



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								=   logging.get_logger(__name__)




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	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(__snake_case  ,			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(__snake_case							)
  _A          =      output_stream[0]

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



@add_end_docstrings(lowerCamelCase			)
class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def __init__(  self		,      *a		,      **a   )		->							Optional[Any]:




   """simple docstring"""







   _A          =      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		,      )		->							List[Any]:




   """simple docstring"""







   return super().__call__(a		,      **a   )
 def lowercase_						(  self		,      a=None		,      **a   )		->							Tuple:




   """simple docstring"""







   _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 lowercase_						(  self		,      a   )		->							Any:




   """simple docstring"""







   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
       _A          =      requests.get(a   ).content
     else:
       with open(a		,      '''rb'''   ) as f:
         _A          =      f.read()

   if isinstance(a		,      a   ):
     _A          =      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'''   )

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

   _A          =      self.feature_extractor(
       a		,      sampling_rate=self.feature_extractor.sampling_rate		,      return_tensors='''pt'''   )
   return processed
 def lowercase_						(  self		,      a   )		->							Tuple:




   """simple docstring"""







   _A          =      self.model(**a   )
   return model_outputs



 def lowercase_						(  self		,      a		,      a=5   )		->							Any:




   """simple docstring"""







   _A          =      model_outputs.logits[0].softmax(-1   )
   _A      ,				_A          =      probs.topk(a   )

   _A          =      scores.tolist()
   _A          =      ids.tolist()

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

   return labels




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case							)				->	list[int]:
  _A          =      [0] * len(__snake_case							)
  for i in range(1  ,			len(__snake_case							)							):
    _A          =      duration_times[i - 1] + waiting_times[i - 1]
  return waiting_times




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case							)				->	list[int]:
  return [
      duration_time + waiting_times[i]
      for i, duration_time in enumerate(__snake_case							)
  ]




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)




def       UpperCAmelCase__	(							__snake_case							)				->	float:
  return sum(__snake_case							) / len(__snake_case							)


if __name__ == "__main__":
    # process id's
    __lowerCamelCase								=   [1, 2, 3]

    # ensure that we actually have processes
    if len(processes) == 0:
        print("""Zero amount of processes""")
        raise SystemExit(0)

    # duration time of all processes
    __lowerCamelCase								=   [1_9, 8, 9]

    # ensure we can match each id to a duration time
    if len(duration_times) != len(processes):
        print("""Unable to match all id's with their duration time""")
        raise SystemExit(0)

    # get the waiting times and the turnaround times
    __lowerCamelCase								=   calculate_waiting_times(duration_times)
    __lowerCamelCase								=   calculate_turnaround_times(duration_times, waiting_times)

    # get the average times
    __lowerCamelCase								=   calculate_average_waiting_time(waiting_times)
    __lowerCamelCase								=   calculate_average_turnaround_time(turnaround_times)

    # print all the results
    print("""Process ID\tDuration Time\tWaiting Time\tTurnaround Time""")
    for i, process in enumerate(processes):
        print(
            f'''{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'''
            f'''{turnaround_times[i]}'''
        )
    print(f'''Average waiting time = {average_waiting_time}''')
    print(f'''Average turn around time = {average_turnaround_time}''')