






"""simple docstring"""



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

from ..utils import logging


A__	:    Union[str, Any]			 = logging.get_logger(__name__)

def 			_snake_case    (					lowerCamelCase__     :   int=None    ,						lowerCamelCase__     :   Optional[Any]=None  )    ->     str:
							return field(default_factory=lambda: default    ,						metadata=lowerCamelCase__  )







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:List[str]             =			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"
			        )
			    },       )

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

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

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


			def      UpperCAmelCase__						(				self					:  Dict ):
										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."       ,	snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  List[str] ):
										return json.dumps(dataclasses.asdict(self )       ,	indent=2 )


			@property
			def      UpperCAmelCase__						(				self					:  Tuple ):
										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      UpperCAmelCase__						(				self					:  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







"""simple docstring"""



from __future__ import annotations

from collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



