



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

from ..utils import logging


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase=None				, _lowerCamelCase=None  ):





							'''simple docstring'''




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



@dataclass
class   UpperCAmelCase_    :
							lowerCamelCase__   			=		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'
							        )
							    }   ,					)

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

							lowerCamelCase__   			=		list_field(
							    default=[8, 32, 128, 512]   ,					metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}   ,					)

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







							def    snake_case__      (   self):

														'''simple docstring'''

														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    snake_case__      (   self):

														'''simple docstring'''

														return json.dumps(dataclasses.asdict(self),			indent=2)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														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    snake_case__      (   self):

														'''simple docstring'''

														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





def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
