


'''simple docstring'''



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


__UpperCAmelCase    :str				       =     logging.get_logger(__name__)



@add_end_docstrings(_a )
class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	str							,				**snake_case  :	Dict    )		->							Any:
          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  :	Tuple							,				snake_case  :	Union[np.ndarray, bytes, str]							,				**snake_case  :	Dict    )		->							List[Any]:
          return super().__call__(snake_case							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	int							,				**snake_case  :	int    )		->							Dict:
          __UpperCAmelCase			:				List[str]													=    {}
          if "candidate_labels" in kwargs:
               __UpperCAmelCase			:				List[Any]													=    kwargs['''candidate_labels''']
          if "hypothesis_template" in kwargs:
               __UpperCAmelCase			:				Tuple													=    kwargs['''hypothesis_template''']

          return preprocess_params, {}, {}

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Optional[int]=None							,				snake_case  :	List[Any]="This is a sound of {}."    )		->							Tuple:
          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
                    __UpperCAmelCase			:				List[str]													=    requests.get(snake_case    ).content
               else:
                    with open(snake_case							,				'''rb'''    ) as f:
                         __UpperCAmelCase			:				Optional[int]													=    f.read()

          if isinstance(snake_case							,				snake_case    ):
               __UpperCAmelCase			:				Optional[Any]													=    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'''    )

          __UpperCAmelCase			:				List[str]													=    self.feature_extractor(
              [audio]							,				sampling_rate=self.feature_extractor.sampling_rate							,				return_tensors='''pt'''    )
          __UpperCAmelCase			:				Tuple													=    candidate_labels
          __UpperCAmelCase			:				Optional[Any]													=    [hypothesis_template.format(snake_case    ) for x in candidate_labels]
          __UpperCAmelCase			:				Union[str, Any]													=    self.tokenizer(snake_case							,				return_tensors=self.framework							,				padding=snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    [text_inputs]
          return inputs

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Union[str, Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    model_inputs.pop('''candidate_labels'''    )
          __UpperCAmelCase			:				List[Any]													=    model_inputs.pop('''text_inputs'''    )
          if isinstance(text_inputs[0]							,				snake_case    ):
               __UpperCAmelCase			:				Tuple													=    text_inputs[0]
          else:
               # Batching case.
               __UpperCAmelCase			:				Any													=    text_inputs[0][0]

          __UpperCAmelCase			:				Optional[int]													=    self.model(**snake_case							,				**snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    {
              '''candidate_labels''': candidate_labels,
              '''logits''': outputs.logits_per_audio,
          }
          return model_outputs






     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Optional[int]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				int													=    model_outputs.pop('''candidate_labels'''    )
          __UpperCAmelCase			:				List[str]													=    model_outputs['''logits'''][0]

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

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



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))