





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 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def 					UpperCAmelCase_		(			self     ,					_A     ):
									for model_result in results.values():
														for batch_size, sequence_length in zip(model_result['bs']     ,					model_result['ss']     ):
																			__A	:						List[str]       =				model_result['result'][batch_size][sequence_length]
																			self.assertIsNotNone(_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				'sshleifer/tiny-gpt2'
									__A	:						Optional[int]       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					eager_mode=_A     ,					multi_process=_A     ,					)
									__A	:						List[str]       =				TensorFlowBenchmark(_A     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				'sgugger/tiny-distilbert-classification'
									__A	:						Any       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					only_pretrain_model=_A     ,					)
									__A	:						Union[str, Any]       =				TensorFlowBenchmark(_A     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				'sshleifer/tiny-gpt2'
									__A	:						Optional[int]       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					)
									__A	:						List[str]       =				TensorFlowBenchmark(_A     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				'sshleifer/tiny-gpt2'
									__A	:						Tuple       =				AutoConfig.from_pretrained(_A     )
									__A	:						str       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					eager_mode=_A     ,					multi_process=_A     ,					)
									__A	:						str       =				TensorFlowBenchmark(_A     ,					[config]     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				'sshleifer/tiny-gpt2'
									__A	:						int       =				AutoConfig.from_pretrained(_A     )
									__A	:						Tuple       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					)
									__A	:						Optional[Any]       =				TensorFlowBenchmark(_A     ,					[config]     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				'sshleifer/tiny-gpt2'
									__A	:						Optional[int]       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					)
									__A	:						int       =				TensorFlowBenchmark(_A     )
									__A	:						Union[str, Any]       =				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result     )
									self.check_results_dict_not_empty(results.memory_train_result     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				'sshleifer/tiny-gpt2'
									__A	:						List[str]       =				AutoConfig.from_pretrained(_A     )
									__A	:						List[str]       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					)
									__A	:						str       =				TensorFlowBenchmark(_A     ,					[config]     )
									__A	:						int       =				benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result     )
									self.check_results_dict_not_empty(results.memory_train_result     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'patrickvonplaten/t5-tiny-random'
									__A	:						Any       =				AutoConfig.from_pretrained(_A     )
									__A	:						Optional[int]       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					multi_process=_A     ,					)
									__A	:						int       =				TensorFlowBenchmark(_A     ,					configs=[config]     )
									__A	:						Tuple       =				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 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				'sshleifer/tiny-gpt2'
									__A	:						int       =				TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]     ,					training=_A     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					use_xla=_A     ,					multi_process=_A     ,					)
									__A	:						Dict       =				TensorFlowBenchmark(_A     )
									__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 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				'sshleifer/tiny-gpt2'
									with tempfile.TemporaryDirectory() as tmp_dir:
														__A	:						Optional[Any]       =				TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]     ,					inference=_A     ,					save_to_csv=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					inference_time_csv_file=os.path.join(_A     ,					'inf_time.csv'     )     ,					inference_memory_csv_file=os.path.join(_A     ,					'inf_mem.csv'     )     ,					env_info_csv_file=os.path.join(_A     ,					'env.csv'     )     ,					multi_process=_A     ,					)
														__A	:						Optional[int]       =				TensorFlowBenchmark(_A     )
														benchmark.run()
														self.assertTrue(Path(os.path.join(_A     ,					'inf_time.csv'     )     ).exists()     )
														self.assertTrue(Path(os.path.join(_A     ,					'inf_mem.csv'     )     ).exists()     )
														self.assertTrue(Path(os.path.join(_A     ,					'env.csv'     )     ).exists()     )





				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'sshleifer/tiny-gpt2'

									def _check_summary_is_not_empty(_A     ):
														self.assertTrue(hasattr(_A     ,					'sequential'     )     )
														self.assertTrue(hasattr(_A     ,					'cumulative'     )     )
														self.assertTrue(hasattr(_A     ,					'current'     )     )
														self.assertTrue(hasattr(_A     ,					'total'     )     )

									with tempfile.TemporaryDirectory() as tmp_dir:
														__A	:						int       =				TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]     ,					inference=_A     ,					sequence_lengths=[8]     ,					batch_sizes=[1]     ,					log_filename=os.path.join(_A     ,					'log.txt'     )     ,					log_print=_A     ,					trace_memory_line_by_line=_A     ,					eager_mode=_A     ,					multi_process=_A     ,					)
														__A	:						Dict       =				TensorFlowBenchmark(_A     )
														__A	:						Union[str, Any]       =				benchmark.run()
														_check_summary_is_not_empty(result.inference_summary     )
														self.assertTrue(Path(os.path.join(_A     ,					'log.txt'     )     ).exists()     )











from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					if not nums:
										return 0
					__A	:						Optional[int]       =				nums[0]
					__A	:						str       =				0
					for num in nums[1:]:
										__A    ,				__A	:						Tuple       =				(
										    max_excluding + num,
										    max(a					,						a  ),
										)
					return max(a					,						a  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




