

import numpy as np
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[Any]       ,     SCREAMING_SNAKE_CASE__     :      Any="utf-8"       ,     SCREAMING_SNAKE_CASE__     :      Dict="surrogatepass" )				-> int:





    '''simple docstring'''






    A__        =  bin(int.from_bytes(text.encode(_lowerCAmelCase       ,     _lowerCAmelCase )       ,     'big' ) )[2:]
    return bits.zfill(8 * ((len(_lowerCAmelCase ) + 7) // 8) )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]="utf-8"       ,     SCREAMING_SNAKE_CASE__     :      int="surrogatepass" )				-> List[Any]:





    '''simple docstring'''






    A__        =  int(_lowerCAmelCase       ,     2 )
    return n.to_bytes((n.bit_length() + 7) // 8       ,     'big' ).decode(_lowerCAmelCase       ,     _lowerCAmelCase ) or "\0"
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> str:





    '''simple docstring'''






    if size_par + len(_lowerCAmelCase ) <= 2**size_par - (len(_lowerCAmelCase ) - 1):
        raise ValueError('size of parity don\'t match with size of data' )

    A__        =  []
    A__        =  []
    A__        =  [bin(_lowerCAmelCase )[2:] for x in range(1       ,     size_par + len(_lowerCAmelCase ) + 1 )]

    # sorted information data for the size of the output data
    A__        =  []
    # data position template + parity
    A__        =  []
    # parity bit counter
    A__        =  0
    # counter position of data bits
    A__        =  0

    for x in range(1       ,     size_par + len(_lowerCAmelCase ) + 1 ):
        # Performs a template of bit positions - who should be given,
        # and who should be parity
        if qtd_bp < size_par:
            if (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
                data_out_gab.append('P' )
                A__        =  qtd_bp + 1
            else:
                data_out_gab.append('D' )
        else:
            data_out_gab.append('D' )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data[cont_data] )
            cont_data += 1
        else:
            data_ord.append(_lowerCAmelCase )

             # Calculates parity
    A__        =  0  # parity bit counter
    for bp in range(1       ,     size_par + 1 ):
        # Bit counter one for a given parity
        A__        =  0
        # counter to control the loop reading
        A__        =  0
        for x in data_ord:
            if x is not None:
                try:
                    A__        =  (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    A__        =  '0'
                if aux == "1" and x == "1":
                    cont_bo += 1
            cont_loop += 1
        parity.append(cont_bo % 2 )

        qtd_bp += 1

    # Mount the message
    A__        =  0  # parity bit counter
    for x in range(0       ,     size_par + len(_lowerCAmelCase ) ):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp] ) )
            cont_bp += 1
        else:
            data_out.append(data_ord[x] )

    return data_out

def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Optional[int] )				-> List[str]:





    '''simple docstring'''






    A__        =  []
    # Parity bit counter
    A__        =  0
    # Counter p data bit reading
    A__        =  0
    # list of parity received
    A__        =  []
    A__        =  []

    for x in range(1       ,     len(_lowerCAmelCase ) + 1 ):
        # Performs a template of bit positions - who should be given,
        #  and who should be parity
        if qtd_bp < size_par and (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
            data_out_gab.append('P' )
            A__        =  qtd_bp + 1
        else:
            data_out_gab.append('D' )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_output.append(data[cont_data] )
        else:
            parity_received.append(data[cont_data] )
        cont_data += 1

    # -----------calculates the parity with the data
    A__        =  []
    A__        =  []
    A__        =  [bin(_lowerCAmelCase )[2:] for x in range(1       ,     size_par + len(_lowerCAmelCase ) + 1 )]

    #  sorted information data for the size of the output data
    A__        =  []
    # Data position feedback + parity
    A__        =  []
    # Parity bit counter
    A__        =  0
    # Counter p data bit reading
    A__        =  0

    for x in range(1       ,     size_par + len(_lowerCAmelCase ) + 1 ):
        # Performs a template position of bits - who should be given,
        # and who should be parity
        if qtd_bp < size_par and (np.log(_lowerCAmelCase ) / np.log(2 )).is_integer():
            data_out_gab.append('P' )
            A__        =  qtd_bp + 1
        else:
            data_out_gab.append('D' )

        # Sorts the data to the new output size
        if data_out_gab[-1] == "D":
            data_ord.append(data_output[cont_data] )
            cont_data += 1
        else:
            data_ord.append(_lowerCAmelCase )

             # Calculates parity
    A__        =  0  # parity bit counter
    for bp in range(1       ,     size_par + 1 ):
        # Bit counter one for a certain parity
        A__        =  0
        # Counter to control loop reading
        A__        =  0
        for x in data_ord:
            if x is not None:
                try:
                    A__        =  (bin_pos[cont_loop])[-1 * (bp)]
                except IndexError:
                    A__        =  '0'
                if aux == "1" and x == "1":
                    cont_bo += 1
            cont_loop += 1
        parity.append(str(cont_bo % 2 ) )

        qtd_bp += 1

    # Mount the message
    A__        =  0  # Parity bit counter
    for x in range(0       ,     size_par + len(_lowerCAmelCase ) ):
        if data_ord[x] is None:
            data_out.append(str(parity[cont_bp] ) )
            cont_bp += 1
        else:
            data_out.append(data_ord[x] )

    A__        =  parity_received == parity
    return data_output, ack







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

_lowerCAmelCase    : List[str]		= logging.get_logger(__name__)



def     lowerCAmelCase		(		_lowerCAmelCase				: bytes    ,				_lowerCAmelCase				: int						):




 """simple docstring"""







 UpperCAmelCase__							=    F'''{sampling_rate}'''
 UpperCAmelCase__							=    "1"
 UpperCAmelCase__							=    "f32le"
 UpperCAmelCase__							=    [
     "ffmpeg",
     "-i",
     "pipe:0",
     "-ac",
     ac,
     "-ar",
     ar,
     "-f",
     format_for_conversion,
     "-hide_banner",
     "-loglevel",
     "quiet",
     "pipe:1",
 ]

 try:
  UpperCAmelCase__							=    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"						)
 UpperCAmelCase__							=    ffmpeg_process.communicate(_lowerCAmelCase						)
 UpperCAmelCase__							=    output_stream[0]

 UpperCAmelCase__							=    np.frombuffer(_lowerCAmelCase    ,				np.floataa						)
 if audio.shape[0] == 0:
  raise ValueError("Malformed soundfile"						)
 return audio


@add_end_docstrings(lowerCAmelCase		)
class       _UpperCamelCase   (					lowerCAmelCase		):
      def __init__(      self :Any			,   *lowerCamelCase :Any			,   **lowerCamelCase :Optional[Any]   )  ->      Optional[int]:
       # Default, might be overriden by the model.config.
       UpperCAmelCase__							=    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 :Optional[Any]			,   lowerCamelCase :Union[np.ndarray, bytes, str]			,   **lowerCamelCase :List[Any]			,   )  ->      Tuple:
       return super().__call__(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Dict=None			,   **lowerCamelCase :str   )  ->      int:
       # No parameters on this pipeline right now
       UpperCAmelCase__							=    {}
       if top_k is not None:
        if top_k > self.model.config.num_labels:
         UpperCAmelCase__							=    self.model.config.num_labels
        UpperCAmelCase__							=    top_k
       return {}, {}, postprocess_params
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Dict   )  ->      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
         UpperCAmelCase__							=    requests.get(lowerCamelCase   ).content
        else:
         with open(lowerCamelCase			,   "rb"   ) as f:
          UpperCAmelCase__							=    f.read()

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        UpperCAmelCase__							=    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"   )

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

         UpperCAmelCase__							=    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"   )

       UpperCAmelCase__							=    self.feature_extractor(
           lowerCamelCase			,   sampling_rate=self.feature_extractor.sampling_rate			,   return_tensors="pt"   )
       return processed
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Union[str, Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    self.model(**lowerCamelCase   )
       return model_outputs







      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]=5   )  ->      Tuple:
       UpperCAmelCase__							=    model_outputs.logits[0].softmax(-1   )
       UpperCAmelCase__			,			UpperCAmelCase__							=    probs.topk(lowerCamelCase   )

       UpperCAmelCase__							=    scores.tolist()
       UpperCAmelCase__							=    ids.tolist()

       UpperCAmelCase__							=    [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase			,   lowerCamelCase   )]

       return labels





