

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTConfig,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
    )
class        _A						:

  def __init__( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=13       ,    _SCREAMING_SNAKE_CASE=7       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=True       ,    _SCREAMING_SNAKE_CASE=99       ,    _SCREAMING_SNAKE_CASE=32       ,    _SCREAMING_SNAKE_CASE=5       ,    _SCREAMING_SNAKE_CASE=4       ,    _SCREAMING_SNAKE_CASE=37       ,    _SCREAMING_SNAKE_CASE="gelu"       ,    _SCREAMING_SNAKE_CASE=0.1       ,    _SCREAMING_SNAKE_CASE=0.1       ,    _SCREAMING_SNAKE_CASE=512       ,    _SCREAMING_SNAKE_CASE=16       ,    _SCREAMING_SNAKE_CASE=2       ,    _SCREAMING_SNAKE_CASE=0.02       ,    _SCREAMING_SNAKE_CASE=3       ,    _SCREAMING_SNAKE_CASE=4       ,    _SCREAMING_SNAKE_CASE=None       ,    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   parent
        SCREAMING_SNAKE_CASE_ :     str							=   batch_size
        SCREAMING_SNAKE_CASE_ :     Tuple							=   seq_length
        SCREAMING_SNAKE_CASE_ :     List[str]							=   is_training
        SCREAMING_SNAKE_CASE_ :     Dict							=   use_token_type_ids
        SCREAMING_SNAKE_CASE_ :     str							=   use_labels
        SCREAMING_SNAKE_CASE_ :     Any							=   vocab_size
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   hidden_size
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   num_hidden_layers
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   num_attention_heads
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   intermediate_size
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   hidden_act
        SCREAMING_SNAKE_CASE_ :     Dict							=   hidden_dropout_prob
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_ :     Dict							=   max_position_embeddings
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   type_vocab_size
        SCREAMING_SNAKE_CASE_ :     List[str]							=   type_sequence_label_size
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   initializer_range
        SCREAMING_SNAKE_CASE_ :     Tuple							=   num_labels
        SCREAMING_SNAKE_CASE_ :     Any							=   num_choices
        SCREAMING_SNAKE_CASE_ :     List[str]							=   scope
        SCREAMING_SNAKE_CASE_ :     Tuple							=   self.vocab_size - 1

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size    )

        SCREAMING_SNAKE_CASE_ :     Any							=   None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   ids_tensor([self.batch_size, self.seq_length]       ,    self.type_vocab_size    )

        SCREAMING_SNAKE_CASE_ :     List[Any]							=   None
        SCREAMING_SNAKE_CASE_ :     Any							=   None
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_ :     List[str]							=   ids_tensor([self.batch_size]       ,    self.type_sequence_label_size    )
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]       ,    self.num_labels    )
              SCREAMING_SNAKE_CASE_ :     str							=   ids_tensor([self.batch_size]       ,    self.num_choices    )

        SCREAMING_SNAKE_CASE_ :     int							=   OpenAIGPTConfig(
            vocab_size=self.vocab_size       ,    n_embd=self.hidden_size       ,    n_layer=self.num_hidden_layers       ,    n_head=self.num_attention_heads       ,    n_positions=self.max_position_embeddings       ,    pad_token_id=self.pad_token_id       ,    )

        SCREAMING_SNAKE_CASE_ :     Dict							=   ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,    2    )

        return (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    *_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   OpenAIGPTModel(config=__snake_case    )
        model.to(__snake_case    )
        model.eval()

        SCREAMING_SNAKE_CASE_ :     str							=   model(__snake_case       ,    token_type_ids=__snake_case       ,    head_mask=__snake_case    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   model(__snake_case       ,    token_type_ids=__snake_case    )
        SCREAMING_SNAKE_CASE_ :     int							=   model(__snake_case    )

        self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    *_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   OpenAIGPTLMHeadModel(__snake_case    )
        model.to(__snake_case    )
        model.eval()

        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   model(__snake_case       ,    token_type_ids=__snake_case       ,    labels=__snake_case    )
        self.parent.assertEqual(result.loss.shape       ,    ()    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    *_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   OpenAIGPTDoubleHeadsModel(__snake_case    )
        model.to(__snake_case    )
        model.eval()

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   model(__snake_case       ,    token_type_ids=__snake_case       ,    labels=__snake_case    )
        self.parent.assertEqual(result.loss.shape       ,    ()    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    *_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   self.num_labels
        SCREAMING_SNAKE_CASE_ :     str							=   OpenAIGPTForSequenceClassification(__snake_case    )
        model.to(__snake_case    )
        model.eval()

        SCREAMING_SNAKE_CASE_ :     str							=   ids_tensor([self.batch_size]       ,    self.type_sequence_label_size    )
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   model(__snake_case       ,    token_type_ids=__snake_case       ,    labels=__snake_case    )
        self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )



  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  (
            SCREAMING_SNAKE_CASE_					
        )				,  					
        ) :     Tuple							=   config_and_inputs
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   {
            'input_ids': input_ids,
            'token_type_ids': token_type_ids,
            'head_mask': head_mask,
        }

        return config, inputs_dict

@require_torch
class        _A						(     lowerCamelCase_      ,   lowerCamelCase_      ,   lowerCamelCase_      ,   unittest.TestCase):
  SCREAMING_SNAKE_CASE			:					List[Any]				=     (
      (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE			:					int				=     (
      (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  SCREAMING_SNAKE_CASE			:					Any				=     (
      {
          '''feature-extraction''': OpenAIGPTModel,
          '''text-classification''': OpenAIGPTForSequenceClassification,
          '''text-generation''': OpenAIGPTLMHeadModel,
          '''zero-shot''': OpenAIGPTForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
              # tiny config could not be created.
              return True

        return False

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=False    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   super()._prepare_for_class(__snake_case       ,    __snake_case       ,    return_labels=__snake_case    )

        if return_labels:
              if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                    SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,    dtype=torch.long       ,    device=__snake_case       ,    )
                    SCREAMING_SNAKE_CASE_ :     Tuple							=   inputs_dict['labels']
                    SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   inputs_dict['labels']
                    SCREAMING_SNAKE_CASE_ :     int							=   torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices)       ,    dtype=torch.long       ,    device=__snake_case       ,    )
                    SCREAMING_SNAKE_CASE_ :     str							=   torch.zeros(
                        self.model_tester.batch_size       ,    dtype=torch.long       ,    device=__snake_case    )
        return inputs_dict

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   OpenAIGPTModelTester(self    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   ConfigTester(self       ,    config_class=__snake_case       ,    n_embd=37    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*__snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*__snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*__snake_case    )

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__snake_case    )



  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_ :     List[str]							=   OpenAIGPTModel.from_pretrained(__snake_case    )
              self.assertIsNotNone(__snake_case    )

@require_torch
class        _A						(     unittest.TestCase):

  @slow
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'    )
        model.to(__snake_case    )
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   torch.tensor([[481, 4735, 544]]       ,    dtype=torch.long       ,    device=__snake_case    )  # the president is
        SCREAMING_SNAKE_CASE_ :     Dict							=   [
            481,
            4735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            4_0477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  # the president is a very good man. " \n " i\'m sure he is, " said the

        SCREAMING_SNAKE_CASE_ :     List[str]							=   model.generate(__snake_case       ,    do_sample=__snake_case    )
        self.assertListEqual(output_ids[0].tolist()       ,    __snake_case    )










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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :List[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :Optional[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,configs=[config]      )
												a__             =     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   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

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




