

from collections import deque






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> 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 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> None:




						'''simple docstring'''
						# total number of mlfq's queues
						__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 		__magic_name__    (		self					)   -> list[str]:




						'''simple docstring'''
						__a        =[]
						for i in range(len(self.finish_queue					)					):
									sequence.append(self.finish_queue[i].process_name					)
						return sequence





			def 		__magic_name__    (		self					,  __snake_case					)   -> list[int]:




						'''simple docstring'''
						__a        =[]
						for i in range(len(__snake_case					)					):
									waiting_times.append(queue[i].waiting_time					)
						return waiting_times





			def 		__magic_name__    (		self					,  __snake_case					)   -> list[int]:




						'''simple docstring'''
						__a        =[]
						for i in range(len(__snake_case					)					):
									turnaround_times.append(queue[i].turnaround_time					)
						return turnaround_times





			def 		__magic_name__    (		self					,  __snake_case					)   -> list[int]:




						'''simple docstring'''
						__a        =[]
						for i in range(len(__snake_case					)					):
									completion_times.append(queue[i].stop_time					)
						return completion_times





			def 		__magic_name__    (		self					,  __snake_case					)   -> list[int]:




						'''simple docstring'''
						return [q.burst_time for q in queue]





			def 		__magic_name__    (		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						process.waiting_time += self.current_time - process.stop_time
						return process.waiting_time





			def 		__magic_name__    (		self					,  __snake_case					)   -> deque[Process]:




						'''simple docstring'''
						__a        =deque()  # sequence deque of finished process
						while len(__snake_case					) != 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(__snake_case					)
									# 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(__snake_case					)

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





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> 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(__snake_case					)					):
									__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(__snake_case					)
									# 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(__snake_case					)
									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(__snake_case					)

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





			def 		__magic_name__    (		self					)   -> deque[Process]:




						'''simple docstring'''

						#  all queues except last one have round_robin algorithm
						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

						_lowerCAmelCase					:			List[str]  				=			Process("P1", 0, 53)
						_lowerCAmelCase					:			Optional[Any]  				=			Process("P2", 0, 17)
						_lowerCAmelCase					:			Optional[Any]  				=			Process("P3", 0, 68)
						_lowerCAmelCase					:			Union[str, Any]  				=			Process("P4", 0, 24)
						_lowerCAmelCase					:			Optional[int]  				=			3
						_lowerCAmelCase					:			str  				=			[17, 25]
						_lowerCAmelCase					:			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])})

						_lowerCAmelCase					:			str  				=			Process("P1", 0, 53)
						_lowerCAmelCase					:			Any  				=			Process("P2", 0, 17)
						_lowerCAmelCase					:			Tuple  				=			Process("P3", 0, 68)
						_lowerCAmelCase					:			Tuple  				=			Process("P4", 0, 24)
						_lowerCAmelCase					:			Tuple  				=			3
						_lowerCAmelCase					:			Tuple  				=			[17, 25]
						_lowerCAmelCase					:			Optional[Any]  				=			deque([Pa, Pa, Pa, Pa])
						_lowerCAmelCase					:			List[str]  				=			MLFQ(number_of_queues, time_slices, queue, 0)
						_lowerCAmelCase					:			Union[str, Any]  				=			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()}'''
						)









import copy
import os
from typing import Union

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


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =num_channels
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from GITConfig
						if config_dict.get('model_type'					) == "git":
									__a        =config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

						__a        =GitVisionConfig(**__snake_case					)
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =use_cache
						__a        =tie_word_embeddings
						__a        =num_image_with_embedding

						__a        =bos_token_id
						__a        =eos_token_id





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






