


from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


SCREAMING_SNAKE_CASE						:Optional[int]			=  logging.get_logger(__name__)


@add_end_docstrings(a       )
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Optional[Any]     ,					**_lowerCAmelCase							:							int	) ->			int:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)

     if self.framework != "pt":
        raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''	)
     # No specific FOR_XXX available yet



  def __call__(  self							:							List[Any]     ,					_lowerCAmelCase							:							Union[np.ndarray, bytes, str]     ,					**_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     return super().__call__(_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					**_lowerCAmelCase							:							str	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       {}
     if "candidate_labels" in kwargs:
        snake_case_            =       kwargs["candidate_labels"]
     if "hypothesis_template" in kwargs:
        snake_case_            =       kwargs["hypothesis_template"]

     return preprocess_params, {}, {}



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							Tuple="This is a sound of {}."	) ->			Dict:




     """simple docstring"""
     if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        if audio.startswith("http://"	) or audio.startswith("https://"	):
           # We need to actually check for a real protocol, otherwise it's impossible to use a local file
           # like http_huggingface_co.png
           snake_case_            =       requests.get(_lowerCAmelCase	).content
        else:
           with open(_lowerCAmelCase     ,					"rb"	) as f:
              snake_case_            =       f.read()

     if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       ffmpeg_read(_lowerCAmelCase     ,					self.feature_extractor.sampling_rate	)

     if not isinstance(_lowerCAmelCase     ,					np.ndarray	):
        raise ValueError("We expect a numpy ndarray as input"	)
     if len(audio.shape	) != 1:
        raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"	)

     snake_case_            =       self.feature_extractor(
         [audio]     ,					sampling_rate=self.feature_extractor.sampling_rate     ,					return_tensors="pt"	)
     snake_case_            =       candidate_labels
     snake_case_            =       [hypothesis_template.format(_lowerCAmelCase	) for x in candidate_labels]
     snake_case_            =       self.tokenizer(_lowerCAmelCase     ,					return_tensors=self.framework     ,					padding=_lowerCAmelCase	)
     snake_case_            =       [text_inputs]
     return inputs



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       model_inputs.pop("candidate_labels"	)
     snake_case_            =       model_inputs.pop("text_inputs"	)
     if isinstance(text_inputs[0]     ,					_lowerCAmelCase	):
        snake_case_            =       text_inputs[0]
     else:
        # Batching case.
        snake_case_            =       text_inputs[0][0]

     snake_case_            =       self.model(**_lowerCAmelCase     ,					**_lowerCAmelCase	)

     snake_case_            =       {
         "candidate_labels": candidate_labels,
         "logits": outputs.logits_per_audio,
     }
     return model_outputs



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[int]	) ->			Any:




     """simple docstring"""
     snake_case_            =       model_outputs.pop("candidate_labels"	)
     snake_case_            =       model_outputs["logits"][0]

     if self.framework == "pt":
        snake_case_            =       logits.softmax(dim=0	)
        snake_case_            =       probs.tolist()
     else:
        raise ValueError("`tf` framework not supported."	)

     snake_case_            =       [
         {"score": score, "label": candidate_label}
         for score, candidate_label in sorted(zip(_lowerCAmelCase     ,					_lowerCAmelCase	)     ,					key=lambda _lowerCAmelCase	: -x[0]	)
     ]
     return result








from __future__ import annotations

from math import gcd



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int = 2		,    lowerCAmelCase_				:int = 1		,    lowerCAmelCase_				:int = 3		,    )->int | None:
   '''simple docstring'''






   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(lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				) -> int:
      return (pow(lowerCAmelCase_		,    2				) + step) % modulus

   for _ in range(lowerCAmelCase_				):
      # These track the position within the cycle detection logic.
      snake_case_            =       seed
      snake_case_            =       seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

         # 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``.
         snake_case_            =       gcd(hare - tortoise		,    lowerCAmelCase_				)

         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.
      snake_case_            =       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

   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  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''',
   )
   SCREAMING_SNAKE_CASE						:List[str]			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:List[Any]			=  pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(F'''{args.num} is probably prime''')
   else:
      SCREAMING_SNAKE_CASE						:List[str]			=  args.num // divisor
      print(F'''{args.num} = {divisor} * {quotient}''')




