



"""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


lowerCAmelCase_   						=   logging.get_logger(__name__)



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





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					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"
		        )
		    }     ,)

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

		lowerCAmelCase			:					List[int]   	=				list_field(
		    default=[8, 3_2, 1_2_8, 5_1_2]     ,metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}     ,)

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







		def 					UpperCAmelCase    (	self						:  Dict						)					->		int:






				"""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.'''			,_snake_case			,)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		str:






				"""simple docstring"""



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







		@property
		def 					UpperCAmelCase    (	self						:  int						)					->		List[str]:






				"""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 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""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


"""simple docstring"""




def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       int     , __snake_case				:       int  ) ->				float:
       lowercase    :				List[Any]       		=							(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total


def   __magic_name__	(      ) ->				int:
       print(sum_of_series(1     , 1     , 10  )  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


