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(  UpperCamelCase_       ,				UpperCamelCase_					)		->  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(UpperCamelCase_       ,				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(UpperCamelCase_					)
 UpperCamelCase      =			output_stream[0]

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






@add_end_docstrings(a_		)
class SCREAMING_SNAKE_CASE_		(			a_		):







   def __init__(    self	:	Any      ,      *lowerCamelCase_	:	Tuple      ,      **lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			5
    super().__init__(*_snake_case      ,      **_snake_case							)

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

    self.check_model_type(_snake_case							)







   def __call__(    self	:	Optional[int]      ,      lowerCamelCase_	:	List[str]      ,      **lowerCamelCase_	:	Union[str, Any]      ,      ):





    """simple docstring"""


    return super().__call__(_snake_case      ,      **_snake_case							)







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	List[str]=None      ,      **lowerCamelCase_	:	int							):





    """simple docstring"""


    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 	lowerCamelCase_  (    self	:	Dict      ,      lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    if isinstance(_snake_case      ,      _snake_case							):
     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(_snake_case							).content
     else:
      with open(_snake_case      ,      """rb"""							) as f:
       UpperCamelCase      =			f.read()

    if isinstance(_snake_case      ,      _snake_case							):
     UpperCamelCase      =			ffmpeg_read(_snake_case      ,      self.feature_extractor.sampling_rate							)

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

    if not isinstance(_snake_case      ,      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(
        _snake_case      ,      sampling_rate=self.feature_extractor.sampling_rate      ,      return_tensors="""pt"""							)
    return processed







   def 	lowerCamelCase_  (    self	:	int      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			self.model(**_snake_case							)
    return model_outputs







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Optional[int]=5							):





    """simple docstring"""


    UpperCamelCase      =			model_outputs.logits[0].softmax(-1							)
    UpperCamelCase      =			probs.topk(_snake_case							)

    UpperCamelCase      =			scores.tolist()
    UpperCamelCase      =			ids.tolist()

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

    return labels

def  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			len(UpperCamelCase_					)
 for i in range(length - 1					):
  UpperCamelCase      =			False
  for j in range(length - 1 - i					):
   if collection[j] > collection[j + 1]:
    UpperCamelCase      =			True
    UpperCamelCase     ,    UpperCamelCase      =			collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
     import doctest
     import time

     doctest.testmod()

     _SCREAMING_SNAKE_CASE   		=			input("""Enter numbers separated by a comma:""").strip()
     _SCREAMING_SNAKE_CASE   		=			[int(item) for item in user_input.split(""",""")]
     _SCREAMING_SNAKE_CASE   		=			time.process_time()
     print(*bubble_sort(unsorted), sep=""",""")
     print(F'''Processing time: {(time.process_time() - start)%1E9 + 7}''')
