





import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowercase       :str       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]=None			,   _lowerCamelCase      :  Any=None     ):





						'''simple docstring'''


						return field(default_factory=lambda: default			,   metadata=lowerCAmelCase__     )



@dataclass
class    UpperCamelCase__     :






							"""simple docstring"""

							snake_case_							 =						list_field(
							    default=[]					,							metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    }					,							)

							snake_case_							 =						list_field(
							    default=[8]					,							metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}							)

							snake_case_							 =						list_field(
							    default=[8, 32, 1_28, 5_12]					,							metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}					,							)

							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}					,							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}					,							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Use FP16 to accelerate inference."}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Benchmark training of model"}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Verbose memory tracing"}							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}					,							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    }					,							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Trace memory line by line"}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Save result to a CSV file"}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Save all print statements in a log file"}							)
							snake_case_							 =						field(default=__SCREAMING_SNAKE_CASE					,							metadata={"help": "Whether to print environment information"}							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    }					,							)
							snake_case_							 =						field(
							    default=f'inference_time_{round(time()							)}.csv'					,							metadata={"help": "CSV filename used if saving time results to csv."}					,							)
							snake_case_							 =						field(
							    default=f'inference_memory_{round(time()							)}.csv'					,							metadata={"help": "CSV filename used if saving memory results to csv."}					,							)
							snake_case_							 =						field(
							    default=f'train_time_{round(time()							)}.csv'					,							metadata={"help": "CSV filename used if saving time results to csv for training."}					,							)
							snake_case_							 =						field(
							    default=f'train_memory_{round(time()							)}.csv'					,							metadata={"help": "CSV filename used if saving memory results to csv for training."}					,							)
							snake_case_							 =						field(
							    default=f'env_info_{round(time()							)}.csv'					,							metadata={"help": "CSV filename used if saving environment information."}					,							)
							snake_case_							 =						field(
							    default=f'log_{round(time()							)}.csv'					,							metadata={"help": "Log filename used if print statements are saved in log."}					,							)
							snake_case_							 =						field(default=3					,							metadata={"help": "Times an experiment will be run."}							)
							snake_case_							 =						field(
							    default=__SCREAMING_SNAKE_CASE					,							metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    }					,							)

							def      A_  (      self				:     List[str]    )	->Tuple:
													warnings.warn(
													    f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
													    " are deprecated in general and it is advised to use external Benchmarking libraries "
													    " to benchmark Transformer models."							,	_a							,	)

							def      A_  (      self				:     Tuple    )	->int:
													return json.dumps(dataclasses.asdict(self    )							,	indent=2    )

							@property
							def      A_  (      self				:     str    )	->Union[str, Any]:
													if len(self.models    ) <= 0:
																			raise ValueError(
																			    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
																			    " bert-base-cased` or `args.models = [\'bert-base-cased\']."    )
													return self.models






							@property
							def      A_  (      self				:     Dict    )	->str:
													if not self.multi_process:
																			return False
													elif self.is_tpu:
																			logger.info("Multiprocessing is currently not possible on TPU."    )
																			return False
													else:
																			return True






from __future__ import annotations

from fractions import Fraction







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						return (
						    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
						)







def       UpperCAmelCase							(     _lowerCamelCase      :  int     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					[]
						SCREAMING_SNAKE_CASE__     :				str   =					11
						SCREAMING_SNAKE_CASE__     :				Any   =					int("1" + "0" * digit_len     )
						for num in range(_lowerCamelCase			,   _lowerCamelCase     ):
												while den <= 99:
																		if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																								if is_digit_cancelling(_lowerCamelCase			,   _lowerCamelCase     ):
																														solutions.append(f"""{num}/{den}"""     )
																		den += 1
												num += 1
												SCREAMING_SNAKE_CASE__     :				str   =					10
						return solutions







def       UpperCAmelCase							(     _lowerCamelCase      :  int = 2     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1.0
						for fraction in fraction_list(_lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					Fraction(_lowerCamelCase     )
												result *= frac.denominator / frac.numerator
						return int(_lowerCamelCase     )


if __name__ == "__main__":
				print(solution())