
import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase							,unittest.TestCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[Any]       =					CanineTokenizer
	_UpperCAmelCase       :		Dict       =					False



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				super().setUp()
				_snake_case		=			CanineTokenizer()
				tokenizer.save_pretrained(self.tmpdirname	)



	@cached_property
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				return CanineTokenizer.from_pretrained('google/canine-s'	)



	def  A							(  self	:    List[str]			,  **lowercase	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			self.tokenizer_class.from_pretrained(self.tmpdirname			,  **lowercase	)
				_snake_case		=			1_024
				return tokenizer



	@require_torch
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.canine_tokenizer
				_snake_case		=			['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
				# fmt: off
				_snake_case		=			[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
				# fmt: on
				_snake_case		=			tokenizer(lowercase			,  padding=lowercase			,  return_tensors='pt'	)
				self.assertIsInstance(lowercase			,  lowercase	)

				_snake_case		=			list(batch.input_ids.numpy()[0]	)

				self.assertListEqual(lowercase			,  lowercase	)

				self.assertEqual((2, 39)			,  batch.input_ids.shape	)
				self.assertEqual((2, 39)			,  batch.attention_mask.shape	)



	@require_torch
	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			self.canine_tokenizer
				_snake_case		=			['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
				_snake_case		=			tokenizer(lowercase			,  padding=lowercase			,  return_tensors='pt'	)
				# check if input_ids, attention_mask and token_type_ids are returned
				self.assertIn('input_ids'			,  lowercase	)
				self.assertIn('attention_mask'			,  lowercase	)
				self.assertIn('token_type_ids'			,  lowercase	)



	@require_torch
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			self.canine_tokenizer
				_snake_case		=			[
				    'What\'s the weater?',
				    'It\'s about 25 degrees.',
				]
				_snake_case		=			tokenizer(
				    text_target=lowercase			,  max_length=32			,  padding='max_length'			,  truncation=lowercase			,  return_tensors='pt'	)
				self.assertEqual(32			,  targets['input_ids'].shape[1]	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										self.assertNotEqual(tokenizer.model_max_length			,  42	)

        # Now let's start the test
				_snake_case		=			self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										# Isolate this from the other tests because we save additional tokens/etc
										_snake_case		=			tempfile.mkdtemp()

										_snake_case		=			' He is very happy, UNwant\u00E9d,running'
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										tokenizer.save_pretrained(lowercase	)

										_snake_case		=			tokenizer.__class__.from_pretrained(lowercase	)
										_snake_case		=			after_tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										self.assertListEqual(lowercase			,  lowercase	)

										shutil.rmtree(lowercase	)

				_snake_case		=			self.get_tokenizers(model_max_length=42	)
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										# Isolate this from the other tests because we save additional tokens/etc
										_snake_case		=			tempfile.mkdtemp()

										_snake_case		=			' He is very happy, UNwant\u00E9d,running'

										_snake_case		=			tokenizer.additional_special_tokens

										# We can add a new special token for Canine as follows:
										_snake_case		=			chr(0xE007	)
										additional_special_tokens.append(lowercase	)
										tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}	)
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										tokenizer.save_pretrained(lowercase	)

										_snake_case		=			tokenizer.__class__.from_pretrained(lowercase	)
										_snake_case		=			after_tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										self.assertListEqual(lowercase			,  lowercase	)
										self.assertIn(lowercase			,  after_tokenizer.additional_special_tokens	)
										self.assertEqual(after_tokenizer.model_max_length			,  42	)

										_snake_case		=			tokenizer.__class__.from_pretrained(lowercase			,  model_max_length=43	)
										self.assertEqual(tokenizer.model_max_length			,  43	)

										shutil.rmtree(lowercase	)



	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers(do_lower_case=lowercase	)
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										_snake_case	,					_snake_case		=			self.get_clean_sequence(lowercase	)

										# a special token for Canine can be defined as follows:
										_snake_case		=			0xE005
										_snake_case		=			chr(lowercase	)

										tokenizer.add_special_tokens({'cls_token': special_token}	)
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										self.assertEqual(len(lowercase	)			,  1	)

										_snake_case		=			tokenizer.decode(ids + encoded_special_token			,  clean_up_tokenization_spaces=lowercase	)
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)

										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										self.assertEqual(lowercase			,  input_encoded + special_token_id	)

										_snake_case		=			tokenizer.decode(lowercase			,  skip_special_tokens=lowercase	)
										self.assertTrue(special_token not in decoded	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers(do_lower_case=lowercase	)
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										_snake_case		=			chr(0xE005	)
										_snake_case		=			chr(0xE006	)

										# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
										tokenizer.add_tokens([SPECIAL_TOKEN_1]			,  special_tokens=lowercase	)
										# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
										# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
										tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]}	)

										_snake_case		=			tokenizer.tokenize(lowercase	)
										_snake_case		=			tokenizer.tokenize(lowercase	)

										self.assertEqual(len(lowercase	)			,  1	)
										self.assertEqual(len(lowercase	)			,  1	)
										self.assertEqual(token_a[0]			,  lowercase	)
										self.assertEqual(token_a[0]			,  lowercase	)



	@require_tokenizers
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers(do_lower_case=lowercase	)
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										# a special token for Canine can be defined as follows:
										_snake_case		=			0xE006
										_snake_case		=			chr(lowercase	)

										_snake_case		=			AddedToken(lowercase			,  lstrip=lowercase	)
										tokenizer.add_special_tokens({'additional_special_tokens': [new_token]}	)

										with tempfile.TemporaryDirectory() as tmp_dir_name:
													tokenizer.save_pretrained(lowercase	)
													tokenizer.from_pretrained(lowercase	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(lowercase	)

										with open(os.path.join(lowercase			,  'special_tokens_map.json'	)			,  encoding='utf-8'	) as json_file:
													_snake_case		=			json.load(lowercase	)

										with open(os.path.join(lowercase			,  'tokenizer_config.json'	)			,  encoding='utf-8'	) as json_file:
													_snake_case		=			json.load(lowercase	)

										# a special token for Canine can be defined as follows:
										_snake_case		=			0xE006
										_snake_case		=			chr(lowercase	)

										_snake_case		=			[new_token_a]
										_snake_case		=			[new_token_a]

										with open(os.path.join(lowercase			,  'special_tokens_map.json'	)			,  'w'			,  encoding='utf-8'	) as outfile:
													json.dump(lowercase			,  lowercase	)
										with open(os.path.join(lowercase			,  'tokenizer_config.json'	)			,  'w'			,  encoding='utf-8'	) as outfile:
													json.dump(lowercase			,  lowercase	)

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										_snake_case		=			tokenizer_class.from_pretrained(lowercase			,  extra_ids=0	)
										self.assertIn(lowercase			,  tokenizer_without_change_in_init.additional_special_tokens	)
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]			,  tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]	)	)			,  )

										_snake_case		=			0xE007
										_snake_case		=			chr(lowercase	)
										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										_snake_case		=			[AddedToken(lowercase			,  lstrip=lowercase	)]
										_snake_case		=			tokenizer_class.from_pretrained(
										    lowercase			,  additional_special_tokens=lowercase			,  extra_ids=0	)

										self.assertIn(lowercase			,  tokenizer.additional_special_tokens	)
										# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    [new_token_a]			,  tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]	)	)	)



	@require_tokenizers
	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers(do_lower_case=lowercase	)
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										_snake_case		=			'hello world'
										if self.space_between_special_tokens:
													_snake_case		=			'[CLS] hello world [SEP]'
										else:
													_snake_case		=			input
										_snake_case		=			tokenizer.encode(lowercase			,  add_special_tokens=lowercase	)
										_snake_case		=			tokenizer.decode(lowercase			,  spaces_between_special_tokens=self.space_between_special_tokens	)
										self.assertIn(lowercase			,  [output, output.lower()]	)



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
										_snake_case		=			[
										    'bos_token',
										    'eos_token',
										    'unk_token',
										    'sep_token',
										    'pad_token',
										    'cls_token',
										    'mask_token',
										]

				_snake_case		=			'a'
				_snake_case		=			ord(lowercase	)

				for attr in attributes_list:
							setattr(lowercase			,  attr + '_id'			,  lowercase	)
							self.assertEqual(getattr(lowercase			,  lowercase	)			,  lowercase	)
							self.assertEqual(getattr(lowercase			,  attr + '_id'	)			,  lowercase	)

							setattr(lowercase			,  attr + '_id'			,  lowercase	)
							self.assertEqual(getattr(lowercase			,  lowercase	)			,  lowercase	)
							self.assertEqual(getattr(lowercase			,  attr + '_id'	)			,  lowercase	)

				setattr(lowercase			,  'additional_special_tokens_ids'			,  []	)
				self.assertListEqual(getattr(lowercase			,  'additional_special_tokens'	)			,  []	)
				self.assertListEqual(getattr(lowercase			,  'additional_special_tokens_ids'	)			,  []	)

				_snake_case		=			0xE006
				_snake_case		=			chr(lowercase	)
				setattr(lowercase			,  'additional_special_tokens_ids'			,  [additional_special_token_id]	)
				self.assertListEqual(getattr(lowercase			,  'additional_special_tokens'	)			,  [additional_special_token]	)
				self.assertListEqual(getattr(lowercase			,  'additional_special_tokens_ids'	)			,  [additional_special_token_id]	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				pass





	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				pass

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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    List[Any]			,  lowercase	:    Dict	):


				'''simple docstring'''


				for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['bs']			,  model_result['ss']	):
										_snake_case		=			model_result['result'][batch_size][sequence_length]
										self.assertIsNotNone(lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			'sgugger/tiny-distilbert-classification'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  only_pretrain_model=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  torchscript=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			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  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  fpaa=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				# set architectures equal to `None`
				_snake_case		=			None
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			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  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  fpaa=lowercase			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result	)
				self.check_results_dict_not_empty(results.memory_inference_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tinier_bart'
				_snake_case		=			AutoConfig.from_pretrained(lowercase	)
				_snake_case		=			PyTorchBenchmarkArguments(
				    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  multi_process=lowercase			,  )
				_snake_case		=			PyTorchBenchmark(lowercase			,  configs=[config]	)
				_snake_case		=			benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result	)
				self.check_results_dict_not_empty(results.memory_train_result	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  save_to_csv=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  inference_time_csv_file=os.path.join(lowercase			,  'inf_time.csv'	)			,  train_memory_csv_file=os.path.join(lowercase			,  'train_mem.csv'	)			,  inference_memory_csv_file=os.path.join(lowercase			,  'inf_mem.csv'	)			,  train_time_csv_file=os.path.join(lowercase			,  'train_time.csv'	)			,  env_info_csv_file=os.path.join(lowercase			,  'env.csv'	)			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							benchmark.run()
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_time.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'inf_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'train_mem.csv'	)	).exists()	)
							self.assertTrue(Path(os.path.join(lowercase			,  'env.csv'	)	).exists()	)





	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				_snake_case		=			'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(lowercase	:    Optional[Any]	):
							self.assertTrue(hasattr(lowercase			,  'sequential'	)	)
							self.assertTrue(hasattr(lowercase			,  'cumulative'	)	)
							self.assertTrue(hasattr(lowercase			,  'current'	)	)
							self.assertTrue(hasattr(lowercase			,  'total'	)	)

				with tempfile.TemporaryDirectory() as tmp_dir:
							_snake_case		=			PyTorchBenchmarkArguments(
							    models=[MODEL_ID]			,  training=lowercase			,  inference=lowercase			,  sequence_lengths=[8]			,  batch_sizes=[1]			,  log_filename=os.path.join(lowercase			,  'log.txt'	)			,  log_print=lowercase			,  trace_memory_line_by_line=lowercase			,  multi_process=lowercase			,  )
							_snake_case		=			PyTorchBenchmark(lowercase	)
							_snake_case		=			benchmark.run()
							_check_summary_is_not_empty(result.inference_summary	)
							_check_summary_is_not_empty(result.train_summary	)
							self.assertTrue(Path(os.path.join(lowercase			,  'log.txt'	)	).exists()	)