
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     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    int ):
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result["""bs"""]  ,		model_result["""ss"""] ):
															UpperCamelCase_:							Dict		=  model_result["""result"""][batch_size][sequence_length]
															self.assertIsNotNone(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							Dict		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							Union[str, Any]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		eager_mode=snake_case_  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							Tuple		=  TensorFlowBenchmark(snake_case_ )
							UpperCamelCase_:							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  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							int		=  """sgugger/tiny-distilbert-classification"""
							UpperCamelCase_:							Optional[int]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		only_pretrain_model=snake_case_  ,		)
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmark(snake_case_ )
							UpperCamelCase_:							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	:    Optional[Any] ):
							UpperCamelCase_:							List[str]		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							Optional[Any]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							int		=  TensorFlowBenchmark(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  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[str] ):
							UpperCamelCase_:							Dict		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							str		=  AutoConfig.from_pretrained(snake_case_ )
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		eager_mode=snake_case_  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							Union[str, Any]		=  TensorFlowBenchmark(snake_case_  ,		[config] )
							UpperCamelCase_:							Tuple		=  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] ):
							UpperCamelCase_:							List[Any]		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							Dict		=  AutoConfig.from_pretrained(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							int		=  TensorFlowBenchmark(snake_case_  ,		[config] )
							UpperCamelCase_:							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	:    Union[str, Any] ):
							UpperCamelCase_:							Union[str, Any]		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmark(snake_case_ )
							UpperCamelCase_:							str		=  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	:    List[str] ):
							UpperCamelCase_:							Optional[int]		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							Union[str, Any]		=  AutoConfig.from_pretrained(snake_case_ )
							UpperCamelCase_:							Tuple		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							Optional[Any]		=  TensorFlowBenchmark(snake_case_  ,		[config] )
							UpperCamelCase_:							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  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Any		=  """patrickvonplaten/t5-tiny-random"""
							UpperCamelCase_:							Union[str, Any]		=  AutoConfig.from_pretrained(snake_case_ )
							UpperCamelCase_:							Optional[int]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							Union[str, Any]		=  TensorFlowBenchmark(snake_case_  ,		configs=[config] )
							UpperCamelCase_:							Optional[int]		=  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  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							List[Any]		=  """sshleifer/tiny-gpt2"""
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]  ,		training=snake_case_  ,		inference=snake_case_  ,		sequence_lengths=[8]  ,		batch_sizes=[1]  ,		use_xla=snake_case_  ,		multi_process=snake_case_  ,		)
							UpperCamelCase_:							List[str]		=  TensorFlowBenchmark(snake_case_ )
							UpperCamelCase_:							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	:    Optional[Any] ):
							UpperCamelCase_:							Optional[Any]		=  """sshleifer/tiny-gpt2"""
							with tempfile.TemporaryDirectory() as tmp_dir:
											UpperCamelCase_:							int		=  TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]  ,		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""" )  ,		inference_memory_csv_file=os.path.join(snake_case_  ,		"""inf_mem.csv""" )  ,		env_info_csv_file=os.path.join(snake_case_  ,		"""env.csv""" )  ,		multi_process=snake_case_  ,		)
											UpperCamelCase_:							List[str]		=  TensorFlowBenchmark(snake_case_ )
											benchmark.run()
											self.assertTrue(Path(os.path.join(snake_case_  ,		"""inf_time.csv""" ) ).exists() )
											self.assertTrue(Path(os.path.join(snake_case_  ,		"""inf_mem.csv""" ) ).exists() )
											self.assertTrue(Path(os.path.join(snake_case_  ,		"""env.csv""" ) ).exists() )




			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							int		=  """sshleifer/tiny-gpt2"""

							def _check_summary_is_not_empty(snake_case_	:    Optional[int] ):
											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:
											UpperCamelCase_:							Any		=  TensorFlowBenchmarkArguments(
											    models=[MODEL_ID]  ,		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_  ,		eager_mode=snake_case_  ,		multi_process=snake_case_  ,		)
											UpperCamelCase_:							Tuple		=  TensorFlowBenchmark(snake_case_ )
											UpperCamelCase_:							Any		=  benchmark.run()
											_check_summary_is_not_empty(result.inference_summary )
											self.assertTrue(Path(os.path.join(snake_case_  ,		"""log.txt""" ) ).exists() )








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		float:
				if mass < 0:
								raise ValueError("""The mass of a body cannot be negative"""  )
				return 0.5 * mass * abs(lowerCamelCase  ) * abs(lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






