

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					:  bytes,    lowerCAmelCase					:  int     )     ->  np.array:
      A    =				f'''{sampling_rate}'''
      A    =				'1'
      A    =				'f32le'
      A    =				[
          'ffmpeg',
          '-i',
          'pipe:0',
          '-ac',
          ac,
          '-ar',
          ar,
          '-f',
          format_for_conversion,
          '-hide_banner',
          '-loglevel',
          'quiet',
          'pipe:1',
      ]

      try:
            A    =				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    =				ffmpeg_process.communicate(lowerCAmelCase     )
      A    =				output_stream[0]

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







@add_end_docstrings(__lowercase			)
class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  def __init__( self	: Optional[int]		,						*UpperCamelCase__	: Optional[Any]		,						**UpperCamelCase__	: Any ):
        # Default, might be overriden by the model.config.
        A    =				5
        super().__init__(*UpperCamelCase__		,						**UpperCamelCase__ )

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

        self.check_model_type(UpperCamelCase__ )







  def __call__( self	: int		,						UpperCamelCase__	: Union[np.ndarray, bytes, str]		,						**UpperCamelCase__	: Any		,						):
        return super().__call__(UpperCamelCase__		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: List[Any]		,						UpperCamelCase__	: Union[str, Any]=None		,						**UpperCamelCase__	: Optional[Any] ):
        # No parameters on this pipeline right now
        A    =				{}
        if top_k is not None:
              if top_k > self.model.config.num_labels:
                    A    =				self.model.config.num_labels
              A    =				top_k
        return {}, {}, postprocess_params







  def 	UpperCamelCase       ( self	: Tuple		,						UpperCamelCase__	: Any ):
        if isinstance(UpperCamelCase__		,						UpperCamelCase__ ):
              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    =				requests.get(UpperCamelCase__ ).content
              else:
                    with open(UpperCamelCase__		,						'rb' ) as f:
                          A    =				f.read()

        if isinstance(UpperCamelCase__		,						UpperCamelCase__ ):
              A    =				ffmpeg_read(UpperCamelCase__		,						self.feature_extractor.sampling_rate )

        if isinstance(UpperCamelCase__		,						UpperCamelCase__ ):
              # 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    =				inputs.pop('raw'		,						UpperCamelCase__ )
              if _inputs is None:
                    # Remove path which will not be used from `datasets`.
                    inputs.pop('path'		,						UpperCamelCase__ )
                    A    =				inputs.pop('array'		,						UpperCamelCase__ )
              A    =				inputs.pop('sampling_rate' )
              A    =				_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    =				F.resample(
                        torch.from_numpy(UpperCamelCase__ )		,						UpperCamelCase__		,						self.feature_extractor.sampling_rate ).numpy()

        if not isinstance(UpperCamelCase__		,						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    =				self.feature_extractor(
            UpperCamelCase__		,						sampling_rate=self.feature_extractor.sampling_rate		,						return_tensors='pt' )
        return processed







  def 	UpperCamelCase       ( self	: Dict		,						UpperCamelCase__	: List[Any] ):
        A    =				self.model(**UpperCamelCase__ )
        return model_outputs







  def 	UpperCamelCase       ( self	: List[Any]		,						UpperCamelCase__	: Optional[Any]		,						UpperCamelCase__	: Union[str, Any]=5 ):
        A    =				model_outputs.logits[0].softmax(-1 )
        A ,	A    =				probs.topk(UpperCamelCase__ )

        A    =				scores.tolist()
        A    =				ids.tolist()

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

        return labels






def 			__UpperCamelCase    (lowerCAmelCase					:  int     )     ->  int:
      barcode //= 10  # exclude the last digit
      A    =				False
      A    =				0

      # extract and check each digit
      while barcode != 0:
            A    =				1 if checker else 3
            s += mult * (barcode % 10)
            barcode //= 10
            A    =				not checker

      return (10 - (s % 10)) % 10


def 			__UpperCamelCase    (lowerCAmelCase					:  int     )     ->  bool:
      return len(str(lowerCAmelCase     )     ) == 13 and get_check_digit(lowerCAmelCase     ) == barcode % 10


def 			__UpperCamelCase    (lowerCAmelCase					:  str     )     ->  int:
      if str(lowerCAmelCase     ).isalpha():
            A    =				f'''Barcode \'{barcode}\' has alphabetic characters.'''
            raise ValueError(lowerCAmelCase     )
      elif int(lowerCAmelCase     ) < 0:
            raise ValueError('The entered barcode has a negative value. Try again.'     )
      else:
            return int(lowerCAmelCase     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  _UpperCAmelCase    	=			get_barcode(input("Barcode: ").strip())

  if is_valid(barcode):
    print(F'''\'{barcode}\' is a valid barcode.''')
  else:
    print(F'''\'{barcode}\' is NOT a valid barcode.''')



