


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

import numpy as np
import torch

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


A      :    Optional[Any]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			['''input_features''', '''is_longer''']





       def __init__(   self							:			str					,      __lowerCamelCase							:			Union[str, Any]=64					,      __lowerCamelCase							:			Optional[Any]=48000					,      __lowerCamelCase							:			str=480					,      __lowerCamelCase							:			Optional[int]=10					,      __lowerCamelCase							:			str=1024					,      __lowerCamelCase							:			List[str]=0.0					,      __lowerCamelCase							:			int=False					,      __lowerCamelCase							:			float = 0					,      __lowerCamelCase							:			float = 14000					,      __lowerCamelCase							:			int = None					,      __lowerCamelCase							:			str = "fusion"					,      __lowerCamelCase							:			str = "repeatpad"					,      **__lowerCamelCase							:			Optional[int]					,      ):


             '''simple docstring'''



             super().__init__(
                 feature_size=__lowerCAmelCase					,      sampling_rate=__lowerCAmelCase					,      padding_value=__lowerCAmelCase					,      return_attention_mask=__lowerCAmelCase					,      **__lowerCAmelCase					,      )
             lowerCamelCase__		:				str    =  top_db
             lowerCamelCase__		:				Any    =  truncation
             lowerCamelCase__		:				List[str]    =  padding
             lowerCamelCase__		:				Union[str, Any]    =  fft_window_size
             lowerCamelCase__		:				Dict    =  (fft_window_size >> 1) + 1
             lowerCamelCase__		:				Optional[int]    =  hop_length
             lowerCamelCase__		:				List[str]    =  max_length_s
             lowerCamelCase__		:				Dict    =  max_length_s * sampling_rate
             lowerCamelCase__		:				List[Any]    =  sampling_rate
             lowerCamelCase__		:				Union[str, Any]    =  frequency_min
             lowerCamelCase__		:				str    =  frequency_max
             lowerCamelCase__		:				int    =  mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins					,      num_mel_filters=__lowerCAmelCase					,      min_frequency=__lowerCAmelCase					,      max_frequency=__lowerCAmelCase					,      sampling_rate=__lowerCAmelCase					,      norm=__lowerCAmelCase					,      mel_scale="htk"					,      )
             lowerCamelCase__		:				int    =  mel_filter_bank(
                 num_frequency_bins=self.nb_frequency_bins					,      num_mel_filters=__lowerCAmelCase					,      min_frequency=__lowerCAmelCase					,      max_frequency=__lowerCAmelCase					,      sampling_rate=__lowerCAmelCase					,      norm="slaney"					,      mel_scale="slaney"					,      )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			np.array					,      __lowerCamelCase							:			Optional[np.array] = None       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  spectrogram(
                 __lowerCAmelCase					,      window_function(self.fft_window_size					,      "hann"       )					,      frame_length=self.fft_window_size					,      hop_length=self.hop_length					,      power=2.0					,      mel_filters=__lowerCAmelCase					,      log_mel="dB"					,      )
             return log_mel_spectrogram.T





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  np.array_split(list(range(0					,      total_frames - chunk_frames + 1       )       )					,      3       )
             if len(ranges[1]       ) == 0:
                   # if the audio is too short, we just use the first chunk
                   lowerCamelCase__		:				Any    =  [0]
             if len(ranges[2]       ) == 0:
                   # if the audio is too short, we just use the first chunk
                   lowerCamelCase__		:				Optional[Any]    =  [0]
             # randomly choose index for each part
             lowerCamelCase__		:				List[str]    =  np.random.choice(ranges[0]       )
             lowerCamelCase__		:				int    =  np.random.choice(ranges[1]       )
             lowerCamelCase__		:				int    =  np.random.choice(ranges[2]       )

             lowerCamelCase__		:				Any    =  mel[idx_front : idx_front + chunk_frames, :]
             lowerCamelCase__		:				str    =  mel[idx_middle : idx_middle + chunk_frames, :]
             lowerCamelCase__		:				List[str]    =  mel[idx_back : idx_back + chunk_frames, :]

             lowerCamelCase__		:				Dict    =  torch.tensor(mel[None, None, :]       )
             lowerCamelCase__		:				Optional[Any]    =  torch.nn.functional.interpolate(
                 __lowerCAmelCase					,      size=[chunk_frames, 64]					,      mode="bilinear"					,      align_corners=__lowerCAmelCase       )
             lowerCamelCase__		:				str    =  mel_shrink[0][0].numpy()
             lowerCamelCase__		:				List[str]    =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,      axis=0       )
             return mel_fusion





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			np.array					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             if waveform.shape[0] > max_length:
                   if truncation == "rand_trunc":
                         lowerCamelCase__		:				int    =  True
                         # random crop to max_length (for compatibility) -> this should be handled by self.pad
                         lowerCamelCase__		:				Tuple    =  len(__lowerCAmelCase       ) - max_length
                         lowerCamelCase__		:				List[str]    =  np.random.randint(0					,      overflow + 1       )
                         lowerCamelCase__		:				Union[str, Any]    =  waveform[idx : idx + max_length]
                         lowerCamelCase__		:				Optional[Any]    =  self._np_extract_fbank_features(__lowerCAmelCase					,      self.mel_filters_slaney       )[None, :]
                   elif truncation == "fusion":
                         lowerCamelCase__		:				List[str]    =  self._np_extract_fbank_features(__lowerCAmelCase					,      self.mel_filters       )
                         lowerCamelCase__		:				Optional[int]    =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                         lowerCamelCase__		:				int    =  mel.shape[0]
                         if chunk_frames == total_frames:
                               # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                               # In this case, we just use the whole audio.
                               lowerCamelCase__		:				List[Any]    =  np.stack([mel, mel, mel, mel]					,      axis=0       )
                               lowerCamelCase__		:				Tuple    =  False
                         else:
                               lowerCamelCase__		:				str    =  self._random_mel_fusion(__lowerCAmelCase					,      __lowerCAmelCase					,      __lowerCAmelCase       )
                               lowerCamelCase__		:				List[Any]    =  True
                   else:
                         raise NotImplementedError(f"data_truncating {truncation} not implemented"       )

             else:
                   lowerCamelCase__		:				Any    =  False
                   # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                   if waveform.shape[0] < max_length:
                         if padding == "repeat":
                               lowerCamelCase__		:				Any    =  int(max_length / len(__lowerCAmelCase       )       )
                               lowerCamelCase__		:				Tuple    =  np.stack(np.tile(__lowerCAmelCase					,      n_repeat + 1       )       )[:max_length]
                         if padding == "repeatpad":
                               lowerCamelCase__		:				Optional[Any]    =  int(max_length / len(__lowerCAmelCase       )       )
                               lowerCamelCase__		:				List[str]    =  np.stack(np.tile(__lowerCAmelCase					,      __lowerCAmelCase       )       )
                         lowerCamelCase__		:				Optional[Any]    =  np.pad(__lowerCAmelCase					,      (0, max_length - waveform.shape[0])					,      mode="constant"					,      constant_values=0       )

                   if truncation == "fusion":
                         lowerCamelCase__		:				Union[str, Any]    =  self._np_extract_fbank_features(__lowerCAmelCase					,      self.mel_filters       )
                         lowerCamelCase__		:				int    =  np.stack([input_mel, input_mel, input_mel, input_mel]					,      axis=0       )
                   else:
                         lowerCamelCase__		:				int    =  self._np_extract_fbank_features(__lowerCAmelCase					,      self.mel_filters_slaney       )[None, :]

             return input_mel, longer





       def __call__(   self							:			int					,      __lowerCamelCase							:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,      __lowerCamelCase							:			str = None					,      __lowerCamelCase							:			Optional[str] = None					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			Optional[Union[str, TensorType]] = None					,      **__lowerCamelCase							:			Optional[int]					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  truncation if truncation is not None else self.truncation
             lowerCamelCase__		:				Tuple    =  padding if padding else self.padding

             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."       )

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

             if is_batched:
                   lowerCamelCase__		:				Dict    =  [np.asarray(__lowerCAmelCase					,      dtype=np.floataa       ) for speech in raw_speech]
             elif not is_batched and not isinstance(__lowerCAmelCase					,      np.ndarray       ):
                   lowerCamelCase__		:				List[Any]    =  np.asarray(__lowerCAmelCase					,      dtype=np.floataa       )
             elif isinstance(__lowerCAmelCase					,      np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                   lowerCamelCase__		:				List[str]    =  raw_speech.astype(np.floataa       )

             # always return batch
             if not is_batched:
                   lowerCamelCase__		:				int    =  [np.asarray(__lowerCAmelCase       )]

             # convert to mel spectrogram, truncate and pad if needed.
             lowerCamelCase__		:				Dict    =  [
                 self._get_input_mel(__lowerCAmelCase					,      max_length if max_length else self.nb_max_samples					,      __lowerCAmelCase					,      __lowerCAmelCase       )
                 for waveform in raw_speech
             ]

             lowerCamelCase__		:				Dict    =  []
             lowerCamelCase__		:				Any    =  []
             for mel, longer in padded_inputs:
                   input_mel.append(__lowerCAmelCase       )
                   is_longer.append(__lowerCAmelCase       )

             if truncation == "fusion" and sum(__lowerCAmelCase       ) == 0:
                   # if no audio is longer than 10s, then randomly select one audio to be longer
                   lowerCamelCase__		:				Union[str, Any]    =  np.random.randint(0					,      len(__lowerCAmelCase       )       )
                   lowerCamelCase__		:				Tuple    =  True

             if isinstance(input_mel[0]					,      __lowerCAmelCase       ):
                   lowerCamelCase__		:				Optional[Any]    =  [np.asarray(__lowerCAmelCase					,      dtype=np.floataa       ) for feature in input_mel]

             # is_longer is a list of bool
             lowerCamelCase__		:				Dict    =  [[longer] for longer in is_longer]

             lowerCamelCase__		:				Optional[int]    =  {"input_features": input_mel, "is_longer": is_longer}
             lowerCamelCase__		:				Tuple    =  BatchFeature(__lowerCAmelCase       )

             if return_tensors is not None:
                   lowerCamelCase__		:				str    =  input_features.convert_to_tensors(__lowerCAmelCase       )

             return input_features









from collections import deque


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					A__        =   process_name  # process name
					A__        =   arrival_time  # arrival time of the process
					# completion time of finished process or last interrupted time
					A__        =   arrival_time
					A__        =   burst_time  # remaining burst time
					A__        =   0  # total time of the process wait in ready queue
					A__        =   0  # time from arrival time to completion time


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    list[int]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					A__        =   number_of_queues
					# time slice of queues that round robin algorithm applied
					A__        =   time_slices
					# unfinished process is in this ready_queue
					A__        =   queue
					# current time
					A__        =   current_time
					# finished process is in this sequence queue
					A__        =   deque()






			def    a_       (			self   :    Dict   )	->	list[str]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(self.finish_queue   )   ):
							sequence.append(self.finish_queue[i].process_name   )
					return sequence






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							waiting_times.append(queue[i].waiting_time   )
					return waiting_times






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							turnaround_times.append(queue[i].turnaround_time   )
					return turnaround_times






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							completion_times.append(queue[i].stop_time   )
					return completion_times






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    deque[Process]   )	->	list[int]:







					"""simple docstring"""






					return [q.burst_time for q in queue]






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Process   )	->	int:







					"""simple docstring"""






					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    deque[Process]   )	->	deque[Process]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of finished process
					while len(__lowerCAmelCase   ) != 0:
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of current process
							self.update_waiting_time(__lowerCAmelCase   )
							# update current time
							self.current_time += cp.burst_time
							# finish the process and set the process's burst-time 0
							A__        =   0
							# set the process's turnaround time because it is finished
							A__        =   self.current_time - cp.arrival_time
							# set the completion time
							A__        =   self.current_time
							# add the process to queue that has finished queue
							finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# FCFS will finish all remaining processes
					return finished






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int   )	->	tuple[deque[Process], deque[Process]]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(__lowerCAmelCase   )   ):
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of unfinished processes
							self.update_waiting_time(__lowerCAmelCase   )
							# if the burst time of process is bigger than time-slice
							if cp.burst_time > time_slice:
									# use CPU for only time-slice
									self.current_time += time_slice
									# update remaining burst time
									cp.burst_time -= time_slice
									# update end point time
									A__        =   self.current_time
									# locate the process behind the queue because it is not finished
									ready_queue.append(__lowerCAmelCase   )
							else:
									# use CPU for remaining burst time
									self.current_time += cp.burst_time
									# set burst time 0 because the process is finished
									A__        =   0
									# set the finish time
									A__        =   self.current_time
									# update the process' turnaround time because it is finished
									A__        =   self.current_time - cp.arrival_time
									# add the process to queue that has finished queue
									finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# return finished processes queue and remaining processes queue
					return finished, ready_queue







			def    a_       (			self   :    List[Any]   )	->	deque[Process]:







					"""simple docstring"""






					for i in range(self.number_of_queues - 1   ):
							A__      ,			A__        =   self.round_robin(
							    self.ready_queue							,	self.time_slices[i]   )
					#  the last queue has first_come_first_served algorithm
					self.first_come_first_served(self.ready_queue   )

					return self.finish_queue


if __name__ == "__main__":
					import doctest

					A    :					Union[str, Any] =      Process('''P1''', 0, 5_3)
					A    :					Optional[Any] =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					int =      Process('''P4''', 0, 2_4)
					A    :					Any =      3
					A    :					List[Any] =      [1_7, 2_5]
					A    :					Optional[Any] =      deque([Pa, Pa, Pa, Pa])

					if len(time_slices) != number_of_queues - 1:
										raise SystemExit(0)

					doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

					A    :					Optional[Any] =      Process('''P1''', 0, 5_3)
					A    :					int =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					Tuple =      Process('''P4''', 0, 2_4)
					A    :					Union[str, Any] =      3
					A    :					Optional[Any] =      [1_7, 2_5]
					A    :					Tuple =      deque([Pa, Pa, Pa, Pa])
					A    :					Optional[int] =      MLFQ(number_of_queues, time_slices, queue, 0)
					A    :					Dict =      mlfq.multi_level_feedback_queue()

					# print total waiting times of processes(P1, P2, P3, P4)
					print(
					    F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print completion times of processes(P1, P2, P3, P4)
					print(
					    F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print total turnaround times of processes(P1, P2, P3, P4)
					print(
					    F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print sequence of finished processes
					print(
					    F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
					)



