




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

from ..utils import logging


_UpperCAmelCase	:						List[str]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase=None,			lowerCamelCase=None   ):
  return field(default_factory=lambda: default,			metadata=lowerCamelCase   )







@dataclass
class    __lowerCAmelCase       :
       _a 					=						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'''
               )
           }      , )

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

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         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 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return json.dumps(dataclasses.asdict(self				)       ,					indent=2				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         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 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = False   ):
  if n == 2:
    return True
  if not n % 2 or n < 2:
    return False
  if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
    return False
  if n > 3317044064679887385961981 and not allow_probable:
    raise ValueError(
        "Warning: upper bound of deterministic test is exceeded. "
        "Pass allow_probable=True to allow probabilistic test. "
        "A return value of True indicates a probable prime."   )
  # array bounds provided by analysis
  lowercase    :Optional[Any]      	=						[
      2047,
      1373653,
      25326001,
      3215031751,
      2152302898747,
      3474749660383,
      341550071728321,
      1,
      3825123056546413051,
      1,
      1,
      318665857834031151167461,
      3317044064679887385961981,
  ]

  lowercase    :List[Any]      	=						[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(lowerCamelCase,			1   ):
    if n < _p:
      # then we have our last prime to check
      lowercase    :Tuple      	=						primes[:idx]
      break
  lowercase	,    lowercase    :str      	=						n - 1, 0
  # break up n -1 into a power of 2 (s) and
  # remaining odd component
  # essentially, solve for d * 2 ** s == n - 1
  while d % 2 == 0:
    d //= 2
    s += 1
  for prime in plist:
    lowercase    :Tuple      	=						False
    for r in range(lowerCamelCase   ):
      lowercase    :Any      	=						pow(lowerCamelCase,			d * 2**r,			lowerCamelCase   )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        lowercase    :Any      	=						True
        # this loop will not determine compositeness
        break
    if pr:
      continue
    # if pr is False, then the above loop never evaluated to true,
    # and the n MUST be composite
    return False
  return True



def       UpperCAmelCase__  (     ):
  assert not miller_rabin(561   )
  assert miller_rabin(563   )
  # 2047

  assert not miller_rabin(838201   )
  assert miller_rabin(838207   )
  # 1_373_653

  assert not miller_rabin(17316001   )
  assert miller_rabin(17316017   )
  # 25_326_001

  assert not miller_rabin(3078386641   )
  assert miller_rabin(3078386653   )
  # 3_215_031_751

  assert not miller_rabin(1713045574801   )
  assert miller_rabin(1713045574819   )
  # 2_152_302_898_747

  assert not miller_rabin(2779799728307   )
  assert miller_rabin(2779799728327   )
  # 3_474_749_660_383

  assert not miller_rabin(113850023909441   )
  assert miller_rabin(113850023909527   )
  # 341_550_071_728_321

  assert not miller_rabin(1275041018848804351   )
  assert miller_rabin(1275041018848804391   )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(79666464458507787791867   )
  assert miller_rabin(79666464458507787791951   )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(552840677446647897660333   )
  assert miller_rabin(552840677446647897660359   )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



