




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def lowercase    (  )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						ArgumentParser(
			    description=(
			        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
			    ) )

			# Optional arguments for the launch helper
			parser.add_argument('--num_cores'       ,    type=SCREAMING_SNAKE_CASE       ,    default=1       ,    help='Number of TPU cores to use (1 or 8).' )

			# positional
			parser.add_argument(
			    'training_script'       ,    type=SCREAMING_SNAKE_CASE       ,    help=(
			        'The full path to the single TPU training '
			        'program/script to be launched in parallel, '
			        'followed by all the arguments for the '
			        'training script'
			    )       ,    )

			# rest from the training program
			parser.add_argument('training_script_args'       ,    nargs=SCREAMING_SNAKE_CASE )

			return parser.parse_args()

def lowercase    (  )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						parse_args()

			# Import training_script as a module.
			SCREAMING_SNAKE_CASE_								=						Path(args.training_script )
			sys.path.append(str(script_fpath.parent.resolve() ) )
			SCREAMING_SNAKE_CASE_								=						script_fpath.stem
			SCREAMING_SNAKE_CASE_								=						importlib.import_module(SCREAMING_SNAKE_CASE )

			# Patch sys.argv
			SCREAMING_SNAKE_CASE_								=						[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores )]

			xmp.spawn(mod._mp_fn       ,    args=()       ,    nprocs=args.num_cores )


if __name__ == "__main__":
				main()






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


SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__()
				SCREAMING_SNAKE_CASE_								=						nn.ModuleList(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[ControlNetOutput, Tuple]:




				"""simple docstring"""



				for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     self.nets      )      ):
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						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:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						down_samples, mid_sample
							else:
										SCREAMING_SNAKE_CASE_								=						[
										    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      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						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
							SCREAMING_SNAKE_CASE_								=						model_path_to_save + f'_{idx}'





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						[]

				# 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`, ...
				SCREAMING_SNAKE_CASE_								=						pretrained_model_path
				while os.path.isdir(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
							controlnets.append(SCREAMING_SNAKE_CASE      )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						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      )
