

'''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


UpperCAmelCase					:			Optional[Any]       =	logging.get_logger(__name__)







def   a__			(    a__=None ,       a__=None		):




     """simple docstring"""


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







@dataclass
class 		lowerCAmelCase__  :



 """simple docstring"""



 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  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """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."""   ,				lowerCamelCase__   ,				)
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      return json.dumps(dataclasses.asdict(self     )   ,				indent=2     )
 @property
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					List[Any]:


      """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[Any]     )    ->					List[Any]:


      """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 A__       (	UpperCAmelCase_ = 6_0_0_8_5_1_4_7_5_1_4_3				):

						try:
												_UpperCamelCase   :						str												=  int(UpperCAmelCase_				)
						except (TypeError, ValueError):
												raise TypeError('Parameter n must be int or castable to int.'				)
						if n <= 0:
												raise ValueError('Parameter n must be greater than or equal to one.'				)
						_UpperCamelCase   :						int												=  1
						_UpperCamelCase   :						Optional[Any]												=  2
						while i * i <= n:
												while n % i == 0:
																		_UpperCamelCase   :						Union[str, Any]												=  i
																		n //= i
												i += 1
						if n > 1:
												_UpperCamelCase   :						Dict												=  n
						return int(UpperCAmelCase_				)


if __name__ == "__main__":
					print(F"""{solution() = }""")


