



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
		import tensorflow as tf

		from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments

@require_tf
class   snake_case__							(unittest.TestCase						):







						"""simple docstring"""




						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Any:
									"""simple docstring"""




									for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["""bs"""]   ,  model_result["""ss"""]    ):
															a__  :		List[Any]					=    model_result['result'][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCAmelCase    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> str:
									"""simple docstring"""




									a__  :		Any					=    'sshleifer/tiny-gpt2'
									a__  :		Optional[Any]					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  eager_mode=__UpperCAmelCase   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		Optional[int]					=    TensorFlowBenchmark(__UpperCAmelCase    )
									a__  :		Union[str, Any]					=    benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result    )
									self.check_results_dict_not_empty(results.memory_inference_result    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Union[str, Any]:
									"""simple docstring"""




									a__  :		List[str]					=    'sgugger/tiny-distilbert-classification'
									a__  :		Union[str, Any]					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  only_pretrain_model=__UpperCAmelCase   ,  )
									a__  :		Any					=    TensorFlowBenchmark(__UpperCAmelCase    )
									a__  :		Union[str, Any]					=    benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result    )
									self.check_results_dict_not_empty(results.memory_inference_result    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Tuple:
									"""simple docstring"""




									a__  :		List[Any]					=    'sshleifer/tiny-gpt2'
									a__  :		Optional[Any]					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		List[str]					=    TensorFlowBenchmark(__UpperCAmelCase    )
									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 				SCREAMING_SNAKE_CASE__(  self    )   -> Tuple:
									"""simple docstring"""




									a__  :		Any					=    'sshleifer/tiny-gpt2'
									a__  :		Tuple					=    AutoConfig.from_pretrained(__UpperCAmelCase    )
									a__  :		str					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  eager_mode=__UpperCAmelCase   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		int					=    TensorFlowBenchmark(__UpperCAmelCase   ,  [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 				SCREAMING_SNAKE_CASE__(  self    )   -> List[Any]:
									"""simple docstring"""




									a__  :		Optional[int]					=    'sshleifer/tiny-gpt2'
									a__  :		List[str]					=    AutoConfig.from_pretrained(__UpperCAmelCase    )
									a__  :		int					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		Optional[Any]					=    TensorFlowBenchmark(__UpperCAmelCase   ,  [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 				SCREAMING_SNAKE_CASE__(  self    )   -> Tuple:
									"""simple docstring"""




									a__  :		Any					=    'sshleifer/tiny-gpt2'
									a__  :		List[Any]					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		List[str]					=    TensorFlowBenchmark(__UpperCAmelCase    )
									a__  :		List[str]					=    benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result    )
									self.check_results_dict_not_empty(results.memory_train_result    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Optional[Any]:
									"""simple docstring"""




									a__  :		Union[str, Any]					=    'sshleifer/tiny-gpt2'
									a__  :		Any					=    AutoConfig.from_pretrained(__UpperCAmelCase    )
									a__  :		Tuple					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		Any					=    TensorFlowBenchmark(__UpperCAmelCase   ,  [config]    )
									a__  :		str					=    benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result    )
									self.check_results_dict_not_empty(results.memory_train_result    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Optional[int]:
									"""simple docstring"""




									a__  :		List[Any]					=    'patrickvonplaten/t5-tiny-random'
									a__  :		Optional[Any]					=    AutoConfig.from_pretrained(__UpperCAmelCase    )
									a__  :		Any					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		Union[str, Any]					=    TensorFlowBenchmark(__UpperCAmelCase   ,  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    )

						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""    )    ) == 0   ,  """Cannot do xla on CPU."""    )
						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Union[str, Any]:
									"""simple docstring"""




									a__  :		List[str]					=    'sshleifer/tiny-gpt2'
									a__  :		Optional[Any]					=    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]   ,  training=__UpperCAmelCase   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  use_xla=__UpperCAmelCase   ,  multi_process=__UpperCAmelCase   ,  )
									a__  :		Any					=    TensorFlowBenchmark(__UpperCAmelCase    )
									a__  :		str					=    benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result    )
									self.check_results_dict_not_empty(results.memory_inference_result    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> str:
									"""simple docstring"""




									a__  :		List[str]					=    'sshleifer/tiny-gpt2'
									with tempfile.TemporaryDirectory() as tmp_dir:
												a__  :		List[str]					=    TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]   ,  inference=__UpperCAmelCase   ,  save_to_csv=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  inference_time_csv_file=os.path.join(__UpperCAmelCase   ,  """inf_time.csv"""    )   ,  inference_memory_csv_file=os.path.join(__UpperCAmelCase   ,  """inf_mem.csv"""    )   ,  env_info_csv_file=os.path.join(__UpperCAmelCase   ,  """env.csv"""    )   ,  multi_process=__UpperCAmelCase   ,  )
												a__  :		Tuple					=    TensorFlowBenchmark(__UpperCAmelCase    )
												benchmark.run()
												self.assertTrue(Path(os.path.join(__UpperCAmelCase   ,  """inf_time.csv"""    )    ).exists()    )
												self.assertTrue(Path(os.path.join(__UpperCAmelCase   ,  """inf_mem.csv"""    )    ).exists()    )
												self.assertTrue(Path(os.path.join(__UpperCAmelCase   ,  """env.csv"""    )    ).exists()    )

						def 				SCREAMING_SNAKE_CASE__(  self    )   -> Optional[int]:
									"""simple docstring"""




									a__  :		int					=    'sshleifer/tiny-gpt2'

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

									with tempfile.TemporaryDirectory() as tmp_dir:
												a__  :		Any					=    TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]   ,  inference=__UpperCAmelCase   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  log_filename=os.path.join(__UpperCAmelCase   ,  """log.txt"""    )   ,  log_print=__UpperCAmelCase   ,  trace_memory_line_by_line=__UpperCAmelCase   ,  eager_mode=__UpperCAmelCase   ,  multi_process=__UpperCAmelCase   ,  )
												a__  :		Any					=    TensorFlowBenchmark(__UpperCAmelCase    )
												a__  :		Optional[Any]					=    benchmark.run()
												_check_summary_is_not_empty(result.inference_summary    )
												self.assertTrue(Path(os.path.join(__UpperCAmelCase   ,  """log.txt"""    )    ).exists()    )











"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				int(_SCREAMING_SNAKE_CASE     )
  if decimal in (0, 1):  # Exit cases for the recursion
    return str(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				divmod(_SCREAMING_SNAKE_CASE	,  2     )
  return binary_recursive(_SCREAMING_SNAKE_CASE     ) + str(_SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				str(_SCREAMING_SNAKE_CASE     ).strip()
  if not number:
    raise ValueError('No input value was provided'     )
  lowerCAmelCase__     :Dict											=				'-' if number.startswith('-'     ) else ''
  lowerCAmelCase__     :int											=				number.lstrip('-'     )
  if not number.isnumeric():
    raise ValueError('Input value is not an integer'     )
  return F"{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE     )     )}"


if __name__ == "__main__":
  from doctest import testmod

  testmod()
