


from __future__ import annotations

import pandas as pd


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		[0] * no_of_processes
       UpperCamelCase    :	Any   	=		[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(_lowerCamelCase       ):
              UpperCamelCase    :	str   	=		burst_time[i]

       UpperCamelCase    :	int   	=		0
       UpperCamelCase    :	Optional[int]   	=		0
       UpperCamelCase    :	Dict   	=		9_9_9_9_9_9_9_9_9
       UpperCamelCase    :	Optional[Any]   	=		0
       UpperCamelCase    :	Optional[Any]   	=		False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(_lowerCamelCase       ):
                     if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                            if remaining_time[j] < minm:
                                   UpperCamelCase    :	str   	=		remaining_time[j]
                                   UpperCamelCase    :	Tuple   	=		j
                                   UpperCamelCase    :	Optional[int]   	=		True

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

              UpperCamelCase    :	int   	=		remaining_time[short]
              if minm == 0:
                     UpperCamelCase    :	Optional[Any]   	=		9_9_9_9_9_9_9_9_9

              if remaining_time[short] == 0:
                     complete += 1
                     UpperCamelCase    :	Optional[int]   	=		False

                     # Find finish time of current process
                     UpperCamelCase    :	Union[str, Any]   	=		increment_time + 1

                     # Calculate waiting time
                     UpperCamelCase    :	Optional[int]   	=		finish_time - arrival_time[short]
                     UpperCamelCase    :	Dict   	=		finar - burst_time[short]

                     if waiting_time[short] < 0:
                            UpperCamelCase    :	Tuple   	=		0

        # Increment time
              increment_time += 1
       return waiting_time


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		[0] * no_of_processes
       for i in range(_lowerCamelCase       ):
              UpperCamelCase    :	Optional[Any]   	=		burst_time[i] + waiting_time[i]
       return turn_around_time


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		0
       UpperCamelCase    :	Optional[Any]   	=		0
       for i in range(_lowerCamelCase       ):
              UpperCamelCase    :	List[Any]   	=		total_waiting_time + waiting_time[i]
              UpperCamelCase    :	str   	=		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")
  __UpperCAmelCase : str 			=				int(input())
  __UpperCAmelCase : Optional[int] 			=				[0] * no_of_processes
  __UpperCAmelCase : str 			=				[0] * no_of_processes
  __UpperCAmelCase : str 			=				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))
    __UpperCAmelCase			,    __UpperCAmelCase : Optional[Any] 			=				map(int, input().split())

  __UpperCAmelCase : Any 			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  __UpperCAmelCase : Any 			=				burst_time
  __UpperCAmelCase : Dict 			=				no_of_processes
  __UpperCAmelCase : Optional[int] 			=				waiting_time
  __UpperCAmelCase : Union[str, Any] 			=				calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  __UpperCAmelCase : Optional[Any] 			=				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)





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					["input_features"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=80 ,					__SCREAMING_SNAKE_CASE=16_000 ,					__SCREAMING_SNAKE_CASE=160 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  feature_size=__SCREAMING_SNAKE_CASE ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					padding_value=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		n_fft
              UpperCamelCase    :	Dict   	=		hop_length
              UpperCamelCase    :	Dict   	=		chunk_length
              UpperCamelCase    :	List[str]   	=		chunk_length * sampling_rate
              UpperCamelCase    :	Dict   	=		self.n_samples // hop_length
              UpperCamelCase    :	str   	=		sampling_rate
              UpperCamelCase    :	Union[str, Any]   	=		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2 ,					num_mel_filters=__SCREAMING_SNAKE_CASE ,					min_frequency=0.0 ,					max_frequency=8_000.0 ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					norm='''slaney''' ,					mel_scale='''slaney''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		spectrogram(
                  __SCREAMING_SNAKE_CASE ,					window_function(self.n_fft ,					'''hann'''						) ,					frame_length=self.n_fft ,					hop_length=self.hop_length ,					power=2.0 ,					mel_filters=self.mel_filters ,					log_mel='''log10''' ,					)
              UpperCamelCase    :	int   	=		log_spec[:, :-1]
              UpperCamelCase    :	int   	=		np.maximum(__SCREAMING_SNAKE_CASE ,					log_spec.max() - 8.0						)
              UpperCamelCase    :	Any   	=		(log_spec + 4.0) / 4.0
              return log_spec






       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  _lowercase   (     __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0.0						):




              """simple docstring"""




              if attention_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		np.array(__SCREAMING_SNAKE_CASE ,					np.intaa						)
                     UpperCamelCase    :	Optional[Any]   	=		[]

                     for vector, length in zip(__SCREAMING_SNAKE_CASE ,					attention_mask.sum(-1						)						):
                            UpperCamelCase    :	Optional[Any]   	=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
                            if length < normed_slice.shape[0]:
                                   UpperCamelCase    :	Optional[int]   	=		padding_value

                            normed_input_values.append(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

              return normed_input_values






       def __call__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "max_length" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """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.__class__.__name__} was trained using a"""
                                f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                                f""" was sampled with {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.'''						)

              UpperCamelCase    :	Tuple   	=		isinstance(__SCREAMING_SNAKE_CASE ,					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}"""						)
              UpperCamelCase    :	Union[str, Any]   	=		is_batched_numpy or (
                  isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)						))
              )

              if is_batched:
                     UpperCamelCase    :	List[Any]   	=		[np.asarray([speech] ,					dtype=np.floataa						).T for speech in raw_speech]
              elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						):
                     UpperCamelCase    :	int   	=		np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						)
              elif isinstance(__SCREAMING_SNAKE_CASE ,					np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                     UpperCamelCase    :	Union[str, Any]   	=		raw_speech.astype(np.floataa						)

              # always return batch
              if not is_batched:
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray([raw_speech]						).T]

              UpperCamelCase    :	Optional[int]   	=		BatchFeature({'''input_features''': raw_speech}						)

              # convert into correct format for padding

              UpperCamelCase    :	Optional[Any]   	=		self.pad(
                  __SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					max_length=max_length if max_length else self.n_samples ,					truncation=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=return_attention_mask or do_normalize ,					)

              # zero-mean and unit-variance normalization
              if do_normalize:
                     UpperCamelCase    :	Optional[Any]   	=		self.zero_mean_unit_var_norm(
                         padded_inputs['''input_features'''] ,					attention_mask=padded_inputs['''attention_mask'''] ,					padding_value=self.padding_value ,					)
                     UpperCamelCase    :	List[str]   	=		np.stack(padded_inputs['''input_features'''] ,					axis=0						)

              # make sure list is in array format
              UpperCamelCase    :	Dict   	=		padded_inputs.get('''input_features'''						).transpose(2 ,					0 ,					1						)

              UpperCamelCase    :	Tuple   	=		[self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

              if isinstance(input_features[0] ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						) for feature in input_features]
              else:
                     UpperCamelCase    :	Dict   	=		input_features

              if return_attention_mask:
                     # rescale from sample (48000) to feature (3000)
                     UpperCamelCase    :	Union[str, Any]   	=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

              if return_tensors is not None:
                     UpperCamelCase    :	Dict   	=		padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE						)

              return padded_inputs






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		copy.deepcopy(self.__dict__						)
              UpperCamelCase    :	List[str]   	=		self.__class__.__name__
              if "mel_filters" in output:
                     del output["mel_filters"]
              return output

