
"""simple docstring"""







from collections import deque




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   process_name  # process name
			__lowerCAmelCase    :		str		    =   arrival_time  # arrival time of the process
			# completion time of finished process or last interrupted time
			__lowerCAmelCase    :		List[Any]		    =   arrival_time
			__lowerCAmelCase    :		Dict		    =   burst_time  # remaining burst time
			__lowerCAmelCase    :		List[Any]		    =   0  # total time of the process wait in ready queue
			__lowerCAmelCase    :		Any		    =   0  # time from arrival time to completion time




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			# total number of mlfq's queues
			__lowerCAmelCase    :		int		    =   number_of_queues
			# time slice of queues that round robin algorithm applied
			__lowerCAmelCase    :		int		    =   time_slices
			# unfinished process is in this ready_queue
			__lowerCAmelCase    :		Optional[int]		    =   queue
			# current time
			__lowerCAmelCase    :		Optional[Any]		    =   current_time
			# finished process is in this sequence queue
			__lowerCAmelCase    :		deque[Process]		    =   deque()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   []
			for i in range(len(self.finish_queue				)				):
				sequence.append(self.finish_queue[i].process_name				)
			return sequence

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   []
			for i in range(len(_UpperCAmelCase				)				):
				waiting_times.append(queue[i].waiting_time				)
			return waiting_times

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   []
			for i in range(len(_UpperCAmelCase				)				):
				turnaround_times.append(queue[i].turnaround_time				)
			return turnaround_times

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   []
			for i in range(len(_UpperCAmelCase				)				):
				completion_times.append(queue[i].stop_time				)
			return completion_times

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return [q.burst_time for q in queue]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			process.waiting_time += self.current_time - process.stop_time
			return process.waiting_time

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		deque[Process]		    =   deque()  # sequence deque of finished process
			while len(_UpperCAmelCase				) != 0:
				__lowerCAmelCase    :		List[str]		    =   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(_UpperCAmelCase				)
				# update current time
				self.current_time += cp.burst_time
				# finish the process and set the process's burst-time 0
				__lowerCAmelCase    :		int		    =   0
				# set the process's turnaround time because it is finished
				__lowerCAmelCase    :		str		    =   self.current_time - cp.arrival_time
				# set the completion time
				__lowerCAmelCase    :		List[str]		    =   self.current_time
				# add the process to queue that has finished queue
				finished.append(_UpperCAmelCase				)

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		deque[Process]		    =   deque()  # sequence deque of terminated process
			# just for 1 cycle and unfinished processes will go back to queue
			for _ in range(len(_UpperCAmelCase				)				):
				__lowerCAmelCase    :		str		    =   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(_UpperCAmelCase				)
				# 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
					__lowerCAmelCase    :		Tuple		    =   self.current_time
					# locate the process behind the queue because it is not finished
					ready_queue.append(_UpperCAmelCase				)
				else:
					# use CPU for remaining burst time
					self.current_time += cp.burst_time
					# set burst time 0 because the process is finished
					__lowerCAmelCase    :		Optional[Any]		    =   0
					# set the finish time
					__lowerCAmelCase    :		Optional[int]		    =   self.current_time
					# update the process' turnaround time because it is finished
					__lowerCAmelCase    :		Dict		    =   self.current_time - cp.arrival_time
					# add the process to queue that has finished queue
					finished.append(_UpperCAmelCase				)

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







		def 							__lowerCamelCase							(   self				):

			#  all queues except last one have round_robin algorithm
			for i in range(self.number_of_queues - 1				):
				__lowerCAmelCase    :		List[str]		    =   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

					lowerCamelCase__           =	Process("""P1""", 0, 53)
					lowerCamelCase__           =	Process("""P2""", 0, 17)
					lowerCamelCase__           =	Process("""P3""", 0, 68)
					lowerCamelCase__           =	Process("""P4""", 0, 24)
					lowerCamelCase__           =	3
					lowerCamelCase__           =	[17, 25]
					lowerCamelCase__           =	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])})

					lowerCamelCase__           =	Process("""P1""", 0, 53)
					lowerCamelCase__           =	Process("""P2""", 0, 17)
					lowerCamelCase__           =	Process("""P3""", 0, 68)
					lowerCamelCase__           =	Process("""P4""", 0, 24)
					lowerCamelCase__           =	3
					lowerCamelCase__           =	[17, 25]
					lowerCamelCase__           =	deque([Pa, Pa, Pa, Pa])
					lowerCamelCase__           =	MLFQ(number_of_queues, time_slices, queue, 0)
					lowerCamelCase__           =	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()}'
					)

"""simple docstring"""







from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			'mctct'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=80_65 ,	_SCREAMING_SNAKE_CASE=15_36 ,	_SCREAMING_SNAKE_CASE=36 ,	_SCREAMING_SNAKE_CASE=61_44 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=3_84 ,	_SCREAMING_SNAKE_CASE=9_20 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=(7,) ,	_SCREAMING_SNAKE_CASE=(3,) ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="sum" ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   vocab_size
			__lowerCAmelCase    :		Any		    =   hidden_size
			__lowerCAmelCase    :		int		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   intermediate_size
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		str		    =   attention_head_dim
			__lowerCAmelCase    :		int		    =   max_position_embeddings
			__lowerCAmelCase    :		List[Any]		    =   layer_norm_eps
			__lowerCAmelCase    :		Tuple		    =   layerdrop
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		List[str]		    =   pad_token_id
			__lowerCAmelCase    :		Union[str, Any]		    =   bos_token_id
			__lowerCAmelCase    :		int		    =   eos_token_id
			__lowerCAmelCase    :		Optional[int]		    =   conv_glu_dim
			__lowerCAmelCase    :		List[Any]		    =   conv_dropout
			__lowerCAmelCase    :		Tuple		    =   num_conv_layers
			__lowerCAmelCase    :		Tuple		    =   input_feat_per_channel
			__lowerCAmelCase    :		Optional[int]		    =   input_channels
			__lowerCAmelCase    :		str		    =   conv_channels
			__lowerCAmelCase    :		str		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Tuple		    =   ctc_zero_infinity

			# prevents config testing fail with exporting to json
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   list(_SCREAMING_SNAKE_CASE				)

			if len(self.conv_kernel				) != self.num_conv_layers:
				raise ValueError(
				    'Configuration for convolutional module is incorrect. '
				    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
				    f"but is `len(config.conv_kernel) = {len(self.conv_kernel				)}`, "
				    f"`config.num_conv_layers = {self.num_conv_layers}`."				)