

'''simple docstring'''

from collections import deque







class   UpperCamelCase__    :


		"""simple docstring"""

		def __init__( self    ,			snake_case    ,			snake_case    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple   					=   process_name  # process name
							UpperCAmelCase		:							Optional[Any]   					=   arrival_time  # arrival time of the process
							# completion time of finished process or last interrupted time
							UpperCAmelCase		:							Any   					=   arrival_time
							UpperCAmelCase		:							List[Any]   					=   burst_time  # remaining burst time
							UpperCAmelCase		:							List[Any]   					=   0  # total time of the process wait in ready queue
							UpperCAmelCase		:							Any   					=   0  # time from arrival time to completion time







class   UpperCamelCase__    :


		"""simple docstring"""

		def __init__( self    ,			snake_case    ,			snake_case    ,			snake_case    ,			snake_case    ,			):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]   					=   number_of_queues
							# time slice of queues that round robin algorithm applied
							UpperCAmelCase		:							Any   					=   time_slices
							# unfinished process is in this ready_queue
							UpperCAmelCase		:							Union[str, Any]   					=   queue
							# current time
							UpperCAmelCase		:							Tuple   					=   current_time
							# finished process is in this sequence queue
							UpperCAmelCase		:							Dict   					=   deque()







		def       A_       ( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]   					=   []
							for i in range(len(self.finish_queue			)			):
												sequence.append(self.finish_queue[i].process_name			)
							return sequence







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]   					=   []
							for i in range(len(__snake_case			)			):
												waiting_times.append(queue[i].waiting_time			)
							return waiting_times







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							int   					=   []
							for i in range(len(__snake_case			)			):
												turnaround_times.append(queue[i].turnaround_time			)
							return turnaround_times







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							int   					=   []
							for i in range(len(__snake_case			)			):
												completion_times.append(queue[i].stop_time			)
							return completion_times







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							return [q.burst_time for q in queue]







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							process.waiting_time += self.current_time - process.stop_time
							return process.waiting_time







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]   					=   deque()  # sequence deque of finished process
							while len(__snake_case			) != 0:
												UpperCAmelCase		:							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(__snake_case			)
												# update current time
												self.current_time += cp.burst_time
												# finish the process and set the process's burst-time 0
												UpperCAmelCase		:							List[str]   					=   0
												# set the process's turnaround time because it is finished
												UpperCAmelCase		:							Tuple   					=   self.current_time - cp.arrival_time
												# set the completion time
												UpperCAmelCase		:							str   					=   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       A_       ( self    ,			snake_case    ,			snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]   					=   deque()  # sequence deque of terminated process
							# just for 1 cycle and unfinished processes will go back to queue
							for _ in range(len(__snake_case			)			):
												UpperCAmelCase		:							int   					=   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
																	UpperCAmelCase		:							List[str]   					=   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
																	UpperCAmelCase		:							Optional[int]   					=   0
																	# set the finish time
																	UpperCAmelCase		:							str   					=   self.current_time
																	# update the process' turnaround time because it is finished
																	UpperCAmelCase		:							Any   					=   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       A_       ( self			):


							'''simple docstring'''

							for i in range(self.number_of_queues - 1			):
												UpperCAmelCase					, UpperCAmelCase		:							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

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

				# print total waiting times of processes(P1, P2, P3, P4)
				print(
				    F'waiting time:\\n        \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:\\n        \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:\\n        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
				)
				# print sequence of finished processes
				print(
				    F'sequence of finished processes:\\n        {mlfq.calculate_sequence_of_finish_queue()}'
				)



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase					:			str  				=			{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			List[Any]  				=			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			str  				=			{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     _snake_case	):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError('boolean value expected'	)






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






			__a        =checkpoint[F'{old_prefix}.in_layers.0.weight']
			__a        =checkpoint[F'{old_prefix}.in_layers.0.bias']
			__a        =checkpoint[F'{old_prefix}.in_layers.2.weight']
			__a        =checkpoint[F'{old_prefix}.in_layers.2.bias']
			__a        =checkpoint[F'{old_prefix}.emb_layers.1.weight']
			__a        =checkpoint[F'{old_prefix}.emb_layers.1.bias']
			__a        =checkpoint[F'{old_prefix}.out_layers.0.weight']
			__a        =checkpoint[F'{old_prefix}.out_layers.0.bias']
			__a        =checkpoint[F'{old_prefix}.out_layers.3.weight']
			__a        =checkpoint[F'{old_prefix}.out_layers.3.bias']

			if has_skip:
						__a        =checkpoint[F'{old_prefix}.skip_connection.weight']
						__a        =checkpoint[F'{old_prefix}.skip_connection.bias']

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






			__a      ,    __a      ,    __a        =checkpoint[F'{old_prefix}.qkv.weight'].chunk(3       ,     dim=0	)
			__a      ,    __a      ,    __a        =checkpoint[F'{old_prefix}.qkv.bias'].chunk(3       ,     dim=0	)

			__a        =checkpoint[F'{old_prefix}.norm.weight']
			__a        =checkpoint[F'{old_prefix}.norm.bias']

			__a        =weight_q.squeeze(-1	).squeeze(-1	)
			__a        =bias_q.squeeze(-1	).squeeze(-1	)
			__a        =weight_k.squeeze(-1	).squeeze(-1	)
			__a        =bias_k.squeeze(-1	).squeeze(-1	)
			__a        =weight_v.squeeze(-1	).squeeze(-1	)
			__a        =bias_v.squeeze(-1	).squeeze(-1	)

			__a        =(
			    checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1	).squeeze(-1	)
			)
			__a        =checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1	).squeeze(-1	)

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        ={}

			__a        =checkpoint['time_embed.0.weight']
			__a        =checkpoint['time_embed.0.bias']
			__a        =checkpoint['time_embed.2.weight']
			__a        =checkpoint['time_embed.2.bias']

			if unet_config["num_class_embeds"] is not None:
						__a        =checkpoint['label_emb.weight']

			__a        =checkpoint['input_blocks.0.0.weight']
			__a        =checkpoint['input_blocks.0.0.bias']

			__a        =unet_config['down_block_types']
			__a        =unet_config['layers_per_block']
			__a        =unet_config['attention_head_dim']
			__a        =unet_config['block_out_channels']
			__a        =1
			__a        =channels_list[0]

			for i, layer_type in enumerate(_snake_case	):
						__a        =channels_list[i]
						__a        =current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(_snake_case	):
												__a        =F'down_blocks.{i}.resnets.{j}'
												__a        =F'input_blocks.{current_layer}.0'
												__a        =True if j == 0 and downsample_block_has_skip else False
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(_snake_case	):
												__a        =F'down_blocks.{i}.resnets.{j}'
												__a        =F'input_blocks.{current_layer}.0'
												__a        =True if j == 0 and downsample_block_has_skip else False
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'down_blocks.{i}.attentions.{j}'
												__a        =F'input_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

						if i != len(_snake_case	) - 1:
									__a        =F'down_blocks.{i}.downsamplers.0'
									__a        =F'input_blocks.{current_layer}.0'
									__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
									current_layer += 1

						__a        =current_channels

			# hardcoded the mid-block for now
			__a        ='mid_block.resnets.0'
			__a        ='middle_block.0'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.attentions.0'
			__a        ='middle_block.1'
			__a        =convert_attention(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.resnets.1'
			__a        ='middle_block.2'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =0
			__a        =unet_config['up_block_types']

			for i, layer_type in enumerate(_snake_case	):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1	):
												__a        =F'up_blocks.{i}.resnets.{j}'
												__a        =F'output_blocks.{current_layer}.0'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												current_layer += 1

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.1'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1	):
												__a        =F'up_blocks.{i}.resnets.{j}'
												__a        =F'output_blocks.{current_layer}.0'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     has_skip=_snake_case	)
												__a        =F'up_blocks.{i}.attentions.{j}'
												__a        =F'output_blocks.{current_layer}.1'
												__a        =convert_attention(
												    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												current_layer += 1

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.2'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =checkpoint['out.0.weight']
			__a        =checkpoint['out.0.bias']
			__a        =checkpoint['out.2.weight']
			__a        =checkpoint['out.2.bias']

			return new_checkpoint


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[Any]  				=			argparse.ArgumentParser()

						parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
						parser.add_argument(
						    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
						)
						parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

						_lowerCAmelCase					:			Optional[Any]  				=			parser.parse_args()
						_lowerCAmelCase					:			Optional[Any]  				=			strabool(args.class_cond)

						_lowerCAmelCase					:			Dict  				=			os.path.basename(args.unet_path)
						print(f'''Checkpoint: {ckpt_name}''')

						# Get U-Net config
						if "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Tuple  				=			IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												_lowerCAmelCase					:			Optional[int]  				=			LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			TEST_UNET_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			None

						_lowerCAmelCase					:			Tuple  				=			con_pt_to_diffuser(args.unet_path, unet_config)

						_lowerCAmelCase					:			Optional[int]  				=			UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Optional[int]  				=			CT_IMAGENET_64_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												_lowerCAmelCase					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

						_lowerCAmelCase					:			str  				=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)






