
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    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]    )     ->   Optional[Any]:
														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    __snake_case (							self	:  List[Any]    )     ->   str:
														__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		:      List[str]										=       TensorFlowBenchmark(_a    )
														__snake_case		:      int										=       benchmark.run()
														self.check_results_dict_not_empty(results.time_inference_result    )
														self.check_results_dict_not_empty(results.memory_inference_result    )

							def    __snake_case (							self	:  str    )     ->   Optional[Any]:
														__snake_case		:      Optional[int]										=       "sgugger/tiny-distilbert-classification"
														__snake_case		:      Dict										=       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,     training=_a   ,     inference=_a   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_a   ,     only_pretrain_model=_a   ,     )
														__snake_case		:      Tuple										=       TensorFlowBenchmark(_a    )
														__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    __snake_case (							self	:  str    )     ->   Any:
														__snake_case		:      Optional[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		:      List[str]										=       TensorFlowBenchmark(_a    )
														__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    __snake_case (							self	:  str    )     ->   str:
														__snake_case		:      Optional[int]										=       "sshleifer/tiny-gpt2"
														__snake_case		:      Any										=       AutoConfig.from_pretrained(_a    )
														__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		:      Union[str, Any]										=       TensorFlowBenchmark(_a   ,     [config]    )
														__snake_case		:      Any										=       benchmark.run()
														self.check_results_dict_not_empty(results.time_inference_result    )
														self.check_results_dict_not_empty(results.memory_inference_result    )

							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														__snake_case		:      Union[str, Any]										=       "sshleifer/tiny-gpt2"
														__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		:      Union[str, Any]										=       TensorFlowBenchmark(_a   ,     [config]    )
														__snake_case		:      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    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      Optional[Any]										=       "sshleifer/tiny-gpt2"
														__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    )
														__snake_case		:      int										=       benchmark.run()
														self.check_results_dict_not_empty(results.time_train_result    )
														self.check_results_dict_not_empty(results.memory_train_result    )

							def    __snake_case (							self	:  Dict    )     ->   Tuple:
														__snake_case		:      Union[str, Any]										=       "sshleifer/tiny-gpt2"
														__snake_case		:      List[str]										=       AutoConfig.from_pretrained(_a    )
														__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[Any]										=       TensorFlowBenchmark(_a   ,     [config]    )
														__snake_case		:      int										=       benchmark.run()
														self.check_results_dict_not_empty(results.time_train_result    )
														self.check_results_dict_not_empty(results.memory_train_result    )

							def    __snake_case (							self	:  Dict    )     ->   Union[str, Any]:
														__snake_case		:      List[Any]										=       "patrickvonplaten/t5-tiny-random"
														__snake_case		:      Optional[Any]										=       AutoConfig.from_pretrained(_a    )
														__snake_case		:      Optional[Any]										=       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,     training=_a   ,     inference=_a   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     multi_process=_a   ,     )
														__snake_case		:      str										=       TensorFlowBenchmark(_a   ,     configs=[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    )

							@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"    )    ) == 0   ,     "Cannot do xla on CPU."    )
							def    __snake_case (							self	:  Optional[Any]    )     ->   Dict:
														__snake_case		:      Union[str, Any]										=       "sshleifer/tiny-gpt2"
														__snake_case		:      List[str]										=       TensorFlowBenchmarkArguments(
														    models=[MODEL_ID]   ,     training=_a   ,     inference=_a   ,     sequence_lengths=[8]   ,     batch_sizes=[1]   ,     use_xla=_a   ,     multi_process=_a   ,     )
														__snake_case		:      List[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    __snake_case (							self	:  Tuple    )     ->   int:
														__snake_case		:      Dict										=       "sshleifer/tiny-gpt2"
														with tempfile.TemporaryDirectory() as tmp_dir:
																					__snake_case		:      Union[str, 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		:      List[str]										=       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    __snake_case (							self	:  str    )     ->   List[Any]:
														__snake_case		:      Optional[Any]										=       "sshleifer/tiny-gpt2"

														def _check_summary_is_not_empty(lowerCamelCase	:  Tuple    ):
																					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		:      List[Any]										=       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		:      Optional[Any]										=       TensorFlowBenchmark(_a    )
																					__snake_case		:      List[Any]										=       benchmark.run()
																					_check_summary_is_not_empty(result.inference_summary    )
																					self.assertTrue(Path(os.path.join(_a   ,     "log.txt"    )    ).exists()    )





def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return x if y == 0 else greatest_common_divisor(__lowerCamelCase							,     x % y     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (x * y) // greatest_common_divisor(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase = 2_0     ):
							__snake_case		:      Optional[Any]										=       1
							for i in range(1							,     n + 1     ):
														__snake_case		:      Any										=       lcm(__lowerCamelCase							,     __lowerCamelCase     )
							return g


if __name__ == "__main__":
			print(f'''{solution() = }''')

