
'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
    import jax.numpy as jnp

if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							{
           """task_specific_params""": {
               """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
               """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
               """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
           }
       }
       a_      :	Union[str, Any]      =							{
           """task_specific_params.summarization.length_penalty""": 1.0,
           """task_specific_params.summarization.max_length""": 1_28,
           """task_specific_params.summarization.min_length""": 12,
           """task_specific_params.summarization.num_beams""": 4,
           """task_specific_params.summarization_cnn.length_penalty""": 2.0,
           """task_specific_params.summarization_cnn.max_length""": 1_42,
           """task_specific_params.summarization_cnn.min_length""": 56,
           """task_specific_params.summarization_cnn.num_beams""": 4,
           """task_specific_params.summarization_xsum.length_penalty""": 1.0,
           """task_specific_params.summarization_xsum.max_length""": 62,
           """task_specific_params.summarization_xsum.min_length""": 11,
           """task_specific_params.summarization_xsum.num_beams""": 6,
       }

       self.assertEqual(flatten_dict(lowerCAmelCase_ )							,		lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							np.random.randn(3							,		4 )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_ )							,		x.transpose() ) )

       a_      :	Union[str, Any]      =							np.random.randn(3							,		4							,		5 )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_							,		axes=(1, 2, 0) )							,		x.transpose((1, 2, 0) ) ) )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							np.random.randn(3							,		4 )
       a_      :	Any      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_ )							,		transpose(lowerCAmelCase_ ).numpy() ) )

       a_      :	int      =							np.random.randn(3							,		4							,		5 )
       a_      :	Optional[Any]      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_							,		axes=(1, 2, 0) )							,		transpose(lowerCAmelCase_							,		axes=(1, 2, 0) ).numpy() ) )


     @require_tf
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							np.random.randn(3							,		4 )
       a_      :	Any      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_ )							,		transpose(lowerCAmelCase_ ).numpy() ) )

       a_      :	Optional[Any]      =							np.random.randn(3							,		4							,		5 )
       a_      :	Optional[int]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_							,		axes=(1, 2, 0) )							,		transpose(lowerCAmelCase_							,		axes=(1, 2, 0) ).numpy() ) )


     @require_flax
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							np.random.randn(3							,		4 )
       a_      :	List[str]      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_ )							,		np.asarray(transpose(lowerCAmelCase_ ) ) ) )

       a_      :	Any      =							np.random.randn(3							,		4							,		5 )
       a_      :	Tuple      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCAmelCase_							,		axes=(1, 2, 0) )							,		np.asarray(transpose(lowerCAmelCase_							,		axes=(1, 2, 0) ) ) ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							np.random.randn(3							,		4 )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(4, 3) )							,		np.reshape(lowerCAmelCase_							,		(4, 3) ) ) )

       a_      :	Any      =							np.random.randn(3							,		4							,		5 )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(12, 5) )							,		np.reshape(lowerCAmelCase_							,		(12, 5) ) ) )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							np.random.randn(3							,		4 )
       a_      :	Union[str, Any]      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(4, 3) )							,		reshape(lowerCAmelCase_							,		(4, 3) ).numpy() ) )

       a_      :	Optional[Any]      =							np.random.randn(3							,		4							,		5 )
       a_      :	Optional[Any]      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(12, 5) )							,		reshape(lowerCAmelCase_							,		(12, 5) ).numpy() ) )


     @require_tf
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							np.random.randn(3							,		4 )
       a_      :	List[str]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(4, 3) )							,		reshape(lowerCAmelCase_							,		(4, 3) ).numpy() ) )

       a_      :	Dict      =							np.random.randn(3							,		4							,		5 )
       a_      :	Union[str, Any]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(12, 5) )							,		reshape(lowerCAmelCase_							,		(12, 5) ).numpy() ) )


     @require_flax
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							np.random.randn(3							,		4 )
       a_      :	Any      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(4, 3) )							,		np.asarray(reshape(lowerCAmelCase_							,		(4, 3) ) ) ) )

       a_      :	Dict      =							np.random.randn(3							,		4							,		5 )
       a_      :	Union[str, Any]      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCAmelCase_							,		(12, 5) )							,		np.asarray(reshape(lowerCAmelCase_							,		(12, 5) ) ) ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							np.random.randn(1							,		3							,		4 )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_ )							,		np.squeeze(lowerCAmelCase_ ) ) )

       a_      :	Optional[int]      =							np.random.randn(1							,		4							,		1							,		5 )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							,		axis=2 )							,		np.squeeze(lowerCAmelCase_							,		axis=2 ) ) )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							np.random.randn(1							,		3							,		4 )
       a_      :	Union[str, Any]      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_ )							,		squeeze(lowerCAmelCase_ ).numpy() ) )

       a_      :	Dict      =							np.random.randn(1							,		4							,		1							,		5 )
       a_      :	List[str]      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							,		axis=2 )							,		squeeze(lowerCAmelCase_							,		axis=2 ).numpy() ) )


     @require_tf
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							np.random.randn(1							,		3							,		4 )
       a_      :	Optional[int]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_ )							,		squeeze(lowerCAmelCase_ ).numpy() ) )

       a_      :	Any      =							np.random.randn(1							,		4							,		1							,		5 )
       a_      :	List[Any]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							,		axis=2 )							,		squeeze(lowerCAmelCase_							,		axis=2 ).numpy() ) )


     @require_flax
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							np.random.randn(1							,		3							,		4 )
       a_      :	Optional[int]      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_ )							,		np.asarray(squeeze(lowerCAmelCase_ ) ) ) )

       a_      :	Any      =							np.random.randn(1							,		4							,		1							,		5 )
       a_      :	str      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCAmelCase_							,		axis=2 )							,		np.asarray(squeeze(lowerCAmelCase_							,		axis=2 ) ) ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							np.random.randn(3							,		4 )
       self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_							,		axis=1 )							,		np.expand_dims(lowerCAmelCase_							,		axis=1 ) ) )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							np.random.randn(3							,		4 )
       a_      :	Any      =							torch.tensor(lowerCAmelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_							,		axis=1 )							,		expand_dims(lowerCAmelCase_							,		axis=1 ).numpy() ) )


     @require_tf
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							np.random.randn(3							,		4 )
       a_      :	List[Any]      =							tf.constant(lowerCAmelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_							,		axis=1 )							,		expand_dims(lowerCAmelCase_							,		axis=1 ).numpy() ) )






     @require_flax
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							np.random.randn(3							,		4 )
       a_      :	List[str]      =							jnp.array(lowerCAmelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCAmelCase_							,		axis=1 )							,		np.asarray(expand_dims(lowerCAmelCase_							,		axis=1 ) ) ) )




'''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() )


