





'''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   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase=None				,						__UpperCAmelCase=None   )    ->       Dict:

   '''simple docstring'''







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







@dataclass
class __a			:
   __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					:      List[Any]     ):

      '''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					:      int     ):

      '''simple docstring'''

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





   @property
   def   UpperCAmelCase__  (       self					:      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					:      Tuple     ):

      '''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'''





import math







def snake_case_     (					lowercase__		):

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,       int(math.sqrt(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



