import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

UpperCAmelCase				 =   logging.get_logger(__name__)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Any						      =    f"""{sampling_rate}"""
     a	:     List[str]						      =    '''1'''
     a	:     List[str]						      =    '''f32le'''
     a	:     Any						      =    [
         '''ffmpeg''',
         '''-i''',
         '''pipe:0''',
         '''-ac''',
         ac,
         '''-ar''',
         ar,
         '''-f''',
         format_for_conversion,
         '''-hide_banner''',
         '''-loglevel''',
         '''quiet''',
         '''pipe:1''',
     ]

     try:
          a	:     Dict						      =    subprocess.Popen(_lowerCAmelCase    ,   stdin=subprocess.PIPE    ,   stdout=subprocess.PIPE							)
     except FileNotFoundError:
          raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''							)
     a	:     str						      =    ffmpeg_process.communicate(_lowerCAmelCase							)
     a	:     Tuple						      =    output_stream[0]

     a	:     Optional[int]						      =    np.frombuffer(_lowerCAmelCase    ,   np.floataa							)
     if audio.shape[0] == 0:
          raise ValueError('''Malformed soundfile'''							)
     return audio






@add_end_docstrings(snake_case__						)
class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   def __init__(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->Optional[int]:
        # Default, might be overriden by the model.config.
        a	:     int						      =    5
        super().__init__(*lowerCAmelCase_				,  **lowerCAmelCase_     )

        if self.framework != "pt":
             raise ValueError(F"""The {self.__class__} is only available in PyTorch."""     )

        self.check_model_type(lowerCAmelCase_     )
   def __call__(     self				,  lowerCAmelCase_				,  **lowerCAmelCase_				,  )						->Tuple:
        return super().__call__(lowerCAmelCase_				,  **lowerCAmelCase_     )
   def _lowercase(     self				,  lowerCAmelCase_=None				,  **lowerCAmelCase_     )						->int:
        # No parameters on this pipeline right now
        a	:     int						      =    {}
        if top_k is not None:
             if top_k > self.model.config.num_labels:
                  a	:     List[str]						      =    self.model.config.num_labels
             a	:     Tuple						      =    top_k
        return {}, {}, postprocess_params
   def _lowercase(     self				,  lowerCAmelCase_     )						->Optional[int]:
        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             if inputs.startswith('''http://'''     ) or inputs.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	:     str						      =    requests.get(lowerCAmelCase_     ).content
             else:
                  with open(lowerCAmelCase_				,  '''rb'''     ) as f:
                       a	:     int						      =    f.read()

        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             a	:     str						      =    ffmpeg_read(lowerCAmelCase_				,  self.feature_extractor.sampling_rate     )

        if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ):
             # Accepting `"array"` which is the key defined in `datasets` for
             # better integration
             if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                  raise ValueError(
                      '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                      '''\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, '''
                      '''containing the sampling_rate associated with that array'''     )

             a	:     Dict						      =    inputs.pop('''raw'''				,  lowerCAmelCase_     )
             if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('''path'''				,  lowerCAmelCase_     )
                  a	:     int						      =    inputs.pop('''array'''				,  lowerCAmelCase_     )
             a	:     str						      =    inputs.pop('''sampling_rate'''     )
             a	:     Dict						      =    _inputs
             if in_sampling_rate != self.feature_extractor.sampling_rate:
                  import torch

                  if is_torchaudio_available():
                       from torchaudio import functional as F
                  else:
                       raise ImportError(
                           '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                           '''The torchaudio package can be installed through: `pip install torchaudio`.'''     )

                  a	:     int						      =    F.resample(
                      torch.from_numpy(lowerCAmelCase_     )				,  lowerCAmelCase_				,  self.feature_extractor.sampling_rate     ).numpy()

        if not isinstance(lowerCAmelCase_				,  np.ndarray     ):
             raise ValueError('''We expect a numpy ndarray as input'''     )
        if len(inputs.shape     ) != 1:
             raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''     )

        a	:     List[Any]						      =    self.feature_extractor(
            lowerCAmelCase_				,  sampling_rate=self.feature_extractor.sampling_rate				,  return_tensors='''pt'''     )
        return processed
   def _lowercase(     self				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[str]						      =    self.model(**lowerCAmelCase_     )
        return model_outputs


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=5     )						->Tuple:
        a	:     int						      =    model_outputs.logits[0].softmax(-1     )
        a      ,				a	:     int						      =    probs.topk(lowerCAmelCase_     )

        a	:     int						      =    scores.tolist()
        a	:     Any						      =    ids.tolist()

        a	:     int						      =    [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_				,  lowerCAmelCase_     )]

        return labels





def     lowerCAmelCase		(		_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: Tuple						):




 """simple docstring"""







 sequence.append(_lowerCAmelCase						)

 UpperCAmelCase__							=    0
 while sequence[index] != target:
  index += 1

 sequence.pop()

 if index == len(_lowerCAmelCase						):
  return None

 return index


if __name__ == "__main__":
  _lowerCAmelCase    : List[Any]		= input("Enter numbers separated by comma:\n").strip()
  _lowerCAmelCase    : Dict		= [int(item) for item in user_input.split(",")]

  _lowerCAmelCase    : Optional[Any]		= input("Enter a single number to be found in the list:\n")
  _lowerCAmelCase    : Tuple		= int(target_input)
  _lowerCAmelCase    : int		= sentinel_linear_search(sequence, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print("Not found")





