

"""simple docstring"""

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   None:
          UpperCAmelCase       =   data
          UpperCAmelCase       =   4_2
          UpperCAmelCase       =   4_2




def       lowercase__  (   lowerCAmelCase			:   Node	)	-> None:






    """simple docstring"""




    UpperCAmelCase       =   0
    UpperCAmelCase       =   x




def       lowercase__  (   lowerCAmelCase			:   Node	,			lowerCAmelCase			:   Node	)	-> None:






    """simple docstring"""




    UpperCAmelCase     ,							UpperCAmelCase       =   find_set(lowerCAmelCase	), find_set(lowerCAmelCase	)
    if x == y:
        return

    elif x.rank > y.rank:
        UpperCAmelCase       =   x
    else:
        UpperCAmelCase       =   y
        if x.rank == y.rank:
            y.rank += 1




def       lowercase__  (   lowerCAmelCase			:   Node	)	-> Node:






    """simple docstring"""




    if x != x.parent:
        UpperCAmelCase       =   find_set(x.parent	)
    return x.parent




def       lowercase__  (   lowerCAmelCase			:   Node	)	-> set:






    """simple docstring"""




    UpperCAmelCase       =   ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    UpperCAmelCase       =   F"{node.data} is not in {sets}"
    raise ValueError(lowerCAmelCase	)




def       lowercase__  (   )	-> None:






    """simple docstring"""




    UpperCAmelCase       =   [Node(lowerCAmelCase	) for i in range(6	)]
    for v in vertex:
        make_set(lowerCAmelCase	)

    union_set(vertex[0]	,			vertex[1]	)
    union_set(vertex[1]	,			vertex[2]	)
    union_set(vertex[3]	,			vertex[4]	)
    union_set(vertex[3]	,			vertex[5]	)

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowerCAmelCase	).isdisjoint(find_python_set(lowerCAmelCase	)	):
                assert find_set(lowerCAmelCase	) != find_set(lowerCAmelCase	)
            else:
                assert find_set(lowerCAmelCase	) == find_set(lowerCAmelCase	)


if __name__ == "__main__":
   test_disjoint_set()





"""simple docstring"""

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

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)




def       lowercase__  (   lowerCAmelCase			:   bytes	,			lowerCAmelCase			:   int	)	-> np.array:






    """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(SCREAMING_SNAKE_CASE_							)
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    *lowercase_				,    **lowercase_							)  ->   List[Any]:
          # Default, might be overriden by the model.config.
          UpperCAmelCase       =   5
          super().__init__(*lowercase_				,    **lowercase_							)

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

          self.check_model_type(lowercase_							)




      def __call__(							self				,    lowercase_				,    **lowercase_				,    )  ->   Tuple:
          return super().__call__(lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_=None				,    **lowercase_							)  ->   Optional[Any]:
          # 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       a_ (							self				,    lowercase_							)  ->   List[str]:
          if isinstance(lowercase_				,    lowercase_							):
              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(lowercase_							).content
              else:
                  with open(lowercase_				,    'rb'							) as f:
                      UpperCAmelCase       =   f.read()

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   ffmpeg_read(lowercase_				,    self.feature_extractor.sampling_rate							)

          if isinstance(lowercase_				,    lowercase_							):
              # 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'				,    lowercase_							)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('path'				,    lowercase_							)
                  UpperCAmelCase       =   inputs.pop('array'				,    lowercase_							)
              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(lowercase_							)				,    lowercase_				,    self.feature_extractor.sampling_rate							).numpy()

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




      def       a_ (							self				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   self.model(**lowercase_							)
          return model_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_=5							)  ->   int:
          UpperCAmelCase       =   model_outputs.logits[0].softmax(-1							)
          UpperCAmelCase     ,							UpperCAmelCase       =   probs.topk(lowercase_							)

          UpperCAmelCase       =   scores.tolist()
          UpperCAmelCase       =   ids.tolist()

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

          return labels



