
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  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      F'''{sampling_rate}'''
   SCREAMING_SNAKE_CASE_	=      '''1'''
   SCREAMING_SNAKE_CASE_	=      '''f32le'''
   SCREAMING_SNAKE_CASE_	=      [
       '''ffmpeg''',
       '''-i''',
       '''pipe:0''',
       '''-ac''',
       ac,
       '''-ar''',
       ar,
       '''-f''',
       format_for_conversion,
       '''-hide_banner''',
       '''-loglevel''',
       '''quiet''',
       '''pipe:1''',
   ]

   try:
      SCREAMING_SNAKE_CASE_	=      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'''						)
   SCREAMING_SNAKE_CASE_	=      ffmpeg_process.communicate(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      output_stream[0]

   SCREAMING_SNAKE_CASE_	=      np.frombuffer(__lowerCamelCase,  np.floataa						)
   if audio.shape[0] == 0:
      raise ValueError('''Malformed soundfile'''						)
   return audio





@add_end_docstrings(__SCREAMING_SNAKE_CASE	)
class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     *_A ,     **_A	)				->				str:
          # Default, might be overriden by the model.config.
          SCREAMING_SNAKE_CASE_	=      5
          super().__init__(*_A ,     **_A	)

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

          self.check_model_type(_A	)



       def __call__(							self ,     _A ,     **_A ,     )				->				int:
          return super().__call__(_A ,     **_A	)



       def     _UpperCamelCase	(							self ,     _A=None ,     **_A	)				->				Union[str, Any]:
          # No parameters on this pipeline right now
          SCREAMING_SNAKE_CASE_	=      {}
          if top_k is not None:
             if top_k > self.model.config.num_labels:
                SCREAMING_SNAKE_CASE_	=      self.model.config.num_labels
             SCREAMING_SNAKE_CASE_	=      top_k
          return {}, {}, postprocess_params



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[Any]:
          if isinstance(_A ,     _A	):
             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
                SCREAMING_SNAKE_CASE_	=      requests.get(_A	).content
             else:
                with open(_A ,     '''rb'''	) as f:
                   SCREAMING_SNAKE_CASE_	=      f.read()

          if isinstance(_A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      ffmpeg_read(_A ,     self.feature_extractor.sampling_rate	)

          if isinstance(_A ,     _A	):
             # 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'''	)

             SCREAMING_SNAKE_CASE_	=      inputs.pop('''raw''' ,     _A	)
             if _inputs is None:
                # Remove path which will not be used from `datasets`.
                inputs.pop('''path''' ,     _A	)
                SCREAMING_SNAKE_CASE_	=      inputs.pop('''array''' ,     _A	)
             SCREAMING_SNAKE_CASE_	=      inputs.pop('''sampling_rate'''	)
             SCREAMING_SNAKE_CASE_	=      _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`.'''	)

                SCREAMING_SNAKE_CASE_	=      F.resample(
                    torch.from_numpy(_A	) ,     _A ,     self.feature_extractor.sampling_rate	).numpy()

          if not isinstance(_A ,     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'''	)

          SCREAMING_SNAKE_CASE_	=      self.feature_extractor(
              _A ,     sampling_rate=self.feature_extractor.sampling_rate ,     return_tensors='''pt'''	)
          return processed



       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          SCREAMING_SNAKE_CASE_	=      self.model(**_A	)
          return model_outputs




       def     _UpperCamelCase	(							self ,     _A ,     _A=5	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      model_outputs.logits[0].softmax(-1	)
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      probs.topk(_A	)

          SCREAMING_SNAKE_CASE_	=      scores.tolist()
          SCREAMING_SNAKE_CASE_	=      ids.tolist()

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

          return labels




from math import sqrt


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      [1]
   for i in range(2,  int(sqrt(__lowerCamelCase						)						) + 1,  1						):
      if number % i == 0:
         values.append(__lowerCamelCase						)
         if int(number // i						) != i:
            values.append(int(number // i						)						)
   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   return sum(factors(__lowerCamelCase						)						) > n


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      factors(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      [[0 for i in range(number + 1						)] for j in range(r + 1						)]
   for i in range(r + 1						):
      SCREAMING_SNAKE_CASE_	=      True

   for i in range(1,  number + 1						):
      SCREAMING_SNAKE_CASE_	=      False

   for i in range(1,  r + 1						):
      for j in range(1,  number + 1						):
         if j < values[i - 1]:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j]
         else:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0


def  A__ (       __lowerCamelCase						):
   return abundant(__lowerCamelCase						) and not semi_perfect(__lowerCamelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")


