



from statistics import mean

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		list:







						"""simple docstring"""

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

						# Sort by arrival time.
						A__	:							Tuple						    = [burst_time[i] for i in np.argsort(__lowercase       )]
						A__	:							Optional[int]						    = [process_name[i] for i in np.argsort(__lowercase       )]
						arrival_time.sort()

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

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

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

						return turn_around_time
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		list:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = [0] * no_of_process
						for i in range(0							,     __lowercase       ):
												A__	:							int						    = turn_around_time[i] - burst_time[i]
						return waiting_time


if __name__ == "__main__":
		snake_case    :						int  =						5
		snake_case    :						List[Any]  =						['A', 'B', 'C', 'D', 'E']
		snake_case    :						Dict  =						[1, 2, 3, 4, 5]
		snake_case    :						Any  =						[1, 2, 3, 4, 5]

		snake_case    :						Dict  =						calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		snake_case    :						List[Any]  =						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}""")




import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							List[str]						    = checkpoint

						A__	:							Dict						    = {}

						A__	:							Union[str, Any]						    = vae_state_dict["encoder.conv_in.weight"]
						A__	:							int						    = vae_state_dict["encoder.conv_in.bias"]
						A__	:							List[Any]						    = vae_state_dict["encoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.conv_out.bias"]
						A__	:							int						    = vae_state_dict["encoder.norm_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.norm_out.bias"]

						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_in.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.conv_in.bias"]
						A__	:							str						    = vae_state_dict["decoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_out.bias"]
						A__	:							Union[str, Any]						    = vae_state_dict["decoder.norm_out.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.norm_out.bias"]

						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.bias"]
						A__	:							Dict						    = vae_state_dict["post_quant_conv.weight"]
						A__	:							Union[str, Any]						    = vae_state_dict["post_quant_conv.bias"]

						# Retrieves the keys for the encoder down blocks only
						A__	:							str						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "encoder.down" in layer}       )
						A__	:							Optional[int]						    = {
						    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						# Retrieves the keys for the decoder up blocks only
						A__	:							Any						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "decoder.up" in layer}       )
						A__	:							Any						    = {
						    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						for i in range(__lowercase       ):
												A__	:							Any						    = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

												if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																		A__	:							Any						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'       )
																		A__	:							Optional[int]						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'       )

												A__	:							Any						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "encoder.mid.block" in key]
						A__	:							Any						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Optional[int]						    = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

												A__	:							Optional[Any]						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							str						    = [key for key in vae_state_dict if "encoder.mid.attn" in key]
						A__	:							List[Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							Optional[int]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )

						for i in range(__lowercase       ):
												A__	:							Dict						    = num_up_blocks - 1 - i
												A__	:							int						    = [
												    key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
												]

												if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																		A__	:							Tuple						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		A__	:							List[Any]						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

												A__	:							str						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							List[str]						    = {"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "decoder.mid.block" in key]
						A__	:							Optional[int]						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Union[str, Any]						    = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

												A__	:							Dict						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							Optional[Any]						    = [key for key in vae_state_dict if "decoder.mid.attn" in key]
						A__	:							Union[str, Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							List[str]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )
						return new_checkpoint
def 	snake_case__       (					__lowercase							,     __lowercase							,     )		->		Tuple:







						"""simple docstring"""

						A__	:							List[str]						    = requests.get(
						    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"       )
						A__	:							Dict						    = io.BytesIO(r.content       )

						A__	:							str						    = OmegaConf.load(__lowercase       )
						A__	:							Any						    = 5_1_2
						A__	:							Optional[Any]						    = "cuda" if torch.cuda.is_available() else "cpu"
						if checkpoint_path.endswith("safetensors"       ):
												from safetensors import safe_open

												A__	:							str						    = {}
												with safe_open(__lowercase							,     framework="pt"							,     device="cpu"       ) as f:
																		for key in f.keys():
																								A__	:							List[str]						    = f.get_tensor(__lowercase       )
						else:
												A__	:							str						    = torch.load(__lowercase							,     map_location=__lowercase       )["state_dict"]

						# Convert the VAE model.
						A__	:							List[str]						    = create_vae_diffusers_config(__lowercase							,     image_size=__lowercase       )
						A__	:							List[Any]						    = custom_convert_ldm_vae_checkpoint(__lowercase							,     __lowercase       )

						A__	:							Dict						    = AutoencoderKL(**__lowercase       )
						vae.load_state_dict(__lowercase       )
						vae.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						List[Any]  =						argparse.ArgumentParser()

		parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

		snake_case    :						int  =						parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)