
"""simple docstring"""





from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder



@dataclass
class __lowerCAmelCase				(					UpperCamelCase_      ):




			"""simple docstring"""







			snake_case	      =				42





class __lowerCAmelCase				(					UpperCamelCase_     ,				UpperCamelCase_      ):




			"""simple docstring"""







			snake_case	      =				True




			@register_to_config
			def __init__(				self						:					int       ,   _snake_case						:					int = 3       ,   _snake_case						:					int = 3       ,   _snake_case						:					Tuple[str] = ("DownEncoderBlock2D",)       ,   _snake_case						:					Tuple[str] = ("UpDecoderBlock2D",)       ,   _snake_case						:					Tuple[int] = (64,)       ,   _snake_case						:					int = 1       ,   _snake_case						:					str = "silu"       ,   _snake_case						:					int = 4       ,   _snake_case						:					int = 32       ,   _snake_case						:					int = 32       ,   _snake_case						:					float = 0.1_8_2_1_5       ,   )      -> List[Any]:







								"""simple docstring"""
								super().__init__()

								# pass init params to Encoder
								A_				 =						Encoder(
								    in_channels=__A       ,   out_channels=__A       ,   down_block_types=__A       ,   block_out_channels=__A       ,   layers_per_block=__A       ,   act_fn=__A       ,   norm_num_groups=__A       ,   double_z=__A       ,   )

								# pass init params to Decoder
								A_				 =						Decoder(
								    in_channels=__A       ,   out_channels=__A       ,   up_block_types=__A       ,   block_out_channels=__A       ,   layers_per_block=__A       ,   norm_num_groups=__A       ,   act_fn=__A       ,   )

								A_				 =						nn.Convad(2 * latent_channels       ,   2 * latent_channels       ,   1	)
								A_				 =						nn.Convad(__A       ,   __A       ,   1	)

								A_				 =						False
								A_				 =						False

								# only relevant if vae tiling is enabled
								A_				 =						self.config.sample_size
								A_				 =						(
								    self.config.sample_size[0]
								    if isinstance(self.config.sample_size       ,   (list, tuple)	)
								    else self.config.sample_size
								)
								A_				 =						int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
								A_				 =						0.2_5




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					int=False	)      -> List[Any]:







								"""simple docstring"""
								if isinstance(__A       ,   (Encoder, Decoder)	):
													A_				 =						value




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					bool = True	)      -> Tuple:







								"""simple docstring"""
								A_				 =						use_tiling




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								self.enable_tiling(__A	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						True




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						False




			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						{}

								def fn_recursive_add_processors(_snake_case						:					str       ,   _snake_case						:					torch.nn.Module       ,   _snake_case						:					Dict[str, AttentionProcessor]	):
													if hasattr(__A       ,   "set_processor"	):
																		A_				 =						module.processor

													for sub_name, child in module.named_children():
																		fn_recursive_add_processors(F'{name}.{sub_name}'       ,   __A       ,   __A	)

													return processors

								for name, module in self.named_children():
													fn_recursive_add_processors(__A       ,   __A       ,   __A	)

								return processors




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						len(self.attn_processors.keys()	)

								if isinstance(__A       ,   __A	) and len(__A	) != count:
													raise ValueError(
													    F'A dict of processors was passed, but the number of processors {len(__A	)} does not match the'
													    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'	)

								def fn_recursive_attn_processor(_snake_case						:					str       ,   _snake_case						:					torch.nn.Module       ,   _snake_case						:					List[str]	):
													if hasattr(__A       ,   "set_processor"	):
																		if not isinstance(__A       ,   __A	):
																							module.set_processor(__A	)
																		else:
																							module.set_processor(processor.pop(F'{name}.processor'	)	)

													for sub_name, child in module.named_children():
																		fn_recursive_attn_processor(F'{name}.{sub_name}'       ,   __A       ,   __A	)

								for name, module in self.named_children():
													fn_recursive_attn_processor(__A       ,   __A       ,   __A	)




			def      lowerCamelCase__     (				self						:					Any	)      -> int:







								"""simple docstring"""
								self.set_attn_processor(AttnProcessor()	)




			@apply_forward_hook
			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = True	)      -> Any:







								"""simple docstring"""
								if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
													return self.tiled_encode(__A       ,   return_dict=__A	)

								if self.use_slicing and x.shape[0] > 1:
													A_				 =						[self.encoder(__A	) for x_slice in x.split(1	)]
													A_				 =						torch.cat(__A	)
								else:
													A_				 =						self.encoder(__A	)

								A_				 =						self.quant_conv(__A	)
								A_				 =						DiagonalGaussianDistribution(__A	)

								if not return_dict:
													return (posterior,)

								return AutoencoderKLOutput(latent_dist=__A	)




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = True	)      -> List[str]:







								"""simple docstring"""
								if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
													return self.tiled_decode(__A       ,   return_dict=__A	)

								A_				 =						self.post_quant_conv(__A	)
								A_				 =						self.decoder(__A	)

								if not return_dict:
													return (dec,)

								return DecoderOutput(sample=__A	)




			@apply_forward_hook
			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = True	)      -> Dict:







								"""simple docstring"""
								if self.use_slicing and z.shape[0] > 1:
													A_				 =						[self._decode(__A	).sample for z_slice in z.split(1	)]
													A_				 =						torch.cat(__A	)
								else:
													A_				 =						self._decode(__A	).sample

								if not return_dict:
													return (decoded,)

								return DecoderOutput(sample=__A	)




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						min(a.shape[2]       ,   b.shape[2]       ,   __A	)
								for y in range(__A	):
													A_				 =						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
								return b




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					str	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						min(a.shape[3]       ,   b.shape[3]       ,   __A	)
								for x in range(__A	):
													A_				 =						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
								return b




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = True	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
								A_				 =						int(self.tile_latent_min_size * self.tile_overlap_factor	)
								A_				 =						self.tile_latent_min_size - blend_extent

								# Split the image into 512x512 tiles and encode them separately.
								A_				 =						[]
								for i in range(0       ,   x.shape[2]       ,   __A	):
													A_				 =						[]
													for j in range(0       ,   x.shape[3]       ,   __A	):
																		A_				 =						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																		A_				 =						self.encoder(__A	)
																		A_				 =						self.quant_conv(__A	)
																		row.append(__A	)
													rows.append(__A	)
								A_				 =						[]
								for i, row in enumerate(__A	):
													A_				 =						[]
													for j, tile in enumerate(__A	):
																		# blend the above tile and the left tile
																		# to the current tile and add the current tile to the result row
																		if i > 0:
																							A_				 =						self.blend_v(rows[i - 1][j]       ,   __A       ,   __A	)
																		if j > 0:
																							A_				 =						self.blend_h(row[j - 1]       ,   __A       ,   __A	)
																		result_row.append(tile[:, :, :row_limit, :row_limit]	)
													result_rows.append(torch.cat(__A       ,   dim=3	)	)

								A_				 =						torch.cat(__A       ,   dim=2	)
								A_				 =						DiagonalGaussianDistribution(__A	)

								if not return_dict:
													return (posterior,)

								return AutoencoderKLOutput(latent_dist=__A	)




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = True	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
								A_				 =						int(self.tile_sample_min_size * self.tile_overlap_factor	)
								A_				 =						self.tile_sample_min_size - blend_extent

								# Split z into overlapping 64x64 tiles and decode them separately.
								# The tiles have an overlap to avoid seams between tiles.
								A_				 =						[]
								for i in range(0       ,   z.shape[2]       ,   __A	):
													A_				 =						[]
													for j in range(0       ,   z.shape[3]       ,   __A	):
																		A_				 =						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																		A_				 =						self.post_quant_conv(__A	)
																		A_				 =						self.decoder(__A	)
																		row.append(__A	)
													rows.append(__A	)
								A_				 =						[]
								for i, row in enumerate(__A	):
													A_				 =						[]
													for j, tile in enumerate(__A	):
																		# blend the above tile and the left tile
																		# to the current tile and add the current tile to the result row
																		if i > 0:
																							A_				 =						self.blend_v(rows[i - 1][j]       ,   __A       ,   __A	)
																		if j > 0:
																							A_				 =						self.blend_h(row[j - 1]       ,   __A       ,   __A	)
																		result_row.append(tile[:, :, :row_limit, :row_limit]	)
													result_rows.append(torch.cat(__A       ,   dim=3	)	)

								A_				 =						torch.cat(__A       ,   dim=2	)
								if not return_dict:
													return (dec,)

								return DecoderOutput(sample=__A	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					torch.FloatTensor       ,   _snake_case						:					bool = False       ,   _snake_case						:					bool = True       ,   _snake_case						:					Optional[torch.Generator] = None       ,   )      -> int:







								"""simple docstring"""
								A_				 =						sample
								A_				 =						self.encode(__A	).latent_dist
								if sample_posterior:
													A_				 =						posterior.sample(generator=__A	)
								else:
													A_				 =						posterior.mode()
								A_				 =						self.decode(__A	).sample

								if not return_dict:
													return (dec,)

								return DecoderOutput(sample=__A	)






"""simple docstring"""





from collections import deque



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					int       ,   _snake_case						:					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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					list[int]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					int       ,   )      -> 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      lowerCamelCase__     (				self						:					List[str]	)      -> list[str]:







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




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													waiting_times.append(queue[i].waiting_time	)
								return waiting_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													turnaround_times.append(queue[i].turnaround_time	)
								return turnaround_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													completion_times.append(queue[i].stop_time	)
								return completion_times




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					deque[Process]	)      -> list[int]:







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




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Process	)      -> int:







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




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					deque[Process]	)      -> 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      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					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(_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      lowerCamelCase__     (				self						:					Any	)      -> 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

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

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




