


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


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







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		int:

       """simple docstring"""

       a_					:		Tuple							=  R'\w+[.]\d+'
       a_					:		List[Any]							=  re.findall(__A  ,    __A				)
       for pat in pats:
              a_					:		Union[str, Any]							=  key.replace(__A  ,    '_'.join(pat.split('.'				)				)				)
       return key







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			Optional[Any]				)	->		str:

       """simple docstring"""

       a_					:		Dict							=  pt_tuple_key[:-1] + ('scale',)
       if (
           any('norm' in str_ for str_ in pt_tuple_key				)
           and (pt_tuple_key[-1] == "bias")
           and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
           and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
       ):
              a_					:		Any							=  pt_tuple_key[:-1] + ('scale',)
              return renamed_pt_tuple_key, pt_tensor
       elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
              a_					:		Tuple							=  pt_tuple_key[:-1] + ('scale',)
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
              a_					:		List[Any]							=  pt_tuple_key[:-1] + ('embedding',)
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       a_					:		int							=  pt_tuple_key[:-1] + ('kernel',)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
              a_					:		Union[str, Any]							=  pt_tensor.transpose(2  ,    3  ,    1  ,    0				)
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       a_					:		Any							=  pt_tuple_key[:-1] + ('kernel',)
       if pt_tuple_key[-1] == "weight":
              a_					:		Union[str, Any]							=  pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       a_					:		Optional[Any]							=  pt_tuple_key[:-1] + ('weight',)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       a_					:		List[str]							=  pt_tuple_key[:-1] + ('bias',)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			Dict  ,    __A :			Any=42				)	->		List[Any]:

       """simple docstring"""

       a_					:		str							=  {k: v.numpy() for k, v in pt_state_dict.items()}

       # Step 2: Since the model is stateless, get random Flax params
       a_					:		int							=  flax_model.init_weights(PRNGKey(__A				)				)

       a_					:		Tuple							=  flatten_dict(__A				)
       a_					:		Dict							=  {}

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              a_					:		Optional[int]							=  rename_key(__A				)
              a_					:		List[Any]							=  tuple(renamed_pt_key.split('.'				)				)

              # Correctly rename weight parameters
              a_							,		a_					:		Optional[Any]							=  rename_key_and_reshape_tensor(__A  ,    __A  ,    __A				)

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""				)

        # also add unexpected weight so that warning is thrown
              a_					:		Optional[int]							=  jnp.asarray(__A				)

       return unflatten_dict(__A				)









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()}'''
					)



