



"""simple docstring"""







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       int     )	-> Optional[Any]:






	"""simple docstring"""





	A__											=					StableDiffusionPipeline.from_pretrained(UpperCAmelCase_,	torch_dtype=torch.floataa     )

	# load LoRA weight from .safetensors
	A__											=					load_file(UpperCAmelCase_     )

	A__											=					[]

	# directly update weight in diffusers model
	for key in state_dict:
		# it is suggested to print out the key, it usually will be something like below
		# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

		# as we have set the alpha beforehand, so just skip
		if ".alpha" in key or key in visited:
			continue

		if "text" in key:
			A__											=					key.split("."     )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"     )[-1].split("_"     )
			A__											=					pipeline.text_encoder
		else:
			A__											=					key.split("."     )[0].split(LORA_PREFIX_UNET + "_"     )[-1].split("_"     )
			A__											=					pipeline.unet

		# find the target layer
		A__											=					layer_infos.pop(0     )
		while len(UpperCAmelCase_     ) > -1:
			try:
				A__											=					curr_layer.__getattr__(UpperCAmelCase_     )
				if len(UpperCAmelCase_     ) > 0:
					A__											=					layer_infos.pop(0     )
				elif len(UpperCAmelCase_     ) == 0:
					break
			except Exception:
				if len(UpperCAmelCase_     ) > 0:
					temp_name += "_" + layer_infos.pop(0     )
				else:
					A__											=					layer_infos.pop(0     )

		A__											=					[]
		if "lora_down" in key:
			pair_keys.append(key.replace("lora_down",	"lora_up"     )     )
			pair_keys.append(UpperCAmelCase_     )
		else:
			pair_keys.append(UpperCAmelCase_     )
			pair_keys.append(key.replace("lora_up",	"lora_down"     )     )

		# update weight
		if len(state_dict[pair_keys[0]].shape     ) == 4:
			A__											=					state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
			A__											=					state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
			curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_,	UpperCAmelCase_     ).unsqueeze(2     ).unsqueeze(3     )
		else:
			A__											=					state_dict[pair_keys[0]].to(torch.floataa     )
			A__											=					state_dict[pair_keys[1]].to(torch.floataa     )
			curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_,	UpperCAmelCase_     )

		# update visited list
		for item in pair_keys:
			visited.append(UpperCAmelCase_     )

	return pipeline


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			UpperCamelCase     =							parser.parse_args()

			UpperCamelCase     =							args.base_model_path
			UpperCamelCase     =							args.checkpoint_path
			UpperCamelCase     =							args.dump_path
			UpperCamelCase     =							args.lora_prefix_unet
			UpperCamelCase     =							args.lora_prefix_text_encoder
			UpperCamelCase     =							args.alpha

			UpperCamelCase     =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			UpperCamelCase     =							pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









"""simple docstring"""







from statistics import mean

import numpy as np

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





	A__											=					0
	# Number of processes finished
	A__											=					0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	A__											=					[0] * no_of_process
	# List to include calculation results
	A__											=					[0] * no_of_process

	# Sort by arrival time.
	A__											=					[burst_time[i] for i in np.argsort(UpperCAmelCase_     )]
	A__											=					[process_name[i] for i in np.argsort(UpperCAmelCase_     )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		A__											=					0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			A__											=					arrival_time[i]

		A__											=					0
		# Index showing the location of the process being performed
		A__											=					0
		# Saves the current response ratio.
		A__											=					0
		for i in range(0,	UpperCAmelCase_     ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				A__											=					(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				A__											=					temp
				A__											=					i

        # Calculate the turn around time
		A__											=					current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		A__											=					1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time

def       _lowerCamelCase				(							UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       list,	UpperCAmelCase_ :       int     )	-> list:






	"""simple docstring"""





	A__											=					[0] * no_of_process
	for i in range(0,	UpperCAmelCase_     ):
		A__											=					turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
			UpperCamelCase     =							5
			UpperCamelCase     =							["""A""", """B""", """C""", """D""", """E"""]
			UpperCamelCase     =							[1, 2, 3, 4, 5]
			UpperCamelCase     =							[1, 2, 3, 4, 5]

			UpperCamelCase     =							calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			UpperCamelCase     =							calculate_waiting_time(
			    process_name, turn_around_time, burst_time, no_of_process
			)

			print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
			for i in range(0, no_of_process):
						print(
						    f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
						    f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
						)

			print(f'average waiting time : {mean(waiting_time):.5f}')
			print(f'average turn around time : {mean(turn_around_time):.5f}')




