



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

from ..utils import logging


lowercase_													=      logging.get_logger(__name__)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]=None					, __lowerCamelCase						:     List[str]=None						)	->    Optional[Any]:
					return field(default_factory=lambda: default					, metadata=__lowerCamelCase						)






@dataclass
class     __a			:
	SCREAMING_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"
	        )
	    }						,     )

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

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

	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Use FP16 to accelerate inference."}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Benchmark training of model"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Verbose memory tracing"}					)
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=SCREAMING_SNAKE_CASE						,     metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }						,     )
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Trace memory line by line"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Save result to a CSV file"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Save all print statements in a log file"}					)
	SCREAMING_SNAKE_CASE  	=							field(default=SCREAMING_SNAKE_CASE						,     metadata={"help": "Whether to print environment information"}					)
	SCREAMING_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."
	        )
	    }						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"inference_time_{round(time()					)}.csv"						,     metadata={"help": "CSV filename used if saving time results to csv."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"inference_memory_{round(time()					)}.csv"						,     metadata={"help": "CSV filename used if saving memory results to csv."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"train_time_{round(time()					)}.csv"						,     metadata={"help": "CSV filename used if saving time results to csv for training."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"train_memory_{round(time()					)}.csv"						,     metadata={"help": "CSV filename used if saving memory results to csv for training."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"env_info_{round(time()					)}.csv"						,     metadata={"help": "CSV filename used if saving environment information."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(
	    default=f"log_{round(time()					)}.csv"						,     metadata={"help": "Log filename used if print statements are saved in log."}						,     )
	SCREAMING_SNAKE_CASE  	=							field(default=3						,     metadata={"help": "Times an experiment will be run."}					)
	SCREAMING_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 		UpperCamelCase				(    self				:     Union[str, Any])->				List[str]:
						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				:     str)->				Union[str, Any]:
						return json.dumps(dataclasses.asdict(self)					,						indent=2)



	@property
	def 		UpperCamelCase				(    self				:     Tuple)->				List[str]:
						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				:     Optional[Any])->				int:
						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        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




