





'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Tuple     	=	"wav2vec2"




   def __init__(					self,	__magic_name__=32,	__magic_name__=768,	__magic_name__=12,	__magic_name__=12,	__magic_name__=3072,	__magic_name__="gelu",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=0.02,	__magic_name__=1E-5,	__magic_name__="group",	__magic_name__="gelu",	__magic_name__=(512, 512, 512, 512, 512, 512, 512),	__magic_name__=(5, 2, 2, 2, 2, 2, 2),	__magic_name__=(10, 3, 3, 3, 3, 2, 2),	__magic_name__=False,	__magic_name__=128,	__magic_name__=16,	__magic_name__=False,	__magic_name__=True,	__magic_name__=0.05,	__magic_name__=10,	__magic_name__=2,	__magic_name__=0.0,	__magic_name__=10,	__magic_name__=0,	__magic_name__=320,	__magic_name__=2,	__magic_name__=0.1,	__magic_name__=100,	__magic_name__=256,	__magic_name__=256,	__magic_name__=0.1,	__magic_name__="sum",	__magic_name__=False,	__magic_name__=False,	__magic_name__=256,	__magic_name__=(512, 512, 512, 512, 1500),	__magic_name__=(5, 3, 3, 1, 1),	__magic_name__=(1, 2, 3, 1, 1),	__magic_name__=512,	__magic_name__=0,	__magic_name__=1,	__magic_name__=2,	__magic_name__=False,	__magic_name__=3,	__magic_name__=2,	__magic_name__=3,	__magic_name__=None,	__magic_name__=None,	**__magic_name__,	)				->   Union[str, Any]:






       """simple docstring"""
       super().__init__(**__magic_name__,	pad_token_id=__magic_name__,	bos_token_id=__magic_name__,	eos_token_id=__magic_name__							)
       UpperCamelCase__							:       str      =   hidden_size
       UpperCamelCase__							:       Dict      =   feat_extract_norm
       UpperCamelCase__							:       Tuple      =   feat_extract_activation
       UpperCamelCase__							:       Union[str, Any]      =   list(__magic_name__							)
       UpperCamelCase__							:       Any      =   list(__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   list(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   conv_bias
       UpperCamelCase__							:       Tuple      =   num_conv_pos_embeddings
       UpperCamelCase__							:       Any      =   num_conv_pos_embedding_groups
       UpperCamelCase__							:       int      =   len(self.conv_dim							)
       UpperCamelCase__							:       Union[str, Any]      =   num_hidden_layers
       UpperCamelCase__							:       Optional[int]      =   intermediate_size
       UpperCamelCase__							:       Optional[int]      =   hidden_act
       UpperCamelCase__							:       Optional[int]      =   num_attention_heads
       UpperCamelCase__							:       Dict      =   hidden_dropout
       UpperCamelCase__							:       Dict      =   attention_dropout
       UpperCamelCase__							:       int      =   activation_dropout
       UpperCamelCase__							:       Optional[int]      =   feat_proj_dropout
       UpperCamelCase__							:       List[Any]      =   final_dropout
       UpperCamelCase__							:       Optional[Any]      =   layerdrop
       UpperCamelCase__							:       Union[str, Any]      =   layer_norm_eps
       UpperCamelCase__							:       Any      =   initializer_range
       UpperCamelCase__							:       str      =   vocab_size
       UpperCamelCase__							:       List[str]      =   do_stable_layer_norm
       UpperCamelCase__							:       List[str]      =   use_weighted_layer_sum

       if (
           (len(self.conv_stride							) != self.num_feat_extract_layers)
           or (len(self.conv_kernel							) != self.num_feat_extract_layers)
           or (len(self.conv_dim							) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
               ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
               f" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"
               f" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."							)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       UpperCamelCase__							:       List[str]      =   apply_spec_augment
       UpperCamelCase__							:       List[Any]      =   mask_time_prob
       UpperCamelCase__							:       Optional[int]      =   mask_time_length
       UpperCamelCase__							:       int      =   mask_time_min_masks
       UpperCamelCase__							:       List[str]      =   mask_feature_prob
       UpperCamelCase__							:       Dict      =   mask_feature_length
       UpperCamelCase__							:       List[Any]      =   mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       UpperCamelCase__							:       Any      =   num_codevectors_per_group
       UpperCamelCase__							:       Tuple      =   num_codevector_groups
       UpperCamelCase__							:       Optional[int]      =   contrastive_logits_temperature
       UpperCamelCase__							:       Dict      =   feat_quantizer_dropout
       UpperCamelCase__							:       List[Any]      =   num_negatives
       UpperCamelCase__							:       List[str]      =   codevector_dim
       UpperCamelCase__							:       List[Any]      =   proj_codevector_dim
       UpperCamelCase__							:       Tuple      =   diversity_loss_weight

       # ctc loss
       UpperCamelCase__							:       Union[str, Any]      =   ctc_loss_reduction
       UpperCamelCase__							:       List[Any]      =   ctc_zero_infinity

       # adapter
       UpperCamelCase__							:       Optional[Any]      =   add_adapter
       UpperCamelCase__							:       int      =   adapter_kernel_size
       UpperCamelCase__							:       List[Any]      =   adapter_stride
       UpperCamelCase__							:       int      =   num_adapter_layers
       UpperCamelCase__							:       Dict      =   output_hidden_size or hidden_size
       UpperCamelCase__							:       Optional[Any]      =   adapter_attn_dim

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       UpperCamelCase__							:       int      =   classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       UpperCamelCase__							:       Optional[int]      =   list(__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   list(__magic_name__							)
       UpperCamelCase__							:       Tuple      =   list(__magic_name__							)
       UpperCamelCase__							:       int      =   xvector_output_dim




   @property
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       return functools.reduce(operator.mul,	self.conv_stride,	1							)








from __future__ import annotations

from math import gcd




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    int = 2	,     __UpperCAmelCase:    int = 1	,     __UpperCAmelCase:    int = 3	,     )   ->			int | None:
    # A value less than 2 can cause an infinite loop in the algorithm.
    if num < 2:
        raise ValueError('''The input value cannot be less than 2'''   )

    # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
    # algorithm struggles to find factors that are divisible by two.
    # As a workaround, we specifically check for two and even inputs.
    #   See: https://math.stackexchange.com/a/2856214/165820
    if num > 2 and num % 2 == 0:
        return 2

    # Pollard's Rho algorithm requires a function that returns pseudorandom
    # values between 0 <= X < ``num``.  It doesn't need to be random in the
    # sense that the output value is cryptographically secure or difficult
    # to calculate, it only needs to be random in the sense that all output
    # values should be equally likely to appear.
    # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
    # However, the success of Pollard's algorithm isn't guaranteed and is
    # determined in part by the initial seed and the chosen random function.
    # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
    # where ``C`` is a value that we can modify between each attempt.
    def rand_fn(__UpperCAmelCase:    int	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    int   ) -> int:
        return (pow(__UpperCAmelCase	,     2   ) + step) % modulus

    for _ in range(__UpperCAmelCase   ):
        # These track the position within the cycle detection logic.
        UpperCamelCase__							:       List[Any]      =   seed
        UpperCamelCase__							:       List[str]      =   seed

        while True:
            # At each iteration, the tortoise moves one step and the hare moves two.
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       List[str]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   rand_fn(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

            # At some point both the tortoise and the hare will enter a cycle whose
            # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
            # the tortoise and hare will end up on the same value modulo ``p``.
            # We can detect when this happens because the position difference between
            # the tortoise and the hare will share a common divisor with ``num``.
            UpperCamelCase__							:       Optional[int]      =   gcd(hare - tortoise	,     __UpperCAmelCase   )

            if divisor == 1:
                # No common divisor yet, just keep searching.
                continue
            else:
                # We found a common divisor!
                if divisor == num:
                    # Unfortunately, the divisor is ``num`` itself and is useless.
                    break
                else:
                    # The divisor is a nontrivial factor of ``num``!
                    return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
        UpperCamelCase__							:       List[str]      =   hare

        # The new step value for the random function can just be incremented.
        # At first the results will be similar to what the old function would
        # have produced, but the value will quickly diverge after a bit.
        step += 1

    # We haven't found a divisor within the requested number of attempts.
    # We were unlucky or ``num`` itself is actually prime.
    return None


if __name__ == "__main__":
  import argparse

  UpperCAmelCase_  							=				argparse.ArgumentParser()
  parser.add_argument(
      'num',
      type=int,
      help='The value to find a divisor of',
  )
  parser.add_argument(
      '--attempts',
      type=int,
      default=3,
      help='The number of attempts before giving up',
  )
  UpperCAmelCase_  							=				parser.parse_args()

  UpperCAmelCase_  							=				pollard_rho(args.num, attempts=args.attempts)
  if divisor is None:
    print(F'''{args.num} is probably prime''')
  else:
    UpperCAmelCase_  							=				args.num // divisor
    print(F'''{args.num} = {divisor} * {quotient}''')

