




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      [0] * len(UpperCamelCase_       )

				for i in range(1      ,    len(UpperCamelCase_       )       ):
								# use last results for better performance - dynamic programming
								_lowerCAmelCase    :    Union[str, Any]										=      prefix_result[i - 1]
								while j > 0 and input_string[i] != input_string[j]:
												_lowerCAmelCase    :    str										=      prefix_result[j - 1]

								if input_string[i] == input_string[j]:
												j += 1
								_lowerCAmelCase    :    Optional[int]										=      j

				return prefix_result




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return max(prefix_function(UpperCamelCase_       )       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """sgugger/tiny-distilbert-classification"""
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	only_pretrain_model=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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(torch_device == """cpu"""		,	"""Cant do half precision"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	fpaa=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    int										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								# set architectures equal to `None`
								_lowerCAmelCase    :    Optional[int]										=      None
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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      )



				@unittest.skipIf(torch_device == """cpu"""		,	"""Can't do half precision"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	fpaa=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[Any]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Tuple										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[int]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    int										=      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      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase    :    Optional[int]										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	save_to_csv=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	inference_time_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )		,	train_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )		,	inference_memory_csv_file=os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )		,	train_time_csv_file=os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )		,	env_info_csv_file=os.path.join(_UpperCAmelCase		,	"""env.csv"""      )		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
												benchmark.run()
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_time.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""inf_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""train_mem.csv"""      )      ).exists()      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""env.csv"""      )      ).exists()      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_UpperCAmelCase      :		int      ):
												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:
												_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
												    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	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		,	multi_process=_UpperCAmelCase		,	)
												_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
												_lowerCAmelCase    :    Any										=      benchmark.run()
												_check_summary_is_not_empty(result.inference_summary      )
												_check_summary_is_not_empty(result.train_summary      )
												self.assertTrue(Path(os.path.join(_UpperCAmelCase		,	"""log.txt"""      )      ).exists()      )

