


'''simple docstring'''



import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
						from comet_ml import OfflineExperiment

if is_tensorboard_available():
						import struct

						import tensorboard.compat.proto.event_pba as event_pba

A__	:      Any				=						logging.getLogger(__name__)





@require_tensorboard
class      UpperCAmelCase_						(unittest.TestCase						):





				"""simple docstring"""






				def    lowercase_       (							self			)       ->  Union[str, Any]:
									__lowerCamelCase     :			str              =	"test_project_with_config"
									with tempfile.TemporaryDirectory() as dirpath:
														__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
														__lowerCamelCase     :			Optional[Any]              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers(lowerCamelCase__    ,  lowerCamelCase__			)
														accelerator.end_training()
														for child in Path(f'{dirpath}/{project_name}'			).glob('*/**'			):
																			__lowerCamelCase     :			List[Any]              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: x.is_file()    ,  child.iterdir()			)			)[0]
														self.assertNotEqual(str(lowerCamelCase__			)    ,  ''			)







				def    lowercase_       (							self			)       ->  Tuple:
									__lowerCamelCase     :			Union[str, Any]              =	"test_project_with_log"
									with tempfile.TemporaryDirectory() as dirpath:
														__lowerCamelCase     :			Any              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
														accelerator.init_trackers(lowerCamelCase__			)
														__lowerCamelCase     :			Tuple              =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(lowerCamelCase__    ,  step=0			)
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														__lowerCamelCase     :			int              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: x.is_file()    ,  Path(f'{dirpath}/{project_name}'			).iterdir()			)			)[0]
														self.assertNotEqual(str(lowerCamelCase__			)    ,  ''			)







				def    lowercase_       (							self			)       ->  Union[str, Any]:
									__lowerCamelCase     :			Optional[Any]              =	"test_project_with_log"
									with tempfile.TemporaryDirectory() as dirpath:
														__lowerCamelCase     :			List[str]              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)
														accelerator.init_trackers(lowerCamelCase__			)
														__lowerCamelCase     :			Dict              =	{"tensor": torch.tensor(1			)}
														accelerator.log(lowerCamelCase__    ,  step=0			)
														accelerator.end_training()
														# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
														# Names are randomly generated each time
														__lowerCamelCase     :			Tuple              =	list(filter(lambda SCREAMING_SNAKE_CASE_			: x.is_file()    ,  Path(f'{dirpath}/{project_name}'			).iterdir()			)			)[0]
														# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
														with open(lowerCamelCase__    ,  'rb'			) as f:
																			__lowerCamelCase     :			Optional[Any]              =	f.read()
														__lowerCamelCase     :			List[str]              =	False
														while data:
																			__lowerCamelCase     :			List[Any]              =	struct.unpack('Q'    ,  data[:8]			)

																			__lowerCamelCase     :			Any              =	data[12 : 12 + int(header[0]			)]  # 8+4
																			__lowerCamelCase     :			Optional[Any]              =	data[12 + int(header[0]			) + 4 :]
																			__lowerCamelCase     :			Dict              =	event_pba.Event()

																			event.ParseFromString(lowerCamelCase__			)
																			if event.HasField('summary'			):
																								for value in event.summary.value:
																													if value.simple_value == 1.0 and value.tag == "tensor":
																																		__lowerCamelCase     :			List[Any]              =	True
														self.assertTrue(lowerCamelCase__    ,  'Converted tensor was not found in the log file!'			)







				def    lowercase_       (							self			)       ->  Any:
									with self.assertRaisesRegex(lowerCamelCase__    ,  'Logging with `tensorboard` requires a `logging_dir`'			):
														__lowerCamelCase     :			str              =	Accelerator(log_with='tensorboard'			)
									with tempfile.TemporaryDirectory() as dirpath:
														__lowerCamelCase     :			int              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__			)







				def    lowercase_       (							self			)       ->  Tuple:
									__lowerCamelCase     :			str              =	ProjectConfiguration(total_limit=30			)
									with tempfile.TemporaryDirectory() as dirpath:
														__lowerCamelCase     :			Dict              =	Accelerator(log_with='tensorboard'    ,  project_dir=lowerCamelCase__    ,  project_config=lowerCamelCase__			)







@require_wandb
@mock.patch.dict(os.environ      ,	{'WANDB_MODE': 'offline'}						)
class      UpperCAmelCase_						(_a      ,	_a						):





				"""simple docstring"""






				def    lowercase_       (							self			)       ->  Optional[Any]:
									super().setUp()
									# wandb let's us override where logs are stored to via the WANDB_DIR env var
									self.add_mocks(mock.patch.dict(os.environ    ,  {'WANDB_DIR': self.tmpdir}			)			)







				@staticmethod
				def    lowercase_       (							SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = True			)       ->  int:
									__lowerCamelCase     :			Any              =	rf'{section} ([\S\s]*?)\n\n'
									if record:
														__lowerCamelCase     :			Any              =	rf'Record: {pattern}'
									__lowerCamelCase     :			Dict              =	re.findall(lowerCamelCase__    ,  lowerCamelCase__			)[0]
									# A config
									if section == "config" or section == "history":
														__lowerCamelCase     :			Optional[Any]              =	re.findall(r'\"([a-zA-Z0-9_.,]+)'    ,  lowerCamelCase__			)
														return {key: val for key, val in zip(cleaned_record[0::2]    ,  cleaned_record[1::2]			)}
									# Everything else
									else:
														return dict(re.findall(r'(\w+): \"([^\s]+)\"'    ,  lowerCamelCase__			)			)







				@skip
				def    lowercase_       (							self			)       ->  Any:
									__lowerCamelCase     :			Any              =	"test_project_with_config"
									__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with='wandb'			)
									__lowerCamelCase     :			int              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
									__lowerCamelCase     :			Optional[int]              =	{"wandb": {"tags": ["my_tag"]}}
									accelerator.init_trackers(lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__			)
									__lowerCamelCase     :			Dict              =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(lowerCamelCase__    ,  step=0			)
									accelerator.end_training()
									# The latest offline log is stored at wandb/latest-run/*.wandb
									for child in Path(f'{self.tmpdir}/wandb/latest-run'			).glob('*'			):
														if child.is_file() and child.suffix == ".wandb":
																			__lowerCamelCase     :			Any              =	subprocess.check_output(
																			    ['wandb', 'sync', '--view', '--verbose', str(lowerCamelCase__			)]    ,  env=os.environ.copy()			).decode('utf8'    ,  'ignore'			)
																			break

        # Check HPS through careful parsing and cleaning
									__lowerCamelCase     :			int              =	self.parse_log(lowerCamelCase__    ,  'config'			)
									self.assertEqual(logged_items['num_iterations']    ,  '12'			)
									self.assertEqual(logged_items['learning_rate']    ,  '0.01'			)
									self.assertEqual(logged_items['some_boolean']    ,  'false'			)
									self.assertEqual(logged_items['some_string']    ,  'some_value'			)
									self.assertEqual(logged_items['some_string']    ,  'some_value'			)

									# Run tags
									__lowerCamelCase     :			Optional[Any]              =	self.parse_log(lowerCamelCase__    ,  'run'    ,  lowerCamelCase__			)
									self.assertEqual(logged_items['tags']    ,  'my_tag'			)

									# Actual logging
									__lowerCamelCase     :			Union[str, Any]              =	self.parse_log(lowerCamelCase__    ,  'history'			)
									self.assertEqual(logged_items['total_loss']    ,  '0.1'			)
									self.assertEqual(logged_items['iteration']    ,  '1'			)
									self.assertEqual(logged_items['my_text']    ,  'some_value'			)
									self.assertEqual(logged_items['_step']    ,  '0'			)







def     UpperCAmelCase__      (			self					:       str   ,					UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       int							)  ->	Optional[int]:
					__lowerCamelCase     :			Dict              =	run_name
					__lowerCamelCase     :			Union[str, Any]              =	OfflineExperiment(project_name=UpperCAmelCase_   ,					offline_directory=UpperCAmelCase_							)
					logger.info(F'Initialized offline CometML project {self.run_name}'							)
					logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'							)





@require_comet_ml
@mock.patch.object(_a      ,	'__init__'      ,	_a						)
class      UpperCAmelCase_						(unittest.TestCase						):





				"""simple docstring"""






				@staticmethod
				def    lowercase_       (							SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = False			)       ->  int:
									for log in log_list:
														__lowerCamelCase     :			Optional[int]              =	json.loads(lowerCamelCase__			)["payload"]
														if is_param and "param" in j.keys():
																			if j["param"]["paramName"] == key:
																								return j["param"]["paramValue"]
														if "log_other" in j.keys():
																			if j["log_other"]["key"] == key:
																								return j["log_other"]["val"]
														if "metric" in j.keys():
																			if j["metric"]["metricName"] == key:
																								return j["metric"]["metricValue"]







				def    lowercase_       (							self			)       ->  str:
									with tempfile.TemporaryDirectory() as d:
														__lowerCamelCase     :			Optional[Any]              =	CometMLTracker('test_project_with_config'    ,  lowerCamelCase__			)
														__lowerCamelCase     :			List[str]              =	Accelerator(log_with=lowerCamelCase__			)
														__lowerCamelCase     :			List[Any]              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers(lowerCamelCase__    ,  lowerCamelCase__			)
														accelerator.end_training()
														__lowerCamelCase     :			Optional[Any]              =	os.listdir(lowerCamelCase__			)[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														__lowerCamelCase     :			List[Any]              =	os.path.join(lowerCamelCase__    ,  lowerCamelCase__			)
														__lowerCamelCase     :			Tuple              =	zipfile.ZipFile(lowerCamelCase__    ,  'r'			)
														__lowerCamelCase     :			Tuple              =	archive.open('messages.json'			).read().decode('utf-8'			)
									__lowerCamelCase     :			List[Any]              =	log.split('\n'			)[:-1]
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'num_iterations'    ,  lowerCamelCase__			)    ,  12			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'learning_rate'    ,  lowerCamelCase__			)    ,  0.0_1			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'some_boolean'    ,  lowerCamelCase__			)    ,  lowerCamelCase__			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'some_string'    ,  lowerCamelCase__			)    ,  'some_value'			)







				def    lowercase_       (							self			)       ->  str:
									with tempfile.TemporaryDirectory() as d:
														__lowerCamelCase     :			int              =	CometMLTracker('test_project_with_config'    ,  lowerCamelCase__			)
														__lowerCamelCase     :			Union[str, Any]              =	Accelerator(log_with=lowerCamelCase__			)
														accelerator.init_trackers(lowerCamelCase__			)
														__lowerCamelCase     :			List[str]              =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(lowerCamelCase__    ,  step=0			)
														accelerator.end_training()
														__lowerCamelCase     :			Optional[Any]              =	os.listdir(lowerCamelCase__			)[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														__lowerCamelCase     :			Dict              =	os.path.join(lowerCamelCase__    ,  lowerCamelCase__			)
														__lowerCamelCase     :			List[Any]              =	zipfile.ZipFile(lowerCamelCase__    ,  'r'			)
														__lowerCamelCase     :			List[str]              =	archive.open('messages.json'			).read().decode('utf-8'			)
									__lowerCamelCase     :			Dict              =	log.split('\n'			)[:-1]
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'curr_step'    ,  lowerCamelCase__			)    ,  0			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'total_loss'			)    ,  0.1			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'iteration'			)    ,  1			)
									self.assertEqual(self.get_value_from_key(lowerCamelCase__    ,  'my_text'			)    ,  'some_value'			)







class      UpperCAmelCase_						(_a						):





				"""simple docstring"""






				lowerCamelCase						:      str							  =    [
				    'total_loss',
				    'iteration',
				    'my_text',
				    'learning_rate',
				    'num_iterations',
				    'some_boolean',
				    'some_string',
				]

				lowerCamelCase						:      Optional[Any]							  =    'my_custom_tracker'
				lowerCamelCase						:      Union[str, Any]							  =    False







				def __init__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Dict:
									__lowerCamelCase     :			Tuple              =	open(f'{dir}/log.csv'    ,  'w+'			)
									__lowerCamelCase     :			Union[str, Any]              =	csv.DictWriter(self.f    ,  fieldnames=self._col_names			)
									self.writer.writeheader()







				@property
				def    lowercase_       (							self			)       ->  Union[str, Any]:
									return self.writer







				def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
									logger.info('Call init'			)
									self.writer.writerow(lowerCamelCase__			)







				def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
									logger.info('Call log'			)
									self.writer.writerow(lowerCamelCase__			)







				def    lowercase_       (							self			)       ->  Optional[int]:
									self.f.close()







class      UpperCAmelCase_						(unittest.TestCase						):





				"""simple docstring"""






				def    lowercase_       (							self			)       ->  Dict:
									with tempfile.TemporaryDirectory() as d:
														__lowerCamelCase     :			Optional[int]              =	MyCustomTracker(lowerCamelCase__			)
														__lowerCamelCase     :			Optional[int]              =	Accelerator(log_with=lowerCamelCase__			)
														__lowerCamelCase     :			List[str]              =	{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
														accelerator.init_trackers('Some name'    ,  lowerCamelCase__			)
														accelerator.end_training()
														with open(f'{d}/log.csv'    ,  'r'			) as f:
																			__lowerCamelCase     :			Dict              =	csv.DictReader(lowerCamelCase__			)
																			__lowerCamelCase     :			Tuple              =	next(lowerCamelCase__			)
																			__lowerCamelCase     :			Union[str, Any]              =	{
																			    "total_loss": "",
																			    "iteration": "",
																			    "my_text": "",
																			    "learning_rate": "0.01",
																			    "num_iterations": "12",
																			    "some_boolean": "False",
																			    "some_string": "some_value",
																			}
																			self.assertDictEqual(lowerCamelCase__    ,  lowerCamelCase__			)







				def    lowercase_       (							self			)       ->  int:
									with tempfile.TemporaryDirectory() as d:
														__lowerCamelCase     :			str              =	MyCustomTracker(lowerCamelCase__			)
														__lowerCamelCase     :			List[str]              =	Accelerator(log_with=lowerCamelCase__			)
														accelerator.init_trackers('Some name'			)
														__lowerCamelCase     :			Optional[Any]              =	{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
														accelerator.log(lowerCamelCase__    ,  step=0			)
														accelerator.end_training()
														with open(f'{d}/log.csv'    ,  'r'			) as f:
																			__lowerCamelCase     :			Dict              =	csv.DictReader(lowerCamelCase__			)
																			__lowerCamelCase     :			Tuple              =	next(lowerCamelCase__			)
																			__lowerCamelCase     :			List[Any]              =	{
																			    "total_loss": "0.1",
																			    "iteration": "1",
																			    "my_text": "some_value",
																			    "learning_rate": "",
																			    "num_iterations": "",
																			    "some_boolean": "",
																			    "some_string": "",
																			}
																			self.assertDictEqual(lowerCamelCase__    ,  lowerCamelCase__			)










'''simple docstring'''



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class      UpperCAmelCase_						(_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






    lowerCamelCase						:      Any							  =    BartTokenizer
    lowerCamelCase						:      Optional[Any]							  =    BartTokenizerFast
    lowerCamelCase						:      Tuple							  =    True
    lowerCamelCase						:      Optional[Any]							  =    filter_roberta_detectors







    def    lowercase_       (							self			)       ->  Optional[int]:
         super().setUp()
         __lowerCamelCase     :			List[Any]              =	[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         __lowerCamelCase     :			Optional[Any]              =	dict(zip(SCREAMING_SNAKE_CASE_    ,  range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
         __lowerCamelCase     :			Union[str, Any]              =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __lowerCamelCase     :			int              =	{'unk_token': '<unk>'}

         __lowerCamelCase     :			int              =	os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES['vocab_file']			)
         __lowerCamelCase     :			str              =	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(SCREAMING_SNAKE_CASE_			) + '\n'			)
         with open(self.merges_file    ,  'w'    ,  encoding='utf-8'			) as fp:
              fp.write('\n'.join(SCREAMING_SNAKE_CASE_			)			)







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
         kwargs.update(self.special_tokens_map			)
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  int:
         kwargs.update(self.special_tokens_map			)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         return "lower newer", "lower newer"







    @cached_property
    def    lowercase_       (							self			)       ->  str:
         return BartTokenizer.from_pretrained('facebook/bart-large'			)







    @cached_property
    def    lowercase_       (							self			)       ->  Any:
         return BartTokenizerFast.from_pretrained('facebook/bart-large'			)







    @require_torch
    def    lowercase_       (							self			)       ->  List[str]:
         __lowerCamelCase     :			str              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __lowerCamelCase     :			Union[str, Any]              =	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  max_length=len(SCREAMING_SNAKE_CASE_			)    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              self.assertEqual((2, 9)    ,  batch.input_ids.shape			)
              self.assertEqual((2, 9)    ,  batch.attention_mask.shape			)
              __lowerCamelCase     :			str              =	batch.input_ids.tolist()[0]
              self.assertListEqual(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
              # Test that special tokens are reset







    @require_torch
    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Optional[int]              =	['A long paragraph for summarization.', 'Another paragraph for summarization.']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __lowerCamelCase     :			Tuple              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  padding=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'    ,  SCREAMING_SNAKE_CASE_			)
              self.assertIn('attention_mask'    ,  SCREAMING_SNAKE_CASE_			)
              self.assertNotIn('labels'    ,  SCREAMING_SNAKE_CASE_			)
              self.assertNotIn('decoder_attention_mask'    ,  SCREAMING_SNAKE_CASE_			)







    @require_torch
    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Any              =	[
             'Summary of the text.',
             'Another summary.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  max_length=32    ,  padding='max_length'    ,  return_tensors='pt'			)
              self.assertEqual(32    ,  targets['input_ids'].shape[1]			)







    @require_torch
    def    lowercase_       (							self			)       ->  Tuple:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __lowerCamelCase     :			Optional[Any]              =	tokenizer(
                  ['I am a small frog' * 10_24, 'I am a small frog']    ,  padding=SCREAMING_SNAKE_CASE_    ,  truncation=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
              self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
              self.assertEqual(batch.input_ids.shape    ,  (2, 10_24)			)







    @require_torch
    def    lowercase_       (							self			)       ->  Optional[Any]:
         __lowerCamelCase     :			Dict              =	['A long paragraph for summarization.']
         __lowerCamelCase     :			str              =	[
             'Summary of the text.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __lowerCamelCase     :			int              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
              __lowerCamelCase     :			Union[str, Any]              =	tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			)
              __lowerCamelCase     :			Optional[Any]              =	inputs['input_ids']
              __lowerCamelCase     :			Dict              =	targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)







    def    lowercase_       (							self			)       ->  List[Any]:
         pass







    def    lowercase_       (							self			)       ->  int:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                   __lowerCamelCase     :			List[str]              =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                   __lowerCamelCase     :			Optional[int]              =	self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_			)
                   __lowerCamelCase     :			Any              =	'A, <mask> AllenNLP sentence.'
                   __lowerCamelCase     :			Tuple              =	tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                   __lowerCamelCase     :			Union[str, Any]              =	tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_    ,  add_special_tokens=SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)

                   # 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']			)    ,  )

                   __lowerCamelCase     :			int              =	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
                   __lowerCamelCase     :			Optional[Any]              =	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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)
                   self.assertSequenceEqual(tokens_r['input_ids']    ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]			)

                   self.assertSequenceEqual(
                       SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)
                   self.assertSequenceEqual(
                       SCREAMING_SNAKE_CASE_    ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']			)






