
# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		lowercase       =    data





	def __iter__(       self       ):
		for element in self.data:
			yield element






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE=True							):
	lowercase       =    Accelerator(even_batches=__SCREAMING_SNAKE_CASE							)
	assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
	return accelerator






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = False							):
	if iterable:
		lowercase       =    DummyIterableDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE							)							)							)
	else:
		lowercase       =    TensorDataset(torch.as_tensor(range(__SCREAMING_SNAKE_CASE							)							)							)

	lowercase       =    DataLoader(__SCREAMING_SNAKE_CASE ,							batch_size=__SCREAMING_SNAKE_CASE							)
	lowercase       =    accelerator.prepare(__SCREAMING_SNAKE_CASE							)

	return dl






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							):
	lowercase       =    create_dataloader(accelerator=__SCREAMING_SNAKE_CASE ,							dataset_size=__SCREAMING_SNAKE_CASE ,							batch_size=__SCREAMING_SNAKE_CASE							)

	lowercase       =    [len(batch[0]							) for batch in dl]

	if accelerator.process_index == 0:
		assert batch_sizes == process_0_expected_batch_sizes
	elif accelerator.process_index == 1:
		assert batch_sizes == process_1_expected_batch_sizes






def 							UpperCAmelCase_		(							):
	lowercase       =    create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    __SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1 ,							process_0_expected_batch_sizes=[1, 1] ,							process_1_expected_batch_sizes=[1, 1] ,							)

	# without padding, we would expect the same number of batches, but different sizes
	verify_dataloader_batch_sizes(
	    __SCREAMING_SNAKE_CASE ,							dataset_size=7 ,							batch_size=2 ,							process_0_expected_batch_sizes=[2, 2] ,							process_1_expected_batch_sizes=[2, 2] ,							)






def 							UpperCAmelCase_		(							):
	lowercase       =    create_accelerator(even_batches=__SCREAMING_SNAKE_CASE							)

	verify_dataloader_batch_sizes(
	    __SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1 ,							process_0_expected_batch_sizes=[1, 1] ,							process_1_expected_batch_sizes=[1] ,							)

	verify_dataloader_batch_sizes(
	    __SCREAMING_SNAKE_CASE ,							dataset_size=7 ,							batch_size=2 ,							process_0_expected_batch_sizes=[2, 2] ,							process_1_expected_batch_sizes=[2, 1] ,							)






def 							UpperCAmelCase_		(							):
	lowercase       =    create_accelerator(even_batches=__SCREAMING_SNAKE_CASE							)

	lowercase       =    torch.nn.Linear(1 ,							1							)
	lowercase       =    accelerator.prepare(__SCREAMING_SNAKE_CASE							)

	lowercase       =    create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1							)

	lowercase       =    []
	with accelerator.join_uneven_inputs([ddp_model]							):
		for batch_idx, batch in enumerate(__SCREAMING_SNAKE_CASE							):
			lowercase       =    ddp_model(batch[0].float()							)
			lowercase       =    output.sum()
			loss.backward()
			batch_idxs.append(__SCREAMING_SNAKE_CASE							)

	accelerator.wait_for_everyone()

	if accelerator.process_index == 0:
		assert batch_idxs == [0, 1]
	elif accelerator.process_index == 1:
		assert batch_idxs == [0]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE							) as w:
		with accelerator.join_uneven_inputs([Mock()]							):
			pass

		assert issubclass(w[-1].category ,							__SCREAMING_SNAKE_CASE							)
		assert "only supported for multi-GPU" in str(w[-1].message							)






def 							UpperCAmelCase_		(							):
	lowercase       =    True
	lowercase       =    False
	lowercase       =    create_accelerator(even_batches=__SCREAMING_SNAKE_CASE							)
	lowercase       =    torch.nn.Linear(1 ,							1							)
	lowercase       =    accelerator.prepare(__SCREAMING_SNAKE_CASE							)
	lowercase       =    create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1							)
	lowercase       =    create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1							)

	with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=__SCREAMING_SNAKE_CASE							):
		lowercase       =    train_dl.batch_sampler.even_batches
		lowercase       =    valid_dl.batch_sampler.even_batches

	assert train_dl_overridden_value == overridden_even_batches
	assert valid_dl_overridden_value == overridden_even_batches
	assert train_dl.batch_sampler.even_batches == default_even_batches
	assert valid_dl.batch_sampler.even_batches == default_even_batches






def 							UpperCAmelCase_		(							):
	lowercase       =    True
	lowercase       =    False
	lowercase       =    create_accelerator(even_batches=__SCREAMING_SNAKE_CASE							)
	lowercase       =    torch.nn.Linear(1 ,							1							)
	lowercase       =    accelerator.prepare(__SCREAMING_SNAKE_CASE							)
	create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1 ,							iterable=__SCREAMING_SNAKE_CASE							)
	lowercase       =    create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1							)

	with warnings.catch_warnings():
		warnings.filterwarnings('ignore'							)
		try:
			with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=__SCREAMING_SNAKE_CASE							):
				lowercase       =    batch_dl.batch_sampler.even_batches
		except AttributeError:
			# ensure attribute error is not raised when processing iterable dl
			raise AssertionError

	assert batch_dl_overridden_value == overridden_even_batches
	assert batch_dl.batch_sampler.even_batches == default_even_batches






def 							UpperCAmelCase_		(							):
	lowercase       =    create_accelerator()
	lowercase       =    torch.nn.Linear(1 ,							1							)
	lowercase       =    accelerator.prepare(__SCREAMING_SNAKE_CASE							)
	create_dataloader(__SCREAMING_SNAKE_CASE ,							dataset_size=3 ,							batch_size=1 ,							iterable=__SCREAMING_SNAKE_CASE							)

	with warnings.catch_warnings(record=__SCREAMING_SNAKE_CASE							) as w:
		with accelerator.join_uneven_inputs([ddp_model] ,							even_batches=__SCREAMING_SNAKE_CASE							):
			pass

		assert issubclass(w[-1].category ,							__SCREAMING_SNAKE_CASE							)
		assert "only supported for map-style datasets" in str(w[-1].message							)






def 							UpperCAmelCase_		(							):
	lowercase       =    create_accelerator()

	accelerator.print('Test that even_batches variable ensures uniform batches across processes'							)
	test_default_ensures_even_batch_sizes()

	accelerator.print('Run tests with even_batches disabled'							)
	test_can_disable_even_batches()

	accelerator.print('Test joining uneven inputs'							)
	test_can_join_uneven_inputs()

	accelerator.print('Test overriding even_batches when joining uneven inputs'							)
	test_join_can_override_even_batches()

	accelerator.print('Test overriding even_batches for mixed dataloader types'							)
	test_join_can_override_for_mixed_type_dataloaders()

	accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'							)
	test_join_raises_warning_for_iterable_when_overriding_even_batches()

	accelerator.print('Test join with non DDP distributed raises warning'							)
	lowercase       =    accelerator.state.distributed_type
	lowercase       =    DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(__SCREAMING_SNAKE_CASE							)
	lowercase       =    original_state


if __name__ == "__main__":
	main()



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       [
	    # (model_name, model_kwargs)
	    ("""bert-base-cased""", {}),
	    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
		with NamedTemporaryFile(mode='w+t'       ) as vocab_file:
			vocab_file.write('\n'.join(snake_case       )       )
			vocab_file.flush()
			lowercase       =    BertTokenizerFast(vocab_file.name       )

		with TemporaryDirectory() as bert_save_dir:
			lowercase       =    BertModel(BertConfig(vocab_size=len(snake_case       )       )       )
			model.save_pretrained(snake_case       )
			self._test_export(snake_case		,    'pt'		,    12		,    snake_case       )





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )
			lowercase       =    quantize(Path(snake_case       )       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )
			lowercase       =    quantize(snake_case       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case=None		,    **snake_case       ):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase       =    Path(snake_case       ).joinpath('model.onnx'       )

			# Remove folder if exists
			if path.parent.exists():
				path.parent.rmdir()

			# Export
			convert(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    **snake_case       )

			return path
		except Exception as e:
			self.fail(snake_case       )





	@require_torch
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'pt'       )





	@require_tf
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TFBertModel

		lowercase       =    TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'tf'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    FeatureExtractionPipeline(snake_case		,    snake_case       )

		lowercase       =    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    infer_shapes(snake_case		,    snake_case       )

		# Assert all variables are present
		self.assertEqual(len(snake_case       )		,    len(snake_case       )       )
		self.assertTrue(all(var_name in shapes for var_name in variable_names       )       )
		self.assertSequenceEqual(variable_names[:3]		,    snake_case       )
		self.assertSequenceEqual(variable_names[3:]		,    snake_case       )

		# Assert inputs are {0: batch, 1: sequence}
		for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
			self.assertDictEqual(shapes[var_name]		,    {0: 'batch', 1: 'sequence'}       )

		# Assert outputs are {0: batch, 1: sequence} and {0: batch}
		self.assertDictEqual(shapes['output_0']		,    {0: 'batch', 1: 'sequence'}       )
		self.assertDictEqual(shapes['output_1']		,    {0: 'batch'}       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase       =    {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase  ,					lowercase       =    ensure_valid_input(FuncContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the same number of args (all are valid)
		self.assertEqual(len(snake_case       )		,    3       )

		# Should have exactly the same input names
		self.assertEqual(set(snake_case       )		,    set(snake_case       )       )

		# Parameter should be reordered according to their respective place in the function:
		# (input_ids, token_type_ids, attention_mask)
		self.assertEqual(snake_case		,    (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])       )

		# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
		lowercase  ,					lowercase       =    ensure_valid_input(FuncNonContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the one arg (all before the one not provided "some_other_args")
		self.assertEqual(len(snake_case       )		,    1       )
		self.assertEqual(len(snake_case       )		,    1       )

		# Should have only "input_ids"
		self.assertEqual(inputs_args[0]		,    tokens['input_ids']       )
		self.assertEqual(ordered_input_names[0]		,    'input_ids'       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )		,    '-test'       )
		self.assertEqual('/home/something/my_fake_model-test.onnx'		,    generated.as_posix()       )

