"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			UpperCamelCase_ ):
						lowerCAmelCase__  :						Optional[Any]													=		['audio_values', 'audio_mask']





						def __init__(       self		,__UpperCAmelCase=20_48		,__UpperCAmelCase=1		,__UpperCAmelCase=[16, 16]		,__UpperCAmelCase=1_28		,__UpperCAmelCase=4_41_00		,__UpperCAmelCase=86		,__UpperCAmelCase=20_48		,__UpperCAmelCase=0.0		,**__UpperCAmelCase		,)					->				Any:
													super().__init__(
													    feature_size=__UpperCAmelCase		,sampling_rate=__UpperCAmelCase		,padding_value=__UpperCAmelCase		,**__UpperCAmelCase		,)

													A__	     =spectrogram_length
													A__	     =num_channels
													A__	     =patch_size
													A__	     =feature_size // self.patch_size[1]
													A__	     =n_fft
													A__	     =sampling_rate // hop_length_to_sampling_rate
													A__	     =sampling_rate
													A__	     =padding_value
													A__	     =mel_filter_bank(
													    num_frequency_bins=1 + n_fft // 2		,num_mel_filters=__UpperCAmelCase		,min_frequency=0.0		,max_frequency=2_2_0_5_0.0		,sampling_rate=__UpperCAmelCase		,norm='slaney'		,mel_scale='slaney'		,).T





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				np.ndarray:
													A__	     =spectrogram(
													    __UpperCAmelCase		,window_function(self.n_fft		,'hann'     )		,frame_length=self.n_fft		,hop_length=self.hop_length		,power=2.0		,mel_filters=self.mel_filters.T		,log_mel='dB'		,db_range=8_0.0		,)
													A__	     =log_spec[:, :-1]
													A__	     =log_spec - 2_0.0
													A__	     =np.clip(log_spec / 4_0.0		,-2.0		,0.0     ) + 1.0
													return log_spec





						def __call__(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,__UpperCAmelCase = False		,**__UpperCAmelCase		,)					->				BatchFeature:

													if sampling_rate is not None:
																				if sampling_rate != self.sampling_rate:
																											raise ValueError(
																											    'This feature extractor is set to support sampling rate'
																											    f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
																											    f''' 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.'     )

													A__	     =isinstance(__UpperCAmelCase		,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}'''     )
													A__	     =is_batched_numpy or (
													    isinstance(__UpperCAmelCase		,(list, tuple)     ) and (isinstance(raw_speech[0]		,(np.ndarray, tuple, list)     ))
													)
													if is_batched:
																				A__	     =[np.asarray([speech]		,dtype=np.floataa     ).T for speech in raw_speech]
													elif not is_batched and not isinstance(__UpperCAmelCase		,np.ndarray     ):
																				A__	     =np.asarray(__UpperCAmelCase		,dtype=np.floataa     )
													elif isinstance(__UpperCAmelCase		,np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
																				A__	     =raw_speech.astype(np.floataa     )
													# always return batch
													if not is_batched:
																				A__	     =[np.asarray([raw_speech]     ).T]

													# Convert audio signals to log mel spectrograms, truncate by time axis
													A__	     =[
													    self._np_extract_fbank_features(waveform.squeeze()     ).T[: self.spectrogram_length] for waveform in raw_speech
													]
													if isinstance(audio_features[0]		,__UpperCAmelCase     ):
																				A__	     =[np.asarray(__UpperCAmelCase		,dtype=np.floataa     ) for feature in audio_features]

													# Create audio attention mask
													A__	     =max(
													    [ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len for feature in audio_features]     )  # The maximum number of audio patches in a batch
													if return_attention_mask:
																				A__	     =[
																				    (ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [1]
																				    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]     ) * self.freq_len) * [0]
																				    for feature in audio_features
																				]
																				A__	     =np.array(__UpperCAmelCase     ).astype(np.floataa     )

													# convert into correct format for padding
													A__	     =max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
													A__	     =np.ones([len(__UpperCAmelCase     ), 1, max_time_len, self.feature_size]     ).astype(np.floataa     )
													A__	     =padded_audio_features * self.padding_value
													for i in range(len(__UpperCAmelCase     )     ):
																				A__	     =audio_features[i]
																				A__	     =feature

													# return as BatchFeature
													if return_attention_mask:
																				A__	     ={'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
													else:
																				A__	     ={'''audio_values''': padded_audio_features}

													A__	     =BatchFeature(data=__UpperCAmelCase		,tensor_type=__UpperCAmelCase     )
													return encoded_inputs






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							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:
																																			A__	     =							remaining_time[j]
																																			A__	     =							j
																																			A__	     =							True

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

														A__	     =							remaining_time[short]
														if minm == 0:
																					A__	     =							999_999_999

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

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

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

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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(input())
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  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											=  map(int, input().split())

				__lowerCamelCase											=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				__lowerCamelCase											=  burst_time
				__lowerCamelCase											=  no_of_processes
				__lowerCamelCase											=  waiting_time
				__lowerCamelCase											=  calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				__lowerCamelCase											=  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)





