

from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def 			UpperCAmelCase__				(			_A      : int							):





  '''simple docstring'''







  a__  =int(number**0.5							)
  return number == sq * sq




def 			UpperCAmelCase__				(			_A      : int	,      _A      : int	,      _A      : int	,      _A      : int	,      _A      : int	,      _A      : int							):





  '''simple docstring'''







  a__  =x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
  a__  =x_den * y_den * z_den
  a__  =gcd(_A	,      _A							)
  top //= hcf
  bottom //= hcf
  return top, bottom




def 			UpperCAmelCase__				(			_A      : int = 35							):





  '''simple docstring'''







  a__  =set()
  a__  =42
  a__  =Fraction(0							)
  a__  =42

  for x_num in range(1	,      order + 1							):
    for x_den in range(x_num + 1	,      order + 1							):
      for y_num in range(1	,      order + 1							):
        for y_den in range(y_num + 1	,      order + 1							):
          # n=1
          a__  =x_num * y_den + x_den * y_num
          a__  =x_den * y_den
          a__  =gcd(_A	,      _A							)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            a__  =add_three(
                _A	,      _A	,      _A	,      _A	,      _A	,      _A							)
            unique_s.add(_A							)

          # n=2
          a__  =(
              x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
          )
          a__  =x_den * x_den * y_den * y_den
          if is_sq(_A							) and is_sq(_A							):
            a__  =int(sqrt(_A							)							)
            a__  =int(sqrt(_A							)							)
            a__  =gcd(_A	,      _A							)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              a__  =add_three(
                  _A	,      _A	,      _A	,      _A	,      _A	,      _A							)
              unique_s.add(_A							)

                    # n=-1
          a__  =x_num * y_num
          a__  =x_den * y_num + x_num * y_den
          a__  =gcd(_A	,      _A							)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            a__  =add_three(
                _A	,      _A	,      _A	,      _A	,      _A	,      _A							)
            unique_s.add(_A							)

          # n=2
          a__  =x_num * x_num * y_num * y_num
          a__  =(
              x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
          )
          if is_sq(_A							) and is_sq(_A							):
            a__  =int(sqrt(_A							)							)
            a__  =int(sqrt(_A							)							)
            a__  =gcd(_A	,      _A							)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              a__  =add_three(
                  _A	,      _A	,      _A	,      _A	,      _A	,      _A							)
              unique_s.add(_A							)

  for num, den in unique_s:
    total += Fraction(_A	,      _A							)

  return total.denominator + total.numerator


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



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


lowerCamelCase           =					logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    **lowercase_       )							->    Dict:






     """simple docstring"""






     super().__init__(**lowercase_       )

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



   def __call__(				self,    lowercase_,    **lowercase_       )							->    Tuple:






     """simple docstring"""






     return super().__call__(lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    int:






     """simple docstring"""






     a__  ={}
     if "candidate_labels" in kwargs:
       a__  =kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
       a__  =kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=None,    lowercase_="This is a sound of {}."       )							->    Union[str, Any]:






     """simple docstring"""






     if isinstance(lowercase_,    lowercase_       ):
       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
         a__  =requests.get(lowercase_       ).content
       else:
         with open(lowercase_,    '''rb'''       ) as f:
           a__  =f.read()

     if isinstance(lowercase_,    lowercase_       ):
       a__  =ffmpeg_read(lowercase_,    self.feature_extractor.sampling_rate       )

     if not isinstance(lowercase_,    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'''       )

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



   def  _UpperCAmelCase (				self,    lowercase_       )							->    str:






     """simple docstring"""






     a__  =model_inputs.pop('''candidate_labels'''       )
     a__  =model_inputs.pop('''text_inputs'''       )
     if isinstance(text_inputs[0],    lowercase_       ):
       a__  =text_inputs[0]
     else:
       # Batching case.
       a__  =text_inputs[0][0]

     a__  =self.model(**lowercase_,    **lowercase_       )

     a__  ={
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_audio,
     }
     return model_outputs



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Any:






     """simple docstring"""






     a__  =model_outputs.pop('''candidate_labels'''       )
     a__  =model_outputs['''logits'''][0]

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

     a__  =[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(lowercase_,    lowercase_       ),    key=lambda lowercase_       : -x[0]       )
     ]
     return result
