
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowercase__		:Any			  =   logging.get_logger(__name__)







class     lowercase		( _lowerCAmelCase		):




							def __init__(    self       ,A__):
												super().__init__()
												lowercase						     =						nn.ModuleList(SCREAMING_SNAKE_CASE_)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = True       ,):
												for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,self.nets)):
																	lowercase						     =						controlnet(
																	    SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,)

																	# merge samples
																	if i == 0:
																						lowercase						     =						down_samples, mid_sample
																	else:
																						lowercase						     =						[
																						    samples_prev + samples_curr
																						    for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)
																						]
																						mid_block_res_sample += mid_sample

												return down_block_res_samples, mid_block_res_sample




							def 			A__				(    self       ,A__       ,A__ = True       ,A__ = None       ,A__ = False       ,A__ = None       ,):
												lowercase						     =						0
												lowercase						     =						save_directory
												for controlnet in self.nets:
																	controlnet.save_pretrained(
																	    SCREAMING_SNAKE_CASE_       ,is_main_process=SCREAMING_SNAKE_CASE_       ,save_function=SCREAMING_SNAKE_CASE_       ,safe_serialization=SCREAMING_SNAKE_CASE_       ,variant=SCREAMING_SNAKE_CASE_       ,)

																	idx += 1
																	lowercase						     =						model_path_to_save + f'_{idx}'





							@classmethod
							def 			A__				(    cls       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						[]

												# load controlnet and append to list until no controlnet directory exists anymore
												# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
												# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
												lowercase						     =						pretrained_model_path
												while os.path.isdir(SCREAMING_SNAKE_CASE_):
																	lowercase						     =						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_)
																	controlnets.append(SCREAMING_SNAKE_CASE_)

																	idx += 1
																	lowercase						     =						pretrained_model_path + f'_{idx}'

												logger.info(f'{len(SCREAMING_SNAKE_CASE_)} controlnets loaded from {pretrained_model_path}.')

												if len(SCREAMING_SNAKE_CASE_) == 0:
																	raise ValueError(
																	    f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE_)}. Expected at least {pretrained_model_path + "_0"}.')

												return cls(SCREAMING_SNAKE_CASE_)




from statistics import mean

import numpy as np



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						0
					# Number of processes finished
					lowercase						     =						0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					lowercase						     =						[0] * no_of_process
					# List to include calculation results
					lowercase						     =						[0] * no_of_process

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

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

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

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

					return turn_around_time



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						[0] * no_of_process
					for i in range(0			,      lowerCAmelCase__		):
										lowercase						     =						turn_around_time[i] - burst_time[i]
					return waiting_time


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

							lowercase__		:List[str]			  =   calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							lowercase__		: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}')




