




'''simple docstring'''





from functools import reduce

_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def      UpperCamelCase_(	snake_case							:       Any = N     ):

							'''simple docstring'''






							return max(
							    # mypy cannot properly interpret reduce
							    int(reduce(lambda snake_case							,				snake_case     : str(int(SCREAMING_SNAKE_CASE__     ) * int(SCREAMING_SNAKE_CASE__     )     )							,				n[i : i + 1_3]     )     )
							    for i in range(len(SCREAMING_SNAKE_CASE__     ) - 1_2     )     )


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


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,**snake_case      ):



           '''simple docstring'''







           super().__init__(**snake_case      )

           if self.framework != "pt":
                  raise ValueError(f"The {self.__class__} is only available in PyTorch."      )
           # No specific FOR_XXX available yet
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      {}
           if "candidate_labels" in kwargs:
                  lowercase							:     List[str]       	=      kwargs["""candidate_labels"""]
           if "hypothesis_template" in kwargs:
                  lowercase							:     Dict       	=      kwargs["""hypothesis_template"""]

           return preprocess_params, {}, {}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,snake_case="This is a sound of {}."      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  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
                         lowercase							:     Optional[Any]       	=      requests.get(snake_case      ).content
                  else:
                         with open(snake_case		,"""rb"""      ) as f:
                                lowercase							:     Union[str, Any]       	=      f.read()

           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     int       	=      ffmpeg_read(snake_case		,self.feature_extractor.sampling_rate      )

           if not isinstance(snake_case		,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"""      )

           lowercase							:     Dict       	=      self.feature_extractor(
               [audio]		,sampling_rate=self.feature_extractor.sampling_rate		,return_tensors="""pt"""      )
           lowercase							:     Tuple       	=      candidate_labels
           lowercase							:     Tuple       	=      [hypothesis_template.format(snake_case      ) for x in candidate_labels]
           lowercase							:     Optional[Any]       	=      self.tokenizer(snake_case		,return_tensors=self.framework		,padding=snake_case      )
           lowercase							:     Optional[Any]       	=      [text_inputs]
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      model_inputs.pop("""candidate_labels"""      )
           lowercase							:     Dict       	=      model_inputs.pop("""text_inputs"""      )
           if isinstance(text_inputs[0]		,snake_case      ):
                  lowercase							:     List[Any]       	=      text_inputs[0]
           else:
                  # Batching case.
                  lowercase							:     Dict       	=      text_inputs[0][0]

           lowercase							:     Optional[Any]       	=      self.model(**snake_case		,**snake_case      )

           lowercase							:     Any       	=      {
               """candidate_labels""": candidate_labels,
               """logits""": outputs.logits_per_audio,
           }
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      model_outputs.pop("""candidate_labels"""      )
           lowercase							:     Any       	=      model_outputs["""logits"""][0]

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

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