

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=['input_values', 'padding_mask']
			def __init__(	self							:       str					,					a							:       int = 1					,					a							:       int = 2_4000					,					a							:       float = 0.0					,					a							:       float = None					,					a							:       float = None					,					**a							:       Optional[Any]					,					)							->				List[Any]:

				"""simple docstring"""

				super().__init__(feature_size=a					,					sampling_rate=a					,					padding_value=a					,					**a		)
				SCREAMING_SNAKE_CASE      :					str    =				chunk_length_s
				SCREAMING_SNAKE_CASE      :					List[str]    =				overlap
			@property
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				if self.chunk_length_s is None:
					return None
				else:
					return int(self.chunk_length_s * self.sampling_rate		)
			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Optional[int]:

				"""simple docstring"""

				if self.chunk_length_s is None or self.overlap is None:
					return None
				else:
					return max(1					,					int((1.0 - self.overlap) * self.chunk_length		)		)






			def __call__(	self							:       Optional[int]					,					a							:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					a							:       Optional[Union[bool, str, PaddingStrategy]] = None					,					a							:       Optional[bool] = False					,					a							:       Optional[int] = None					,					a							:       Optional[Union[str, TensorType]] = None					,					a							:       Optional[int] = None					,					)							->				BatchFeature:

				"""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 audio 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."		)

				if padding and truncation:
					raise ValueError("Both padding and truncation were set. Make sure you only set one."		)
				elif padding is None:
					# by default let's pad the inputs
					SCREAMING_SNAKE_CASE      :					str    =				True

				SCREAMING_SNAKE_CASE      :					List[Any]    =				bool(
				    isinstance(a					,					(list, tuple)		) and (isinstance(raw_audio[0]					,					(np.ndarray, tuple, list)		))		)

				if is_batched:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				[np.asarray(a					,					dtype=np.floataa		).T for audio in raw_audio]
				elif not is_batched and not isinstance(a					,					np.ndarray		):
					SCREAMING_SNAKE_CASE      :					Any    =				np.asarray(a					,					dtype=np.floataa		)
				elif isinstance(a					,					np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				raw_audio.astype(np.floataa		)

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

				# verify inputs are valid
				for idx, example in enumerate(a		):
					if example.ndim > 2:
						raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}"		)
					if self.feature_size == 1 and example.ndim != 1:
						raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels"		)
					if self.feature_size == 2 and example.shape[-1] != 2:
						raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels"		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
				SCREAMING_SNAKE_CASE      :					Tuple    =				BatchFeature({"input_values": raw_audio}		)
				if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
					if truncation:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				min(array.shape[0] for array in raw_audio		)
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				int(np.floor(max_length / self.chunk_stride		)		)
						SCREAMING_SNAKE_CASE      :					Dict    =				(nb_step - 1) * self.chunk_stride + self.chunk_length
					elif padding:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				max(array.shape[0] for array in raw_audio		)
						SCREAMING_SNAKE_CASE      :					int    =				int(np.ceil(max_length / self.chunk_stride		)		)
						SCREAMING_SNAKE_CASE      :					List[Any]    =				(nb_step - 1) * self.chunk_stride + self.chunk_length
						SCREAMING_SNAKE_CASE      :					str    =				"max_length"
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				input_values

        # normal padding on batch
				if padded_inputs is None:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.pad(
					    a					,					max_length=a					,					truncation=a					,					padding=a					,					return_attention_mask=a					,					)
					if padding:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				padded_inputs.pop("attention_mask"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				[]
				for example in padded_inputs.pop("input_values"		):
					if self.feature_size == 1:
						SCREAMING_SNAKE_CASE      :					Tuple    =				example[..., None]
					input_values.append(example.T		)

				SCREAMING_SNAKE_CASE      :					Dict    =				input_values
				if return_tensors is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				padded_inputs.convert_to_tensors(a		)

				return padded_inputs






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()}'''
					)



