





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    ):







		def        lowercase       (    self ,						__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["bs"] ,						model_result["ss"]				):
																	_snake_case       :					Union[str, Any]				       =   model_result["result"][batch_size][sequence_length]
																	self.assertIsNotNone(A__				)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					Optional[Any]				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						eager_mode=A__ ,						multi_process=A__ ,						)
							_snake_case       :					str				       =   TensorFlowBenchmark(A__				)
							_snake_case       :					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        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

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







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Dict				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					Union[str, Any]				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=A__ ,						)
							_snake_case       :					List[str]				       =   TensorFlowBenchmark(A__				)
							_snake_case       :					Tuple				       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result				)
							self.check_results_dict_not_empty(results.memory_inference_result				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Dict				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					List[Any]				       =   AutoConfig.from_pretrained(A__				)
							_snake_case       :					List[Any]				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						eager_mode=A__ ,						multi_process=A__ ,						)
							_snake_case       :					Any				       =   TensorFlowBenchmark(A__ ,						[config]				)
							_snake_case       :					Dict				       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result				)
							self.check_results_dict_not_empty(results.memory_inference_result				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					str				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					Any				       =   AutoConfig.from_pretrained(A__				)
							_snake_case       :					int				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=A__ ,						)
							_snake_case       :					Any				       =   TensorFlowBenchmark(A__ ,						[config]				)
							_snake_case       :					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        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					Dict				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=A__ ,						)
							_snake_case       :					Union[str, Any]				       =   TensorFlowBenchmark(A__				)
							_snake_case       :					Tuple				       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result				)
							self.check_results_dict_not_empty(results.memory_train_result				)







		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

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







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   "patrickvonplaten/t5-tiny-random"
							_snake_case       :					Dict				       =   AutoConfig.from_pretrained(A__				)
							_snake_case       :					int				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						multi_process=A__ ,						)
							_snake_case       :					List[str]				       =   TensorFlowBenchmark(A__ ,						configs=[config]				)
							_snake_case       :					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        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   "sshleifer/tiny-gpt2"
							_snake_case       :					Dict				       =   TensorFlowBenchmarkArguments(
							    models=[MODEL_ID] ,						training=A__ ,						inference=A__ ,						sequence_lengths=[8] ,						batch_sizes=[1] ,						use_xla=A__ ,						multi_process=A__ ,						)
							_snake_case       :					Optional[Any]				       =   TensorFlowBenchmark(A__				)
							_snake_case       :					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        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					str				       =   "sshleifer/tiny-gpt2"
							with tempfile.TemporaryDirectory() as tmp_dir:
												_snake_case       :					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__ ,						)
												_snake_case       :					Dict				       =   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        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					int				       =   "sshleifer/tiny-gpt2"

							def _check_summary_is_not_empty(__snake_case				):
												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:
												_snake_case       :					Dict				       =   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__ ,						)
												_snake_case       :					Tuple				       =   TensorFlowBenchmark(A__				)
												_snake_case       :					List[str]				       =   benchmark.run()
												_check_summary_is_not_empty(result.inference_summary				)
												self.assertTrue(Path(os.path.join(A__ ,						"log.txt"				)				).exists()				)






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


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

							testmod()