from __future__ import annotations

import pandas as pd



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[int]			,					__UpperCamelCase   :	list[int]			,					__UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[0] * no_of_processes
  SCREAMING_SNAKE_CASE__      							=				[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				burst_time[i]

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				9_99_99_99_99
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(__UpperCamelCase     ):
      if arrival_time[j] <= increment_time and remaining_time[j] > 0:
        if remaining_time[j] < minm:
          SCREAMING_SNAKE_CASE__      							=				remaining_time[j]
          SCREAMING_SNAKE_CASE__      							=				j
          SCREAMING_SNAKE_CASE__      							=				True

    if not check:
      increment_time += 1
      continue
    remaining_time[short] -= 1

    SCREAMING_SNAKE_CASE__      							=				remaining_time[short]
    if minm == 0:
      SCREAMING_SNAKE_CASE__      							=				9_99_99_99_99

    if remaining_time[short] == 0:
      complete += 1
      SCREAMING_SNAKE_CASE__      							=				False

      # Find finish time of current process
      SCREAMING_SNAKE_CASE__      							=				increment_time + 1

      # Calculate waiting time
      SCREAMING_SNAKE_CASE__      							=				finish_time - arrival_time[short]
      SCREAMING_SNAKE_CASE__      							=				finar - burst_time[short]

      if waiting_time[short] < 0:
        SCREAMING_SNAKE_CASE__      							=				0

        # Increment time
    increment_time += 1
  return waiting_time



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[int]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	list[int]     )							-> list[int]:







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[int]			,					__UpperCamelCase   :	list[int]			,					__UpperCamelCase   :	int     )							-> None:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				total_waiting_time + waiting_time[i]
    SCREAMING_SNAKE_CASE__      							=				total_turn_around_time + turn_around_time[i]
  print(f"""Average waiting time = {total_waiting_time / no_of_processes:.5f}"""     )
  print("""Average turn around time ="""			,					total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
   print('''Enter how many process you want to analyze''')
   __lowerCamelCase      :			int							       =		int(input())
   __lowerCamelCase      :			Optional[Any]							       =		[0] * no_of_processes
   __lowerCamelCase      :			List[Any]							       =		[0] * no_of_processes
   __lowerCamelCase      :			Tuple							       =		list(range(1, no_of_processes + 1))

   for i in range(no_of_processes):
      print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
      __lowerCamelCase       ,						__lowerCamelCase      :			Optional[Any]							       =		map(int, input().split())

   __lowerCamelCase      :			Dict							       =		calculate_waitingtime(arrival_time, burst_time, no_of_processes)

   __lowerCamelCase      :			int							       =		burst_time
   __lowerCamelCase      :			str							       =		no_of_processes
   __lowerCamelCase      :			List[str]							       =		waiting_time
   __lowerCamelCase      :			List[Any]							       =		calculate_turnaroundtime(bt, n, wt)

   calculate_average_times(waiting_time, turn_around_time, no_of_processes)

   __lowerCamelCase      :			Optional[int]							       =		pd.DataFrame(
       list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
       columns=[
           '''Process''',
           '''BurstTime''',
           '''ArrivalTime''',
           '''WaitingTime''',
           '''TurnAroundTime''',
       ],
   )

   # Printing the dataFrame
   pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
   print(fcfs)

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__lowerCamelCase      :			Any							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["input_features", "attention_mask"]




   def __init__(   self      :    int	,     _lowercase      :    Dict=80	,     _lowercase      :    Union[str, Any]=1_60_00	,     _lowercase      :    str=0.0	,     _lowercase      :    int=10	,     _lowercase      :    str=25	,     _lowercase      :    Union[str, Any]="hamming_window"	,     _lowercase      :    Dict=3_27_68.0	,     _lowercase      :    Optional[int]=0.97	,     _lowercase      :    Optional[int]=1.0	,     _lowercase      :    int=True	,     _lowercase      :    Union[str, Any]=True	,     _lowercase      :    Optional[int]=False	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(feature_size=_lowercase	,     sampling_rate=_lowercase	,     padding_value=_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				hop_length
     SCREAMING_SNAKE_CASE__      							=				win_length
     SCREAMING_SNAKE_CASE__      							=				frame_signal_scale
     SCREAMING_SNAKE_CASE__      							=				preemphasis_coeff
     SCREAMING_SNAKE_CASE__      							=				mel_floor
     SCREAMING_SNAKE_CASE__      							=				normalize_means
     SCREAMING_SNAKE_CASE__      							=				normalize_vars
     SCREAMING_SNAKE_CASE__      							=				win_function
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask

     SCREAMING_SNAKE_CASE__      							=				win_length * sampling_rate // 10_00
     SCREAMING_SNAKE_CASE__      							=				hop_length * sampling_rate // 10_00

     SCREAMING_SNAKE_CASE__      							=				optimal_fft_length(self.sample_size				)
     SCREAMING_SNAKE_CASE__      							=				(self.n_fft // 2) + 1




   def      __a						(   self      :    str	,     _lowercase      :    np.array				):



     """simple docstring"""






     if self.win_function == "hamming_window":
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function	,     periodic=_lowercase				)
     else:
       SCREAMING_SNAKE_CASE__      							=				window_function(window_length=self.sample_size	,     name=self.win_function				)

     SCREAMING_SNAKE_CASE__      							=				mel_filter_bank(
         num_frequency_bins=self.n_freqs	,     num_mel_filters=self.feature_size	,     min_frequency=0.0	,     max_frequency=self.sampling_rate / 2.0	,     sampling_rate=self.sampling_rate	,     )

     SCREAMING_SNAKE_CASE__      							=				spectrogram(
         one_waveform * self.frame_signal_scale	,     window=_lowercase	,     frame_length=self.sample_size	,     hop_length=self.sample_stride	,     fft_length=self.n_fft	,     center=_lowercase	,     preemphasis=self.preemphasis_coeff	,     mel_filters=_lowercase	,     mel_floor=self.mel_floor	,     log_mel="""log"""	,     )
     return msfc_features.T




   def      __a						(   self      :    Tuple	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     if self.normalize_means:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].mean(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.subtract(_lowercase	,     _lowercase				)
     if self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				x[:input_length].std(axis=0				)
       SCREAMING_SNAKE_CASE__      							=				np.divide(_lowercase	,     _lowercase				)

     if input_length < x.shape[0]:
       SCREAMING_SNAKE_CASE__      							=				padding_value

     # make sure array is in float32
     SCREAMING_SNAKE_CASE__      							=				x.astype(np.floataa				)

     return x




   def      __a						(   self      :    int	,     _lowercase      :    List[np.ndarray]	,     _lowercase      :    Optional[np.ndarray] = None				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [self._normalize_one(_lowercase	,     _lowercase	,     self.padding_value				) for x, n in zip(_lowercase	,     _lowercase				)]




   def __call__(   self      :    Dict	,     _lowercase      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     _lowercase      :    Union[bool, str, PaddingStrategy] = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    bool = False	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    Optional[bool] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    Optional[int] = None	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             f""" {self.sampling_rate} and not {sampling_rate}."""				)
     else:
       logger.warning(
           """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
           """Failing to do so can result in silent errors that might be hard to debug."""				)

     SCREAMING_SNAKE_CASE__      							=				isinstance(_lowercase	,     np.ndarray				) and len(raw_speech.shape				) > 1
     if is_batched_numpy and len(raw_speech.shape				) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
     SCREAMING_SNAKE_CASE__      							=				is_batched_numpy or (
         isinstance(_lowercase	,     (list, tuple)				) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)				))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowercase	,     np.ndarray				):
       SCREAMING_SNAKE_CASE__      							=				np.asarray(_lowercase	,     dtype=np.floataa				)
     elif isinstance(_lowercase	,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
       SCREAMING_SNAKE_CASE__      							=				raw_speech.astype(np.floataa				)

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE__      							=				[raw_speech]

     # extract fbank features
     SCREAMING_SNAKE_CASE__      							=				[self._extract_mfsc_features(_lowercase				) for one_waveform in raw_speech]

     # convert into correct format for padding
     SCREAMING_SNAKE_CASE__      							=				BatchFeature({"""input_features""": features}				)

     SCREAMING_SNAKE_CASE__      							=				self.pad(
         _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     truncation=_lowercase	,     pad_to_multiple_of=_lowercase	,     return_attention_mask=_lowercase	,     **_lowercase	,     )
     # make sure list is in array format
     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""input_features"""				)
     if isinstance(input_features[0]	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.floataa				) for feature in input_features]

     SCREAMING_SNAKE_CASE__      							=				padded_inputs.get("""attention_mask"""				)
     if attention_mask is not None:
       SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase	,     dtype=np.intaa				) for array in attention_mask]

     if self.normalize_means or self.normalize_vars:
       SCREAMING_SNAKE_CASE__      							=				(
           np.array(_lowercase	,     dtype=np.intaa				)
           if self._get_padding_strategies(_lowercase	,     max_length=_lowercase				) is not PaddingStrategy.DO_NOT_PAD
           and padding
           else None
       )
       SCREAMING_SNAKE_CASE__      							=				self.normalize(
           padded_inputs["""input_features"""]	,     attention_mask=_lowercase				)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE__      							=				padded_inputs.convert_to_tensors(_lowercase				)

     return padded_inputs
