





'''simple docstring'''

from __future__ import annotations

from statistics import mean

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       list[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[0] * no_of_processes
   __SCREAMING_SNAKE_CASE   				=							[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(A__   ):
      __SCREAMING_SNAKE_CASE   				=							burst_time[i]
   __SCREAMING_SNAKE_CASE   				=							[]

   __SCREAMING_SNAKE_CASE   				=							0
   __SCREAMING_SNAKE_CASE   				=							0

   # When processes are not completed,
   # A process whose arrival time has passed \
   # and has remaining execution time is put into the ready_process.
   # The shortest process in the ready_process, target_process is executed.

   while completed != no_of_processes:
      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							-1

      for i in range(A__   ):
         if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
            ready_process.append(A__   )

      if len(A__   ) > 0:
         __SCREAMING_SNAKE_CASE   				=							ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               __SCREAMING_SNAKE_CASE   				=							i
         total_time += burst_time[target_process]
         completed += 1
         __SCREAMING_SNAKE_CASE   				=							0
         __SCREAMING_SNAKE_CASE   				=							(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       list[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[0] * no_of_processes
   for i in range(A__   ):
      __SCREAMING_SNAKE_CASE   				=							burst_time[i] + waiting_time[i]
   return turn_around_time


if __name__ == "__main__":
    print("[TEST CASE 01]")

    a   	=    4
    a   	=    [2, 5, 3, 7]
    a   	=    [0, 0, 0, 0]
    a   	=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
    a   	=    calculate_turnaroundtime(
        burst_time, no_of_processes, waiting_time
    )

    # Printing the Result
    print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
    for i, process_id in enumerate(list(range(1, 5))):
        print(
            F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
            F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
        )
    print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
    print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')








from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        if self.framework != "pt":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''   )
        # No specific FOR_XXX available yet




  def __call__(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			={}
        if "candidate_labels" in kwargs:
              _lowercase      			=kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
              _lowercase      			=kwargs['hypothesis_template']

        return preprocess_params, {}, {}




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase="This is a sound of {}."   )    ->					Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              if audio.startswith('http://'   ) or audio.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
                    _lowercase      			=requests.get(lowerCAmelCase   ).content
              else:
                    with open(lowerCAmelCase				,  'rb'   ) as f:
                          _lowercase      			=f.read()

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=ffmpeg_read(lowerCAmelCase				,  self.feature_extractor.sampling_rate   )

        if not isinstance(lowerCAmelCase				,  np.ndarray   ):
              raise ValueError('We expect a numpy ndarray as input'   )
        if len(audio.shape   ) != 1:
              raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'   )

        _lowercase      			=self.feature_extractor(
            [audio]				,  sampling_rate=self.feature_extractor.sampling_rate				,  return_tensors='pt'   )
        _lowercase      			=candidate_labels
        _lowercase      			=[hypothesis_template.format(lowerCAmelCase   ) for x in candidate_labels]
        _lowercase      			=self.tokenizer(lowerCAmelCase				,  return_tensors=self.framework				,  padding=lowerCAmelCase   )
        _lowercase      			=[text_inputs]
        return inputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('candidate_labels'   )
        _lowercase      			=model_inputs.pop('text_inputs'   )
        if isinstance(text_inputs[0]				,  lowerCAmelCase   ):
              _lowercase      			=text_inputs[0]
        else:
              # Batching case.
              _lowercase      			=text_inputs[0][0]

        _lowercase      			=self.model(**lowerCAmelCase				,  **lowerCAmelCase   )

        _lowercase      			={
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_audio,
        }
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=model_outputs.pop('candidate_labels'   )
        _lowercase      			=model_outputs['logits'][0]

        if self.framework == "pt":
              _lowercase      			=logits.softmax(dim=0   )
              _lowercase      			=probs.tolist()
        else:
              raise ValueError('`tf` framework not supported.'   )

        _lowercase      			=[
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase				,  lowerCAmelCase   )				,  key=lambda lowerCAmelCase   : -x[0]   )
        ]
        return result


