,a_ )
lowerCAmelCase__ = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=a_ )
| 193 | 0 |
import math
def _lowerCAmelCase ( _a : list , _a : int = 0 , _a : int = 0 ) -> list:
lowerCAmelCase_ : List[str] = end or len(_a )
for i in range(_a , _a ):
lowerCAmelCase_ : List[Any] = i
lowerCAmelCase_ : Tuple = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
lowerCAmelCase_ : Tuple = array[temp_index - 1]
temp_index -= 1
lowerCAmelCase_ : Optional[Any] = temp_index_value
return array
def _lowerCAmelCase ( _a : list , _a : int , _a : int ) -> None: # Max Heap
lowerCAmelCase_ : List[str] = index
lowerCAmelCase_ : Tuple = 2 * index + 1 # Left Node
lowerCAmelCase_ : Any = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
lowerCAmelCase_ : Optional[int] = left_index
if right_index < heap_size and array[largest] < array[right_index]:
lowerCAmelCase_ : Union[str, Any] = right_index
if largest != index:
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = array[largest], array[index]
heapify(_a , _a , _a )
def _lowerCAmelCase ( _a : list ) -> list:
lowerCAmelCase_ : List[Any] = len(_a )
for i in range(n // 2 , -1 , -1 ):
heapify(_a , _a , _a )
for i in range(n - 1 , 0 , -1 ):
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = array[0], array[i]
heapify(_a , 0 , _a )
return array
def _lowerCAmelCase ( _a : list , _a : int , _a : int , _a : int ) -> int:
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _lowerCAmelCase ( _a : list , _a : int , _a : int , _a : int ) -> int:
lowerCAmelCase_ : Union[str, Any] = low
lowerCAmelCase_ : List[Any] = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
lowerCAmelCase_ , lowerCAmelCase_ : Any = array[j], array[i]
i += 1
def _lowerCAmelCase ( _a : list ) -> list:
if len(_a ) == 0:
return array
lowerCAmelCase_ : int = 2 * math.ceil(math.loga(len(_a ) ) )
lowerCAmelCase_ : Tuple = 16
return intro_sort(_a , 0 , len(_a ) , _a , _a )
def _lowerCAmelCase ( _a : list , _a : int , _a : int , _a : int , _a : int ) -> list:
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(_a )
max_depth -= 1
lowerCAmelCase_ : Tuple = median_of_a(_a , _a , start + ((end - start) // 2) + 1 , end - 1 )
lowerCAmelCase_ : Optional[int] = partition(_a , _a , _a , _a )
intro_sort(_a , _a , _a , _a , _a )
lowerCAmelCase_ : int = p
return insertion_sort(_a , _a , _a )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : List[Any] = input("""Enter numbers separated by a comma : """).strip()
UpperCAmelCase_ : Optional[int] = [float(item) for item in user_input.split(""",""")]
print(sort(unsorted))
| 440 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase__ ( unittest.TestCase ):
__UpperCamelCase = inspect.getfile(accelerate.test_utils )
__UpperCamelCase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_cli.py"""] )
__UpperCamelCase = ["""accelerate""", """launch"""]
__UpperCamelCase = Path.home() / """.cache/huggingface/accelerate"""
__UpperCamelCase = """default_config.yaml"""
__UpperCamelCase = config_folder / config_file
__UpperCamelCase = config_folder / """_default_config.yaml"""
__UpperCamelCase = Path("""tests/test_configs""" )
@classmethod
def UpperCAmelCase__ ( cls ):
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def UpperCAmelCase__ ( cls ):
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : int = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def UpperCAmelCase__ ( self ):
for config in sorted(self.test_config_path.glob("""**/*.yaml""" ) ):
with self.subTest(config_file=_lowercase ):
execute_subprocess_async(
self.base_cmd + ["""--config_file""", str(_lowercase ), self.test_file_path] , env=os.environ.copy() )
def UpperCAmelCase__ ( self ):
execute_subprocess_async(["""accelerate""", """test"""] , env=os.environ.copy() )
class lowercase__ ( unittest.TestCase ):
__UpperCamelCase = """test-tpu"""
__UpperCamelCase = """us-central1-a"""
__UpperCamelCase = """ls"""
__UpperCamelCase = ["""accelerate""", """tpu-config"""]
__UpperCamelCase = """cd /usr/share"""
__UpperCamelCase = """tests/test_samples/test_command_file.sh"""
__UpperCamelCase = """Running gcloud compute tpus tpu-vm ssh"""
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command""",
self.command,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Optional[Any] = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] , return_stdout=_lowercase )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Tuple = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--command""",
self.command,
"""--command""",
"""echo \"Hello World\"""",
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : str = run_command(
self.cmd
+ ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Optional[Any] = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command_file""",
self.command_file,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Tuple = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
def UpperCAmelCase__ ( self ):
lowerCAmelCase_ : Any = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--install_accelerate""",
"""--accelerate_version""",
"""12.0.0""",
"""--debug""",
] , return_stdout=_lowercase , )
self.assertIn(
F'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all' , _lowercase , )
| 440 | 1 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
if n == 1 or not isinstance(_lowercase ,_lowercase ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase = [0, 1]
for i in range(2 ,n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = 0
UpperCamelCase = 2
while digits < n:
index += 1
UpperCamelCase = len(str(fibonacci(_lowercase ) ) )
return index
def __snake_case ( _lowercase = 1000 ):
"""simple docstring"""
return fibonacci_digits_index(_lowercase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 34 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {'vocab_file': 'vocab.txt'}
SCREAMING_SNAKE_CASE_ = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
SCREAMING_SNAKE_CASE_ = {
'openbmb/cpm-ant-10b': 1024,
}
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = collections.OrderedDict()
with open(_lowercase ,'''r''' ,encoding='''utf-8''' ) as reader:
UpperCamelCase = reader.readlines()
for index, token in enumerate(_lowercase ):
UpperCamelCase = token.rstrip('''\n''' )
UpperCamelCase = index
return vocab
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase_ , lowerCamelCase_="<unk>" , lowerCamelCase_=2_0_0) -> Any:
UpperCamelCase = vocab
UpperCamelCase = unk_token
UpperCamelCase = max_input_chars_per_word
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Union[str, Any]:
UpperCamelCase = list(lowerCamelCase_)
if len(lowerCamelCase_) > self.max_input_chars_per_word:
return [self.unk_token]
UpperCamelCase = 0
UpperCamelCase = []
while start < len(lowerCamelCase_):
UpperCamelCase = len(lowerCamelCase_)
UpperCamelCase = None
while start < end:
UpperCamelCase = ''''''.join(chars[start:end])
if substr in self.vocab:
UpperCamelCase = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token)
start += 1
else:
sub_tokens.append(lowerCamelCase_)
UpperCamelCase = end
return sub_tokens
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ['''input_ids''', '''attention_mask''']
A_ = False
def __init__( self , lowerCamelCase_ , lowerCamelCase_="<d>" , lowerCamelCase_="</d>" , lowerCamelCase_="<s>" , lowerCamelCase_="</s>" , lowerCamelCase_="<pad>" , lowerCamelCase_="<unk>" , lowerCamelCase_="</n>" , lowerCamelCase_="</_>" , lowerCamelCase_="left" , **lowerCamelCase_ , ) -> List[str]:
requires_backends(self , ['''jieba'''])
super().__init__(
bod_token=lowerCamelCase_ , eod_token=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , line_token=lowerCamelCase_ , space_token=lowerCamelCase_ , padding_side=lowerCamelCase_ , **lowerCamelCase_ , )
UpperCamelCase = bod_token
UpperCamelCase = eod_token
UpperCamelCase = load_vocab(lowerCamelCase_)
UpperCamelCase = self.encoder[space_token]
UpperCamelCase = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
UpperCamelCase = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowerCamelCase_: x[1]))
UpperCamelCase = {v: k for k, v in self.encoder.items()}
UpperCamelCase = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token)
@property
def UpperCAmelCase__ ( self) -> Dict:
return self.encoder[self.bod_token]
@property
def UpperCAmelCase__ ( self) -> str:
return self.encoder[self.eod_token]
@property
def UpperCAmelCase__ ( self) -> List[Any]:
return self.encoder["\n"]
@property
def UpperCAmelCase__ ( self) -> int:
return len(self.encoder)
def UpperCAmelCase__ ( self) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Any:
UpperCamelCase = []
for x in jieba.cut(lowerCamelCase_ , cut_all=lowerCamelCase_):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase_))
return output_tokens
def UpperCAmelCase__ ( self , lowerCamelCase_ , **lowerCamelCase_) -> Tuple:
UpperCamelCase = [i for i in token_ids if i >= 0]
UpperCamelCase = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(lowerCamelCase_ , **lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Dict:
return token in self.encoder
def UpperCAmelCase__ ( self , lowerCamelCase_) -> str:
return "".join(lowerCamelCase_)
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Optional[int]:
return self.encoder.get(lowerCamelCase_ , self.encoder.get(self.unk_token))
def UpperCAmelCase__ ( self , lowerCamelCase_) -> Dict:
return self.decoder.get(lowerCamelCase_ , self.unk_token)
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> Tuple[str]:
if os.path.isdir(lowerCamelCase_):
UpperCamelCase = os.path.join(
lowerCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
else:
UpperCamelCase = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
UpperCamelCase = 0
if " " in self.encoder:
UpperCamelCase = self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
UpperCamelCase = self.encoder['''\n''']
del self.encoder["\n"]
UpperCamelCase = collections.OrderedDict(sorted(self.encoder.items() , key=lambda lowerCamelCase_: x[1]))
with open(lowerCamelCase_ , '''w''' , encoding='''utf-8''') as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
''' Please check that the vocabulary is not corrupted!''')
UpperCamelCase = token_index
writer.write(token + '''\n''')
index += 1
return (vocab_file,)
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None) -> List[int]:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = False) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase_ , token_ids_a=lowerCamelCase_ , already_has_special_tokens=lowerCamelCase_)
if token_ids_a is not None:
return [1] + ([0] * len(lowerCamelCase_)) + [1] + ([0] * len(lowerCamelCase_))
return [1] + ([0] * len(lowerCamelCase_))
| 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
"""LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LiltForQuestionAnswering""",
"""LiltForSequenceClassification""",
"""LiltForTokenClassification""",
"""LiltModel""",
"""LiltPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 470 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[Any]:
lowerCAmelCase__ : List[str] = 10
lowerCAmelCase__ : Optional[Any] = datasets.Features(
{
'''tokens''': datasets.Sequence(datasets.Value('''string''' ) ),
'''labels''': datasets.Sequence(datasets.ClassLabel(names=['''negative''', '''positive'''] ) ),
'''answers''': datasets.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
'''id''': datasets.Value('''int64''' ),
} )
lowerCAmelCase__ : List[Any] = datasets.Dataset.from_dict(
{
'''tokens''': [['''foo'''] * 5] * n,
'''labels''': [[1] * 5] * n,
'''answers''': [{'''answer_start''': [97], '''text''': ['''1976''']}] * 10,
'''id''': list(range(UpperCamelCase ) ),
} , features=UpperCamelCase , )
return dataset
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : str = str(tmp_path_factory.mktemp('''data''' ) / '''file.arrow''' )
dataset.map(cache_file_name=UpperCamelCase )
return filename
# FILE_CONTENT + files
lowerCAmelCase_ = """\
Text data.
Second line of data."""
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt'''
lowerCAmelCase__ : int = FILE_CONTENT
with open(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase )
return filename
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
import bza
lowerCAmelCase__ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.bz2'''
lowerCAmelCase__ : Optional[Any] = bytes(UpperCamelCase , '''utf-8''' )
with bza.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
import gzip
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''file.txt.gz''' )
lowerCAmelCase__ : Any = bytes(UpperCamelCase , '''utf-8''' )
with gzip.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Tuple:
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.lz4'''
lowerCAmelCase__ : Tuple = bytes(UpperCamelCase , '''utf-8''' )
with lza.frame.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase__ : int = tmp_path_factory.mktemp('''data''' ) / '''file.txt.7z'''
with pyazr.SevenZipFile(UpperCamelCase , '''w''' ) as archive:
archive.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
import tarfile
lowerCAmelCase__ : List[str] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
import lzma
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.xz'''
lowerCAmelCase__ : str = bytes(UpperCamelCase , '''utf-8''' )
with lzma.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Any:
import zipfile
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[str]:
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''file.txt.zst'''
lowerCAmelCase__ : int = bytes(UpperCamelCase , '''utf-8''' )
with zstd.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''file.xml'''
lowerCAmelCase__ : Tuple = textwrap.dedent(
'''\
<?xml version="1.0" encoding="UTF-8" ?>
<tmx version="1.4">
<header segtype="sentence" srclang="ca" />
<body>
<tu>
<tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>
<tuv xml:lang="en"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>
<tuv xml:lang="en"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>
<tuv xml:lang="en"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>
<tuv xml:lang="en"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>
<tuv xml:lang="en"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>''' )
with open(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase )
return filename
lowerCAmelCase_ = [
{"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
lowerCAmelCase_ = [
{"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
{"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
lowerCAmelCase_ = {
"""col_1""": ["""0""", """1""", """2""", """3"""],
"""col_2""": [0, 1, 2, 3],
"""col_3""": [0.0, 1.0, 2.0, 3.0],
}
lowerCAmelCase_ = [
{"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
{"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]
lowerCAmelCase_ = [
{"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Tuple:
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : str = datasets.Dataset.from_dict(UpperCamelCase )
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.arrow''' )
dataset.map(cache_file_name=UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.sqlite''' )
with contextlib.closing(sqlitea.connect(UpperCamelCase ) ) as con:
lowerCAmelCase__ : int = con.cursor()
cur.execute('''CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)''' )
for item in DATA:
cur.execute('''INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)''' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> int:
lowerCAmelCase__ : List[str] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.csv''' )
with open(UpperCamelCase , '''w''' , newline='''''' ) as f:
lowerCAmelCase__ : List[str] = csv.DictWriter(UpperCamelCase , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.csv''' )
with open(UpperCamelCase , '''w''' , newline='''''' ) as f:
lowerCAmelCase__ : Tuple = csv.DictWriter(UpperCamelCase , fieldnames=['''col_1''', '''col_2''', '''col_3'''] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[str]:
import bza
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.bz2'''
with open(UpperCamelCase , '''rb''' ) as f:
lowerCAmelCase__ : List[str] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(UpperCamelCase , '''wb''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(csv_path.replace('''.csv''' , '''.CSV''' ) ) )
f.write(UpperCamelCase , arcname=os.path.basename(csva_path.replace('''.csv''' , '''.CSV''' ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> str:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.csv.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : str = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.parquet''' )
lowerCAmelCase__ : List[Any] = pa.schema(
{
'''col_1''': pa.string(),
'''col_2''': pa.intaa(),
'''col_3''': pa.floataa(),
} )
with open(UpperCamelCase , '''wb''' ) as f:
lowerCAmelCase__ : str = pq.ParquetWriter(UpperCamelCase , schema=UpperCamelCase )
lowerCAmelCase__ : List[Any] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase ) )] for k in DATA[0]} , schema=UpperCamelCase )
writer.write_table(UpperCamelCase )
writer.close()
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : List[Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
lowerCAmelCase__ : List[str] = {'''data''': DATA}
with open(UpperCamelCase , '''w''' ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Tuple:
lowerCAmelCase__ : Tuple = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.json''' )
lowerCAmelCase__ : Optional[Any] = {'''data''': DATA_DICT_OF_LISTS}
with open(UpperCamelCase , '''w''' ) as f:
json.dump(UpperCamelCase , UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> int:
lowerCAmelCase__ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> str:
lowerCAmelCase__ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_312.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA_312:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Optional[int] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset-str.jsonl''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in DATA_STR:
f.write(json.dumps(UpperCamelCase ) + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> int:
import gzip
lowerCAmelCase__ : Dict = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt.gz''' )
with open(UpperCamelCase , '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase , '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> List[Any]:
import gzip
lowerCAmelCase__ : int = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.gz''' )
with open(UpperCamelCase , '''rb''' ) as orig_file:
with gzip.open(UpperCamelCase , '''wb''' ) as zipped_file:
zipped_file.writelines(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''nested''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.jsonl.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCAmelCase__ : Optional[Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.add(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Union[str, Any] = tmp_path_factory.mktemp('''data''' ) / '''dataset_nested.jsonl.tar'''
with tarfile.TarFile(UpperCamelCase , '''w''' ) as f:
f.add(UpperCamelCase , arcname=os.path.join('''nested''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[Any]:
lowerCAmelCase__ : Dict = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : Any = str(tmp_path_factory.mktemp('''data''' ) / '''dataset.txt''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Any:
lowerCAmelCase__ : int = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : int = str(tmp_path_factory.mktemp('''data''' ) / '''dataset2.txt''' )
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Optional[int]:
lowerCAmelCase__ : Optional[Any] = ['''0''', '''1''', '''2''', '''3''']
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.abc'''
with open(UpperCamelCase , '''w''' ) as f:
for item in data:
f.write(item + '''\n''' )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
lowerCAmelCase__ : Optional[int] = tmp_path_factory.mktemp('''data''' ) / '''dataset.text.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Any = tmp_path_factory.mktemp('''data''' ) / '''dataset_with_dir.text.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
f.write(UpperCamelCase , arcname=os.path.join('''main_dir''' , os.path.basename(UpperCamelCase ) ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
lowerCAmelCase__ : str = tmp_path_factory.mktemp('''data''' ) / '''dataset.ext.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename('''unsupported.ext''' ) )
f.write(UpperCamelCase , arcname=os.path.basename('''unsupported_2.ext''' ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : List[Any] = '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third'''] )
lowerCAmelCase__ : Union[str, Any] = str(tmp_path_factory.mktemp('''data''' ) / '''dataset_with_unicode_new_lines.txt''' )
with open(UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(UpperCamelCase )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[int]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_image_rgb.jpg''' )
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( ) -> Optional[Any]:
return os.path.join('''tests''' , '''features''' , '''data''' , '''test_audio_44100.wav''' )
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Tuple = tmp_path_factory.mktemp('''data''' ) / '''dataset.img.zip'''
with zipfile.ZipFile(UpperCamelCase , '''w''' ) as f:
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ) )
f.write(UpperCamelCase , arcname=os.path.basename(UpperCamelCase ).replace('''.jpg''' , '''2.jpg''' ) )
return path
@pytest.fixture(scope='''session''' )
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
lowerCAmelCase__ : Optional[Any] = tmp_path_factory.mktemp('''data_dir''' )
(data_dir / "subdir").mkdir()
with open(data_dir / '''subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden file
with open(data_dir / '''subdir''' / '''.test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '''.subdir''' / '''train.txt''' , '''w''' ) as f:
f.write('''foo\n''' * 10 )
with open(data_dir / '''.subdir''' / '''test.txt''' , '''w''' ) as f:
f.write('''bar\n''' * 10 )
return data_dir
| 470 | 1 |
import math
import sys
def __lowerCAmelCase ( UpperCamelCase ) -> str:
lowerCAmelCase__ : Dict = ''''''
try:
with open(UpperCamelCase , '''rb''' ) as binary_file:
lowerCAmelCase__ : str = binary_file.read()
for dat in data:
lowerCAmelCase__ : Optional[int] = F"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def __lowerCAmelCase ( UpperCamelCase ) -> str:
lowerCAmelCase__ : Any = {'''0''': '''0''', '''1''': '''1'''}
lowerCAmelCase__ , lowerCAmelCase__ : Dict = '''''', ''''''
lowerCAmelCase__ : Tuple = len(UpperCamelCase )
for i in range(len(UpperCamelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
lowerCAmelCase__ : Dict = lexicon[curr_string]
result += last_match_id
lowerCAmelCase__ : Optional[Any] = last_match_id + '''0'''
if math.loga(UpperCamelCase ).is_integer():
lowerCAmelCase__ : int = {}
for curr_key in list(UpperCamelCase ):
lowerCAmelCase__ : Optional[int] = lexicon.pop(UpperCamelCase )
lowerCAmelCase__ : Tuple = new_lex
lowerCAmelCase__ : Dict = last_match_id + '''1'''
index += 1
lowerCAmelCase__ : Tuple = ''''''
return result
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> None:
lowerCAmelCase__ : Optional[int] = 8
try:
with open(UpperCamelCase , '''wb''' ) as opened_file:
lowerCAmelCase__ : Optional[Any] = [
to_write[i : i + byte_length]
for i in range(0 , len(UpperCamelCase ) , UpperCamelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(UpperCamelCase , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def __lowerCAmelCase ( UpperCamelCase ) -> str:
lowerCAmelCase__ : Any = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
lowerCAmelCase__ : Optional[Any] = data_bits[counter:]
lowerCAmelCase__ : List[str] = data_bits[counter + 1 :]
return data_bits
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase ) -> None:
lowerCAmelCase__ : Optional[int] = read_file_binary(UpperCamelCase )
lowerCAmelCase__ : Any = remove_prefix(UpperCamelCase )
lowerCAmelCase__ : str = decompress_data(UpperCamelCase )
write_file_binary(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 678 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class _lowerCAmelCase ( _lowercase ):
A__ = 'donut-swin'
A__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __UpperCAmelCase=224 , __UpperCAmelCase=4 , __UpperCAmelCase=3 , __UpperCAmelCase=96 , __UpperCAmelCase=[2, 2, 6, 2] , __UpperCAmelCase=[3, 6, 12, 24] , __UpperCAmelCase=7 , __UpperCAmelCase=4.0 , __UpperCAmelCase=True , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase="gelu" , __UpperCAmelCase=False , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-5 , **__UpperCAmelCase , ):
super().__init__(**__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = image_size
lowerCAmelCase__ : List[str] = patch_size
lowerCAmelCase__ : int = num_channels
lowerCAmelCase__ : Optional[Any] = embed_dim
lowerCAmelCase__ : int = depths
lowerCAmelCase__ : Dict = len(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = num_heads
lowerCAmelCase__ : Dict = window_size
lowerCAmelCase__ : str = mlp_ratio
lowerCAmelCase__ : Optional[int] = qkv_bias
lowerCAmelCase__ : Any = hidden_dropout_prob
lowerCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
lowerCAmelCase__ : List[str] = drop_path_rate
lowerCAmelCase__ : Tuple = hidden_act
lowerCAmelCase__ : List[str] = use_absolute_embeddings
lowerCAmelCase__ : Dict = layer_norm_eps
lowerCAmelCase__ : Any = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase__ : List[Any] = int(embed_dim * 2 ** (len(__UpperCAmelCase ) - 1) )
| 678 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
def __init__( self , lowercase , lowercase , lowercase = None , lowercase = None , lowercase = False , **lowercase , ) -> Union[str, Any]:
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
lowerCamelCase_ = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
lowerCamelCase_ = self.builder.as_dataset(
split="train" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = None , **lowercase , ) -> Any:
if num_proc is not None and num_proc <= 0:
raise ValueError(f'num_proc {num_proc} must be an integer > 0.' )
lowerCamelCase_ = dataset
lowerCamelCase_ = name
lowerCamelCase_ = con
lowerCamelCase_ = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowerCamelCase_ = num_proc
lowerCamelCase_ = to_sql_kwargs
def SCREAMING_SNAKE_CASE_( self ) -> int:
lowerCamelCase_ = self.to_sql_kwargs.pop("sql" , _a )
lowerCamelCase_ = self.to_sql_kwargs.pop("con" , _a )
lowerCamelCase_ = self.to_sql_kwargs.pop("index" , _a )
lowerCamelCase_ = self._write(index=_a , **self.to_sql_kwargs )
return written
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = args
lowerCamelCase_ = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
lowerCamelCase_ = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
lowerCamelCase_ = batch.to_pandas()
lowerCamelCase_ = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def SCREAMING_SNAKE_CASE_( self , lowercase , **lowercase ) -> int:
lowerCamelCase_ = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
lowerCamelCase_ = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 712 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__A =datasets.logging.get_logger(__name__)
__A ='''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
__A ='''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
__A ='''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__="dummy_doc" ):
lowerCamelCase_ = {doc: key_lines}
lowerCamelCase_ = {doc: sys_lines}
lowerCamelCase_ = {}
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ = 0
lowerCamelCase_ , lowerCamelCase_ = reader.get_doc_mentions(lowerCamelCase__ , key_doc_lines[doc] , lowerCamelCase__ )
key_singletons_num += singletons_num
if NP_only or min_span:
lowerCamelCase_ = reader.set_annotated_parse_trees(lowerCamelCase__ , key_doc_lines[doc] , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ , lowerCamelCase_ = reader.get_doc_mentions(lowerCamelCase__ , sys_doc_lines[doc] , lowerCamelCase__ )
sys_singletons_num += singletons_num
if NP_only or min_span:
lowerCamelCase_ = reader.set_annotated_parse_trees(lowerCamelCase__ , key_doc_lines[doc] , lowerCamelCase__ , lowerCamelCase__ )
if remove_nested:
lowerCamelCase_ , lowerCamelCase_ = reader.remove_nested_coref_mentions(lowerCamelCase__ , lowerCamelCase__ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
lowerCamelCase_ , lowerCamelCase_ = reader.remove_nested_coref_mentions(lowerCamelCase__ , lowerCamelCase__ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
lowerCamelCase_ = reader.get_mention_assignments(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = reader.get_mention_assignments(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
"Number of removed nested coreferring mentions in the key "
F'annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}' )
logger.info(
"Number of resulting singleton clusters in the key "
F'annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}' )
if not keep_singletons:
logger.info(
F'{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '
"files, respectively" )
return doc_coref_infos
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = get_coref_infos(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = {}
lowerCamelCase_ = 0
lowerCamelCase_ = 0
for name, metric in metrics:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = evaluator.evaluate_documents(lowerCamelCase__ , lowerCamelCase__ , beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F'{name}/recall': recall, F'{name}/precision': precision, F'{name}/f1': fa} )
logger.info(
name.ljust(1_0 ) , F'Recall: {recall * 1_0_0:.2f}' , F' Precision: {precision * 1_0_0:.2f}' , F' F1: {fa * 1_0_0:.2f}' , )
if conll_subparts_num == 3:
lowerCamelCase_ = (conll / 3) * 1_0_0
logger.info(F'CoNLL score: {conll:.2f}' )
output_scores.update({"conll_score": conll} )
return output_scores
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = False
for line in key_lines:
if not line.startswith("#" ):
if len(line.split() ) > 6:
lowerCamelCase_ = line.split()[5]
if not parse_col == "-":
lowerCamelCase_ = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_( self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Sequence(datasets.Value("string" ) ),
} ) , codebase_urls=["https://github.com/ns-moosavi/coval"] , reference_urls=[
"https://github.com/ns-moosavi/coval",
"https://www.aclweb.org/anthology/P16-1060",
"http://www.conll.cemantix.org/2012/data.html",
] , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase=True , lowercase=False , lowercase=False , lowercase=False ) -> Dict:
lowerCamelCase_ = [
("mentions", evaluator.mentions),
("muc", evaluator.muc),
("bcub", evaluator.b_cubed),
("ceafe", evaluator.ceafe),
("lea", evaluator.lea),
]
if min_span:
lowerCamelCase_ = util.check_gold_parse_annotation(lowercase )
if not has_gold_parse:
raise NotImplementedError("References should have gold parse annotation to use 'min_span'." )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
lowerCamelCase_ = evaluate(
key_lines=lowercase , sys_lines=lowercase , metrics=lowercase , NP_only=lowercase , remove_nested=lowercase , keep_singletons=lowercase , min_span=lowercase , )
return score
| 313 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ (tf.keras.optimizers.schedules.LearningRateSchedule ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1.0 , SCREAMING_SNAKE_CASE_ = None , ) -> Any:
super().__init__()
__lowerCamelCase : Optional[Any] = initial_learning_rate
__lowerCamelCase : Optional[Any] = warmup_steps
__lowerCamelCase : Union[str, Any] = power
__lowerCamelCase : Optional[int] = decay_schedule_fn
__lowerCamelCase : Any = name
def __call__( self , SCREAMING_SNAKE_CASE_ ) -> str:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCamelCase : str = tf.cast(SCREAMING_SNAKE_CASE_ , tf.floataa )
__lowerCamelCase : Optional[int] = tf.cast(self.warmup_steps , tf.floataa )
__lowerCamelCase : List[Any] = global_step_float / warmup_steps_float
__lowerCamelCase : Optional[Any] = self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=SCREAMING_SNAKE_CASE_ , )
def lowercase_ ( self ) -> Optional[Any]:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def UpperCAmelCase__ ( UpperCAmelCase_ : float , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 0.9 , UpperCAmelCase_ : float = 0.999 , UpperCAmelCase_ : float = 1e-8 , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : Optional[float] = None , UpperCAmelCase_ : float = 0.0 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : Optional[List[str]] = None , ) -> int:
__lowerCamelCase : int = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=UpperCAmelCase_ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=UpperCAmelCase_ , )
if num_warmup_steps:
__lowerCamelCase : str = WarmUp(
initial_learning_rate=UpperCAmelCase_ , decay_schedule_fn=UpperCAmelCase_ , warmup_steps=UpperCAmelCase_ , )
if weight_decay_rate > 0.0:
__lowerCamelCase : List[Any] = AdamWeightDecay(
learning_rate=UpperCAmelCase_ , weight_decay_rate=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , epsilon=UpperCAmelCase_ , clipnorm=UpperCAmelCase_ , global_clipnorm=UpperCAmelCase_ , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=UpperCAmelCase_ , )
else:
__lowerCamelCase : Tuple = tf.keras.optimizers.Adam(
learning_rate=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , beta_a=UpperCAmelCase_ , epsilon=UpperCAmelCase_ , clipnorm=UpperCAmelCase_ , global_clipnorm=UpperCAmelCase_ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.0_0_1 , SCREAMING_SNAKE_CASE_ = 0.9 , SCREAMING_SNAKE_CASE_ = 0.9_9_9 , SCREAMING_SNAKE_CASE_ = 1E-7 , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "AdamWeightDecay" , **SCREAMING_SNAKE_CASE_ , ) -> int:
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = weight_decay_rate
__lowerCamelCase : str = include_in_weight_decay
__lowerCamelCase : List[Any] = exclude_from_weight_decay
@classmethod
def lowercase_ ( cls , SCREAMING_SNAKE_CASE_ ) -> Dict:
__lowerCamelCase : Any = {'WarmUp': WarmUp}
return super(SCREAMING_SNAKE_CASE_ , cls ).from_config(SCREAMING_SNAKE_CASE_ , custom_objects=SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
super(SCREAMING_SNAKE_CASE_ , self )._prepare_local(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
__lowerCamelCase : Tuple = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
__lowerCamelCase , __lowerCamelCase : Optional[Any] = list(zip(*SCREAMING_SNAKE_CASE_ ) )
return super(SCREAMING_SNAKE_CASE_ , self ).apply_gradients(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , name=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCamelCase : Optional[int] = apply_state or {}
__lowerCamelCase : Dict = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCamelCase : List[Any] = self._fallback_apply_state(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> str:
__lowerCamelCase , __lowerCamelCase : Dict = self._get_lr(var.device , var.dtype.base_dtype , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = self._decay_weights_op(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tf.control_dependencies([decay] ):
return super(SCREAMING_SNAKE_CASE_ , self )._resource_apply_dense(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Union[str, Any]:
__lowerCamelCase , __lowerCamelCase : Tuple = self._get_lr(var.device , var.dtype.base_dtype , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = self._decay_weights_op(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tf.control_dependencies([decay] ):
return super(SCREAMING_SNAKE_CASE_ , self )._resource_apply_sparse(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : Any = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ ) -> Dict:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is not None:
return False
return True
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self ) -> Tuple:
__lowerCamelCase : Tuple = []
__lowerCamelCase : Optional[Any] = None
@property
def lowercase_ ( self ) -> List[str]:
if self._accum_steps is None:
__lowerCamelCase : Tuple = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=SCREAMING_SNAKE_CASE_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def lowercase_ ( self ) -> List[str]:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , SCREAMING_SNAKE_CASE_ ) -> str:
if not self._gradients:
__lowerCamelCase : List[str] = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(SCREAMING_SNAKE_CASE_ ) , trainable=SCREAMING_SNAKE_CASE_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(SCREAMING_SNAKE_CASE_ ) != len(self._gradients ):
raise ValueError(f'Expected {len(self._gradients )} gradients, but got {len(SCREAMING_SNAKE_CASE_ )}' )
for accum_gradient, gradient in zip(self._gradients , SCREAMING_SNAKE_CASE_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(SCREAMING_SNAKE_CASE_ )
self._accum_steps.assign_add(1 )
def lowercase_ ( self ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(SCREAMING_SNAKE_CASE_ ) )
| 13 |
'''simple docstring'''
A__ : dict[tuple[int, int, int], int] = {}
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
__lowerCamelCase : List[Any] = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
__lowerCamelCase : Tuple = _calculate(days - 1 , UpperCAmelCase_ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
__lowerCamelCase : int = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
__lowerCamelCase : List[Any] = _calculate(days - 1 , UpperCAmelCase_ , 0 )
__lowerCamelCase : Optional[int] = state_late + state_absent + state_ontime
__lowerCamelCase : Union[str, Any] = prizestrings
return prizestrings
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 30 ) -> int:
return _calculate(UpperCAmelCase_ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 13 | 1 |
"""simple docstring"""
import inspect
import unittest
class __UpperCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self : Tuple ) -> Any:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self : List[Any] ) -> Any:
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
a__ : Any = inspect.getmembers(a_ , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
a__ : str = "k-diffusion"
elif backend == "invisible_watermark":
a__ : List[Any] = "invisible-watermark"
assert backend in deps, F"{backend} is not in the deps table!"
| 251 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def lowercase__ ( lowerCAmelCase__ : Union[str, Any] ) -> str:
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def lowercase__ ( lowerCAmelCase__ : List[str] ) -> str:
'''simple docstring'''
a__ : Any = create_tensor(lowerCAmelCase__ )
a__ : Optional[Any] = gather(lowerCAmelCase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def lowercase__ ( lowerCAmelCase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
a__ : str = [state.process_index]
a__ : Optional[int] = gather_object(lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) == state.num_processes, F"{gathered_obj}, {len(lowerCAmelCase__ )} != {state.num_processes}"
assert gathered_obj == list(range(state.num_processes ) ), F"{gathered_obj} != {list(range(state.num_processes ) )}"
def lowercase__ ( lowerCAmelCase__ : Optional[Any] ) -> Tuple:
'''simple docstring'''
a__ : str = create_tensor(lowerCAmelCase__ )
a__ : Any = broadcast(lowerCAmelCase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def lowercase__ ( lowerCAmelCase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
a__ : Any = torch.arange(state.num_processes + 1 ).to(state.device )
else:
a__ : Union[str, Any] = torch.arange(state.num_processes ).to(state.device )
a__ : List[Any] = pad_across_processes(lowerCAmelCase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def lowercase__ ( lowerCAmelCase__ : Dict ) -> str:
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
a__ : List[str] = create_tensor(lowerCAmelCase__ )
a__ : Union[str, Any] = reduce(lowerCAmelCase__ , "sum" )
a__ : List[str] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ ), F"{reduced_tensor} != {truth_tensor}"
def lowercase__ ( lowerCAmelCase__ : List[str] ) -> int:
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
a__ : Tuple = create_tensor(lowerCAmelCase__ )
a__ : Dict = reduce(lowerCAmelCase__ , "mean" )
a__ : Tuple = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ ), F"{reduced_tensor} != {truth_tensor}"
def lowercase__ ( lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
def lowercase__ ( ) -> Optional[int]:
'''simple docstring'''
a__ : List[str] = PartialState()
state.print(F"State: {state}" )
state.print("testing gather" )
test_gather(lowerCAmelCase__ )
state.print("testing gather_object" )
test_gather_object(lowerCAmelCase__ )
state.print("testing broadcast" )
test_broadcast(lowerCAmelCase__ )
state.print("testing pad_across_processes" )
test_pad_across_processes(lowerCAmelCase__ )
state.print("testing reduce_sum" )
test_reduce_sum(lowerCAmelCase__ )
state.print("testing reduce_mean" )
test_reduce_mean(lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 251 | 1 |
class A__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] ):
a__ : str = name
a__ : Optional[int] = value
a__ : Dict = weight
def __repr__( self : Union[str, Any] ):
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _UpperCamelCase( self : Dict ):
return self.value
def _UpperCamelCase( self : Optional[Any] ):
return self.name
def _UpperCamelCase( self : Optional[Any] ):
return self.weight
def _UpperCamelCase( self : Optional[int] ):
return self.value / self.weight
def UpperCamelCase_ ( __a , __a , __a ) -> Optional[Any]:
a__ : Optional[Any] = []
for i in range(len(__a ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def UpperCamelCase_ ( __a , __a , __a ) -> Union[str, Any]:
a__ : List[str] = sorted(__a , key=__a , reverse=__a )
a__ : List[Any] = []
a__, a__ : Union[str, Any] = 0.0, 0.0
for i in range(len(__a ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def UpperCamelCase_ ( ) -> Union[str, Any]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 37 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowerCamelCase , [-1 * len(lowerCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
__magic_name__ : Tuple =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__magic_name__ : Tuple =heapq.heappop(lowerCamelCase )[1][0]
chosen_vertices.add(lowerCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__magic_name__ : Tuple =elem[1][1].index(lowerCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowerCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Optional[int] = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 21 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = ["pixel_values"]
def __init__( self : List[str] , __lowerCamelCase : bool = True , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , __lowerCamelCase : bool = True , __lowerCamelCase : Dict[str, int] = None , __lowerCamelCase : bool = True , __lowerCamelCase : Union[int, float] = 1 / 255 , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , __lowerCamelCase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **__lowerCamelCase : List[Any] , ) -> None:
super().__init__(**__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = size if size is not None else {'''shortest_edge''': 224}
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase , param_name='''crop_size''' )
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = resample
SCREAMING_SNAKE_CASE__ = do_center_crop
SCREAMING_SNAKE_CASE__ = crop_size
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowercase_ ( self : List[Any] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Dict[str, int] , __lowerCamelCase : PILImageResampling = PILImageResampling.BICUBIC , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : List[str] , ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ = int((256 / 224) * size['''shortest_edge'''] )
SCREAMING_SNAKE_CASE__ = get_resize_output_image_size(__lowerCamelCase , size=__lowerCamelCase , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
return resize(
__lowerCamelCase , size=(size_dict['''height'''], size_dict['''width''']) , resample=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def lowercase_ ( self : Optional[Any] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Dict[str, int] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : Optional[int] , ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
return center_crop(__lowerCamelCase , size=(size['''height'''], size['''width''']) , data_format=__lowerCamelCase , **__lowerCamelCase )
def lowercase_ ( self : str , __lowerCamelCase : np.ndarray , __lowerCamelCase : Union[int, float] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : str , ) -> np.ndarray:
return rescale(__lowerCamelCase , scale=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def lowercase_ ( self : List[str] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Union[float, List[float]] , __lowerCamelCase : Union[float, List[float]] , __lowerCamelCase : Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase : int , ) -> np.ndarray:
return normalize(__lowerCamelCase , mean=__lowerCamelCase , std=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def lowercase_ ( self : List[Any] , __lowerCamelCase : ImageInput , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[Dict[str, int]] = None , __lowerCamelCase : PILImageResampling = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[Dict[str, int]] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[float] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[Union[float, Iterable[float]]] = None , __lowerCamelCase : Optional[Union[float, Iterable[float]]] = None , __lowerCamelCase : Optional[TensorType] = None , __lowerCamelCase : ChannelDimension = ChannelDimension.FIRST , **__lowerCamelCase : Any , ) -> BatchFeature:
SCREAMING_SNAKE_CASE__ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase , default_to_square=__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ = get_size_dict(__lowerCamelCase , param_name='''crop_size''' )
SCREAMING_SNAKE_CASE__ = make_list_of_images(__lowerCamelCase )
if not valid_images(__lowerCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ = [to_numpy_array(__lowerCamelCase ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ = [self.resize(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE__ = [self.center_crop(__lowerCamelCase , __lowerCamelCase ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ = [self.rescale(__lowerCamelCase , __lowerCamelCase ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE__ = [self.normalize(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) for image in images]
SCREAMING_SNAKE_CASE__ = [to_channel_dimension_format(__lowerCamelCase , __lowerCamelCase ) for image in images]
SCREAMING_SNAKE_CASE__ = {'''pixel_values''': images}
return BatchFeature(data=__lowerCamelCase , tensor_type=__lowerCamelCase )
| 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Dict = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Tuple = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
_SCREAMING_SNAKE_CASE : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 472 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 9.80665
def __snake_case ( _lowercase ,_lowercase ,_lowercase = g ):
"""simple docstring"""
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volume < 0:
raise ValueError('''Impossible Object volume''' )
if gravity <= 0:
raise ValueError('''Impossible Gravity''' )
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 34 |
"""simple docstring"""
import operator
def __snake_case ( _lowercase ,_lowercase = False ,_lowercase = None ):
"""simple docstring"""
UpperCamelCase = operator.lt if reverse else operator.gt
UpperCamelCase = solution or []
if not arr:
return solution
UpperCamelCase = [arr.pop(0 )]
for i, item in enumerate(_lowercase ):
if _operator(_lowercase ,sublist[-1] ):
sublist.append(_lowercase )
arr.pop(_lowercase )
# merging sublist into solution list
if not solution:
solution.extend(_lowercase )
else:
while sublist:
UpperCamelCase = sublist.pop(0 )
for i, xx in enumerate(_lowercase ):
if not _operator(_lowercase ,_lowercase ):
solution.insert(_lowercase ,_lowercase )
break
else:
solution.append(_lowercase )
strand_sort(_lowercase ,_lowercase ,_lowercase )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : List[str] = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Any = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
a_ : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 484 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Dict = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 484 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCamelCase ( lowercase_ = "isbn/0140328726" ) -> dict:
'''simple docstring'''
lowercase__ : Dict = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
lowercase__ : Optional[Any] = F'{olid} is not a valid Open Library olid'
raise ValueError(lowercase_ )
return requests.get(F'https://openlibrary.org/{new_olid}.json' ).json()
def UpperCamelCase ( lowercase_ ) -> dict:
'''simple docstring'''
lowercase__ : Tuple = {
"""title""": """Title""",
"""publish_date""": """Publish date""",
"""authors""": """Authors""",
"""number_of_pages""": """Number of pages:""",
"""first_sentence""": """First sentence""",
"""isbn_10""": """ISBN (10)""",
"""isbn_13""": """ISBN (13)""",
}
lowercase__ : List[Any] = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
lowercase__ : Any = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
lowercase__ : Tuple = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(lowercase_ , lowercase_ ):
lowercase__ : List[str] = """, """.join(lowercase_ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowerCamelCase__ : Tuple = input("""\nEnter the ISBN code to search (or 'quit' to stop): """).strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (1_0, 1_3) or not isbn.isdigit():
print(f'''Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.''')
continue
print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')
try:
lowerCamelCase__ : Optional[Any] = summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
print("""\n""".join(f'''{key}: {value}''' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f'''Sorry, there are no results for ISBN: {isbn}.''')
| 12 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCamelCase__ : Any = datasets.utils.logging.get_logger(__name__)
class _snake_case ( folder_based_builder.FolderBasedBuilderConfig ):
__lowerCAmelCase : bool = None
__lowerCAmelCase : bool = None
class _snake_case ( folder_based_builder.FolderBasedBuilder ):
__lowerCAmelCase : Optional[Any] = datasets.Audio()
__lowerCAmelCase : Union[str, Any] = 'audio'
__lowerCAmelCase : str = AudioFolderConfig
__lowerCAmelCase : List[str] # definition at the bottom of the script
__lowerCAmelCase : Optional[int] = AudioClassification(audio_column='audio' , label_column='label' )
lowerCamelCase__ : int = [
""".aiff""",
""".au""",
""".avr""",
""".caf""",
""".flac""",
""".htk""",
""".svx""",
""".mat4""",
""".mat5""",
""".mpc2k""",
""".ogg""",
""".paf""",
""".pvf""",
""".raw""",
""".rf64""",
""".sd2""",
""".sds""",
""".ircam""",
""".voc""",
""".w64""",
""".wav""",
""".nist""",
""".wavex""",
""".wve""",
""".xi""",
""".mp3""",
""".opus""",
]
lowerCamelCase__ : int = AUDIO_EXTENSIONS
| 12 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 83 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
_SCREAMING_SNAKE_CASE = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
_SCREAMING_SNAKE_CASE = {'facebook/blenderbot_small-90M': 512}
def snake_case ( snake_case__ :Tuple) -> str:
_A = set()
_A = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
_A = char
_A = set(snake_case__)
return pairs
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :List[Any] = VOCAB_FILES_NAMES
lowerCamelCase :Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase :int = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="__start__" , lowerCAmelCase_="__end__" , lowerCAmelCase_="__unk__" , lowerCAmelCase_="__null__" , **lowerCAmelCase_ , ) -> int:
super().__init__(unk_token=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , **lowerCAmelCase_ )
with open(lowerCAmelCase_ , encoding="""utf-8""" ) as vocab_handle:
_A = json.load(lowerCAmelCase_ )
_A = {v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase_ , encoding="""utf-8""" ) as merges_handle:
_A = merges_handle.read().split("""\n""" )[1:-1]
_A = [tuple(merge.split() ) for merge in merges]
_A = dict(zip(lowerCAmelCase_ , range(len(lowerCAmelCase_ ) ) ) )
_A = {}
@property
def UpperCAmelCase ( self ) -> int:
return len(self.encoder )
def UpperCAmelCase ( self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> str:
if token in self.cache:
return self.cache[token]
_A = re.sub("""([.,!?()])""" , r""" \1""" , lowerCAmelCase_ )
_A = re.sub("""(')""" , r""" \1 """ , lowerCAmelCase_ )
_A = re.sub(r"""\s{2,}""" , """ """ , lowerCAmelCase_ )
if "\n" in token:
_A = token.replace("""\n""" , """ __newln__""" )
_A = token.split(""" """ )
_A = []
for token in tokens:
if not len(lowerCAmelCase_ ):
continue
_A = token.lower()
_A = tuple(lowerCAmelCase_ )
_A = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
_A = get_pairs(lowerCAmelCase_ )
if not pairs:
words.append(lowerCAmelCase_ )
continue
while True:
_A = min(lowerCAmelCase_ , key=lambda lowerCAmelCase_ : self.bpe_ranks.get(lowerCAmelCase_ , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_A , _A = bigram
_A = []
_A = 0
while i < len(lowerCAmelCase_ ):
try:
_A = word.index(lowerCAmelCase_ , lowerCAmelCase_ )
new_word.extend(word[i:j] )
_A = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowerCAmelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_A = tuple(lowerCAmelCase_ )
_A = new_word
if len(lowerCAmelCase_ ) == 1:
break
else:
_A = get_pairs(lowerCAmelCase_ )
_A = """@@ """.join(lowerCAmelCase_ )
_A = word[:-4]
_A = word
words.append(lowerCAmelCase_ )
return " ".join(lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> List[str]:
_A = []
_A = re.findall(r"""\S+\n?""" , lowerCAmelCase_ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase_ ).split(""" """ ) ) )
return split_tokens
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> int:
_A = token.lower()
return self.encoder.get(lowerCAmelCase_ , self.encoder.get(self.unk_token ) )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> str:
return self.decoder.get(lowerCAmelCase_ , self.unk_token )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> str:
_A = """ """.join(lowerCAmelCase_ ).replace("""@@ """ , """""" ).strip()
return out_string
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ = None ) -> Tuple[str]:
if not os.path.isdir(lowerCAmelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
lowerCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_A = os.path.join(
lowerCAmelCase_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(lowerCAmelCase_ , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCAmelCase_ , ensure_ascii=lowerCAmelCase_ ) + """\n""" )
_A = 0
with open(lowerCAmelCase_ , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCAmelCase_ : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
""" Please check that the tokenizer is not corrupted!""" )
_A = token_index
writer.write(""" """.join(lowerCAmelCase_ ) + """\n""" )
index += 1
return vocab_file, merge_file
| 83 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __snake_case :
def __init__( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = data
lowerCAmelCase__ = [0X67452301, 0XEFCDAB89, 0X98BADCFE, 0X10325476, 0XC3D2E1F0]
@staticmethod
def SCREAMING_SNAKE_CASE_ ( a_ ,a_ ):
"""simple docstring"""
return ((n << b) | (n >> (32 - b))) & 0XFFFFFFFF
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = b'''\x80''' + b'''\x00''' * (63 - (len(self.data ) + 8) % 64)
lowerCAmelCase__ = self.data + padding + struct.pack('>Q' ,8 * len(self.data ) )
return padded_data
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return [
self.padded_data[i : i + 64] for i in range(0 ,len(self.padded_data ) ,64 )
]
def SCREAMING_SNAKE_CASE_ ( self ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = list(struct.unpack('>16L' ,snake_case_ ) ) + [0] * 64
for i in range(16 ,80 ):
lowerCAmelCase__ = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) ,1 )
return w
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.padding()
lowerCAmelCase__ = self.split_blocks()
for block in self.blocks:
lowerCAmelCase__ = self.expand_block(snake_case_ )
lowerCAmelCase__ = self.h
for i in range(0 ,80 ):
if 0 <= i < 20:
lowerCAmelCase__ = (b & c) | ((~b) & d)
lowerCAmelCase__ = 0X5A827999
elif 20 <= i < 40:
lowerCAmelCase__ = b ^ c ^ d
lowerCAmelCase__ = 0X6ED9EBA1
elif 40 <= i < 60:
lowerCAmelCase__ = (b & c) | (b & d) | (c & d)
lowerCAmelCase__ = 0X8F1BBCDC
elif 60 <= i < 80:
lowerCAmelCase__ = b ^ c ^ d
lowerCAmelCase__ = 0XCA62C1D6
lowerCAmelCase__ = (
self.rotate(snake_case_ ,5 ) + f + e + k + expanded_block[i] & 0XFFFFFFFF,
a,
self.rotate(snake_case_ ,30 ),
c,
d,
)
lowerCAmelCase__ = (
self.h[0] + a & 0XFFFFFFFF,
self.h[1] + b & 0XFFFFFFFF,
self.h[2] + c & 0XFFFFFFFF,
self.h[3] + d & 0XFFFFFFFF,
self.h[4] + e & 0XFFFFFFFF,
)
return ("{:08x}" * 5).format(*self.h )
def UpperCAmelCase_ ( ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = B'''Test String'''
assert SHAaHash(lowerCamelCase_ ).final_hash() == hashlib.shaa(lowerCamelCase_ ).hexdigest() # noqa: S324
def UpperCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = argparse.ArgumentParser(description='Process some strings or files' )
parser.add_argument(
'--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument('--file' , dest='input_file' , help='Hash contents of a file' )
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
lowerCAmelCase__ = f.read()
else:
lowerCAmelCase__ = bytes(lowerCamelCase_ , 'utf-8' )
print(SHAaHash(lowerCamelCase_ ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 193 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class _SCREAMING_SNAKE_CASE ( snake_case ):
lowerCamelCase_ = 'markuplm'
def __init__( self : List[Any] , snake_case_ : List[str]=3_0522 , snake_case_ : str=768 , snake_case_ : str=12 , snake_case_ : Optional[Any]=12 , snake_case_ : Any=3072 , snake_case_ : Dict="gelu" , snake_case_ : Dict=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : int=512 , snake_case_ : Optional[Any]=2 , snake_case_ : int=0.02 , snake_case_ : Optional[Any]=1E-12 , snake_case_ : Dict=0 , snake_case_ : Optional[int]=0 , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=256 , snake_case_ : Union[str, Any]=1024 , snake_case_ : Optional[Any]=216 , snake_case_ : Optional[Any]=1001 , snake_case_ : Tuple=32 , snake_case_ : str=50 , snake_case_ : int="absolute" , snake_case_ : List[Any]=True , snake_case_ : List[Any]=None , **snake_case_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ , )
A : int = vocab_size
A : Dict = hidden_size
A : str = num_hidden_layers
A : List[Any] = num_attention_heads
A : int = hidden_act
A : List[Any] = intermediate_size
A : Optional[Any] = hidden_dropout_prob
A : Tuple = attention_probs_dropout_prob
A : str = max_position_embeddings
A : Dict = type_vocab_size
A : Optional[int] = initializer_range
A : Optional[Any] = layer_norm_eps
A : Any = position_embedding_type
A : List[Any] = use_cache
A : List[str] = classifier_dropout
# additional properties
A : Optional[Any] = max_depth
A : Tuple = max_xpath_tag_unit_embeddings
A : str = max_xpath_subs_unit_embeddings
A : Dict = tag_pad_id
A : Dict = subs_pad_id
A : List[str] = xpath_unit_hidden_size
| 256 | 0 |
from __future__ import annotations
from typing import Any
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
create_state_space_tree(UpperCAmelCase__ , [] , 0 )
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if index == len(UpperCAmelCase__ ):
print(UpperCAmelCase__ )
return
create_state_space_tree(UpperCAmelCase__ , UpperCAmelCase__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(UpperCAmelCase__ , UpperCAmelCase__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
a_ : list[Any] = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(['A', 'B', 'C'])
generate_all_subsequences(seq)
| 710 |
from math import pi
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 484 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a__ : str =logging.get_logger(__name__)
a__ : Any ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
a__ : Dict ={
"""vocab_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
),
"""squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
),
},
}
a__ : int ={
"""squeezebert/squeezebert-uncased""": 512,
"""squeezebert/squeezebert-mnli""": 512,
"""squeezebert/squeezebert-mnli-headless""": 512,
}
a__ : List[str] ={
"""squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}
class snake_case ( a_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] =VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Tuple =PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : Optional[int] =PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ : Any =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : List[Any] =SqueezeBertTokenizer
def __init__( self : List[str] , __A : Optional[int]=None , __A : Optional[int]=None , __A : Optional[Any]=True , __A : List[str]="[UNK]" , __A : Optional[Any]="[SEP]" , __A : Any="[PAD]" , __A : Tuple="[CLS]" , __A : Dict="[MASK]" , __A : Any=True , __A : List[Any]=None , **__A : str , ):
super().__init__(
_snake_case , tokenizer_file=_snake_case , do_lower_case=_snake_case , unk_token=_snake_case , sep_token=_snake_case , pad_token=_snake_case , cls_token=_snake_case , mask_token=_snake_case , tokenize_chinese_chars=_snake_case , strip_accents=_snake_case , **_snake_case , )
__UpperCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , _snake_case ) != do_lower_case
or normalizer_state.get('strip_accents' , _snake_case ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , _snake_case ) != tokenize_chinese_chars
):
__UpperCamelCase = getattr(_snake_case , normalizer_state.pop('type' ) )
__UpperCamelCase = do_lower_case
__UpperCamelCase = strip_accents
__UpperCamelCase = tokenize_chinese_chars
__UpperCamelCase = normalizer_class(**_snake_case )
__UpperCamelCase = do_lower_case
def _lowerCamelCase ( self : str , __A : List[str] , __A : Optional[Any]=None ):
__UpperCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowerCamelCase ( self : Any , __A : List[str] , __A : str = None ):
__UpperCamelCase = [self.sep_token_id]
__UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCamelCase ( self : Tuple , __A : List[Any] , __A : Optional[Any] = None ):
__UpperCamelCase = self._tokenizer.model.save(_snake_case , name=_snake_case )
return tuple(_snake_case )
| 399 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
_UpperCamelCase : Union[str, Any] = checkpoint
_UpperCamelCase : int = {}
_UpperCamelCase : int = vae_state_dict['''encoder.conv_in.weight''']
_UpperCamelCase : Tuple = vae_state_dict['''encoder.conv_in.bias''']
_UpperCamelCase : Tuple = vae_state_dict['''encoder.conv_out.weight''']
_UpperCamelCase : Any = vae_state_dict['''encoder.conv_out.bias''']
_UpperCamelCase : List[Any] = vae_state_dict['''encoder.norm_out.weight''']
_UpperCamelCase : str = vae_state_dict['''encoder.norm_out.bias''']
_UpperCamelCase : str = vae_state_dict['''decoder.conv_in.weight''']
_UpperCamelCase : List[Any] = vae_state_dict['''decoder.conv_in.bias''']
_UpperCamelCase : List[str] = vae_state_dict['''decoder.conv_out.weight''']
_UpperCamelCase : List[str] = vae_state_dict['''decoder.conv_out.bias''']
_UpperCamelCase : int = vae_state_dict['''decoder.norm_out.weight''']
_UpperCamelCase : Dict = vae_state_dict['''decoder.norm_out.bias''']
_UpperCamelCase : Optional[int] = vae_state_dict['''quant_conv.weight''']
_UpperCamelCase : int = vae_state_dict['''quant_conv.bias''']
_UpperCamelCase : List[Any] = vae_state_dict['''post_quant_conv.weight''']
_UpperCamelCase : Optional[int] = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
_UpperCamelCase : Optional[int] = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
_UpperCamelCase : Tuple = {
layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(UpperCamelCase )
}
# Retrieves the keys for the decoder up blocks only
_UpperCamelCase : Any = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
_UpperCamelCase : int = {
layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(UpperCamelCase )
}
for i in range(UpperCamelCase ):
_UpperCamelCase : Any = [key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]
if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
_UpperCamelCase : Optional[int] = vae_state_dict.pop(
f'''encoder.down.{i}.downsample.conv.weight''' )
_UpperCamelCase : Dict = vae_state_dict.pop(
f'''encoder.down.{i}.downsample.conv.bias''' )
_UpperCamelCase : List[str] = renew_vae_resnet_paths(UpperCamelCase )
_UpperCamelCase : Union[str, Any] = {'''old''': f'''down.{i}.block''', '''new''': f'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
_UpperCamelCase : List[str] = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
_UpperCamelCase : Tuple = 2
for i in range(1 ,num_mid_res_blocks + 1 ):
_UpperCamelCase : Optional[int] = [key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]
_UpperCamelCase : List[str] = renew_vae_resnet_paths(UpperCamelCase )
_UpperCamelCase : Tuple = {'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
_UpperCamelCase : Tuple = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
_UpperCamelCase : List[str] = renew_vae_attention_paths(UpperCamelCase )
_UpperCamelCase : Any = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
conv_attn_to_linear(UpperCamelCase )
for i in range(UpperCamelCase ):
_UpperCamelCase : Union[str, Any] = num_up_blocks - 1 - i
_UpperCamelCase : Optional[int] = [
key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
]
if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
_UpperCamelCase : Tuple = vae_state_dict[
f'''decoder.up.{block_id}.upsample.conv.weight'''
]
_UpperCamelCase : Any = vae_state_dict[
f'''decoder.up.{block_id}.upsample.conv.bias'''
]
_UpperCamelCase : Any = renew_vae_resnet_paths(UpperCamelCase )
_UpperCamelCase : Any = {'''old''': f'''up.{block_id}.block''', '''new''': f'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
_UpperCamelCase : List[Any] = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
_UpperCamelCase : Optional[Any] = 2
for i in range(1 ,num_mid_res_blocks + 1 ):
_UpperCamelCase : int = [key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]
_UpperCamelCase : Optional[int] = renew_vae_resnet_paths(UpperCamelCase )
_UpperCamelCase : Optional[Any] = {'''old''': f'''mid.block_{i}''', '''new''': f'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
_UpperCamelCase : Tuple = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
_UpperCamelCase : Tuple = renew_vae_attention_paths(UpperCamelCase )
_UpperCamelCase : Dict = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,additional_replacements=[meta_path] ,config=UpperCamelCase )
conv_attn_to_linear(UpperCamelCase )
return new_checkpoint
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,) -> List[str]:
# Only support V1
_UpperCamelCase : Tuple = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
_UpperCamelCase : List[Any] = io.BytesIO(r.content )
_UpperCamelCase : Optional[int] = OmegaConf.load(UpperCamelCase )
_UpperCamelCase : str = 5_12
_UpperCamelCase : int = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
_UpperCamelCase : str = {}
with safe_open(UpperCamelCase ,framework='''pt''' ,device='''cpu''' ) as f:
for key in f.keys():
_UpperCamelCase : Union[str, Any] = f.get_tensor(UpperCamelCase )
else:
_UpperCamelCase : str = torch.load(UpperCamelCase ,map_location=UpperCamelCase )['''state_dict''']
# Convert the VAE model.
_UpperCamelCase : Dict = create_vae_diffusers_config(UpperCamelCase ,image_size=UpperCamelCase )
_UpperCamelCase : str = custom_convert_ldm_vae_checkpoint(UpperCamelCase ,UpperCamelCase )
_UpperCamelCase : Dict = AutoencoderKL(**UpperCamelCase )
vae.load_state_dict(UpperCamelCase )
vae.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
_UpperCAmelCase : str = argparse.ArgumentParser()
parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
_UpperCAmelCase : int = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 683 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def lowerCamelCase ( _snake_case ,_snake_case = 0.0 ,_snake_case = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 254 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase__ = logging.get_logger(__name__)
@dataclass
class a ( lowercase ):
UpperCamelCase : Dict = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **UpperCamelCase_ ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
UpperCAmelCase__ : int = deprecated_arg[3:]
UpperCAmelCase__ : Tuple = not kwargs.pop(UpperCamelCase_ )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
UpperCAmelCase__ : Dict = kwargs.pop('tpu_name' , self.tpu_name )
UpperCAmelCase__ : Tuple = kwargs.pop('device_idx' , self.device_idx )
UpperCAmelCase__ : List[str] = kwargs.pop('eager_mode' , self.eager_mode )
UpperCAmelCase__ : Any = kwargs.pop('use_xla' , self.use_xla )
super().__init__(**UpperCamelCase_ )
UpperCamelCase : str = field(
default=lowercase , metadata={"""help""": """Name of TPU"""} , )
UpperCamelCase : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
UpperCamelCase : bool = field(default=lowercase , metadata={"""help""": """Benchmark models in eager model."""} )
UpperCamelCase : bool = field(
default=lowercase , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
UpperCAmelCase__ : Optional[Any] = None
if self.tpu:
try:
if self.tpu_name:
UpperCAmelCase__ : str = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
UpperCAmelCase__ : Optional[int] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
UpperCAmelCase__ : Tuple = None
return tpu
@cached_property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
UpperCAmelCase__ : Union[str, Any] = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , 'GPU' )
UpperCAmelCase__ : Any = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , 'GPU' ) # disable GPU
UpperCAmelCase__ : Any = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
return self._setup_tpu is not None
@property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
return self._setup_strategy
@property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
return tf.config.list_physical_devices('GPU' )
@property
def __snake_case ( self ):
requires_backends(self , ['tf'] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __snake_case ( self ):
return self.n_gpu > 0
| 254 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
_SCREAMING_SNAKE_CASE =sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_UpperCamelCase ) )
return round(_UpperCamelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 405 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a: Optional[int] = logging.get_logger(__name__)
_a: Optional[Any] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = 'yolos'
def __init__( self : List[str] , lowerCAmelCase : List[Any]=768 , lowerCAmelCase : Optional[int]=12 , lowerCAmelCase : List[Any]=12 , lowerCAmelCase : List[Any]=3_072 , lowerCAmelCase : str="gelu" , lowerCAmelCase : int=0.0 , lowerCAmelCase : List[str]=0.0 , lowerCAmelCase : Dict=0.02 , lowerCAmelCase : List[str]=1e-12 , lowerCAmelCase : Union[str, Any]=[512, 864] , lowerCAmelCase : int=16 , lowerCAmelCase : Optional[Any]=3 , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : str=100 , lowerCAmelCase : Any=True , lowerCAmelCase : Tuple=False , lowerCAmelCase : List[str]=1 , lowerCAmelCase : Union[str, Any]=5 , lowerCAmelCase : int=2 , lowerCAmelCase : List[str]=5 , lowerCAmelCase : Optional[int]=2 , lowerCAmelCase : Any=0.1 , **lowerCAmelCase : Any , ):
'''simple docstring'''
super().__init__(**lowerCAmelCase )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = qkv_bias
UpperCAmelCase_ = num_detection_tokens
UpperCAmelCase_ = use_mid_position_embeddings
UpperCAmelCase_ = auxiliary_loss
# Hungarian matcher
UpperCAmelCase_ = class_cost
UpperCAmelCase_ = bbox_cost
UpperCAmelCase_ = giou_cost
# Loss coefficients
UpperCAmelCase_ = bbox_loss_coefficient
UpperCAmelCase_ = giou_loss_coefficient
UpperCAmelCase_ = eos_coefficient
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = version.parse('1.11' )
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __A ( self : Optional[int] ):
'''simple docstring'''
return 1e-4
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return 12
| 162 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _lowerCAmelCase ( UpperCAmelCase__ : List[str] ) ->str:
A__ : Tuple = {}
A__ : Union[str, Any] = tokenizer(example["""content"""], truncation=UpperCAmelCase__ )["""input_ids"""]
A__ : Any = len(example["""content"""] ) / len(output["""input_ids"""] )
return output
A_ = HfArgumentParser(PretokenizationArguments)
A_ = parser.parse_args()
if args.num_workers is None:
A_ = multiprocessing.cpu_count()
A_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
A_ = time.time()
A_ = load_dataset(args.dataset_name, split='''train''')
print(F'Dataset loaded in {time.time()-t_start:.2f}s')
A_ = time.time()
A_ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')
A_ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')
| 498 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : str, UpperCAmelCase__ : List[Any] ) ->List[Any]:
A__ : Union[str, Any] = [1]
for i in range(2, UpperCAmelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
A__ : Optional[int] = []
A__ : List[str] = list(range(UpperCAmelCase__ ) )
# Find permutation
while factorials:
A__ : Optional[int] = factorials.pop()
A__ , A__ : Optional[int] = divmod(UpperCAmelCase__, UpperCAmelCase__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 498 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def __magic_name__ ( lowercase = "." ):
for dir_path, dir_names, filenames in os.walk(lowercase ):
SCREAMING_SNAKE_CASE_: Any =[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowercase )[1] in (".py", ".ipynb"):
yield os.path.join(lowercase , lowercase ).lstrip("""./""" )
def __magic_name__ ( lowercase ):
return f'''{i * " "}*''' if i else "\n##"
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Dict =old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowercase ) or old_parts[i] != new_part) and new_part:
print(f'''{md_prefix(lowercase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def __magic_name__ ( lowercase = "." ):
SCREAMING_SNAKE_CASE_: Any =""""""
for filepath in sorted(good_file_paths(lowercase ) ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =os.path.split(lowercase )
if filepath != old_path:
SCREAMING_SNAKE_CASE_: List[str] =print_path(lowercase , lowercase )
SCREAMING_SNAKE_CASE_: Tuple =(filepath.count(os.sep ) + 1) if filepath else 0
SCREAMING_SNAKE_CASE_: Union[str, Any] =f'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(f'''{md_prefix(lowercase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(""".""")
| 409 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( UpperCAmelCase__ ):
def __init__( self : Any , lowerCAmelCase : NestedDataStructureLike[PathLike] , lowerCAmelCase : Optional[NamedSplit] = None , lowerCAmelCase : Optional[Features] = None , lowerCAmelCase : str = None , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : Optional[int] = None , **lowerCAmelCase : Union[str, Any] , ) -> str:
'''simple docstring'''
super().__init__(
lowerCAmelCase , split=lowerCAmelCase , features=lowerCAmelCase , cache_dir=lowerCAmelCase , keep_in_memory=lowerCAmelCase , streaming=lowerCAmelCase , num_proc=lowerCAmelCase , **lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: Union[str, Any] =path_or_paths if isinstance(lowerCAmelCase , lowerCAmelCase ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE_: str =Text(
cache_dir=lowerCAmelCase , data_files=lowerCAmelCase , features=lowerCAmelCase , **lowerCAmelCase , )
def lowerCamelCase__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE_: Union[str, Any] =self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE_: List[Any] =None
SCREAMING_SNAKE_CASE_: Tuple =None
SCREAMING_SNAKE_CASE_: List[Any] =None
SCREAMING_SNAKE_CASE_: Dict =None
self.builder.download_and_prepare(
download_config=lowerCAmelCase , download_mode=lowerCAmelCase , verification_mode=lowerCAmelCase , base_path=lowerCAmelCase , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE_: List[str] =self.builder.as_dataset(
split=self.split , verification_mode=lowerCAmelCase , in_memory=self.keep_in_memory )
return dataset
| 409 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
SCREAMING_SNAKE_CASE__ : str = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
for attribute in key.split('.' ):
a : Optional[int] = getattr(UpperCAmelCase__ , UpperCAmelCase__ )
if weight_type is not None:
a : str = getattr(UpperCAmelCase__ , UpperCAmelCase__ ).shape
else:
a : Dict = hf_pointer.shape
assert hf_shape == value.shape, (
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}'
)
if weight_type == "weight":
a : List[str] = value
elif weight_type == "weight_g":
a : int = value
elif weight_type == "weight_v":
a : Union[str, Any] = value
elif weight_type == "bias":
a : Dict = value
else:
a : Optional[Any] = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Union[str, Any]:
a : Dict = []
a : Optional[Any] = fairseq_model.state_dict()
a : Optional[Any] = hf_model.feature_extractor
a : int = hf_model.adapter
for name, value in fairseq_dict.items():
a : List[Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , hf_model.config.feat_extract_norm == 'group' , )
a : str = True
elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.'] ):
load_adapter(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
a : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
a : Optional[Any] = True
if "*" in mapped_key:
a : str = name.split(UpperCAmelCase__ )[0].split('.' )[-2]
a : Optional[int] = mapped_key.replace('*' , UpperCAmelCase__ )
if "weight_g" in name:
a : List[str] = 'weight_g'
elif "weight_v" in name:
a : Union[str, Any] = 'weight_v'
elif "bias" in name:
a : int = 'bias'
elif "weight" in name:
a : str = 'weight'
else:
a : Any = None
set_recursively(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase__ )
logger.warning(F'Unused weights: {unused_weights}' )
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Tuple:
a : List[str] = full_name.split('conv_layers.' )[-1]
a : str = name.split('.' )
a : Any = int(items[0] )
a : List[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
a : Dict = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
a : int = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
a : int = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
a : Dict = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(UpperCAmelCase__ )
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
a : Union[str, Any] = full_name.split('adaptor.' )[-1]
a : Union[str, Any] = name.split('.' )
if items[1].isdigit():
a : Tuple = int(items[1] )
else:
a : Optional[Any] = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
a : Optional[int] = value
logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
a : Optional[Any] = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
a : Tuple = value
logger.info(F'Adapter proj layer bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
a : Optional[Any] = value
logger.info(F'Adapter proj layer weight was initialized from {full_name}.' )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
a : Optional[Any] = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
a : Any = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.' )
else:
unused_weights.append(UpperCAmelCase__ )
def A_ ( UpperCAmelCase__ ) -> int:
a , a : Tuple = emb.weight.shape
a : List[str] = nn.Linear(UpperCAmelCase__ , UpperCAmelCase__ , bias=UpperCAmelCase__ )
a : List[Any] = emb.weight.data
return lin_layer
@torch.no_grad()
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ) -> Optional[int]:
a : Optional[int] = WavaVecaConfig.from_pretrained(
UpperCAmelCase__ , add_adapter=UpperCAmelCase__ , adapter_stride=UpperCAmelCase__ , adapter_kernel_size=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , output_hidden_size=UpperCAmelCase__ , )
a : Union[str, Any] = MBartConfig.from_pretrained(UpperCAmelCase__ )
# load model
a , a , a : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'config_yaml': config_yaml_path,
'data': '/'.join(dict_path.split('/' )[:-1] ),
'w2v_path': checkpoint_path,
'load_pretrained_decoder_from': None,
} , )
a : Any = model[0].eval()
# load feature extractor
a : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ )
# set weights for wav2vec2 encoder
a : Dict = WavaVecaModel(UpperCAmelCase__ )
recursively_load_weights_wavaveca(model.encoder , UpperCAmelCase__ )
# load decoder weights
a : List[str] = MBartForCausalLM(UpperCAmelCase__ )
a , a : Optional[int] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=UpperCAmelCase__ )
logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
a : Any = SpeechEncoderDecoderModel(encoder=UpperCAmelCase__ , decoder=UpperCAmelCase__ )
a : Tuple = False
a : Optional[int] = MBartaaTokenizer(UpperCAmelCase__ )
tokenizer.save_pretrained(UpperCAmelCase__ )
a : Optional[int] = hf_wavavec.config.to_dict()
a : Union[str, Any] = tokenizer.pad_token_id
a : Any = tokenizer.bos_token_id
a : Any = tokenizer.eos_token_id
a : str = 'mbart50'
a : str = 'wav2vec2'
a : List[str] = tokenizer.eos_token_id
a : int = 25_0004
a : Union[str, Any] = tokenizer.eos_token_id
a : Any = SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__ )
hf_wavavec.save_pretrained(UpperCAmelCase__ )
feature_extractor.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
parser.add_argument(
"--encoder_config_path",
default="facebook/wav2vec2-xls-r-1b",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/mbart-large-50-one-to-many-mmt",
type=str,
help="Path to hf decoder checkpoint config",
)
parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 509 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( _UpperCAmelCase ):
"""simple docstring"""
lowercase : Tuple = ["image_processor", "tokenizer"]
lowercase : str = "AutoImageProcessor"
lowercase : List[Any] = "AutoTokenizer"
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
a : Dict = self.image_processor
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ) -> Dict:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
a : int = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a : Optional[Any] = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a : int = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def lowercase_ ( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> Tuple:
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ ( self , *__UpperCAmelCase , **__UpperCAmelCase ) -> str:
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def lowercase_ ( self ) -> Any:
return ["input_ids", "attention_mask", "pixel_values"]
| 509 | 1 |
'''simple docstring'''
import argparse
import copy
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
A_ = {}
with open(__lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
A_ = []
_list.append([line.split()[1], line.split()[2]] )
A_ = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
A_ = []
_list.append([line.split()[0], line.split()[2]] )
A_ = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> str:
with open(__lowerCamelCase ) as f:
A_ = f.read(1 )
A_ = start_node
A_ = []
A_ = start_node
A_ = 0
while visiting not in first_solution:
A_ = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(__lowerCamelCase ) and k[0] not in first_solution:
A_ = k[1]
A_ = k[0]
first_solution.append(__lowerCamelCase )
A_ = distance_of_first_solution + int(__lowerCamelCase )
A_ = best_node
first_solution.append(__lowerCamelCase )
A_ = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
A_ = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
A_ = []
for n in solution[1:-1]:
A_ = solution.index(__lowerCamelCase )
for kn in solution[1:-1]:
A_ = solution.index(__lowerCamelCase )
if n == kn:
continue
A_ = copy.deepcopy(__lowerCamelCase )
A_ = kn
A_ = n
A_ = 0
for k in _tmp[:-1]:
A_ = _tmp[_tmp.index(__lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
A_ = distance + int(i[1] )
_tmp.append(__lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
A_ = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda SCREAMING_SNAKE_CASE_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> int:
A_ = 1
A_ = first_solution
A_ = []
A_ = distance_of_first_solution
A_ = solution
while count <= iters:
A_ = find_neighborhood(__lowerCamelCase ,__lowerCamelCase )
A_ = 0
A_ = neighborhood[index_of_best_solution]
A_ = len(__lowerCamelCase ) - 1
A_ = False
while not found:
A_ = 0
while i < len(__lowerCamelCase ):
if best_solution[i] != solution[i]:
A_ = best_solution[i]
A_ = solution[i]
break
A_ = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
A_ = True
A_ = best_solution[:-1]
A_ = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
A_ = cost
A_ = solution
else:
A_ = index_of_best_solution + 1
A_ = neighborhood[index_of_best_solution]
if len(__lowerCamelCase ) >= size:
tabu_list.pop(0 )
A_ = count + 1
return best_solution_ever, best_cost
def lowerCamelCase( SCREAMING_SNAKE_CASE_=None ) -> Optional[Any]:
A_ = generate_neighbours(args.File )
A_ , A_ = generate_first_solution(
args.File ,__lowerCamelCase )
A_ , A_ = tabu_search(
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ,args.Iterations ,args.Size ,)
print(F'Best solution: {best_sol}, with total distance: {best_cost}.' )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 366 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def __A ( __lowerCamelCase ) -> List[str]:
return 1.0 / (1.0 + np.exp(-_outputs ))
def __A ( __lowerCamelCase ) -> List[str]:
a = np.max(_outputs , axis=-1 , keepdims=__lowerCamelCase )
a = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=__lowerCamelCase )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = '''sigmoid'''
UpperCamelCase__ = '''softmax'''
UpperCamelCase__ = '''none'''
@add_end_docstrings(
__magic_name__ , r'''
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
''' , )
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = False
UpperCamelCase__ = ClassificationFunction.NONE
def __init__( self :List[str] , **__magic_name__ :List[Any] ):
'''simple docstring'''
super().__init__(**__magic_name__ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase__ ( self :Any , __magic_name__ :int=None , __magic_name__ :Any=None , __magic_name__ :Union[str, Any]="" , **__magic_name__ :Tuple ):
'''simple docstring'''
a = tokenizer_kwargs
a = {}
if hasattr(self.model.config , """return_all_scores""" ) and return_all_scores is None:
a = self.model.config.return_all_scores
if isinstance(__magic_name__ , __magic_name__ ) or top_k is None:
a = top_k
a = False
elif return_all_scores is not None:
warnings.warn(
"""`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"""
""" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""" , __magic_name__ , )
if return_all_scores:
a = None
else:
a = 1
if isinstance(__magic_name__ , __magic_name__ ):
a = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
a = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self :Dict , *__magic_name__ :Optional[int] , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
a = super().__call__(*__magic_name__ , **__magic_name__ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
a = """top_k""" not in kwargs
if isinstance(args[0] , __magic_name__ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :Optional[Any] , **__magic_name__ :Optional[Any] ):
'''simple docstring'''
a = self.framework
if isinstance(__magic_name__ , __magic_name__ ):
return self.tokenizer(**__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) == 1 and isinstance(inputs[0] , __magic_name__ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__magic_name__ , **__magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"""The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
""" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.""" )
return self.tokenizer(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
def lowerCamelCase__ ( self :List[str] , __magic_name__ :Tuple ):
'''simple docstring'''
return self.model(**__magic_name__ )
def lowerCamelCase__ ( self :Dict , __magic_name__ :Union[str, Any] , __magic_name__ :int=None , __magic_name__ :Union[str, Any]=1 , __magic_name__ :Tuple=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
a = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
a = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , """function_to_apply""" ) and function_to_apply is None:
a = self.model.config.function_to_apply
else:
a = ClassificationFunction.NONE
a = model_outputs["""logits"""][0]
a = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
a = sigmoid(__magic_name__ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
a = softmax(__magic_name__ )
elif function_to_apply == ClassificationFunction.NONE:
a = outputs
else:
raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
a = [
{"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__magic_name__ )
]
if not _legacy:
dict_scores.sort(key=lambda __magic_name__ : x["score"] , reverse=__magic_name__ )
if top_k is not None:
a = dict_scores[:top_k]
return dict_scores
| 468 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a__ : Tuple = 2
class __magic_name__ :
def __init__( self , *, # begin keyword-only arguments
__magic_name__="<s>" , __magic_name__="<pad>" , __magic_name__="</s>" , __magic_name__="<unk>" , __magic_name__=None , ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = bos, unk, pad, eos
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = {}
_lowerCAmelCase = self.add_symbol(__magic_name__ )
_lowerCAmelCase = self.add_symbol(__magic_name__ )
_lowerCAmelCase = self.add_symbol(__magic_name__ )
_lowerCAmelCase = self.add_symbol(__magic_name__ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(__magic_name__ )
_lowerCAmelCase = len(self.symbols )
def __eq__( self , __magic_name__ ):
"""simple docstring"""
return self.indices == other.indices
def __getitem__( self , __magic_name__ ):
"""simple docstring"""
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self ):
"""simple docstring"""
return len(self.symbols )
def __contains__( self , __magic_name__ ):
"""simple docstring"""
return sym in self.indices
@classmethod
def _lowerCamelCase ( cls , __magic_name__ ):
"""simple docstring"""
_lowerCAmelCase = cls()
d.add_from_file(__magic_name__ )
return d
def _lowerCamelCase ( self , __magic_name__ , __magic_name__=1 , __magic_name__=False ):
"""simple docstring"""
if word in self.indices and not overwrite:
_lowerCAmelCase = self.indices[word]
_lowerCAmelCase = self.count[idx] + n
return idx
else:
_lowerCAmelCase = len(self.symbols )
_lowerCAmelCase = idx
self.symbols.append(__magic_name__ )
self.count.append(__magic_name__ )
return idx
def _lowerCamelCase ( self , __magic_name__ ):
"""simple docstring"""
return 0
def _lowerCamelCase ( self , __magic_name__ ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
try:
with open(__magic_name__ , 'r' , encoding='utf-8' ) as fd:
self.add_from_file(__magic_name__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(__magic_name__ ) )
return
_lowerCAmelCase = f.readlines()
_lowerCAmelCase = self._load_meta(__magic_name__ )
for line in lines[indices_start_line:]:
try:
_lowerCAmelCase , _lowerCAmelCase = line.rstrip().rsplit(' ' , 1 )
if field == "#fairseq:overwrite":
_lowerCAmelCase = True
_lowerCAmelCase , _lowerCAmelCase = line.rsplit(' ' , 1 )
else:
_lowerCAmelCase = False
_lowerCAmelCase = int(__magic_name__ )
_lowerCAmelCase = line
if word in self and not overwrite:
raise RuntimeError(
'Duplicate word found when loading Dictionary: \'{}\'. '
'Duplicate words can overwrite earlier ones by adding the '
'#fairseq:overwrite flag at the end of the corresponding row '
'in the dictionary file. If using the Camembert model, please '
'download an updated copy of the model file.'.format(__magic_name__ ) )
self.add_symbol(__magic_name__ , n=__magic_name__ , overwrite=__magic_name__ )
except ValueError:
raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'' )
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = dict((re.sub(R'@@$', '', __lowerCamelCase ), v) if k.endswith('@@' ) else (re.sub(R'$', '</w>', __lowerCamelCase ), v) for k, v in d.items() )
_lowerCAmelCase = '<s> <pad> </s> <unk>'.split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
_lowerCAmelCase = d[k] # restore
return da
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
# prep
if not os.path.exists(__lowerCamelCase ):
raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!''' )
os.makedirs(__lowerCamelCase, exist_ok=__lowerCamelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
_lowerCAmelCase = os.path.join(__lowerCamelCase, 'checkpoint.pt' )
if not os.path.isfile(__lowerCamelCase ):
raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' )
_lowerCAmelCase = torch.load(__lowerCamelCase, map_location='cpu' )
_lowerCAmelCase = chkpt['cfg']['model']
# dicts
_lowerCAmelCase = os.path.join(__lowerCamelCase, 'dict.txt' )
if not os.path.isfile(__lowerCamelCase ):
raise ValueError(F'''path to the file {dict_file} does not exist!''' )
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
_lowerCAmelCase = rewrite_dict_keys(src_dict.indices )
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase, VOCAB_FILES_NAMES['vocab_file'] )
print(F'''Generating {src_vocab_file} of {src_vocab_size} records''' )
with open(__lowerCamelCase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCamelCase, ensure_ascii=__lowerCamelCase, indent=__lowerCamelCase ) )
# merges_file (bpecodes)
_lowerCAmelCase = os.path.join(__lowerCamelCase, 'bpecodes' )
if not os.path.isfile(__lowerCamelCase ):
raise ValueError(F'''path to the file {bpecodes_file} does not exist!''' )
_lowerCAmelCase = os.path.join(__lowerCamelCase, VOCAB_FILES_NAMES['merges_file'] )
shutil.copyfile(__lowerCamelCase, __lowerCamelCase )
# model config
_lowerCAmelCase = os.path.join(__lowerCamelCase, 'config.json' )
_lowerCAmelCase = {
'activation_dropout': args['activation_dropout'],
'architectures': ['BioGptForCausalLM'],
'attention_probs_dropout_prob': args['attention_dropout'],
'bos_token_id': 0,
'eos_token_id': 2,
'hidden_act': args['activation_fn'],
'hidden_dropout_prob': args['dropout'],
'hidden_size': args['decoder_embed_dim'],
'initializer_range': 0.02,
'intermediate_size': args['decoder_ffn_embed_dim'],
'layer_norm_eps': 1e-12,
'layerdrop': args['decoder_layerdrop'],
'max_position_embeddings': args['max_target_positions'],
'model_type': 'biogpt',
'num_attention_heads': args['decoder_attention_heads'],
'num_hidden_layers': args['decoder_layers'],
'pad_token_id': 1,
'scale_embedding': not args['no_scale_embedding'],
'tie_word_embeddings': args['share_decoder_input_output_embed'],
'vocab_size': src_vocab_size,
}
# good hparam defaults to start with
print(F'''Generating {biogpt_model_config_file}''' )
with open(__lowerCamelCase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCamelCase, ensure_ascii=__lowerCamelCase, indent=__lowerCamelCase ) )
# tokenizer config
_lowerCAmelCase = os.path.join(__lowerCamelCase, __lowerCamelCase )
_lowerCAmelCase = {
'bos_token': '<s>',
'eos_token': '</s>',
'model_max_length': 1_0_2_4,
'pad_token': '<pad>',
'special_tokens_map_file': None,
'tokenizer_class': 'BioGptTokenizer',
'unk_token': '<unk>',
}
print(F'''Generating {biogpt_tokenizer_config_file}''' )
with open(__lowerCamelCase, 'w', encoding='utf-8' ) as f:
f.write(json.dumps(__lowerCamelCase, ensure_ascii=__lowerCamelCase, indent=__lowerCamelCase ) )
# model
_lowerCAmelCase = chkpt['model']
# remove unneeded keys
_lowerCAmelCase = [
'decoder.version',
]
for k in ignore_keys:
model_state_dict.pop(__lowerCamelCase, __lowerCamelCase )
_lowerCAmelCase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith('output_projection.weight' ):
_lowerCAmelCase = model_state_dict.pop(__lowerCamelCase )
else:
_lowerCAmelCase = model_state_dict.pop(__lowerCamelCase )
_lowerCAmelCase = BioGptConfig.from_pretrained(__lowerCamelCase )
_lowerCAmelCase = BioGptForCausalLM(__lowerCamelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCamelCase )
# save
_lowerCAmelCase = os.path.join(__lowerCamelCase, __lowerCamelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCamelCase, __lowerCamelCase )
print('Conversion is done!' )
if __name__ == "__main__":
a__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a__ : str = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 712 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class __magic_name__ ( unittest.TestCase ):
def _lowerCamelCase ( self , __magic_name__ ):
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss'] ):
_lowerCAmelCase = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(__magic_name__ )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sgugger/tiny-distilbert-classification'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , only_pretrain_model=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , torchscript=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , fpaa=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ )
# set architectures equal to `None`
_lowerCAmelCase = None
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == 'cpu' , 'Can\'t do half precision' )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , fpaa=__magic_name__ , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tinier_bart'
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tinier_bart'
_lowerCAmelCase = AutoConfig.from_pretrained(__magic_name__ )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , save_to_csv=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__magic_name__ , 'inf_time.csv' ) , train_memory_csv_file=os.path.join(__magic_name__ , 'train_mem.csv' ) , inference_memory_csv_file=os.path.join(__magic_name__ , 'inf_mem.csv' ) , train_time_csv_file=os.path.join(__magic_name__ , 'train_time.csv' ) , env_info_csv_file=os.path.join(__magic_name__ , 'env.csv' ) , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
benchmark.run()
self.assertTrue(Path(os.path.join(__magic_name__ , 'inf_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__magic_name__ , 'train_time.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__magic_name__ , 'inf_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__magic_name__ , 'train_mem.csv' ) ).exists() )
self.assertTrue(Path(os.path.join(__magic_name__ , 'env.csv' ) ).exists() )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowerCAmelCase = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(__magic_name__ ):
self.assertTrue(hasattr(__magic_name__ , 'sequential' ) )
self.assertTrue(hasattr(__magic_name__ , 'cumulative' ) )
self.assertTrue(hasattr(__magic_name__ , 'current' ) )
self.assertTrue(hasattr(__magic_name__ , 'total' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__magic_name__ , inference=__magic_name__ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__magic_name__ , 'log.txt' ) , log_print=__magic_name__ , trace_memory_line_by_line=__magic_name__ , multi_process=__magic_name__ , )
_lowerCAmelCase = PyTorchBenchmark(__magic_name__ )
_lowerCAmelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__magic_name__ , 'log.txt' ) ).exists() )
| 309 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class snake_case :
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase=13 , UpperCamelCase=7 , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=99 , UpperCamelCase=32 , UpperCamelCase=5 , UpperCamelCase=4 , UpperCamelCase=4 , UpperCamelCase="gelu" , UpperCamelCase=0.0 , UpperCamelCase=0.1 , UpperCamelCase=True , UpperCamelCase=512 , UpperCamelCase=16 , UpperCamelCase=2 , UpperCamelCase=0.02 , UpperCamelCase=3 , UpperCamelCase=4 , UpperCamelCase=None , ):
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_multiple_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout
lowerCamelCase_ = attention_dropout
lowerCamelCase_ = weight_tying
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, input_ids, input_mask, token_labels
def snake_case ( self ):
"""simple docstring"""
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase , initializer_range=self.initializer_range , )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ = True
return config, input_ids, input_mask, token_labels
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = GPTNeoXJapaneseModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = True
lowerCamelCase_ = GPTNeoXJapaneseModel(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = GPTNeoXJapaneseForCausalLM(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = True
lowerCamelCase_ = GPTNeoXJapaneseForCausalLM(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
# first forward pass
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , use_cache=UpperCamelCase )
lowerCamelCase_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCamelCase_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCamelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCamelCase_ = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , output_hidden_states=UpperCamelCase )
lowerCamelCase_ = output_from_no_past["hidden_states"][0]
lowerCamelCase_ = model(
UpperCamelCase , attention_mask=UpperCamelCase , past_key_values=UpperCamelCase , output_hidden_states=UpperCamelCase , )["hidden_states"][0]
# select random slice
lowerCamelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCamelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase , UpperCamelCase , atol=1e-3 ) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class snake_case ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
_lowerCamelCase = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
_lowerCamelCase = (
{"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = GPTNeoXJapaneseModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
# This regression test was failing with PyTorch < 1.3
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
lowerCamelCase_ = None
self.model_tester.create_and_check_model_as_decoder(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase , UpperCamelCase , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = "abeja/gpt-neox-japanese-2.7b"
lowerCamelCase_ = ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]
lowerCamelCase_ = [
"データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
"100年後に必要とされる会社は、「人」が中心の会社です。",
"フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
"国境の長いトンネルを抜けると、そこは雪国だった。",
"美味しい日本食といえば、やっぱりお寿司ですよね。",
]
lowerCamelCase_ = GPTNeoXJapaneseTokenizer.from_pretrained(UpperCamelCase )
lowerCamelCase_ = GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCamelCase )
lowerCamelCase_ = []
for prompt in prompts:
lowerCamelCase_ = tokenizer(UpperCamelCase , return_tensors="pt" ).input_ids
lowerCamelCase_ = model.generate(UpperCamelCase , max_length=50 )
lowerCamelCase_ = tokenizer.batch_decode(UpperCamelCase , skip_special_tokens=UpperCamelCase )
predicted_outputs += generated_string
self.assertListEqual(UpperCamelCase , UpperCamelCase )
| 675 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : Any = get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")
@require_sentencepiece
@require_tokenizers
class snake_case ( lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = GPTSwaTokenizer
_lowerCamelCase = False
_lowerCamelCase = True
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = GPTSwaTokenizer(UpperCamelCase , eos_token="<unk>" , bos_token="<unk>" , pad_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = "This is a test"
lowerCamelCase_ = "This is a test"
return input_text, output_text
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = "<s>"
lowerCamelCase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase ) , UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase ) , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(UpperCamelCase ) , 2000 )
def snake_case ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = GPTSwaTokenizer(UpperCamelCase )
lowerCamelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , [465, 287, 265, 631, 842] )
lowerCamelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
# fmt: off
self.assertListEqual(
UpperCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] , )
# fmt: on
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase )
self.assertListEqual(
UpperCamelCase , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(UpperCamelCase )
# fmt: off
self.assertListEqual(
UpperCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] )
# fmt: on
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = GPTSwaTokenizer(UpperCamelCase )
lowerCamelCase_ = ["This is a test", "I was born in 92000, and this is falsé."]
lowerCamelCase_ = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(UpperCamelCase , UpperCamelCase ):
self.assertListEqual(tokenizer.encode_fast(UpperCamelCase ) , UpperCamelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(UpperCamelCase , UpperCamelCase ):
self.assertEqual(tokenizer.decode_fast(UpperCamelCase ) , UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = [
"<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')",
"Hey there, how are you doing this fine day?",
"This is a text with a trailing spaces followed by a dot .",
"Häj sväjs lillebrör! =)",
"Det är inget fel på Mr. Cool",
]
# fmt: off
lowerCamelCase_ = {"input_ids": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase , model_name="AI-Sweden/gpt-sw3-126m" , sequences=UpperCamelCase , )
| 675 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def lowerCamelCase__ ( UpperCAmelCase_=32 , UpperCAmelCase_=10 , UpperCAmelCase_=1_00 , UpperCAmelCase_=10_26 , UpperCAmelCase_=True , UpperCAmelCase_="data/tokenized_stories_train_wikitext103.jbl" , UpperCAmelCase_="igf_context_pairs.jbl" , )-> List[str]:
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
UpperCamelCase , UpperCamelCase = generate_datasets(
UpperCAmelCase_ , UpperCAmelCase_ , number=UpperCAmelCase_ , min_len=10_26 , trim=UpperCAmelCase_ )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
UpperCamelCase = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# load pretrained model
UpperCamelCase = load_gpta("gpt2" ).to(UpperCAmelCase_ )
print("computing perplexity on objective set" )
UpperCamelCase = compute_perplexity(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ).item()
print("perplexity on objective set:" , UpperCAmelCase_ )
# collect igf pairs and save to file demo.jbl
collect_objective_set(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_=15 , UpperCAmelCase_=1_28 , UpperCAmelCase_=1_00 , UpperCAmelCase_="igf_model.pt" , )-> int:
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
UpperCamelCase = GPTaLMHeadModel.from_pretrained("gpt2" )
# Initialize secondary learner to use embedding weights of model
UpperCamelCase = SecondaryLearner(UpperCAmelCase_ )
# Train secondary learner
UpperCamelCase = train_secondary_learner(
UpperCAmelCase_ , UpperCAmelCase_ , max_epochs=UpperCAmelCase_ , batch_size=UpperCAmelCase_ , eval_freq=1_00 , igf_model_path=UpperCAmelCase_ , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=32 , UpperCAmelCase_=10_00 , UpperCAmelCase_=16 , UpperCAmelCase_=1.0 , UpperCAmelCase_=recopy_gpta , UpperCAmelCase_=None , UpperCAmelCase_=10 , UpperCAmelCase_="gpt2_finetuned.pt" , )-> Optional[int]:
"""simple docstring"""
UpperCamelCase = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
UpperCamelCase = RandomSampler(UpperCAmelCase_ )
UpperCamelCase = DataLoader(UpperCAmelCase_ , sampler=UpperCAmelCase_ )
UpperCamelCase = max_steps // (len(UpperCAmelCase_ )) + 1
UpperCamelCase = 0
UpperCamelCase = torch.zeros((1, context_len) , dtype=torch.long , device=UpperCAmelCase_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase = recopy_model(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
model.train()
if secondary_learner is not None:
secondary_learner.to(UpperCAmelCase_ )
secondary_learner.eval()
UpperCamelCase = []
UpperCamelCase = 0
UpperCamelCase = []
UpperCamelCase = []
# Compute the performance of the transformer model at the beginning
UpperCamelCase = compute_perplexity(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
test_perps.append(UpperCAmelCase_ )
print("Test perplexity, step" , UpperCAmelCase_ , ":" , UpperCAmelCase_ )
for epoch in range(int(UpperCAmelCase_ ) ):
for step, example in enumerate(UpperCAmelCase_ ):
torch.cuda.empty_cache()
UpperCamelCase = random.randint(0 , example.size(2 ) - context_len - 1 )
UpperCamelCase = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
UpperCamelCase = model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
UpperCamelCase = True
if secondary_learner is not None:
UpperCamelCase = secondary_learner.forward(
torch.tensor(UpperCAmelCase_ , dtype=torch.long , device=UpperCAmelCase_ ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(UpperCAmelCase_ ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
UpperCamelCase = -1
if predicted_q < threshold:
UpperCamelCase = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
UpperCamelCase = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
UpperCamelCase = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
UpperCamelCase = compute_perplexity(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
test_perps.append(UpperCAmelCase_ )
print("Test perplexity, step" , UpperCAmelCase_ , ":" , UpperCAmelCase_ )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , UpperCAmelCase_ )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def lowerCamelCase__ ( )-> List[str]:
"""simple docstring"""
UpperCamelCase = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )
# Required parameters
parser.add_argument(
"--data_dir" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="The input data dir. Should contain data files for WikiText." , )
parser.add_argument(
"--model_name_or_path" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--data_file" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help=(
"A jbl file containing tokenized data which can be split as objective dataset, "
"train_dataset and test_dataset."
) , )
parser.add_argument(
"--igf_data_file" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help="A jbl file containing the context and information gain pairs to train secondary learner." , )
parser.add_argument(
"--output_dir" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , required=UpperCAmelCase_ , help="The output directory where the final fine-tuned model is stored." , )
parser.add_argument(
"--tokenizer_name" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument("--seed" , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help="A seed for reproducible training." )
parser.add_argument(
"--context_len" , default=32 , type=UpperCAmelCase_ , help=(
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
) , )
parser.add_argument(
"--size_objective_set" , default=1_00 , type=UpperCAmelCase_ , help="number of articles that are long enough to be used as our objective set" , )
parser.add_argument(
"--eval_freq" , default=1_00 , type=UpperCAmelCase_ , help="secondary model evaluation is triggered at eval_freq" )
parser.add_argument("--max_steps" , default=10_00 , type=UpperCAmelCase_ , help="To calculate training epochs" )
parser.add_argument(
"--secondary_learner_batch_size" , default=1_28 , type=UpperCAmelCase_ , help="batch size of training data for secondary learner" , )
parser.add_argument(
"--batch_size" , default=16 , type=UpperCAmelCase_ , help="batch size of training data of language model(gpt2) " )
parser.add_argument(
"--eval_interval" , default=10 , type=UpperCAmelCase_ , help=(
"decay the selectivity of our secondary learner filter from"
"1 standard deviation above average to 1 below average after 10 batches"
) , )
parser.add_argument(
"--number" , default=1_00 , type=UpperCAmelCase_ , help="The number of examples split to be used as objective_set/test_data" )
parser.add_argument(
"--min_len" , default=10_26 , type=UpperCAmelCase_ , help="The minimum length of the article to be used as objective set" )
parser.add_argument(
"--secondary_learner_max_epochs" , default=15 , type=UpperCAmelCase_ , help="number of epochs to train secondary learner" )
parser.add_argument("--trim" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="truncate the example if it exceeds context length" )
parser.add_argument(
"--threshold" , default=1.0 , type=UpperCAmelCase_ , help=(
"The threshold value used by secondary learner to filter the train_data and allow only"
" informative data as input to the model"
) , )
parser.add_argument("--finetuned_model_name" , default="gpt2_finetuned.pt" , type=UpperCAmelCase_ , help="finetuned_model_name" )
parser.add_argument(
"--recopy_model" , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help="Reset the model to the original pretrained GPT-2 weights after each iteration" , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=1_00 , min_len=10_26 , trim=UpperCAmelCase_ , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , )
# Load train data for secondary learner
UpperCamelCase = joblib.load("data/IGF_values.jbl" )
# Train secondary learner
UpperCamelCase = training_secondary_learner(
UpperCAmelCase_ , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="igf_model.pt" , )
# load pretrained gpt2 model
UpperCamelCase = GPTaLMHeadModel.from_pretrained("gpt2" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
UpperCamelCase , UpperCamelCase = generate_datasets(
context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=1_00 , min_len=10_26 , trim=UpperCAmelCase_ )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=UpperCAmelCase_ , secondary_learner=UpperCAmelCase_ , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , )
if __name__ == "__main__":
main()
| 556 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Any = (EulerDiscreteScheduler,)
UpperCamelCase_ : Dict = 10
def _SCREAMING_SNAKE_CASE ( self : Any , **UpperCAmelCase_ : str )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = {
"num_train_timesteps": 1_100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**UpperCAmelCase_ )
return config
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> Any:
"""simple docstring"""
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : int )-> Any:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str )-> Dict:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> int:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str )-> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCamelCase = sample.to(UpperCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
UpperCamelCase = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ )
UpperCamelCase = output.prev_sample
UpperCamelCase = torch.sum(torch.abs(UpperCAmelCase_ ) )
UpperCamelCase = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : List[str] )-> str:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config(prediction_type="v_prediction" )
UpperCamelCase = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
UpperCamelCase = sample.to(UpperCAmelCase_ )
for i, t in enumerate(scheduler.timesteps ):
UpperCamelCase = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ )
UpperCamelCase = output.prev_sample
UpperCamelCase = torch.sum(torch.abs(UpperCAmelCase_ ) )
UpperCamelCase = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 0.0002 ) < 1e-2
assert abs(result_mean.item() - 2.2_676e-06 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : List[str] )-> Tuple:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase_ )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
UpperCamelCase = sample.to(UpperCAmelCase_ )
for t in scheduler.timesteps:
UpperCamelCase = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ )
UpperCamelCase = output.prev_sample
UpperCamelCase = torch.sum(torch.abs(UpperCAmelCase_ ) )
UpperCamelCase = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 10.0807 ) < 1e-2
assert abs(result_mean.item() - 0.0131 ) < 1e-3
def _SCREAMING_SNAKE_CASE ( self : Optional[int] )-> Any:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**UpperCAmelCase_ , use_karras_sigmas=UpperCAmelCase_ )
scheduler.set_timesteps(self.num_inference_steps , device=UpperCAmelCase_ )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
UpperCamelCase = sample.to(UpperCAmelCase_ )
for t in scheduler.timesteps:
UpperCamelCase = scheduler.scale_model_input(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ )
UpperCamelCase = scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_ )
UpperCamelCase = output.prev_sample
UpperCamelCase = torch.sum(torch.abs(UpperCAmelCase_ ) )
UpperCamelCase = torch.mean(torch.abs(UpperCAmelCase_ ) )
assert abs(result_sum.item() - 124.52299499511719 ) < 1e-2
assert abs(result_mean.item() - 0.16213932633399963 ) < 1e-3
| 556 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
UpperCamelCase = ["bert-base-uncased", "bert-base-cased"]
UpperCamelCase = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
class lowerCAmelCase_ ( tf.keras.Model ):
def __init__( self , _lowerCAmelCase ):
super().__init__()
_lowercase : Optional[int] = tokenizer
_lowercase : Dict = AutoConfig.from_pretrained(_lowerCAmelCase )
_lowercase : Any = TFAutoModel.from_config(_lowerCAmelCase )
def __a ( self , _lowerCAmelCase ):
_lowercase : int = self.tokenizer(_lowerCAmelCase )
_lowercase : int = self.bert(**_lowerCAmelCase )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class lowerCAmelCase_ ( unittest.TestCase ):
def __a ( self ):
super().setUp()
_lowercase : int = [
BertTokenizer.from_pretrained(_lowerCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
_lowercase : Optional[int] = [TFBertTokenizer.from_pretrained(_lowerCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(_lowerCAmelCase , use_fast_bert_tokenizer=_lowerCAmelCase )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
_lowercase : Any = [
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
_lowercase : Union[str, Any] = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __a ( self ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
_lowercase : str = tokenizer(_lowerCAmelCase , return_tensors='tf' , padding='longest' )
_lowercase : int = tf_tokenizer(_lowerCAmelCase )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def __a ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowercase : Union[str, Any] = tf_tokenizer(self.paired_sentences )
_lowercase : Any = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def __a ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowercase : Dict = tf.function(_lowerCAmelCase )
for test_inputs in (self.test_sentences, self.paired_sentences):
_lowercase : List[str] = tf.constant(_lowerCAmelCase )
_lowercase : Union[str, Any] = compiled_tokenizer(_lowerCAmelCase )
_lowercase : Any = tf_tokenizer(_lowerCAmelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __a ( self ):
for tf_tokenizer in self.tf_tokenizers:
_lowercase : Dict = ModelToSave(tokenizer=_lowerCAmelCase )
_lowercase : Any = tf.convert_to_tensor(self.test_sentences )
_lowercase : List[Any] = model(_lowerCAmelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_lowercase : int = Path(_lowerCAmelCase ) / 'saved.model'
model.save(_lowerCAmelCase )
_lowercase : Any = tf.keras.models.load_model(_lowerCAmelCase )
_lowercase : List[Any] = loaded_model(_lowerCAmelCase )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 66 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
"""kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
"""kssteven/ibert-roberta-large-mnli""": (
"""https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
),
}
class UpperCAmelCase ( A_ ):
A__ : Optional[int] = "ibert"
def __init__(self : List[Any] , snake_case__ : int=3_05_22 , snake_case__ : int=7_68 , snake_case__ : Any=12 , snake_case__ : str=12 , snake_case__ : Optional[int]=30_72 , snake_case__ : Union[str, Any]="gelu" , snake_case__ : Any=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : str=5_12 , snake_case__ : Optional[int]=2 , snake_case__ : Any=0.02 , snake_case__ : List[Any]=1e-12 , snake_case__ : Optional[Any]=1 , snake_case__ : List[Any]=0 , snake_case__ : str=2 , snake_case__ : Tuple="absolute" , snake_case__ : List[str]=False , snake_case__ : Tuple="none" , **snake_case__ : Tuple , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
snake_case : str = vocab_size
snake_case : Tuple = hidden_size
snake_case : Optional[Any] = num_hidden_layers
snake_case : Optional[int] = num_attention_heads
snake_case : List[Any] = hidden_act
snake_case : Optional[Any] = intermediate_size
snake_case : Dict = hidden_dropout_prob
snake_case : List[str] = attention_probs_dropout_prob
snake_case : List[str] = max_position_embeddings
snake_case : Union[str, Any] = type_vocab_size
snake_case : Optional[int] = initializer_range
snake_case : Optional[int] = layer_norm_eps
snake_case : int = position_embedding_type
snake_case : Union[str, Any] = quant_mode
snake_case : Dict = force_dequant
class UpperCAmelCase ( A_ ):
@property
def _SCREAMING_SNAKE_CASE (self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 204 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowercase ( _lowercase ):
"""simple docstring"""
def __lt__( self , __snake_case):
return self[-1] < other[-1]
def __eq__( self , __snake_case):
return self[-1] == other[-1]
def lowerCamelCase_ ( UpperCAmelCase_ : list ) -> list:
'''simple docstring'''
_UpperCamelCase : list[Stack] = []
# sort into stacks
for element in collection:
_UpperCamelCase : Any = Stack([element] )
_UpperCamelCase : Optional[int] = bisect_left(UpperCAmelCase_ , UpperCAmelCase_ )
if i != len(UpperCAmelCase_ ):
stacks[i].append(UpperCAmelCase_ )
else:
stacks.append(UpperCAmelCase_ )
# use a heap-based merge to merge stack efficiently
_UpperCamelCase : str = merge(*(reversed(UpperCAmelCase_ ) for stack in stacks) )
return collection
if __name__ == "__main__":
lowerCAmelCase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowerCAmelCase__ = [int(item) for item in user_input.split(""",""")]
print(patience_sort(unsorted))
| 711 |
lowerCAmelCase__ = range(2, 2_0 + 1)
lowerCAmelCase__ = [1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase__ = {}
def lowerCamelCase_ ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any , UpperCAmelCase_ : int ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : Dict = sum(a_i[j] for j in range(UpperCAmelCase_ , len(UpperCAmelCase_ ) ) )
_UpperCamelCase : Optional[int] = sum(a_i[j] * base[j] for j in range(min(len(UpperCAmelCase_ ) , UpperCAmelCase_ ) ) )
_UpperCamelCase , _UpperCamelCase : Dict = 0, 0
_UpperCamelCase : Optional[int] = n - i
_UpperCamelCase : Union[str, Any] = memo.get(UpperCAmelCase_ )
if sub_memo is not None:
_UpperCamelCase : str = sub_memo.get(UpperCAmelCase_ )
if jumps is not None and len(UpperCAmelCase_ ) > 0:
# find and make the largest jump without going over
_UpperCamelCase : str = -1
for _k in range(len(UpperCAmelCase_ ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_UpperCamelCase : Optional[Any] = _k
break
if max_jump >= 0:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Optional[Any] = jumps[max_jump]
# since the difference between jumps is cached, add c
_UpperCamelCase : Tuple = diff + c
for j in range(min(UpperCAmelCase_ , len(UpperCAmelCase_ ) ) ):
_UpperCamelCase , _UpperCamelCase : Dict = divmod(UpperCAmelCase_ , 1_0 )
if new_c > 0:
add(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
else:
_UpperCamelCase : Union[str, Any] = []
else:
_UpperCamelCase : List[Any] = {c: []}
_UpperCamelCase : Optional[int] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_UpperCamelCase , _UpperCamelCase : Optional[Any] = next_term(UpperCAmelCase_ , k - 1 , i + dn , UpperCAmelCase_ )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_UpperCamelCase , _UpperCamelCase : Any = compute(UpperCAmelCase_ , UpperCAmelCase_ , i + dn , UpperCAmelCase_ )
diff += _diff
dn += terms_jumped
_UpperCamelCase : List[str] = sub_memo[c]
# keep jumps sorted by # of terms skipped
_UpperCamelCase : Union[str, Any] = 0
while j < len(UpperCAmelCase_ ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(UpperCAmelCase_ , (diff, dn, k) )
return (diff, dn)
def lowerCamelCase_ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : Any ) -> Dict:
'''simple docstring'''
if i >= n:
return 0, i
if k > len(UpperCAmelCase_ ):
a_i.extend([0 for _ in range(k - len(UpperCAmelCase_ ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_UpperCamelCase : Any = i
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Any = 0, 0, 0
for j in range(len(UpperCAmelCase_ ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_UpperCamelCase : Union[str, Any] = ds_c + ds_b
diff += addend
_UpperCamelCase : Union[str, Any] = 0
for j in range(UpperCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = a_i[j] + addend
_UpperCamelCase , _UpperCamelCase : Any = divmod(UpperCAmelCase_ , 1_0 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
return diff, i - start_i
def lowerCamelCase_ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ) -> Dict:
'''simple docstring'''
for j in range(UpperCAmelCase_ , len(UpperCAmelCase_ ) ):
_UpperCamelCase : List[str] = digits[j] + addend
if s >= 1_0:
_UpperCamelCase , _UpperCamelCase : Any = divmod(UpperCAmelCase_ , 1_0 )
_UpperCamelCase : Union[str, Any] = addend // 1_0 + quotient
else:
_UpperCamelCase : Dict = s
_UpperCamelCase : Optional[Any] = addend // 1_0
if addend == 0:
break
while addend > 0:
_UpperCamelCase , _UpperCamelCase : Dict = divmod(UpperCAmelCase_ , 1_0 )
digits.append(UpperCAmelCase_ )
def lowerCamelCase_ ( UpperCAmelCase_ : int = 1_0**1_5 ) -> int:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = [1]
_UpperCamelCase : Optional[int] = 1
_UpperCamelCase : int = 0
while True:
_UpperCamelCase , _UpperCamelCase : List[Any] = next_term(UpperCAmelCase_ , 2_0 , i + dn , UpperCAmelCase_ )
dn += terms_jumped
if dn == n - i:
break
_UpperCamelCase : str = 0
for j in range(len(UpperCAmelCase_ ) ):
a_n += digits[j] * 1_0**j
return a_n
if __name__ == "__main__":
print(f'{solution() = }')
| 648 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _lowerCAmelCase ( __a ):
_lowercase =42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 290 |
from math import loga
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 290 | 1 |
from functools import reduce
lowerCamelCase__ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def lowercase_ ( SCREAMING_SNAKE_CASE : str = N ):
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str(int(SCREAMING_SNAKE_CASE ) * int(SCREAMING_SNAKE_CASE ) ) , n[i : i + 13] ) )
for i in range(len(SCREAMING_SNAKE_CASE ) - 12 ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 702 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ =StableDiffusionSAGPipeline
lowerCAmelCase__ =TEXT_TO_IMAGE_PARAMS
lowerCAmelCase__ =TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase__ =TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ =TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase__ =False
def UpperCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
snake_case__ : List[str] =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
snake_case__ : List[str] =DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__SCREAMING_SNAKE_CASE , set_alpha_to_one=__SCREAMING_SNAKE_CASE , )
torch.manual_seed(0 )
snake_case__ : int =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case__ : Optional[Any] =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case__ : Optional[int] =CLIPTextModel(__SCREAMING_SNAKE_CASE )
snake_case__ : List[str] =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case__ : Optional[int] ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=0 ) -> Dict:
"""simple docstring"""
if str(__SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
snake_case__ : Optional[int] =torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
snake_case__ : Dict =torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
snake_case__ : Optional[Any] ={
'''prompt''': '''.''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 1.0,
'''sag_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Tuple =StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
snake_case__ : Any =sag_pipe.to(__SCREAMING_SNAKE_CASE )
sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
snake_case__ : str ='''.'''
snake_case__ : Tuple =torch.manual_seed(0 )
snake_case__ : Optional[int] =sag_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
snake_case__ : Optional[int] =output.images
snake_case__ : Dict =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case__ : Optional[Any] =np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def UpperCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
snake_case__ : Tuple =StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
snake_case__ : Any =sag_pipe.to(__SCREAMING_SNAKE_CASE )
sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
snake_case__ : Dict ='''.'''
snake_case__ : Union[str, Any] =torch.manual_seed(0 )
snake_case__ : int =sag_pipe(
[prompt] , generator=__SCREAMING_SNAKE_CASE , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' )
snake_case__ : List[Any] =output.images
snake_case__ : List[str] =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case__ : Union[str, Any] =np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def UpperCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Optional[Any] =StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
snake_case__ : Dict =sag_pipe.to(__SCREAMING_SNAKE_CASE )
sag_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
snake_case__ : Optional[int] ='''.'''
snake_case__ : str =torch.manual_seed(0 )
snake_case__ : Any =sag_pipe(
[prompt] , width=768 , height=512 , generator=__SCREAMING_SNAKE_CASE , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='''np''' , )
snake_case__ : Any =output.images
assert image.shape == (1, 512, 768, 3)
| 408 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_lowerCamelCase =1.0_5_4_5_7_1_8_1_7E-3_4 # unit of ℏ : J * s
_lowerCamelCase =3E8 # unit of c : m * s^-1
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
lowerCamelCase : str = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCamelCase : Union[str, Any] = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCamelCase : Union[str, Any] = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 681 |
from functools import reduce
_A = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCamelCase__ ( __lowerCAmelCase : str = N ):
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda __lowerCAmelCase , __lowerCAmelCase : str(int(__lowerCAmelCase ) * int(__lowerCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(__lowerCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 290 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
__lowerCAmelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def _lowercase ( a__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = {}
with open(A__ , "r" ) as file:
for line_number, line in enumerate(A__ ):
_UpperCamelCase = line.strip()
if line:
_UpperCamelCase = line.split()
_UpperCamelCase = line_number
_UpperCamelCase = words[0]
_UpperCamelCase = value
return result
def _lowercase ( a__ : Union[str, Any] , a__ : Tuple , a__ : str , a__ : Union[str, Any] , a__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
for attribute in key.split("." ):
_UpperCamelCase = getattr(A__ , A__ )
_UpperCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
_UpperCamelCase = PARAM_MAPPING[full_name.split("." )[-1]]
_UpperCamelCase = "param"
if weight_type is not None and weight_type != "param":
_UpperCamelCase = getattr(A__ , A__ ).shape
elif weight_type is not None and weight_type == "param":
_UpperCamelCase = hf_pointer
for attribute in hf_param_name.split("." ):
_UpperCamelCase = getattr(A__ , A__ )
_UpperCamelCase = shape_pointer.shape
# let's reduce dimension
_UpperCamelCase = value[0]
else:
_UpperCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split("." ):
_UpperCamelCase = getattr(A__ , A__ )
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _lowercase ( a__ : List[str] , a__ : Union[str, Any] , a__ : str , a__ : List[Any] , a__ : int ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A__ ):
_UpperCamelCase = PARAM_MAPPING[full_name.split("." )[-1]]
_UpperCamelCase = "param"
if weight_type is not None and weight_type != "param":
_UpperCamelCase = ".".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_UpperCamelCase = ".".join([key, hf_param_name] )
else:
_UpperCamelCase = key
_UpperCamelCase = value if "lm_head" in full_key else value[0]
__lowerCAmelCase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def _lowercase ( a__ : Any , a__ : List[Any] , a__ : Any=None , a__ : Union[str, Any]=None ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = False
for key, mapped_key in MAPPING.items():
_UpperCamelCase = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(A__ )[0].split("." )[-2]
_UpperCamelCase = mapped_key.replace("*" , A__ )
if "weight_g" in name:
_UpperCamelCase = "weight_g"
elif "weight_v" in name:
_UpperCamelCase = "weight_v"
elif "bias" in name:
_UpperCamelCase = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCamelCase = "weight"
else:
_UpperCamelCase = None
if hf_dict is not None:
rename_dict(A__ , A__ , A__ , A__ , A__ )
else:
set_recursively(A__ , A__ , A__ , A__ , A__ )
return is_used
return is_used
def _lowercase ( a__ : Union[str, Any] , a__ : Tuple , a__ : int ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
A__ , A__ , A__ , A__ , hf_model.config.feat_extract_norm == "group" , )
_UpperCamelCase = True
else:
_UpperCamelCase = load_wavaveca_layer(A__ , A__ , A__ )
if not is_used:
unused_weights.append(A__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def _lowercase ( a__ : int , a__ : int , a__ : str , a__ : int , a__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = full_name.split("conv_layers." )[-1]
_UpperCamelCase = name.split("." )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
_UpperCamelCase = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(A__ )
@torch.no_grad()
def _lowercase ( a__ : List[Any] , a__ : Optional[int] , a__ : Dict=None , a__ : Tuple=None , a__ : List[str]=True , a__ : Optional[int]=False ) -> Optional[int]:
"""simple docstring"""
if config_path is not None:
_UpperCamelCase = WavaVecaConfig.from_pretrained(A__ )
else:
_UpperCamelCase = WavaVecaConfig()
if is_seq_class:
_UpperCamelCase = read_txt_into_dict(A__ )
_UpperCamelCase = idalabel
_UpperCamelCase = WavaVecaForSequenceClassification(A__ )
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
feature_extractor.save_pretrained(A__ )
elif is_finetuned:
if dict_path:
_UpperCamelCase = Dictionary.load(A__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCamelCase = target_dict.pad_index
_UpperCamelCase = target_dict.bos_index
_UpperCamelCase = target_dict.eos_index
_UpperCamelCase = len(target_dict.symbols )
_UpperCamelCase = os.path.join(A__ , "vocab.json" )
if not os.path.isdir(A__ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A__ ) )
return
os.makedirs(A__ , exist_ok=A__ )
_UpperCamelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_UpperCamelCase = 0
_UpperCamelCase = 1
with open(A__ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(A__ , A__ )
_UpperCamelCase = WavaVecaCTCTokenizer(
A__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=A__ , )
_UpperCamelCase = True if config.feat_extract_norm == "layer" else False
_UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=A__ , return_attention_mask=A__ , )
_UpperCamelCase = WavaVecaProcessor(feature_extractor=A__ , tokenizer=A__ )
processor.save_pretrained(A__ )
_UpperCamelCase = WavaVecaForCTC(A__ )
else:
_UpperCamelCase = WavaVecaForPreTraining(A__ )
if is_finetuned or is_seq_class:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
_UpperCamelCase = argparse.Namespace(task="audio_pretraining" )
_UpperCamelCase = fairseq.tasks.setup_task(A__ )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A__ )
_UpperCamelCase = model[0].eval()
recursively_load_weights(A__ , A__ , not is_finetuned )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 714 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_ ( lowercase , lowercase ):
@register_to_config
def __init__( self , *,
lowerCamelCase_ = 4 , lowerCamelCase_ = 7_68 , lowerCamelCase_ , lowerCamelCase_ , ) -> int:
"""simple docstring"""
super().__init__()
_UpperCamelCase = nn.Parameter(torch.zeros(lowerCamelCase_ ) )
# parameters for additional clip time embeddings
_UpperCamelCase = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
_UpperCamelCase = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
# parameters for encoder hidden states
_UpperCamelCase = clip_extra_context_tokens
_UpperCamelCase = nn.Linear(
lowerCamelCase_ , self.clip_extra_context_tokens * cross_attention_dim )
_UpperCamelCase = nn.Linear(lowerCamelCase_ , lowerCamelCase_ )
_UpperCamelCase = nn.LayerNorm(lowerCamelCase_ )
def lowercase ( self , *, lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
_UpperCamelCase = image_embeddings.shape[0]
_UpperCamelCase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
_UpperCamelCase = classifier_free_guidance_embeddings.expand(
lowerCamelCase_ , -1 )
_UpperCamelCase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
_UpperCamelCase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
_UpperCamelCase = self.embedding_proj(lowerCamelCase_ )
_UpperCamelCase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase_ )
_UpperCamelCase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
_UpperCamelCase = self.clip_extra_context_tokens_proj(lowerCamelCase_ )
_UpperCamelCase = clip_extra_context_tokens.reshape(lowerCamelCase_ , -1 , self.clip_extra_context_tokens )
_UpperCamelCase = clip_extra_context_tokens.permute(0 , 2 , 1 )
_UpperCamelCase = self.encoder_hidden_states_proj(lowerCamelCase_ )
_UpperCamelCase = self.text_encoder_hidden_states_norm(lowerCamelCase_ )
_UpperCamelCase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 589 | 0 |
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ = {}
def lowercase_ ( self : int ) -> List[Any]:
print(self.vertex )
for i in self.vertex:
print(_UpperCamelCase , ''' -> ''' , ''' -> '''.join([str(_UpperCamelCase ) for j in self.vertex[i]] ) )
def lowercase_ ( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : int ) -> Tuple:
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_UpperCamelCase )
else:
# else make a new vertex
SCREAMING_SNAKE_CASE__ = [to_vertex]
def lowercase_ ( self : int ) -> Any:
# visited array for storing already visited nodes
SCREAMING_SNAKE_CASE__ = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_UpperCamelCase , _UpperCamelCase )
def lowercase_ ( self : int , __lowerCamelCase : int , __lowerCamelCase : list ) -> int:
# mark start vertex as visited
SCREAMING_SNAKE_CASE__ = True
print(_UpperCamelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[Any] = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('''DFS:''')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 493 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__=1_0_2_4 , __magic_name__=1_0_2_4 , __magic_name__=False , **__magic_name__ ):
_lowercase: List[Any] = AutoTokenizer.from_pretrained(__magic_name__ )
_lowercase: Dict = SeqaSeqDataset(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , type_path="train" , **__magic_name__ )
_lowercase: Union[str, Any] = tok.pad_token_id
def get_lens(__magic_name__ ):
_lowercase: Union[str, Any] = tqdm(
DataLoader(__magic_name__ , batch_size=5_1_2 , num_workers=8 , shuffle=__magic_name__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
_lowercase: Dict = []
for batch in dl:
_lowercase: Any = batch["input_ids"].ne(__magic_name__ ).sum(1 ).tolist()
_lowercase: Dict = batch["labels"].ne(__magic_name__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(__magic_name__ , __magic_name__ ):
max_lens.append(max(__magic_name__ , __magic_name__ ) )
else:
max_lens.extend(__magic_name__ )
return max_lens
_lowercase: Optional[Any] = get_lens(__magic_name__ )
_lowercase: Tuple = SeqaSeqDataset(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , type_path="val" , **__magic_name__ )
_lowercase: Union[str, Any] = get_lens(__magic_name__ )
pickle_save(__magic_name__ , train_ds.len_file )
pickle_save(__magic_name__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 226 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
lowerCAmelCase__ = random.Random()
def __lowerCamelCase ( __a : Optional[int] , __a : Dict=1.0 , __a : Optional[Any]=None , __a : Any=None ) -> List[Any]:
if rng is None:
_lowercase =global_rng
_lowercase =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class _a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=7 , lowerCAmelCase_=400 , lowerCAmelCase_=2000 , lowerCAmelCase_=2048 , lowerCAmelCase_=128 , lowerCAmelCase_=1 , lowerCAmelCase_=512 , lowerCAmelCase_=30 , lowerCAmelCase_=44100 , ):
_lowercase =parent
_lowercase =batch_size
_lowercase =min_seq_length
_lowercase =max_seq_length
_lowercase =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_lowercase =spectrogram_length
_lowercase =feature_size
_lowercase =num_audio_channels
_lowercase =hop_length
_lowercase =chunk_length
_lowercase =sampling_rate
def __lowerCAmelCase ( self ):
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __lowerCAmelCase ( self , lowerCAmelCase_=False , lowerCAmelCase_=False ):
def _flatten(lowerCAmelCase_ ):
return list(itertools.chain(*lowerCAmelCase_ ) )
if equal_length:
_lowercase =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_lowercase =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_lowercase =[np.asarray(lowerCAmelCase_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _a ( lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = TvltFeatureExtractor
def __lowerCAmelCase ( self ):
_lowercase =TvltFeatureExtractionTester(self )
def __lowerCAmelCase ( self ):
_lowercase =self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCAmelCase_ , "spectrogram_length" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , "feature_size" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , "num_audio_channels" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , "hop_length" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , "chunk_length" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , "sampling_rate" ) )
def __lowerCAmelCase ( self ):
_lowercase =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowercase =feat_extract_first.save_pretrained(lowerCAmelCase_ )[0]
check_json_file_has_correct_format(lowerCAmelCase_ )
_lowercase =self.feature_extraction_class.from_pretrained(lowerCAmelCase_ )
_lowercase =feat_extract_first.to_dict()
_lowercase =feat_extract_second.to_dict()
_lowercase =dict_first.pop("mel_filters" )
_lowercase =dict_second.pop("mel_filters" )
self.assertTrue(np.allclose(lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self ):
_lowercase =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowercase =os.path.join(lowerCAmelCase_ , "feat_extract.json" )
feat_extract_first.to_json_file(lowerCAmelCase_ )
_lowercase =self.feature_extraction_class.from_json_file(lowerCAmelCase_ )
_lowercase =feat_extract_first.to_dict()
_lowercase =feat_extract_second.to_dict()
_lowercase =dict_first.pop("mel_filters" )
_lowercase =dict_second.pop("mel_filters" )
self.assertTrue(np.allclose(lowerCAmelCase_ , lowerCAmelCase_ ) )
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def __lowerCAmelCase ( self ):
# Initialize feature_extractor
_lowercase =self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
_lowercase =[floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_lowercase =[np.asarray(lowerCAmelCase_ ) for speech_input in speech_inputs]
# Test not batched input
_lowercase =feature_extractor(np_speech_inputs[0] , return_tensors="np" , sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
_lowercase =feature_extractor(lowerCAmelCase_ , return_tensors="np" , sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
_lowercase =feature_extractor(
lowerCAmelCase_ , return_tensors="np" , sampling_rate=44100 , mask_audio=lowerCAmelCase_ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
_lowercase =[floats_list((1, x) )[0] for x in (800, 800, 800)]
_lowercase =np.asarray(lowerCAmelCase_ )
_lowercase =feature_extractor(lowerCAmelCase_ , return_tensors="np" , sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
_lowercase =load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_lowercase =ds.sort("id" ).select(range(lowerCAmelCase_ ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def __lowerCAmelCase ( self ):
_lowercase =self._load_datasamples(1 )
_lowercase =TvltFeatureExtractor()
_lowercase =feature_extractor(lowerCAmelCase_ , return_tensors="pt" ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 192, 128) )
_lowercase =torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , lowerCAmelCase_ , atol=1e-4 ) )
| 594 |
import random
from .binary_exp_mod import bin_exp_mod
def __lowerCamelCase ( __a : List[Any] , __a : Optional[Any]=1_000 ) -> Union[str, Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_lowercase =n - 1
_lowercase =0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
_lowercase =0
while count < prec:
_lowercase =random.randint(2 , n - 1 )
_lowercase =bin_exp_mod(__a , __a , __a )
if b != 1:
_lowercase =True
for _ in range(__a ):
if b == n - 1:
_lowercase =False
break
_lowercase =b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
lowerCAmelCase__ = abs(int(input("Enter bound : ").strip()))
print("Here's the list of primes:")
print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 594 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_A = logging.get_logger(__name__)
_A = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
_A = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
_A = {
"gpt2": 1_024,
"gpt2-medium": 1_024,
"gpt2-large": 1_024,
"gpt2-xl": 1_024,
"distilgpt2": 1_024,
}
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : Optional[Any] = VOCAB_FILES_NAMES
_snake_case : str = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : List[str] = ['input_ids', 'attention_mask']
_snake_case : List[Any] = GPTaTokenizer
def __init__( self : Union[str, Any] , A_ : Union[str, Any]=None , A_ : Optional[int]=None , A_ : Dict=None , A_ : List[Any]="<|endoftext|>" , A_ : Dict="<|endoftext|>" , A_ : List[Any]="<|endoftext|>" , A_ : List[str]=False , **A_ : str , )-> List[Any]:
super().__init__(
A_ , A_ , tokenizer_file=A_ , unk_token=A_ , bos_token=A_ , eos_token=A_ , add_prefix_space=A_ , **A_ , )
__UpperCamelCase = kwargs.pop("add_bos_token" , A_ )
__UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , A_ ) != add_prefix_space:
__UpperCamelCase = getattr(A_ , pre_tok_state.pop("type" ) )
__UpperCamelCase = add_prefix_space
__UpperCamelCase = pre_tok_class(**A_ )
__UpperCamelCase = add_prefix_space
def A ( self : str , *A_ : Tuple , **A_ : int )-> BatchEncoding:
__UpperCamelCase = kwargs.get("is_split_into_words" , A_ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*A_ , **A_ )
def A ( self : int , *A_ : Optional[int] , **A_ : List[Any] )-> BatchEncoding:
__UpperCamelCase = kwargs.get("is_split_into_words" , A_ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*A_ , **A_ )
def A ( self : int , A_ : str , A_ : Optional[str] = None )-> Tuple[str]:
__UpperCamelCase = self._tokenizer.model.save(A_ , name=A_ )
return tuple(A_ )
def A ( self : Optional[int] , A_ : "Conversation" )-> List[int]:
__UpperCamelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(A_ , add_special_tokens=A_ ) + [self.eos_token_id] )
if len(A_ ) > self.model_max_length:
__UpperCamelCase = input_ids[-self.model_max_length :]
return input_ids
| 505 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self : Optional[int] )-> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A ( self : Optional[Any] )-> int:
torch.manual_seed(0 )
__UpperCamelCase = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("AttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "AttnUpBlock2D") , )
return model
@property
def A ( self : str )-> List[Any]:
torch.manual_seed(0 )
__UpperCamelCase = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , cross_attention_dim=10 , )
return model
@property
def A ( self : str )-> Optional[Any]:
torch.manual_seed(0 )
__UpperCamelCase = AutoencoderKL(
sample_size=(1_28, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D") , up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D") , )
__UpperCamelCase = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_28, 1_28) , down_block_types=("AttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "AttnUpBlock2D") , )
return vqvae, unet
@slow
def A ( self : Tuple )-> List[str]:
__UpperCamelCase = "cpu" # ensure determinism for the device-dependent torch.Generator
__UpperCamelCase = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
__UpperCamelCase = DDPMScheduler()
__UpperCamelCase = AudioDiffusionPipeline(vqvae=A_ , unet=self.dummy_unet , mel=A_ , scheduler=A_ )
__UpperCamelCase = pipe.to(A_ )
pipe.set_progress_bar_config(disable=A_ )
__UpperCamelCase = torch.Generator(device=A_ ).manual_seed(42 )
__UpperCamelCase = pipe(generator=A_ , steps=4 )
__UpperCamelCase = output.audios[0]
__UpperCamelCase = output.images[0]
__UpperCamelCase = torch.Generator(device=A_ ).manual_seed(42 )
__UpperCamelCase = pipe(generator=A_ , steps=4 , return_dict=A_ )
__UpperCamelCase = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
__UpperCamelCase = np.frombuffer(image_from_tuple.tobytes() , dtype="uint8" )[:10]
__UpperCamelCase = np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
__UpperCamelCase = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
__UpperCamelCase = DDIMScheduler()
__UpperCamelCase = self.dummy_vqvae_and_unet
__UpperCamelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=A_ , scheduler=A_ )
__UpperCamelCase = pipe.to(A_ )
pipe.set_progress_bar_config(disable=A_ )
np.random.seed(0 )
__UpperCamelCase = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
__UpperCamelCase = torch.Generator(device=A_ ).manual_seed(42 )
__UpperCamelCase = pipe(raw_audio=A_ , generator=A_ , start_step=5 , steps=10 )
__UpperCamelCase = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
__UpperCamelCase = np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
__UpperCamelCase = self.dummy_unet_condition
__UpperCamelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=A_ , mel=A_ , scheduler=A_ )
__UpperCamelCase = pipe.to(A_ )
pipe.set_progress_bar_config(disable=A_ )
np.random.seed(0 )
__UpperCamelCase = torch.rand((1, 1, 10) )
__UpperCamelCase = pipe(generator=A_ , encoding=A_ )
__UpperCamelCase = output.images[0]
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
__UpperCamelCase = np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self : str )-> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Optional[int] )-> Union[str, Any]:
__UpperCamelCase = torch_device
__UpperCamelCase = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
__UpperCamelCase = pipe.to(A_ )
pipe.set_progress_bar_config(disable=A_ )
__UpperCamelCase = torch.Generator(device=A_ ).manual_seed(42 )
__UpperCamelCase = pipe(generator=A_ )
__UpperCamelCase = output.audios[0]
__UpperCamelCase = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
__UpperCamelCase = np.frombuffer(image.tobytes() , dtype="uint8" )[:10]
__UpperCamelCase = np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 505 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _lowerCAmelCase ( *_lowerCAmelCase )-> Optional[Any]:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__UpperCAmelCase = list(_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) ):
__UpperCAmelCase = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = [
'CUDA out of memory.', # CUDA OOM
'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.', # CUDNN SNAFU
'DefaultCPUAllocator: can\'t allocate memory', # CPU OOM
]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def _lowerCAmelCase ( _lowerCAmelCase = None , _lowerCAmelCase = 1_28 )-> Dict:
if function is None:
return functools.partial(_lowerCAmelCase , starting_batch_size=_lowerCAmelCase )
__UpperCAmelCase = starting_batch_size
def decorator(*_lowerCAmelCase , **_lowerCAmelCase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
__UpperCAmelCase = list(inspect.signature(_lowerCAmelCase ).parameters.keys() )
# Guard against user error
if len(_lowerCAmelCase ) < (len(_lowerCAmelCase ) + 1):
__UpperCAmelCase = ', '.join([F'{arg}={value}' for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
F'Batch size was passed into `{function.__name__}` as the first argument when called.'
F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`' )
while True:
if batch_size == 0:
raise RuntimeError('No executable batch size found, reached zero.' )
try:
return function(_lowerCAmelCase , *_lowerCAmelCase , **_lowerCAmelCase )
except Exception as e:
if should_reduce_batch_size(_lowerCAmelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 617 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self , __A=None , __A=3 , __A=True , __A=True , __A=None , **__A , ):
super().__init__(**__A )
__UpperCAmelCase = backbone
__UpperCAmelCase = num_channels
__UpperCAmelCase = features_only
__UpperCAmelCase = use_pretrained_backbone
__UpperCAmelCase = True
__UpperCAmelCase = out_indices if out_indices is not None else (-1,)
| 617 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class _snake_case :
# setable values
_A : Optional[int] = None
_A : Optional[jnp.ndarray] = None
_A : Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def __UpperCamelCase ( cls : Union[str, Any] ):
return cls()
@dataclass
class _snake_case ( _a ):
_A : jnp.ndarray
_A : jnp.ndarray
_A : KarrasVeSchedulerState
class _snake_case ( _a , _a ):
@property
def __UpperCamelCase ( self : List[str] ):
return True
@register_to_config
def __init__( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : float = 0.02 ,SCREAMING_SNAKE_CASE__ : float = 100 ,SCREAMING_SNAKE_CASE__ : float = 1.007 ,SCREAMING_SNAKE_CASE__ : float = 80 ,SCREAMING_SNAKE_CASE__ : float = 0.05 ,SCREAMING_SNAKE_CASE__ : float = 50 ,):
pass
def __UpperCamelCase ( self : Any ):
return KarrasVeSchedulerState.create()
def __UpperCamelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : KarrasVeSchedulerState ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Tuple = () ):
SCREAMING_SNAKE_CASE:int = jnp.arange(0 ,SCREAMING_SNAKE_CASE__ )[::-1].copy()
SCREAMING_SNAKE_CASE:Tuple = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=SCREAMING_SNAKE_CASE__ ,schedule=jnp.array(SCREAMING_SNAKE_CASE__ ,dtype=jnp.floataa ) ,timesteps=SCREAMING_SNAKE_CASE__ ,)
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : KarrasVeSchedulerState ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : float ,SCREAMING_SNAKE_CASE__ : random.KeyArray ,):
if self.config.s_min <= sigma <= self.config.s_max:
SCREAMING_SNAKE_CASE:Dict = min(self.config.s_churn / state.num_inference_steps ,2**0.5 - 1 )
else:
SCREAMING_SNAKE_CASE:Tuple = 0
# sample eps ~ N(0, S_noise^2 * I)
SCREAMING_SNAKE_CASE:Dict = random.split(SCREAMING_SNAKE_CASE__ ,num=1 )
SCREAMING_SNAKE_CASE:Any = self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__ ,shape=sample.shape )
SCREAMING_SNAKE_CASE:Tuple = sigma + gamma * sigma
SCREAMING_SNAKE_CASE:Optional[Any] = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def __UpperCamelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : KarrasVeSchedulerState ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : float ,SCREAMING_SNAKE_CASE__ : float ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : bool = True ,):
SCREAMING_SNAKE_CASE:Union[str, Any] = sample_hat + sigma_hat * model_output
SCREAMING_SNAKE_CASE:str = (sample_hat - pred_original_sample) / sigma_hat
SCREAMING_SNAKE_CASE:List[str] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__ ,derivative=SCREAMING_SNAKE_CASE__ ,state=SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : KarrasVeSchedulerState ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : float ,SCREAMING_SNAKE_CASE__ : float ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : jnp.ndarray ,SCREAMING_SNAKE_CASE__ : bool = True ,):
SCREAMING_SNAKE_CASE:Optional[Any] = sample_prev + sigma_prev * model_output
SCREAMING_SNAKE_CASE:str = (sample_prev - pred_original_sample) / sigma_prev
SCREAMING_SNAKE_CASE:Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__ ,derivative=SCREAMING_SNAKE_CASE__ ,state=SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : KarrasVeSchedulerState ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Any ):
raise NotImplementedError()
| 143 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _snake_case ( _a , _a , unittest.TestCase ):
_A : List[Any] = IFInpaintingPipeline
_A : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_A : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_A : List[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __UpperCamelCase ( self : Dict ):
return self._get_dummy_components()
def __UpperCamelCase ( self : Dict ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0 ):
if str(SCREAMING_SNAKE_CASE__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE:Any = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE:int = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Union[str, Any] = floats_tensor((1, 3, 32, 32) ,rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = floats_tensor((1, 3, 32, 32) ,rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() ,reason="XFormers attention is only available with CUDA and `xformers` installed" ,)
def __UpperCamelCase ( self : List[str] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def __UpperCamelCase ( self : Any ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" ,reason="float16 requires CUDA" )
def __UpperCamelCase ( self : Any ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __UpperCamelCase ( self : Optional[int] ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __UpperCamelCase ( self : Any ):
self._test_save_load_local()
def __UpperCamelCase ( self : int ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 ,)
| 143 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
),
"squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"squeezebert/squeezebert-uncased": (
"https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli": (
"https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
),
"squeezebert/squeezebert-mnli-headless": (
"https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
),
},
}
__A = {
"squeezebert/squeezebert-uncased": 512,
"squeezebert/squeezebert-mnli": 512,
"squeezebert/squeezebert-mnli-headless": 512,
}
__A = {
"squeezebert/squeezebert-uncased": {"do_lower_case": True},
"squeezebert/squeezebert-mnli": {"do_lower_case": True},
"squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_INIT_CONFIGURATION
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = SqueezeBertTokenizer
def __init__(self : Any , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Optional[int]="[UNK]" , UpperCAmelCase_ : int="[SEP]" , UpperCAmelCase_ : Optional[Any]="[PAD]" , UpperCAmelCase_ : Optional[int]="[CLS]" , UpperCAmelCase_ : str="[MASK]" , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : Optional[Any]=None , **UpperCAmelCase_ : Dict , ) ->Union[str, Any]:
'''simple docstring'''
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , tokenize_chinese_chars=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCamelCase__: Dict =json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase" , UpperCAmelCase_) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase_) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase_) != tokenize_chinese_chars
):
lowerCamelCase__: Any =getattr(UpperCAmelCase_ , normalizer_state.pop("type"))
lowerCamelCase__: Union[str, Any] =do_lower_case
lowerCamelCase__: Optional[Any] =strip_accents
lowerCamelCase__: Optional[int] =tokenize_chinese_chars
lowerCamelCase__: int =normalizer_class(**UpperCAmelCase_)
lowerCamelCase__: Any =do_lower_case
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str=None) ->Dict:
'''simple docstring'''
lowerCamelCase__: List[str] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None) ->List[int]:
'''simple docstring'''
lowerCamelCase__: Dict =[self.sep_token_id]
lowerCamelCase__: Tuple =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None) ->Tuple[str]:
'''simple docstring'''
lowerCamelCase__: Optional[int] =self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_)
return tuple(UpperCAmelCase_)
| 437 |
from __future__ import annotations
def lowerCAmelCase_ ( __a , __a , __a , ) -> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor" )
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor" )
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 437 | 1 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
UpperCAmelCase_ : Any = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : List[Any] = """token-classification"""
def __init__( self : Dict , __lowerCamelCase : Any ):
if type(__lowerCamelCase ) == dict:
UpperCamelCase :Optional[int] = Namespace(**__lowerCamelCase )
UpperCamelCase :Union[str, Any] = import_module("""tasks""" )
try:
UpperCamelCase :List[str] = getattr(__lowerCamelCase , hparams.task_type )
UpperCamelCase :TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
UpperCamelCase :Any = self.token_classification_task.get_labels(hparams.labels )
UpperCamelCase :List[str] = CrossEntropyLoss().ignore_index
super().__init__(__lowerCamelCase , len(self.labels ) , self.mode )
def _A ( self : str , **__lowerCamelCase : Any ):
return self.model(**__lowerCamelCase )
def _A ( self : str , __lowerCamelCase : int , __lowerCamelCase : int ):
UpperCamelCase :int = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
UpperCamelCase :Any = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
UpperCamelCase :Optional[Any] = self(**__lowerCamelCase )
UpperCamelCase :Union[str, Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _A ( self : Tuple ):
UpperCamelCase :Dict = self.hparams
for mode in ["train", "dev", "test"]:
UpperCamelCase :str = self._feature_file(__lowerCamelCase )
if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , __lowerCamelCase )
UpperCamelCase :Optional[Any] = torch.load(__lowerCamelCase )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
UpperCamelCase :Any = self.token_classification_task.read_examples_from_file(args.data_dir , __lowerCamelCase )
UpperCamelCase :Optional[int] = self.token_classification_task.convert_examples_to_features(
__lowerCamelCase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=__lowerCamelCase , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , __lowerCamelCase )
torch.save(__lowerCamelCase , __lowerCamelCase )
def _A ( self : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool = False ):
UpperCamelCase :List[Any] = self._feature_file(__lowerCamelCase )
logger.info("""Loading features from cached file %s""" , __lowerCamelCase )
UpperCamelCase :Any = torch.load(__lowerCamelCase )
UpperCamelCase :List[Any] = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
UpperCamelCase :Union[str, Any] = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
UpperCamelCase :Dict = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
UpperCamelCase :Tuple = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
UpperCamelCase :int = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , batch_size=__lowerCamelCase )
def _A ( self : Optional[Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[str] ):
"""Compute validation""" ""
UpperCamelCase :Optional[int] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
UpperCamelCase :int = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
UpperCamelCase :Dict = self(**__lowerCamelCase )
UpperCamelCase , UpperCamelCase :int = outputs[:2]
UpperCamelCase :Union[str, Any] = logits.detach().cpu().numpy()
UpperCamelCase :Union[str, Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _A ( self : str , __lowerCamelCase : int ):
UpperCamelCase :Dict = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
UpperCamelCase :Union[str, Any] = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
UpperCamelCase :Union[str, Any] = np.argmax(__lowerCamelCase , axis=2 )
UpperCamelCase :Union[str, Any] = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
UpperCamelCase :Tuple = dict(enumerate(self.labels ) )
UpperCamelCase :Optional[int] = [[] for _ in range(out_label_ids.shape[0] )]
UpperCamelCase :Tuple = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
UpperCamelCase :Union[str, Any] = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"""precision""": precision_score(__lowerCamelCase , __lowerCamelCase ),
"""recall""": recall_score(__lowerCamelCase , __lowerCamelCase ),
"""f1""": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
UpperCamelCase :Dict = dict(results.items() )
UpperCamelCase :Tuple = results
return ret, preds_list, out_label_list
def _A ( self : Optional[Any] , __lowerCamelCase : int ):
# when stable
UpperCamelCase , UpperCamelCase , UpperCamelCase :List[str] = self._eval_end(__lowerCamelCase )
UpperCamelCase :Any = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _A ( self : Dict , __lowerCamelCase : Optional[int] ):
# updating to test_epoch_end instead of deprecated test_end
UpperCamelCase , UpperCamelCase , UpperCamelCase :Optional[Any] = self._eval_end(__lowerCamelCase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
UpperCamelCase :Tuple = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _A ( __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
# Add NER specific options
BaseTransformer.add_model_specific_args(__lowerCamelCase , __lowerCamelCase )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=__lowerCamelCase , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=128 , type=__lowerCamelCase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=__lowerCamelCase , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=__lowerCamelCase , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
UpperCAmelCase_ : str = NERTransformer.add_model_specific_args(parser, os.getcwd())
UpperCAmelCase_ : Tuple = parser.parse_args()
UpperCAmelCase_ : int = NERTransformer(args)
UpperCAmelCase_ : int = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
UpperCAmelCase_ : Any = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
UpperCAmelCase_ : Any = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 590 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
"""simple docstring"""
if isinstance(__magic_name__ , np.ndarray ):
return list(tensor.shape )
UpperCamelCase :List[Any] = tf.shape(__magic_name__ )
if tensor.shape == tf.TensorShape(__magic_name__ ):
return dynamic
UpperCamelCase :Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__magic_name__ )]
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tf.Tensor , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[str] = None ) -> tf.Tensor:
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1E-9 , axis=__magic_name__ , name=__magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[str] , __magic_name__ : List[str] , __magic_name__ : List[str] , __magic_name__ : Tuple=1E-5 , __magic_name__ : Optional[Any]=-1 ) -> int:
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__magic_name__ , __magic_name__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
UpperCamelCase , UpperCamelCase :Tuple = tf.nn.moments(__magic_name__ , axes=[axis] , keepdims=__magic_name__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
UpperCamelCase :str = [1] * inputs.shape.rank
UpperCamelCase :int = shape_list(__magic_name__ )[axis]
UpperCamelCase :Optional[Any] = tf.reshape(__magic_name__ , __magic_name__ )
UpperCamelCase :Tuple = tf.reshape(__magic_name__ , __magic_name__ )
# Compute layer normalization using the batch_normalization
# function.
UpperCamelCase :Union[str, Any] = tf.nn.batch_normalization(
__magic_name__ , __magic_name__ , __magic_name__ , offset=__magic_name__ , scale=__magic_name__ , variance_epsilon=__magic_name__ , )
return outputs
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[str] , __magic_name__ : List[Any]=0 , __magic_name__ : Any=-1 ) -> str:
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
UpperCamelCase :Optional[Any] = tf.shape(__magic_name__ )
UpperCamelCase :Optional[int] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
UpperCamelCase :int = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__magic_name__ , __magic_name__ )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tf.Tensor ) -> tf.Tensor:
"""simple docstring"""
if not isinstance(__magic_name__ , tf.Tensor ):
UpperCamelCase :Union[str, Any] = tf.convert_to_tensor(__magic_name__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
UpperCamelCase :Optional[Any] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
UpperCamelCase :List[str] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
UpperCamelCase :Dict = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tf.Tensor , __magic_name__ : int , __magic_name__ : str = "input_ids" ) -> None:
"""simple docstring"""
tf.debugging.assert_less(
__magic_name__ , tf.cast(__magic_name__ , dtype=tensor.dtype ) , message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(__magic_name__ )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : List[str] ) -> Dict:
"""simple docstring"""
UpperCamelCase :List[Any] = 6_4512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
UpperCamelCase :Dict = [x for x in data if len(__magic_name__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
f"""bytes: {bad_attributes}""" )
UpperCamelCase :Tuple = np.asarray(__magic_name__ )
UpperCamelCase :Optional[Any] = 1
UpperCamelCase :Tuple = np.array_split(__magic_name__ , __magic_name__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
UpperCamelCase :Union[str, Any] = np.array_split(__magic_name__ , __magic_name__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__magic_name__ ):
UpperCamelCase :List[str] = chunk_data
else:
UpperCamelCase :Any = data
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[str, Any] , __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
if name in group.attrs:
UpperCamelCase :List[Any] = [n.decode("""utf8""" ) if hasattr(__magic_name__ , """decode""" ) else n for n in group.attrs[name]]
else:
UpperCamelCase :Tuple = []
UpperCamelCase :int = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(__magic_name__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> Any:
"""simple docstring"""
def _expand_single_ad_tensor(__magic_name__ : List[Any] ):
if isinstance(__magic_name__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__magic_name__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , __magic_name__ )
| 590 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase ( _lowerCAmelCase , _lowerCAmelCase=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
else:
return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )
def lowercase ( _lowerCAmelCase , _lowerCAmelCase=0 ):
UpperCAmelCase__ = []
for old_item in old_list:
UpperCAmelCase__ = old_item.replace("""in_layers.0""" , """norm1""" )
UpperCAmelCase__ = new_item.replace("""in_layers.2""" , """conv1""" )
UpperCAmelCase__ = new_item.replace("""out_layers.0""" , """norm2""" )
UpperCAmelCase__ = new_item.replace("""out_layers.3""" , """conv2""" )
UpperCAmelCase__ = new_item.replace("""emb_layers.1""" , """time_emb_proj""" )
UpperCAmelCase__ = new_item.replace("""skip_connection""" , """conv_shortcut""" )
UpperCAmelCase__ = shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def lowercase ( _lowerCAmelCase , _lowerCAmelCase=0 ):
UpperCAmelCase__ = []
for old_item in old_list:
UpperCAmelCase__ = old_item
UpperCAmelCase__ = new_item.replace("""norm.weight""" , """group_norm.weight""" )
UpperCAmelCase__ = new_item.replace("""norm.bias""" , """group_norm.bias""" )
UpperCAmelCase__ = new_item.replace("""proj_out.weight""" , """proj_attn.weight""" )
UpperCAmelCase__ = new_item.replace("""proj_out.bias""" , """proj_attn.bias""" )
UpperCAmelCase__ = shave_segments(__UpperCamelCase , n_shave_prefix_segments=__UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def lowercase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None ):
assert isinstance(__UpperCamelCase , __UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
UpperCAmelCase__ = old_checkpoint[path]
UpperCAmelCase__ = old_tensor.shape[0] // 3
UpperCAmelCase__ = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
UpperCAmelCase__ = old_tensor.shape[0] // config['num_head_channels'] // 3
UpperCAmelCase__ = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
UpperCAmelCase__ = old_tensor.split(channels // num_heads , dim=1 )
UpperCAmelCase__ = query.reshape(__UpperCamelCase )
UpperCAmelCase__ = key.reshape(__UpperCamelCase )
UpperCAmelCase__ = value.reshape(__UpperCamelCase )
for path in paths:
UpperCAmelCase__ = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
UpperCAmelCase__ = new_path.replace("""middle_block.0""" , """mid_block.resnets.0""" )
UpperCAmelCase__ = new_path.replace("""middle_block.1""" , """mid_block.attentions.0""" )
UpperCAmelCase__ = new_path.replace("""middle_block.2""" , """mid_block.resnets.1""" )
if additional_replacements is not None:
for replacement in additional_replacements:
UpperCAmelCase__ = new_path.replace(replacement["""old"""] , replacement["""new"""] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
UpperCAmelCase__ = old_checkpoint[path['old']][:, :, 0]
else:
UpperCAmelCase__ = old_checkpoint[path['old']]
def lowercase ( _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ = {}
UpperCAmelCase__ = checkpoint['time_embed.0.weight']
UpperCAmelCase__ = checkpoint['time_embed.0.bias']
UpperCAmelCase__ = checkpoint['time_embed.2.weight']
UpperCAmelCase__ = checkpoint['time_embed.2.bias']
UpperCAmelCase__ = checkpoint['input_blocks.0.0.weight']
UpperCAmelCase__ = checkpoint['input_blocks.0.0.bias']
UpperCAmelCase__ = checkpoint['out.0.weight']
UpperCAmelCase__ = checkpoint['out.0.bias']
UpperCAmelCase__ = checkpoint['out.2.weight']
UpperCAmelCase__ = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
UpperCAmelCase__ = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
UpperCAmelCase__ = {
layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the middle blocks only
UpperCAmelCase__ = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
UpperCAmelCase__ = {
layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
# Retrieves the keys for the output blocks only
UpperCAmelCase__ = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
UpperCAmelCase__ = {
layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
for layer_id in range(__UpperCamelCase )
}
for i in range(1 , __UpperCamelCase ):
UpperCAmelCase__ = (i - 1) // (config['num_res_blocks'] + 1)
UpperCAmelCase__ = (i - 1) % (config['num_res_blocks'] + 1)
UpperCAmelCase__ = [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
UpperCAmelCase__ = [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
UpperCAmelCase__ = checkpoint[
F'''input_blocks.{i}.0.op.weight'''
]
UpperCAmelCase__ = checkpoint[
F'''input_blocks.{i}.0.op.bias'''
]
continue
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase )
UpperCAmelCase__ = {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
UpperCAmelCase__ = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path, resnet_op] , config=__UpperCamelCase )
if len(__UpperCamelCase ):
UpperCAmelCase__ = renew_attention_paths(__UpperCamelCase )
UpperCAmelCase__ = {
'old': F'''input_blocks.{i}.1''',
'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
UpperCAmelCase__ = {
F'''input_blocks.{i}.1.qkv.bias''': {
'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''input_blocks.{i}.1.qkv.weight''': {
'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase , )
UpperCAmelCase__ = middle_blocks[0]
UpperCAmelCase__ = middle_blocks[1]
UpperCAmelCase__ = middle_blocks[2]
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase )
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , config=__UpperCamelCase )
UpperCAmelCase__ = renew_attention_paths(__UpperCamelCase )
UpperCAmelCase__ = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , attention_paths_to_split=__UpperCamelCase , config=__UpperCamelCase )
for i in range(__UpperCamelCase ):
UpperCAmelCase__ = i // (config['num_res_blocks'] + 1)
UpperCAmelCase__ = i % (config['num_res_blocks'] + 1)
UpperCAmelCase__ = [shave_segments(__UpperCamelCase , 2 ) for name in output_blocks[i]]
UpperCAmelCase__ = {}
for layer in output_block_layers:
UpperCAmelCase__ = layer.split(""".""" )[0], shave_segments(__UpperCamelCase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(__UpperCamelCase )
else:
UpperCAmelCase__ = [layer_name]
if len(__UpperCamelCase ) > 1:
UpperCAmelCase__ = [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
UpperCAmelCase__ = [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase )
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase )
UpperCAmelCase__ = {'old': F'''output_blocks.{i}.0''', 'new': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , config=__UpperCamelCase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
UpperCAmelCase__ = list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
UpperCAmelCase__ = checkpoint[
F'''output_blocks.{i}.{index}.conv.weight'''
]
UpperCAmelCase__ = checkpoint[
F'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(__UpperCamelCase ) == 2:
UpperCAmelCase__ = []
if len(__UpperCamelCase ):
UpperCAmelCase__ = renew_attention_paths(__UpperCamelCase )
UpperCAmelCase__ = {
'old': F'''output_blocks.{i}.1''',
'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
UpperCAmelCase__ = {
F'''output_blocks.{i}.1.qkv.bias''': {
'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
F'''output_blocks.{i}.1.qkv.weight''': {
'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions ) else None , config=__UpperCamelCase , )
else:
UpperCAmelCase__ = renew_resnet_paths(__UpperCamelCase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
UpperCAmelCase__ = '.'.join(["""output_blocks""", str(__UpperCamelCase ), path["""old"""]] )
UpperCAmelCase__ = '.'.join(["""up_blocks""", str(__UpperCamelCase ), """resnets""", str(__UpperCamelCase ), path["""new"""]] )
UpperCAmelCase__ = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
snake_case__ : Tuple = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the architecture.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
snake_case__ : Dict = parser.parse_args()
snake_case__ : Any = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
snake_case__ : str = json.loads(f.read())
snake_case__ : Dict = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
snake_case__ : Tuple = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
snake_case__ : Union[str, Any] = DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
snake_case__ : Union[str, Any] = VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
snake_case__ : List[str] = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 392 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _a ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : str = LongformerTokenizer
A : List[str] = True
A : Optional[int] = LongformerTokenizerFast
A : Tuple = True
def UpperCamelCase_ ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE : Any = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
SCREAMING_SNAKE_CASE : Optional[Any] = dict(zip(A, range(len(A ) ) ) )
SCREAMING_SNAKE_CASE : str = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
SCREAMING_SNAKE_CASE : Tuple = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file, 'w', encoding='utf-8' ) as fp:
fp.write(json.dumps(A ) + '\n' )
with open(self.merges_file, 'w', encoding='utf-8' ) as fp:
fp.write('\n'.join(A ) )
def UpperCamelCase_ ( self, **A ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname, **A )
def UpperCamelCase_ ( self, **A ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = 'lower newer'
SCREAMING_SNAKE_CASE : Union[str, Any] = 'lower newer'
return input_text, output_text
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.tokenizer_class(self.vocab_file, self.merges_file, **self.special_tokens_map )
SCREAMING_SNAKE_CASE : Optional[Any] = 'lower newer'
SCREAMING_SNAKE_CASE : List[str] = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize(A ) # , add_prefix_space=True)
self.assertListEqual(A, A )
SCREAMING_SNAKE_CASE : List[Any] = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE : Union[str, Any] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ), A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!', add_special_tokens=A ), [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418', add_special_tokens=A ), [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2], )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.encode('sequence builders', add_special_tokens=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.encode('multi-sequence build', add_special_tokens=A )
SCREAMING_SNAKE_CASE : int = tokenizer.encode(
'sequence builders', add_special_tokens=A, add_prefix_space=A )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode(
'sequence builders', 'multi-sequence build', add_special_tokens=A, add_prefix_space=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(A )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.build_inputs_with_special_tokens(A, A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Optional[int] = 'Encode this sequence.'
SCREAMING_SNAKE_CASE : List[str] = tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode(A, add_special_tokens=A, add_prefix_space=A )
SCREAMING_SNAKE_CASE : Dict = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(A, A )
SCREAMING_SNAKE_CASE : str = tokenizer.encode(A, add_special_tokens=A, add_prefix_space=A )
SCREAMING_SNAKE_CASE : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(A, A )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode(A, add_special_tokens=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(A, A )
# Testing spaces after special tokens
SCREAMING_SNAKE_CASE : Optional[int] = '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(A, lstrip=A, rstrip=A )} ) # mask token has a left space
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.convert_tokens_to_ids(A )
SCREAMING_SNAKE_CASE : List[str] = 'Encode <mask> sequence'
SCREAMING_SNAKE_CASE : List[str] = 'Encode <mask>sequence'
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode(A )
SCREAMING_SNAKE_CASE : Tuple = encoded.index(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(A, A )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.encode(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = encoded.index(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(A, A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE : Optional[int] = self.rust_tokenizer_class.from_pretrained(A, **A )
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer_class.from_pretrained(A, **A )
SCREAMING_SNAKE_CASE : Optional[Any] = 'A, <mask> AllenNLP sentence.'
SCREAMING_SNAKE_CASE : Any = tokenizer_r.encode_plus(A, add_special_tokens=A, return_token_type_ids=A )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_p.encode_plus(A, add_special_tokens=A, return_token_type_ids=A )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ), sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ), sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ), )
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'], [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'], [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
A, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
A, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def UpperCamelCase_ ( self ):
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2 ):
SCREAMING_SNAKE_CASE : List[Any] = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : Tuple = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
SCREAMING_SNAKE_CASE : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'], A )
self.assertEqual(post_processor_state['add_prefix_space'], A )
self.assertEqual(post_processor_state['trim_offsets'], A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE : str = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE : Tuple = F"{text_of_1_token} {text_of_1_token}"
SCREAMING_SNAKE_CASE : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (len(A ) + 1, len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (len(A ) + 1, len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : List[str] = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (len(A ), len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : Any = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (len(A ), len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : Any = F" {text}"
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
SCREAMING_SNAKE_CASE : str = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (1 + len(A ) + 1, 1 + len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : str = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (1 + len(A ), 1 + len(A ) + 1 + len(A )), )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
A, use_fast=A, add_prefix_space=A, trim_offsets=A )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r(A, return_offsets_mapping=A, add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1], (1 + len(A ), 1 + len(A ) + 1 + len(A )), )
| 28 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase__ ( lowerCAmelCase__ :list[list[float]] ) -> list[list[float]]:
'''simple docstring'''
lowercase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(lowerCAmelCase__ ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
lowercase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError("""This matrix has no inverse.""" )
# Creates a copy of the matrix with swapped positions of the elements
lowercase = [[0.0, 0.0], [0.0, 0.0]]
lowercase , lowercase = matrix[1][1], matrix[0][0]
lowercase , lowercase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(lowerCAmelCase__ ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(lowerCAmelCase__ ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
lowercase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError("""This matrix has no inverse.""" )
# Creating cofactor matrix
lowercase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
lowercase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
lowercase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
lowercase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
lowercase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
lowercase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
lowercase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
lowercase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
lowercase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
lowercase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
lowercase = array(lowerCAmelCase__ )
for i in range(3 ):
for j in range(3 ):
lowercase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
lowercase = array(lowerCAmelCase__ )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(lowerCAmelCase__ )
# Calculate the inverse of the matrix
return [[float(d(lowerCAmelCase__ ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""" )
| 197 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] , lowerCAmelCase__ :list[float] ) -> float:
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , lowercase = divmod(len(lowerCAmelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase : List[Any] =[float(x) for x in input("""Enter the elements of first array: """).split()]
__lowerCAmelCase : List[Any] =[float(x) for x in input("""Enter the elements of second array: """).split()]
print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 197 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
lowercase_ = NewType('''DataClass''', Any)
lowercase_ = NewType('''DataClassType''', Any)
def __lowerCAmelCase ( __lowerCamelCase : Union[str, Any] ) -> int:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def __lowerCAmelCase ( __lowerCamelCase : list ) -> Callable[[str], Any]:
__lowerCAmelCase ={str(__lowerCamelCase ): choice for choice in choices}
return lambda __lowerCamelCase : str_to_choice.get(__lowerCamelCase , __lowerCamelCase )
def __lowerCAmelCase ( *,
__lowerCamelCase : Union[str, List[str]] = None , __lowerCamelCase : str = None , __lowerCamelCase : Any = dataclasses.MISSING , __lowerCamelCase : Callable[[], Any] = dataclasses.MISSING , __lowerCamelCase : dict = None , **__lowerCamelCase : int , ) -> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__lowerCAmelCase ={}
if aliases is not None:
__lowerCAmelCase =aliases
if help is not None:
__lowerCAmelCase =help
return dataclasses.field(metadata=__lowerCamelCase , default=__lowerCamelCase , default_factory=__lowerCamelCase , **__lowerCamelCase )
class __a ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = 42
def __init__( self : List[Any] , snake_case_ : Union[DataClassType, Iterable[DataClassType]] , **snake_case_ : Dict)-> List[str]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__lowerCAmelCase =ArgumentDefaultsHelpFormatter
super().__init__(**snake_case_)
if dataclasses.is_dataclass(snake_case_):
__lowerCAmelCase =[dataclass_types]
__lowerCAmelCase =list(snake_case_)
for dtype in self.dataclass_types:
self._add_dataclass_arguments(snake_case_)
@staticmethod
def UpperCamelCase ( snake_case_ : ArgumentParser , snake_case_ : dataclasses.Field)-> str:
__lowerCAmelCase =F"""--{field.name}"""
__lowerCAmelCase =field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , snake_case_):
raise RuntimeError(
"""Unresolved type detected, which should have been done with the help of """
"""`typing.get_type_hints` method by default""")
__lowerCAmelCase =kwargs.pop("""aliases""" , [])
if isinstance(snake_case_ , snake_case_):
__lowerCAmelCase =[aliases]
__lowerCAmelCase =getattr(field.type , """__origin__""" , field.type)
if origin_type is Union or (hasattr(snake_case_ , """UnionType""") and isinstance(snake_case_ , types.UnionType)):
if str not in field.type.__args__ and (
len(field.type.__args__) != 2 or type(snake_case_) not in field.type.__args__
):
raise ValueError(
"""Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
""" the argument parser only supports one type per argument."""
F""" Problem encountered in field '{field.name}'.""")
if type(snake_case_) not in field.type.__args__:
# filter `str` in Union
__lowerCAmelCase =field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__lowerCAmelCase =getattr(field.type , """__origin__""" , field.type)
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__lowerCAmelCase =(
field.type.__args__[0] if isinstance(snake_case_ , field.type.__args__[1]) else field.type.__args__[1]
)
__lowerCAmelCase =getattr(field.type , """__origin__""" , field.type)
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__lowerCAmelCase ={}
if origin_type is Literal or (isinstance(field.type , snake_case_) and issubclass(field.type , snake_case_)):
if origin_type is Literal:
__lowerCAmelCase =field.type.__args__
else:
__lowerCAmelCase =[x.value for x in field.type]
__lowerCAmelCase =make_choice_type_function(kwargs["""choices"""])
if field.default is not dataclasses.MISSING:
__lowerCAmelCase =field.default
else:
__lowerCAmelCase =True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__lowerCAmelCase =copy(snake_case_)
# Hack because type=bool in argparse does not behave as we want.
__lowerCAmelCase =string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__lowerCAmelCase =False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__lowerCAmelCase =default
# This tells argparse we accept 0 or 1 value after --field_name
__lowerCAmelCase ="""?"""
# This is the value that will get picked if we do --field_name (without value)
__lowerCAmelCase =True
elif isclass(snake_case_) and issubclass(snake_case_ , snake_case_):
__lowerCAmelCase =field.type.__args__[0]
__lowerCAmelCase ="""+"""
if field.default_factory is not dataclasses.MISSING:
__lowerCAmelCase =field.default_factory()
elif field.default is dataclasses.MISSING:
__lowerCAmelCase =True
else:
__lowerCAmelCase =field.type
if field.default is not dataclasses.MISSING:
__lowerCAmelCase =field.default
elif field.default_factory is not dataclasses.MISSING:
__lowerCAmelCase =field.default_factory()
else:
__lowerCAmelCase =True
parser.add_argument(snake_case_ , *snake_case_ , **snake_case_)
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__lowerCAmelCase =False
parser.add_argument(F"""--no_{field.name}""" , action="""store_false""" , dest=field.name , **snake_case_)
def UpperCamelCase ( self : Optional[int] , snake_case_ : DataClassType)-> Dict:
if hasattr(snake_case_ , """_argument_group_name"""):
__lowerCAmelCase =self.add_argument_group(dtype._argument_group_name)
else:
__lowerCAmelCase =self
try:
__lowerCAmelCase =get_type_hints(snake_case_)
except NameError:
raise RuntimeError(
F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
"""removing line of `from __future__ import annotations` which opts in Postponed """
"""Evaluation of Annotations (PEP 563)""")
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(snake_case_):
__lowerCAmelCase =""".""".join(map(snake_case_ , sys.version_info[:3]))
raise RuntimeError(
F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
"""line of `from __future__ import annotations` which opts in union types as """
"""`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
"""support Python versions that lower than 3.10, you need to use """
"""`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
"""`X | None`.""") from ex
raise
for field in dataclasses.fields(snake_case_):
if not field.init:
continue
__lowerCAmelCase =type_hints[field.name]
self._parse_dataclass_field(snake_case_ , snake_case_)
def UpperCamelCase ( self : Optional[Any] , snake_case_ : Dict=None , snake_case_ : Optional[Any]=False , snake_case_ : Optional[int]=True , snake_case_ : Optional[Any]=None , snake_case_ : List[Any]=None , )-> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
__lowerCAmelCase =[]
if args_filename:
args_files.append(Path(snake_case_))
elif look_for_args_file and len(sys.argv):
args_files.append(Path(sys.argv[0]).with_suffix(""".args"""))
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__lowerCAmelCase =ArgumentParser()
args_file_parser.add_argument(snake_case_ , type=snake_case_ , action="""append""")
# Use only remaining args for further parsing (remove the args_file_flag)
__lowerCAmelCase , __lowerCAmelCase =args_file_parser.parse_known_args(args=snake_case_)
__lowerCAmelCase =vars(snake_case_).get(args_file_flag.lstrip("""-""") , snake_case_)
if cmd_args_file_paths:
args_files.extend([Path(snake_case_) for p in cmd_args_file_paths])
__lowerCAmelCase =[]
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__lowerCAmelCase =file_args + args if args is not None else file_args + sys.argv[1:]
__lowerCAmelCase , __lowerCAmelCase =self.parse_known_args(args=snake_case_)
__lowerCAmelCase =[]
for dtype in self.dataclass_types:
__lowerCAmelCase ={f.name for f in dataclasses.fields(snake_case_) if f.init}
__lowerCAmelCase ={k: v for k, v in vars(snake_case_).items() if k in keys}
for k in keys:
delattr(snake_case_ , snake_case_)
__lowerCAmelCase =dtype(**snake_case_)
outputs.append(snake_case_)
if len(namespace.__dict__) > 0:
# additional namespace.
outputs.append(snake_case_)
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""")
return (*outputs,)
def UpperCamelCase ( self : Union[str, Any] , snake_case_ : Dict[str, Any] , snake_case_ : bool = False)-> Tuple[DataClass, ...]:
__lowerCAmelCase =set(args.keys())
__lowerCAmelCase =[]
for dtype in self.dataclass_types:
__lowerCAmelCase ={f.name for f in dataclasses.fields(snake_case_) if f.init}
__lowerCAmelCase ={k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys())
__lowerCAmelCase =dtype(**snake_case_)
outputs.append(snake_case_)
if not allow_extra_keys and unused_keys:
raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(snake_case_)}""")
return tuple(snake_case_)
def UpperCamelCase ( self : Dict , snake_case_ : str , snake_case_ : bool = False)-> Tuple[DataClass, ...]:
with open(Path(snake_case_) , encoding="""utf-8""") as open_json_file:
__lowerCAmelCase =json.loads(open_json_file.read())
__lowerCAmelCase =self.parse_dict(snake_case_ , allow_extra_keys=snake_case_)
return tuple(snake_case_)
def UpperCamelCase ( self : Tuple , snake_case_ : str , snake_case_ : bool = False)-> Tuple[DataClass, ...]:
__lowerCAmelCase =self.parse_dict(yaml.safe_load(Path(snake_case_).read_text()) , allow_extra_keys=snake_case_)
return tuple(snake_case_)
| 354 |
from manim import *
class __a ( SCREAMING_SNAKE_CASE ):
def UpperCamelCase ( self : Tuple)-> Dict:
__lowerCAmelCase =Rectangle(height=0.5 , width=0.5)
__lowerCAmelCase =Rectangle(height=0.4_6 , width=0.4_6).set_stroke(width=0)
__lowerCAmelCase =[mem.copy() for i in range(6)]
__lowerCAmelCase =[mem.copy() for i in range(6)]
__lowerCAmelCase =VGroup(*snake_case_).arrange(snake_case_ , buff=0)
__lowerCAmelCase =VGroup(*snake_case_).arrange(snake_case_ , buff=0)
__lowerCAmelCase =VGroup(snake_case_ , snake_case_).arrange(snake_case_ , buff=0)
__lowerCAmelCase =Text("""CPU""" , font_size=24)
__lowerCAmelCase =Group(snake_case_ , snake_case_).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_)
cpu.move_to([-2.5, -0.5, 0])
self.add(snake_case_)
__lowerCAmelCase =[mem.copy() for i in range(1)]
__lowerCAmelCase =VGroup(*snake_case_).arrange(snake_case_ , buff=0)
__lowerCAmelCase =Text("""GPU""" , font_size=24)
__lowerCAmelCase =Group(snake_case_ , snake_case_).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_)
gpu.align_to(snake_case_ , snake_case_)
gpu.set_x(gpu.get_x() - 1)
self.add(snake_case_)
__lowerCAmelCase =[mem.copy() for i in range(6)]
__lowerCAmelCase =VGroup(*snake_case_).arrange(snake_case_ , buff=0)
__lowerCAmelCase =Text("""Model""" , font_size=24)
__lowerCAmelCase =Group(snake_case_ , snake_case_).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_)
model.move_to([3, -1.0, 0])
self.play(
Create(snake_case_ , run_time=1) , Create(snake_case_ , run_time=1) , Create(snake_case_ , run_time=1) , )
__lowerCAmelCase =MarkupText(
F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
__lowerCAmelCase =Square(side_length=2.2)
key.move_to([-5, 2, 0])
__lowerCAmelCase =MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0])
step_a.move_to([2, 2, 0])
self.play(Write(snake_case_ , run_time=2.5) , Write(snake_case_) , Write(snake_case_))
self.add(snake_case_)
__lowerCAmelCase =[]
__lowerCAmelCase =[]
__lowerCAmelCase =[]
for i, rect in enumerate(snake_case_):
__lowerCAmelCase =Rectangle(height=0.4_6 , width=0.4_6).set_stroke(width=0.0).set_fill(snake_case_ , opacity=0.7)
cpu_target.move_to(snake_case_)
cpu_target.generate_target()
__lowerCAmelCase =0.4_6 / 4
__lowerCAmelCase =0.4_6 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT) , buff=0.0_2 , direction=snake_case_)
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1)
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=snake_case_ , buff=0.0)
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=snake_case_ , buff=0.0)
cpu_targs.append(snake_case_)
first_animations.append(rect.animate(run_time=0.5).set_stroke(snake_case_))
second_animations.append(MoveToTarget(snake_case_ , run_time=1.5))
self.play(*snake_case_)
self.play(*snake_case_)
self.wait()
| 354 | 1 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowercase :
def __init__( self ) -> Any:
"""simple docstring"""
UpperCamelCase = ''
UpperCamelCase = ''
UpperCamelCase = []
UpperCamelCase = 0
UpperCamelCase = 256
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
def __UpperCamelCase ( self , A_ ) -> Dict:
"""simple docstring"""
UpperCamelCase = cva.imread(A_ , 0 )
UpperCamelCase = copy.deepcopy(self.img )
UpperCamelCase , UpperCamelCase , UpperCamelCase = plt.hist(self.img.ravel() , 256 , [0, 256] , label='x' )
UpperCamelCase = np.sum(A_ )
for i in range(len(A_ ) ):
UpperCamelCase = x[i] / self.k
self.sk += prk
UpperCamelCase = (self.L - 1) * self.sk
if self.rem != 0:
UpperCamelCase = int(last % last )
UpperCamelCase = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(A_ )
UpperCamelCase = int(np.ma.count(self.img ) / self.img[1].size )
UpperCamelCase = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
UpperCamelCase = self.img[j][i]
if num != self.last_list[num]:
UpperCamelCase = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(5_000 )
cva.destroyAllWindows()
if __name__ == "__main__":
_UpperCAmelCase : Any = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
_UpperCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 3 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase : str = "scheduler_config.json"
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Tuple = 1
__lowercase : int = 2
__lowercase : List[Any] = 3
__lowercase : str = 4
__lowercase : Optional[Any] = 5
@dataclass
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : jnp.ndarray
class lowercase :
__lowercase : Union[str, Any] = SCHEDULER_CONFIG_NAME
__lowercase : Dict = ["dtype"]
__lowercase : List[Any] = []
__lowercase : Dict = True
@classmethod
def __UpperCamelCase ( cls , A_ = None , A_ = None , A_=False , **A_ , ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = cls.load_config(
pretrained_model_name_or_path=A_ , subfolder=A_ , return_unused_kwargs=A_ , **A_ , )
UpperCamelCase , UpperCamelCase = cls.from_config(A_ , return_unused_kwargs=A_ , **A_ )
if hasattr(A_ , 'create_state' ) and getattr(A_ , 'has_state' , A_ ):
UpperCamelCase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def __UpperCamelCase ( self , A_ , A_ = False , **A_ ) -> str:
"""simple docstring"""
self.save_config(save_directory=A_ , push_to_hub=A_ , **A_ )
@property
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
return self._get_compatibles()
@classmethod
def __UpperCamelCase ( cls ) -> int:
"""simple docstring"""
UpperCamelCase = list(set([cls.__name__] + cls._compatibles ) )
UpperCamelCase = importlib.import_module(__name__.split('.' )[0] )
UpperCamelCase = [
getattr(A_ , A_ ) for c in compatible_classes_str if hasattr(A_ , A_ )
]
return compatible_classes
def A ( lowercase , lowercase ) -> jnp.ndarray:
'''simple docstring'''
assert len(lowercase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase ) - x.ndim) ) , lowercase )
def A ( lowercase , lowercase=0.9_9_9 , lowercase=jnp.floataa ) -> jnp.ndarray:
'''simple docstring'''
def alpha_bar(lowercase ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
UpperCamelCase = []
for i in range(lowercase ):
UpperCamelCase = i / num_diffusion_timesteps
UpperCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(lowercase ) / alpha_bar(lowercase ) , lowercase ) )
return jnp.array(lowercase , dtype=lowercase )
@flax.struct.dataclass
class lowercase :
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
@classmethod
def __UpperCamelCase ( cls , A_ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = scheduler.config
if config.trained_betas is not None:
UpperCamelCase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
UpperCamelCase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCamelCase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCamelCase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
UpperCamelCase = 1.0 - betas
UpperCamelCase = jnp.cumprod(A_ , axis=0 )
return cls(
alphas=A_ , betas=A_ , alphas_cumprod=A_ , )
def A ( lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
UpperCamelCase = state.alphas_cumprod
UpperCamelCase = alphas_cumprod[timesteps] ** 0.5
UpperCamelCase = sqrt_alpha_prod.flatten()
UpperCamelCase = broadcast_to_shape_from_left(lowercase , original_samples.shape )
UpperCamelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCamelCase = sqrt_one_minus_alpha_prod.flatten()
UpperCamelCase = broadcast_to_shape_from_left(lowercase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def A ( lowercase , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = get_sqrt_alpha_prod(lowercase , lowercase , lowercase , lowercase )
UpperCamelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def A ( lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = get_sqrt_alpha_prod(lowercase , lowercase , lowercase , lowercase )
UpperCamelCase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 3 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class snake_case_ ( lowercase__ ):
"""simple docstring"""
def __init__(self: Union[str, Any] , __UpperCAmelCase: str ) -> Dict:
'''simple docstring'''
super().__init__()
__a : Optional[Any] = nn.ModuleList(__UpperCAmelCase )
def UpperCAmelCase__ (self: Dict , __UpperCAmelCase: Optional[Any] , __UpperCAmelCase: List[Any] , __UpperCAmelCase: Optional[Any] , __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[Any] , __UpperCAmelCase: Dict = None , __UpperCAmelCase: str = None , __UpperCAmelCase: Dict = None , __UpperCAmelCase: List[Any] = None , __UpperCAmelCase: Optional[int] = False , __UpperCAmelCase: Any = True , ) -> Dict:
'''simple docstring'''
for i, (image, scale, controlnet) in enumerate(zip(__UpperCAmelCase , __UpperCAmelCase , self.nets ) ):
__a , __a : Tuple = controlnet(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
# merge samples
if i == 0:
__a , __a : int = down_samples, mid_sample
else:
__a : Union[str, Any] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__UpperCAmelCase , __UpperCAmelCase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def UpperCAmelCase__ (self: Optional[Any] , __UpperCAmelCase: Dict , __UpperCAmelCase: List[str] = True , __UpperCAmelCase: List[str] = None , __UpperCAmelCase: Any = False , __UpperCAmelCase: Optional[Any] = None , ) -> Dict:
'''simple docstring'''
__a : Optional[int] = 0
__a : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__UpperCAmelCase , is_main_process=__UpperCAmelCase , save_function=__UpperCAmelCase , safe_serialization=__UpperCAmelCase , variant=__UpperCAmelCase , )
idx += 1
__a : Dict = model_path_to_save + f'_{idx}'
@classmethod
def UpperCAmelCase__ (cls: int , __UpperCAmelCase: List[Any] , **__UpperCAmelCase: Optional[Any] ) -> Dict:
'''simple docstring'''
__a : Any = 0
__a : List[Any] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
__a : Tuple = pretrained_model_path
while os.path.isdir(__UpperCAmelCase ):
__a : Any = ControlNetModel.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
controlnets.append(__UpperCAmelCase )
idx += 1
__a : Optional[int] = pretrained_model_path + f'_{idx}'
logger.info(f'{len(__UpperCAmelCase )} controlnets loaded from {pretrained_model_path}.' )
if len(__UpperCAmelCase ) == 0:
raise ValueError(
f'No ControlNets found under {os.path.dirname(__UpperCAmelCase )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(__UpperCAmelCase )
| 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 579 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class A ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : torch.FloatTensor
class A ( lowerCamelCase_ , lowerCamelCase_ ):
@register_to_config
def __init__( self : Optional[Any] , __UpperCAmelCase : int = 16 , __UpperCAmelCase : int = 88 , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : int = 1 , __UpperCAmelCase : float = 0.0 , __UpperCAmelCase : int = 32 , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : bool = False , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : str = "geglu" , __UpperCAmelCase : bool = True , __UpperCAmelCase : bool = True , ) -> str:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = attention_head_dim
UpperCamelCase_ = num_attention_heads * attention_head_dim
UpperCamelCase_ = in_channels
UpperCamelCase_ = torch.nn.GroupNorm(num_groups=__UpperCAmelCase , num_channels=__UpperCAmelCase , eps=1E-6 , affine=__UpperCAmelCase )
UpperCamelCase_ = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
# 3. Define transformers blocks
UpperCamelCase_ = nn.ModuleList(
[
BasicTransformerBlock(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , dropout=__UpperCAmelCase , cross_attention_dim=__UpperCAmelCase , activation_fn=__UpperCAmelCase , attention_bias=__UpperCAmelCase , double_self_attention=__UpperCAmelCase , norm_elementwise_affine=__UpperCAmelCase , )
for d in range(__UpperCAmelCase )
] )
UpperCamelCase_ = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
def lowercase__ ( self : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict=None , __UpperCAmelCase : int=None , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : str=1 , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : bool = True , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = hidden_states.shape
UpperCamelCase_ = batch_frames // num_frames
UpperCamelCase_ = hidden_states
UpperCamelCase_ = hidden_states[None, :].reshape(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase_ = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
UpperCamelCase_ = self.norm(__UpperCAmelCase )
UpperCamelCase_ = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase_ = self.proj_in(__UpperCAmelCase )
# 2. Blocks
for block in self.transformer_blocks:
UpperCamelCase_ = block(
__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , timestep=__UpperCAmelCase , cross_attention_kwargs=__UpperCAmelCase , class_labels=__UpperCAmelCase , )
# 3. Output
UpperCamelCase_ = self.proj_out(__UpperCAmelCase )
UpperCamelCase_ = (
hidden_states[None, None, :]
.reshape(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
UpperCamelCase_ = hidden_states.reshape(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase_ = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
| 706 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
__a : Union[str, Any] = logging.get_logger(__name__)
# General docstring
__a : List[Any] = """MobileNetV1Config"""
# Base docstring
__a : List[Any] = """google/mobilenet_v1_1.0_224"""
__a : Optional[Any] = [1, 10_24, 7, 7]
# Image classification docstring
__a : Optional[int] = """google/mobilenet_v1_1.0_224"""
__a : str = """tabby, tabby cat"""
__a : List[str] = [
"""google/mobilenet_v1_1.0_224""",
"""google/mobilenet_v1_0.75_192""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def a_ ( __snake_case , __snake_case , __snake_case=None ) -> Any:
'''simple docstring'''
UpperCamelCase_ = {}
if isinstance(__snake_case , __snake_case ):
UpperCamelCase_ = model.mobilenet_va
else:
UpperCamelCase_ = model
UpperCamelCase_ = 'MobilenetV1/Conv2d_0/'
UpperCamelCase_ = backbone.conv_stem.convolution.weight
UpperCamelCase_ = backbone.conv_stem.normalization.bias
UpperCamelCase_ = backbone.conv_stem.normalization.weight
UpperCamelCase_ = backbone.conv_stem.normalization.running_mean
UpperCamelCase_ = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
UpperCamelCase_ = i + 1
UpperCamelCase_ = i * 2
UpperCamelCase_ = backbone.layer[pt_index]
UpperCamelCase_ = F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
UpperCamelCase_ = pointer.convolution.weight
UpperCamelCase_ = pointer.normalization.bias
UpperCamelCase_ = pointer.normalization.weight
UpperCamelCase_ = pointer.normalization.running_mean
UpperCamelCase_ = pointer.normalization.running_var
UpperCamelCase_ = backbone.layer[pt_index + 1]
UpperCamelCase_ = F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
UpperCamelCase_ = pointer.convolution.weight
UpperCamelCase_ = pointer.normalization.bias
UpperCamelCase_ = pointer.normalization.weight
UpperCamelCase_ = pointer.normalization.running_mean
UpperCamelCase_ = pointer.normalization.running_var
if isinstance(__snake_case , __snake_case ):
UpperCamelCase_ = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
UpperCamelCase_ = model.classifier.weight
UpperCamelCase_ = model.classifier.bias
return tf_to_pt_map
def a_ ( __snake_case , __snake_case , __snake_case ) -> Optional[int]:
'''simple docstring'''
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
UpperCamelCase_ = tf.train.list_variables(__snake_case )
UpperCamelCase_ = {}
for name, shape in init_vars:
logger.info(F'''Loading TF weight {name} with shape {shape}''' )
UpperCamelCase_ = tf.train.load_variable(__snake_case , __snake_case )
UpperCamelCase_ = array
# Build TF to PyTorch weights loading map
UpperCamelCase_ = _build_tf_to_pytorch_map(__snake_case , __snake_case , __snake_case )
for name, pointer in tf_to_pt_map.items():
logger.info(F'''Importing {name}''' )
if name not in tf_weights:
logger.info(F'''{name} not in tf pre-trained weights, skipping''' )
continue
UpperCamelCase_ = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
UpperCamelCase_ = np.transpose(__snake_case , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
UpperCamelCase_ = array.squeeze().transpose()
else:
UpperCamelCase_ = np.transpose(__snake_case , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(F'''Initialize PyTorch weight {name} {array.shape}''' )
UpperCamelCase_ = torch.from_numpy(__snake_case )
tf_weights.pop(__snake_case , __snake_case )
tf_weights.pop(name + '/RMSProp' , __snake_case )
tf_weights.pop(name + '/RMSProp_1' , __snake_case )
tf_weights.pop(name + '/ExponentialMovingAverage' , __snake_case )
logger.info(F'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
return model
def a_ ( __snake_case , __snake_case ) -> torch.Tensor:
'''simple docstring'''
UpperCamelCase_ , UpperCamelCase_ = features.shape[-2:]
UpperCamelCase_ , UpperCamelCase_ = conv_layer.stride
UpperCamelCase_ , UpperCamelCase_ = conv_layer.kernel_size
if in_height % stride_height == 0:
UpperCamelCase_ = max(kernel_height - stride_height , 0 )
else:
UpperCamelCase_ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
UpperCamelCase_ = max(kernel_width - stride_width , 0 )
else:
UpperCamelCase_ = max(kernel_width - (in_width % stride_width) , 0 )
UpperCamelCase_ = pad_along_width // 2
UpperCamelCase_ = pad_along_width - pad_left
UpperCamelCase_ = pad_along_height // 2
UpperCamelCase_ = pad_along_height - pad_top
UpperCamelCase_ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__snake_case , __snake_case , 'constant' , 0.0 )
class A ( nn.Module ):
def __init__( self : Any , __UpperCAmelCase : MobileNetVaConfig , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] = 1 , __UpperCAmelCase : Optional[int] = 1 , __UpperCAmelCase : bool = False , __UpperCAmelCase : Optional[bool] = True , __UpperCAmelCase : Optional[bool or str] = True , ) -> None:
"""simple docstring"""
super().__init__()
UpperCamelCase_ = config
if in_channels % groups != 0:
raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
UpperCamelCase_ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
UpperCamelCase_ = nn.Convad(
in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=__UpperCAmelCase , stride=__UpperCAmelCase , padding=__UpperCAmelCase , groups=__UpperCAmelCase , bias=__UpperCAmelCase , padding_mode='zeros' , )
if use_normalization:
UpperCamelCase_ = nn.BatchNormad(
num_features=__UpperCAmelCase , eps=config.layer_norm_eps , momentum=0.9_997 , affine=__UpperCAmelCase , track_running_stats=__UpperCAmelCase , )
else:
UpperCamelCase_ = None
if use_activation:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCamelCase_ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __UpperCAmelCase ):
UpperCamelCase_ = ACTaFN[config.hidden_act]
else:
UpperCamelCase_ = config.hidden_act
else:
UpperCamelCase_ = None
def lowercase__ ( self : Tuple , __UpperCAmelCase : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
if self.config.tf_padding:
UpperCamelCase_ = apply_tf_padding(__UpperCAmelCase , self.convolution )
UpperCamelCase_ = self.convolution(__UpperCAmelCase )
if self.normalization is not None:
UpperCamelCase_ = self.normalization(__UpperCAmelCase )
if self.activation is not None:
UpperCamelCase_ = self.activation(__UpperCAmelCase )
return features
class A ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Tuple = MobileNetVaConfig
_SCREAMING_SNAKE_CASE : List[Any] = load_tf_weights_in_mobilenet_va
_SCREAMING_SNAKE_CASE : Dict = '''mobilenet_v1'''
_SCREAMING_SNAKE_CASE : Tuple = '''pixel_values'''
_SCREAMING_SNAKE_CASE : Tuple = False
def lowercase__ ( self : List[str] , __UpperCAmelCase : Union[nn.Linear, nn.Convad] ) -> None:
"""simple docstring"""
if isinstance(__UpperCAmelCase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__UpperCAmelCase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
__a : Any = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__a : Optional[Any] = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.''' , lowerCamelCase_ , )
class A ( lowerCamelCase_ ):
def __init__( self : Dict , __UpperCAmelCase : MobileNetVaConfig , __UpperCAmelCase : bool = True ) -> Optional[int]:
"""simple docstring"""
super().__init__(__UpperCAmelCase )
UpperCamelCase_ = config
UpperCamelCase_ = 32
UpperCamelCase_ = max(int(depth * config.depth_multiplier ) , config.min_depth )
UpperCamelCase_ = MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=config.num_channels , out_channels=__UpperCAmelCase , kernel_size=3 , stride=2 , )
UpperCamelCase_ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
UpperCamelCase_ = nn.ModuleList()
for i in range(13 ):
UpperCamelCase_ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
UpperCamelCase_ = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=3 , stride=strides[i] , groups=__UpperCAmelCase , ) )
self.layer.append(
MobileNetVaConvLayer(
__UpperCAmelCase , in_channels=__UpperCAmelCase , out_channels=__UpperCAmelCase , kernel_size=1 , ) )
UpperCamelCase_ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def lowercase__ ( self : Dict , __UpperCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowercase__ ( self : List[str] , __UpperCAmelCase : Optional[torch.Tensor] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
"""simple docstring"""
UpperCamelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
UpperCamelCase_ = self.conv_stem(__UpperCAmelCase )
UpperCamelCase_ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
UpperCamelCase_ = layer_module(__UpperCAmelCase )
if output_hidden_states:
UpperCamelCase_ = all_hidden_states + (hidden_states,)
UpperCamelCase_ = hidden_states
if self.pooler is not None:
UpperCamelCase_ = torch.flatten(self.pooler(__UpperCAmelCase ) , start_dim=1 )
else:
UpperCamelCase_ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__UpperCAmelCase , pooler_output=__UpperCAmelCase , hidden_states=__UpperCAmelCase , )
@add_start_docstrings(
'''
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , lowerCamelCase_ , )
class A ( lowerCamelCase_ ):
def __init__( self : Union[str, Any] , __UpperCAmelCase : MobileNetVaConfig ) -> None:
"""simple docstring"""
super().__init__(__UpperCAmelCase )
UpperCamelCase_ = config.num_labels
UpperCamelCase_ = MobileNetVaModel(__UpperCAmelCase )
UpperCamelCase_ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
UpperCamelCase_ = nn.Dropout(config.classifier_dropout_prob , inplace=__UpperCAmelCase )
UpperCamelCase_ = nn.Linear(__UpperCAmelCase , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowercase__ ( self : Any , __UpperCAmelCase : Optional[torch.Tensor] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[torch.Tensor] = None , __UpperCAmelCase : Optional[bool] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
"""simple docstring"""
UpperCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCamelCase_ = self.mobilenet_va(__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
UpperCamelCase_ = outputs.pooler_output if return_dict else outputs[1]
UpperCamelCase_ = self.classifier(self.dropout(__UpperCAmelCase ) )
UpperCamelCase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCamelCase_ = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCamelCase_ = 'single_label_classification'
else:
UpperCamelCase_ = 'multi_label_classification'
if self.config.problem_type == "regression":
UpperCamelCase_ = MSELoss()
if self.num_labels == 1:
UpperCamelCase_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCamelCase_ = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
UpperCamelCase_ = CrossEntropyLoss()
UpperCamelCase_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCamelCase_ = BCEWithLogitsLoss()
UpperCamelCase_ = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
if not return_dict:
UpperCamelCase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__UpperCAmelCase , logits=__UpperCAmelCase , hidden_states=outputs.hidden_states , )
| 559 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
a_ : Optional[int] = "M-CLIP"
def __init__(self , UpperCAmelCase=1_0_2_4 , UpperCAmelCase=7_6_8 , **UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =transformerDimSize
__UpperCAmelCase =imageDimSize
super().__init__(**SCREAMING_SNAKE_CASE__)
class _SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
a_ : int = MCLIPConfig
def __init__(self , UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__)
__UpperCAmelCase =XLMRobertaModel(SCREAMING_SNAKE_CASE__)
__UpperCAmelCase =torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims)
def A__ (self , UpperCAmelCase , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =self.transformer(input_ids=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__)[0]
__UpperCAmelCase =(embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
return self.LinearTransformation(SCREAMING_SNAKE_CASE__), embs
| 132 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
_snake_case = {
'''bart''': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''bert''': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-base-cased-finetuned-mrpc''': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''dpr''': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''gpt2''': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlnet''': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm''': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm-roberta''': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''transfo-xl''': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''openai-gpt''': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''roberta''': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''layoutlm''': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''roberta-large-mnli''': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''camembert''': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''flaubert''': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert''': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert-base-distilled-squad''': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert''': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert-visual-feature-encoder''': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''ctrl''': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''albert''': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''t5''': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''electra''': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''wav2vec2''': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=False , _lowercase=True ) -> List[Any]:
if model_type not in MODEL_CLASSES:
raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.' )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
UpperCamelCase = cached_file(_lowercase , _lowercase , force_download=not use_cached_models )
UpperCamelCase = config_class.from_json_file(_lowercase )
UpperCamelCase = True
UpperCamelCase = True
print(F'Building TensorFlow model from configuration: {config}' )
UpperCamelCase = model_class(_lowercase )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
UpperCamelCase = cached_file(
_lowercase , _lowercase , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
UpperCamelCase = load_pytorch_checkpoint_in_tfa_model(_lowercase , _lowercase )
if compare_with_pt_model:
UpperCamelCase = tf_model(tf_model.dummy_inputs , training=_lowercase ) # build the network
UpperCamelCase = torch.load(_lowercase , map_location='cpu' )
UpperCamelCase = pt_model_class.from_pretrained(
pretrained_model_name_or_path=_lowercase , config=_lowercase , state_dict=_lowercase )
with torch.no_grad():
UpperCamelCase = pt_model(**pt_model.dummy_inputs )
UpperCamelCase = pto[0].numpy()
UpperCamelCase = tfo[0].numpy()
UpperCamelCase = np.amax(np.abs(np_pt - np_tf ) )
print(F'Max absolute difference between models outputs {diff}' )
assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'
# Save pytorch-model
print(F'Save TensorFlow model to {tf_dump_path}' )
tf_model.save_weights(_lowercase , save_format='h5' )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=False , _lowercase=False , _lowercase=False , ) -> int:
if args_model_type is None:
UpperCamelCase = list(MODEL_CLASSES.keys() )
else:
UpperCamelCase = [args_model_type]
for j, model_type in enumerate(_lowercase , start=1 ):
print('=' * 100 )
print(F' Converting model type {j}/{len(_lowercase )}: {model_type}' )
print('=' * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.' )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
UpperCamelCase = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
UpperCamelCase = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(_lowercase , _lowercase ) , start=1 ):
print('-' * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F' Skipping finetuned checkpoint {model_shortcut_name}' )
continue
UpperCamelCase = model_shortcut_name
elif only_convert_finetuned_models:
print(F' Skipping not finetuned checkpoint {model_shortcut_name}' )
continue
print(
F' Converting checkpoint {i}/{len(_lowercase )}: {model_shortcut_name} - model_type {model_type}' )
print('-' * 100 )
if config_shortcut_name in aws_config_map:
UpperCamelCase = cached_file(_lowercase , _lowercase , force_download=not use_cached_models )
else:
UpperCamelCase = config_shortcut_name
if model_shortcut_name in aws_model_maps:
UpperCamelCase = cached_file(_lowercase , _lowercase , force_download=not use_cached_models )
else:
UpperCamelCase = model_shortcut_name
if os.path.isfile(_lowercase ):
UpperCamelCase = 'converted_model'
convert_pt_checkpoint_to_tf(
model_type=_lowercase , pytorch_checkpoint_path=_lowercase , config_file=_lowercase , tf_dump_path=os.path.join(_lowercase , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=_lowercase , )
if remove_cached_files:
os.remove(_lowercase )
os.remove(_lowercase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
)
parser.add_argument(
'''--model_type''',
default=None,
type=str,
help=(
F"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
'''convert all the models from AWS.'''
),
)
parser.add_argument(
'''--pytorch_checkpoint_path''',
default=None,
type=str,
help=(
'''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
'''If not given, will download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
help=(
'''The config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture. If not given and '''
'''--pytorch_checkpoint_path is not given or is a shortcut name '''
'''use the configuration associated to the shortcut name on the AWS'''
),
)
parser.add_argument(
'''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
)
parser.add_argument(
'''--use_cached_models''',
action='''store_true''',
help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
)
parser.add_argument(
'''--remove_cached_files''',
action='''store_true''',
help='''Remove pytorch models after conversion (save memory when converting in batches).''',
)
parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
_snake_case = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : Optional[Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
A : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 356 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A : Tuple = r"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''rag'''
lowerCamelCase__ = True
def __init__( self : Any , __magic_name__ : Optional[int]=None , __magic_name__ : Dict=True , __magic_name__ : List[Any]=None , __magic_name__ : Optional[Any]=None , __magic_name__ : List[Any]=None , __magic_name__ : Any=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Any=" / " , __magic_name__ : int=" // " , __magic_name__ : Any=5 , __magic_name__ : Dict=300 , __magic_name__ : Optional[Any]=768 , __magic_name__ : str=8 , __magic_name__ : List[Any]="wiki_dpr" , __magic_name__ : Any="train" , __magic_name__ : Any="compressed" , __magic_name__ : List[Any]=None , __magic_name__ : Optional[int]=None , __magic_name__ : Optional[int]=False , __magic_name__ : Union[str, Any]=False , __magic_name__ : List[str]=0.0 , __magic_name__ : Dict=True , __magic_name__ : str=False , __magic_name__ : int=False , __magic_name__ : Tuple=False , __magic_name__ : Tuple=True , __magic_name__ : Dict=None , **__magic_name__ : int , ) -> List[str]:
super().__init__(
bos_token_id=__magic_name__ , pad_token_id=__magic_name__ , eos_token_id=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , prefix=__magic_name__ , vocab_size=__magic_name__ , **__magic_name__ , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
SCREAMING_SNAKE_CASE_ = kwargs.pop("question_encoder" )
SCREAMING_SNAKE_CASE_ = question_encoder_config.pop("model_type" )
SCREAMING_SNAKE_CASE_ = kwargs.pop("generator" )
SCREAMING_SNAKE_CASE_ = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = reduce_loss
SCREAMING_SNAKE_CASE_ = label_smoothing
SCREAMING_SNAKE_CASE_ = exclude_bos_score
SCREAMING_SNAKE_CASE_ = do_marginalize
SCREAMING_SNAKE_CASE_ = title_sep
SCREAMING_SNAKE_CASE_ = doc_sep
SCREAMING_SNAKE_CASE_ = n_docs
SCREAMING_SNAKE_CASE_ = max_combined_length
SCREAMING_SNAKE_CASE_ = dataset
SCREAMING_SNAKE_CASE_ = dataset_split
SCREAMING_SNAKE_CASE_ = index_name
SCREAMING_SNAKE_CASE_ = retrieval_vector_size
SCREAMING_SNAKE_CASE_ = retrieval_batch_size
SCREAMING_SNAKE_CASE_ = passages_path
SCREAMING_SNAKE_CASE_ = index_path
SCREAMING_SNAKE_CASE_ = use_dummy_dataset
SCREAMING_SNAKE_CASE_ = output_retrieved
SCREAMING_SNAKE_CASE_ = do_deduplication
SCREAMING_SNAKE_CASE_ = use_cache
if self.forced_eos_token_id is None:
SCREAMING_SNAKE_CASE_ = getattr(self.generator , "forced_eos_token_id" , __magic_name__ )
@classmethod
def __A ( cls : Dict , __magic_name__ : PretrainedConfig , __magic_name__ : PretrainedConfig , **__magic_name__ : List[str] ) -> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **__magic_name__ )
def __A ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.question_encoder.to_dict()
SCREAMING_SNAKE_CASE_ = self.generator.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 356 | 1 |
import warnings
from .generation import TFGenerationMixin
class lowerCAmelCase_ ( a__ ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead." , a__ , )
| 40 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _lowerCAmelCase ( lowercase : str , lowercase : str , **lowercase : Tuple ) ->Tuple:
"""simple docstring"""
lowercase__ = AutoConfig.from_pretrained(lowercase , **lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_config(lowercase )
model.save_pretrained(lowercase )
AutoTokenizer.from_pretrained(lowercase ).save_pretrained(lowercase )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 161 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
__lowerCAmelCase : List[Any] = "hf-internal-testing/tiny-random-bert"
__lowerCAmelCase : int = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
__lowerCAmelCase : List[str] = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class A ( unittest.TestCase ):
def snake_case__ ( self : List[str] ) -> Union[str, Any]:
__UpperCAmelCase = cached_file(__a , __a )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(__a ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(__a , __a ) ) )
with open(os.path.join(__a , '''refs''' , '''main''' ) ) as f:
__UpperCAmelCase = f.read()
self.assertEqual(__a , os.path.join(__a , '''snapshots''' , __a , __a ) )
self.assertTrue(os.path.isfile(__a ) )
# File is cached at the same place the second time.
__UpperCAmelCase = cached_file(__a , __a )
self.assertEqual(__a , __a )
# Using a specific revision to test the full commit hash.
__UpperCAmelCase = cached_file(__a , __a , revision='''9b8c223''' )
self.assertEqual(__a , os.path.join(__a , '''snapshots''' , __a , __a ) )
def snake_case__ ( self : Tuple ) -> List[Any]:
with self.assertRaisesRegex(__a , '''is not a valid model identifier''' ):
__UpperCAmelCase = cached_file('''tiny-random-bert''' , __a )
with self.assertRaisesRegex(__a , '''is not a valid git identifier''' ):
__UpperCAmelCase = cached_file(__a , __a , revision='''aaaa''' )
with self.assertRaisesRegex(__a , '''does not appear to have a file named''' ):
__UpperCAmelCase = cached_file(__a , '''conf''' )
def snake_case__ ( self : List[Any] ) -> Optional[int]:
with self.assertRaisesRegex(__a , '''does not appear to have a file named''' ):
__UpperCAmelCase = cached_file(__a , '''conf''' )
with open(os.path.join(__a , '''refs''' , '''main''' ) ) as f:
__UpperCAmelCase = f.read()
self.assertTrue(os.path.isfile(os.path.join(__a , '''.no_exist''' , __a , '''conf''' ) ) )
__UpperCAmelCase = cached_file(__a , '''conf''' , _raise_exceptions_for_missing_entries=__a )
self.assertIsNone(__a )
__UpperCAmelCase = cached_file(__a , '''conf''' , local_files_only=__a , _raise_exceptions_for_missing_entries=__a )
self.assertIsNone(__a )
__UpperCAmelCase = mock.Mock()
__UpperCAmelCase = 5_0_0
__UpperCAmelCase = {}
__UpperCAmelCase = HTTPError
__UpperCAmelCase = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=__a ) as mock_head:
__UpperCAmelCase = cached_file(__a , '''conf''' , _raise_exceptions_for_connection_errors=__a )
self.assertIsNone(__a )
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self : Optional[Any] ) -> Optional[Any]:
self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __a ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __a ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __a ) )
def snake_case__ ( self : Optional[int] ) -> int:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('''bert-base-cased''' , '''ahah.txt''' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(__a , '''is not a valid model identifier''' ):
get_file_from_repo('''bert-base-case''' , __a )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(__a , '''is not a valid git identifier''' ):
get_file_from_repo('''bert-base-cased''' , __a , revision='''ahaha''' )
__UpperCAmelCase = get_file_from_repo('''bert-base-cased''' , __a )
# The name is the cached name which is not very easy to test, so instead we load the content.
__UpperCAmelCase = json.loads(open(__a , '''r''' ).read() )
self.assertEqual(config['''hidden_size'''] , 7_6_8 )
def snake_case__ ( self : Optional[int] ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
__UpperCAmelCase = Path(__a ) / '''a.txt'''
filename.touch()
self.assertEqual(get_file_from_repo(__a , '''a.txt''' ) , str(__a ) )
self.assertIsNone(get_file_from_repo(__a , '''b.txt''' ) )
| 710 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase ( UpperCamelCase__ : str = "AAPL" ):
"""simple docstring"""
__UpperCAmelCase = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__UpperCAmelCase = BeautifulSoup(requests.get(UpperCamelCase__ ).text , '''html.parser''' )
__UpperCAmelCase = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 654 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__SCREAMING_SNAKE_CASE =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__SCREAMING_SNAKE_CASE =typing.Union[np.floataa, int, float] # noqa: UP007
def a (_lowerCAmelCase , _lowerCAmelCase ):
return np.sqrt(np.sum((np.asarray(_lowercase ) - np.asarray(_lowercase )) ** 2 ) )
def a (_lowerCAmelCase , _lowerCAmelCase ):
return sum((va - va) ** 2 for va, va in zip(_lowercase , _lowercase ) ) ** (1 / 2)
if __name__ == "__main__":
def a ():
from timeit import timeit
print('''Without Numpy''' )
print(
timeit(
'''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' , number=1_0_0_0_0 , globals=globals() , ) )
print('''With Numpy''' )
print(
timeit(
'''euclidean_distance([1, 2, 3], [4, 5, 6])''' , number=1_0_0_0_0 , globals=globals() , ) )
benchmark()
| 234 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
__A = logging.get_logger(__name__)
@add_end_docstrings(snake_case )
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: int , *__A: Tuple , **__A: Optional[Any] ) -> Tuple:
super().__init__(*__A , **__A )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def __A ( self: Tuple , __A: Union[str, Any]=None , __A: int=None , __A: Optional[Any]=None ) -> Optional[Any]:
_A = {}
_A = {}
if prompt is not None:
_A = prompt
if generate_kwargs is not None:
_A = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
_A = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self: Any , __A: Union[str, List[str], "Image.Image", List["Image.Image"]] , **__A: str ) -> Dict:
return super().__call__(__A , **__A )
def __A ( self: Any , __A: Dict , __A: int=None ) -> int:
_A = load_image(__A )
if prompt is not None:
if not isinstance(__A , __A ):
raise ValueError(
f"""Received an invalid text input, got - {type(__A )} - but expected a single string. """
'''Note also that one single text can be provided for conditional image to text generation.''' )
_A = self.model.config.model_type
if model_type == "git":
_A = self.image_processor(images=__A , return_tensors=self.framework )
_A = self.tokenizer(text=__A , add_special_tokens=__A ).input_ids
_A = [self.tokenizer.cls_token_id] + input_ids
_A = torch.tensor(__A ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
_A = self.image_processor(images=__A , header_text=__A , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A = self.image_processor(images=__A , return_tensors=self.framework )
_A = self.tokenizer(__A , return_tensors=self.framework )
model_inputs.update(__A )
else:
raise ValueError(f"""Model type {model_type} does not support conditional text generation""" )
else:
_A = self.image_processor(images=__A , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A = None
return model_inputs
def __A ( self: Optional[int] , __A: Optional[Any] , __A: Optional[int]=None ) -> Any:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __A )
and all(x is None for x in model_inputs['''input_ids'''] )
):
_A = None
if generate_kwargs is None:
_A = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A = model_inputs.pop(self.model.main_input_name )
_A = self.model.generate(__A , **__A , **__A )
return model_outputs
def __A ( self: str , __A: Optional[int] ) -> Optional[int]:
_A = []
for output_ids in model_outputs:
_A = {
'''generated_text''': self.tokenizer.decode(
__A , skip_special_tokens=__A , )
}
records.append(__A )
return records
| 484 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
_snake_case = sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo" ):
'''simple docstring'''
with open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="dataset" ) , "r" ) as f:
_snake_case = json.load(SCREAMING_SNAKE_CASE__ )
_snake_case = {}
_snake_case = []
_snake_case = []
for key, info in class_info.items():
_snake_case = info["name"]
class_names.append(info["name"] )
if info["isthing"]:
thing_ids.append(int(SCREAMING_SNAKE_CASE__ ) )
_snake_case = thing_ids
_snake_case = class_names
return metadata
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=[0.5, 0.5, 0.5] , lowerCamelCase=[0.5, 0.5, 0.5] , lowerCamelCase=10 , lowerCamelCase=False , lowerCamelCase=255 , lowerCamelCase="shi-labs/oneformer_demo" , lowerCamelCase="ade20k_panoptic.json" , lowerCamelCase=10 , ):
_snake_case = parent
_snake_case = batch_size
_snake_case = num_channels
_snake_case = min_resolution
_snake_case = max_resolution
_snake_case = do_resize
_snake_case = {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
_snake_case = do_normalize
_snake_case = image_mean
_snake_case = image_std
_snake_case = class_info_file
_snake_case = prepare_metadata(lowerCamelCase , lowerCamelCase )
_snake_case = num_text
_snake_case = repo_path
# for the post_process_functions
_snake_case = 2
_snake_case = 10
_snake_case = 10
_snake_case = 3
_snake_case = 4
_snake_case = num_labels
_snake_case = do_reduce_labels
_snake_case = ignore_index
def UpperCamelCase( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCamelCase( self , lowerCamelCase , lowerCamelCase=False ):
if not batched:
_snake_case = image_inputs[0]
if isinstance(lowerCamelCase , Image.Image ):
_snake_case , _snake_case = image.size
else:
_snake_case , _snake_case = image.shape[1], image.shape[2]
if w < h:
_snake_case = int(self.size["shortest_edge"] * h / w )
_snake_case = self.size["shortest_edge"]
elif w > h:
_snake_case = self.size["shortest_edge"]
_snake_case = int(self.size["shortest_edge"] * w / h )
else:
_snake_case = self.size["shortest_edge"]
_snake_case = self.size["shortest_edge"]
else:
_snake_case = []
for image in image_inputs:
_snake_case , _snake_case = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_snake_case = max(lowerCamelCase , key=lambda lowerCamelCase : item[0] )[0]
_snake_case = max(lowerCamelCase , key=lambda lowerCamelCase : item[1] )[1]
return expected_height, expected_width
def UpperCamelCase( self ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( __UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
UpperCAmelCase__ : Tuple = image_processing_class
def UpperCamelCase( self ):
_snake_case = OneFormerImageProcessorTester(self )
@property
def UpperCamelCase( self ):
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCamelCase( self ):
_snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , "image_mean" ) )
self.assertTrue(hasattr(lowerCamelCase , "image_std" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_normalize" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_resize" ) )
self.assertTrue(hasattr(lowerCamelCase , "size" ) )
self.assertTrue(hasattr(lowerCamelCase , "ignore_index" ) )
self.assertTrue(hasattr(lowerCamelCase , "class_info_file" ) )
self.assertTrue(hasattr(lowerCamelCase , "num_text" ) )
self.assertTrue(hasattr(lowerCamelCase , "repo_path" ) )
self.assertTrue(hasattr(lowerCamelCase , "metadata" ) )
self.assertTrue(hasattr(lowerCamelCase , "do_reduce_labels" ) )
def UpperCamelCase( self ):
pass
def UpperCamelCase( self ):
# Initialize image_processor
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_snake_case = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase )
_snake_case = image_processor(
lowerCamelCase , ["semantic"] * len(lowerCamelCase ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase( self ):
# Initialize image_processor
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_snake_case = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_snake_case = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase )
_snake_case = image_processor(
lowerCamelCase , ["semantic"] * len(lowerCamelCase ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase( self ):
# Initialize image_processor
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_snake_case = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_snake_case = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case , _snake_case = self.image_processing_tester.get_expected_values(lowerCamelCase , batched=lowerCamelCase )
_snake_case = image_processor(
lowerCamelCase , ["semantic"] * len(lowerCamelCase ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase( self , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase="np" ):
_snake_case = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
_snake_case = self.image_processing_tester.num_labels
_snake_case = None
_snake_case = None
_snake_case = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCamelCase )
if with_segmentation_maps:
_snake_case = num_labels
if is_instance_map:
_snake_case = list(range(lowerCamelCase ) ) * 2
_snake_case = dict(enumerate(lowerCamelCase ) )
_snake_case = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
_snake_case = [Image.fromarray(lowerCamelCase ) for annotation in annotations]
_snake_case = image_processor(
lowerCamelCase , ["semantic"] * len(lowerCamelCase ) , lowerCamelCase , return_tensors="pt" , instance_id_to_semantic_id=lowerCamelCase , pad_and_return_pixel_mask=lowerCamelCase , )
return inputs
def UpperCamelCase( self ):
pass
def UpperCamelCase( self ):
def common(lowerCamelCase=False , lowerCamelCase=None ):
_snake_case = self.comm_get_image_processor_inputs(
with_segmentation_maps=lowerCamelCase , is_instance_map=lowerCamelCase , segmentation_type=lowerCamelCase )
_snake_case = inputs["mask_labels"]
_snake_case = inputs["class_labels"]
_snake_case = inputs["pixel_values"]
_snake_case = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(lowerCamelCase ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=lowerCamelCase )
common(is_instance_map=lowerCamelCase , segmentation_type="pil" )
common(is_instance_map=lowerCamelCase , segmentation_type="pil" )
def UpperCamelCase( self ):
_snake_case = np.zeros((20, 50) )
_snake_case = 1
_snake_case = 1
_snake_case = 1
_snake_case = binary_mask_to_rle(lowerCamelCase )
self.assertEqual(len(lowerCamelCase ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def UpperCamelCase( self ):
_snake_case = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_snake_case = self.image_processing_tester.get_fake_oneformer_outputs()
_snake_case = fature_extractor.post_process_semantic_segmentation(lowerCamelCase )
self.assertEqual(len(lowerCamelCase ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
_snake_case = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
_snake_case = fature_extractor.post_process_semantic_segmentation(lowerCamelCase , target_sizes=lowerCamelCase )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def UpperCamelCase( self ):
_snake_case = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_snake_case = self.image_processing_tester.get_fake_oneformer_outputs()
_snake_case = image_processor.post_process_instance_segmentation(lowerCamelCase , threshold=0 )
self.assertTrue(len(lowerCamelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , lowerCamelCase )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCamelCase( self ):
_snake_case = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_snake_case = self.image_processing_tester.get_fake_oneformer_outputs()
_snake_case = image_processor.post_process_panoptic_segmentation(lowerCamelCase , threshold=0 )
self.assertTrue(len(lowerCamelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , lowerCamelCase )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 368 | 1 |
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