
'''simple docstring'''

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case:  str								=			get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					ReformerTokenizer
     a_    	=					ReformerTokenizerFast
     a_    	=					True
     a_    	=					False
     a_    	=					True


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().setUp()

       a_      :	Optional[Any]      =							ReformerTokenizer(__a							,		keep_accents=__a )
       tokenizer.save_pretrained(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""<s>"""
       a_      :	Union[str, Any]      =							1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a )							,		__a )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a )							,		__a )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							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(__a )							,		10_00 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.assertEqual(self.get_tokenizer().vocab_size							,		10_00 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if not self.test_rust_tokenizer:
         return

       a_      :	Dict      =							self.get_tokenizer()
       a_      :	int      =							self.get_rust_tokenizer()

       a_      :	List[str]      =							"""I was born in 92000, and this is falsé."""

       a_      :	str      =							tokenizer.tokenize(__a )
       a_      :	Tuple      =							rust_tokenizer.tokenize(__a )
       self.assertListEqual(__a							,		__a )

       a_      :	Dict      =							tokenizer.encode(__a							,		add_special_tokens=__a )
       a_      :	List[Any]      =							rust_tokenizer.encode(__a							,		add_special_tokens=__a )
       self.assertListEqual(__a							,		__a )

       a_      :	List[Any]      =							self.get_rust_tokenizer()
       a_      :	List[str]      =							tokenizer.encode(__a )
       a_      :	List[Any]      =							rust_tokenizer.encode(__a )
       self.assertListEqual(__a							,		__a )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=15 ):

       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
           a_      :	Dict      =							self.rust_tokenizer_class.from_pretrained(__a							,		**__a )

           # Simple input
           a_      :	Any      =							"""This is a simple input"""
           a_      :	Dict      =							["""This is a simple input 1""", """This is a simple input 2"""]
           a_      :	List[Any]      =							("""This is a simple input""", """This is a pair""")
           a_      :	int      =							[
               ("""This is a simple input 1""", """This is a simple input 2"""),
               ("""This is a simple pair 1""", """This is a simple pair 2"""),
           ]

           # Simple input tests
           self.assertRaises(__a							,		tokenizer_r.encode							,		__a							,		max_length=__a							,		padding="""max_length""" )

           # Simple input
           self.assertRaises(__a							,		tokenizer_r.encode_plus							,		__a							,		max_length=__a							,		padding="""max_length""" )

           # Simple input
           self.assertRaises(
               __a							,		tokenizer_r.batch_encode_plus							,		__a							,		max_length=__a							,		padding="""max_length"""							,		)

           # Pair input
           self.assertRaises(__a							,		tokenizer_r.encode							,		__a							,		max_length=__a							,		padding="""max_length""" )

           # Pair input
           self.assertRaises(__a							,		tokenizer_r.encode_plus							,		__a							,		max_length=__a							,		padding="""max_length""" )

           # Pair input
           self.assertRaises(
               __a							,		tokenizer_r.batch_encode_plus							,		__a							,		max_length=__a							,		padding="""max_length"""							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							ReformerTokenizer(__a							,		keep_accents=__a )

       a_      :	Dict      =							tokenizer.tokenize("""This is a test""" )
       self.assertListEqual(__a							,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(__a )							,		[2_85, 46, 10, 1_70, 3_82]							,		)

       a_      :	str      =							tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
       self.assertListEqual(
           __a							,		[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]							,		)
       a_      :	Any      =							tokenizer.convert_tokens_to_ids(__a )
       self.assertListEqual(
           __a							,		[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,		)

       a_      :	Tuple      =							tokenizer.convert_ids_to_tokens(__a )
       self.assertListEqual(
           __a							,		[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]							,		)


     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""Hello World!"""
       a_      :	List[Any]      =							[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

       self.assertListEqual(__a							,		self.big_tokenizer.encode(__a ) )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							(
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
       )
       a_      :	Tuple      =							[
           1_08,
           2_65,
           24,
           1_11,
           4,
           2_58,
           1_56,
           35,
           28,
           2_75,
           3,
           2_59,
           2_97,
           2_60,
           84,
           4,
           35,
           1_10,
           44,
           8,
           2_59,
           91,
           2_68,
           21,
           11,
           2_09,
           2_74,
           1_09,
           2_66,
           2_77,
           1_17,
           86,
           93,
           3_15,
           2_58,
           2_78,
           2_58,
           2_77,
           2_58,
           0,
           2_58,
           2_88,
           2_58,
           3_19,
           2_58,
           0,
           2_58,
           0,
           2_58,
           0,
           2_58,
           0,
           2_58,
           2_87,
           2_58,
           3_15,
           2_58,
           2_89,
           2_58,
           2_78,
           99,
           2_69,
           2_66,
           2_62,
           8,
           2_59,
           2_41,
           4,
           2_17,
           2_30,
           2_68,
           2_66,
           55,
           1_68,
           1_06,
           75,
           1_93,
           2_66,
           2_23,
           27,
           49,
           26,
           2_82,
           25,
           2_64,
           2_99,
           19,
           26,
           0,
           2_58,
           2_77,
           1_17,
           86,
           93,
           1_76,
           1_83,
           2_70,
           11,
           2_62,
           42,
           61,
           2_65,
       ]

       self.assertListEqual(__a							,		self.big_tokenizer.encode(__a ) )


     @require_torch
     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       a_      :	Tuple      =							list(self.big_tokenizer.get_vocab().keys() )[:10]
       a_      :	Optional[int]      =							""" """.join(__a )
       a_      :	List[Any]      =							self.big_tokenizer.encode_plus(__a							,		return_tensors="""pt""" )
       a_      :	str      =							self.big_tokenizer.batch_encode_plus([sequence, sequence]							,		return_tensors="""pt""" )

       a_      :	Optional[Any]      =							ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       a_      :	Union[str, Any]      =							encoded_sequence["""input_ids"""].shape
       a_      :	str      =							ReformerModel(__a )

       # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**__a )
         model(**__a )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """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, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # This tokenizer does not know some characters like ")".
       # That is the reason why we use very simple texts here.
       # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
       a_      :	str      =							[
           """This is a very simple sentence.""",
           """The quick brown fox jumps over the lazy dog.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=__a							,		model_name="""google/reformer-crime-and-punishment"""							,		revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""							,		padding=__a							,		sequences=__a							,		)




'''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  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["""bs"""]							,		model_result["""ss"""] ):
           a_      :	str      =							model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	List[Any]      =							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'''

       a_      :	Optional[int]      =							"""sgugger/tiny-distilbert-classification"""
       a_      :	str      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		only_pretrain_model=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							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'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		torchscript=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							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'''

       a_      :	int      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		fpaa=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Dict      =							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'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Dict      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       # set architectures equal to `None`
       a_      :	Any      =							None
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Any      =							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'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Tuple      =							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'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		fpaa=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[str]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[int]      =							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'''

       a_      :	List[Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[str]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	List[str]      =							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'''

       a_      :	Dict      =							"""sshleifer/tinier_bart"""
       a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Dict      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Optional[Any]      =							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'''

       a_      :	Union[str, Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Tuple      =							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'''

       a_      :	Optional[int]      =							"""sshleifer/tinier_bart"""
       a_      :	int      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Dict      =							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'''

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






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(lowerCAmelCase_ ):
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""sequential""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""cumulative""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""current""" ) )
         self.assertTrue(hasattr(lowerCAmelCase_							,		"""total""" ) )

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


