
"""simple docstring"""






import argparse
import os
import re


lowerCamelCase__ :       List[str]						     =			"src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase__ :       str						     =			re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
lowerCamelCase__ :       Union[str, Any]						     =			re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")





def __A						(   a_ :							str		,		a_ :							bool = False     )->							Tuple:




		'''simple docstring'''






		with open(a_		,		'''r'''		,		encoding='''utf-8'''     ) as f:
				SCREAMING_SNAKE_CASE       :     Tuple				= f.read()

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= content.split('''\n'''     )
		SCREAMING_SNAKE_CASE       :     List[str]				= []
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
		while line_idx < len(a_     ):
				if _re_intro_mapping.search(lines[line_idx]     ) is not None:
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= len(re.search(r'''^(\s*)\S'''		,		lines[line_idx]     ).groups()[0]     ) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(''' ''' * indent + '''('''     ):
								new_lines.append(lines[line_idx]     )
								line_idx += 1

						SCREAMING_SNAKE_CASE       :     Tuple				= []
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										SCREAMING_SNAKE_CASE       :     Tuple				= line_idx
										while not lines[line_idx].startswith(''' ''' * indent + ''')'''     ):
												line_idx += 1
										blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]     )     )
								else:
										blocks.append(lines[line_idx]     )
								line_idx += 1

						# Sort blocks by their identifiers
						SCREAMING_SNAKE_CASE       :     Union[str, Any]				= sorted(a_		,		key=lambda a_     : _re_identifier.search(a_     ).groups()[0]     )
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]     )
						line_idx += 1

		if overwrite:
				with open(a_		,		'''w'''		,		encoding='''utf-8'''     ) as f:
						f.write('''\n'''.join(a_     )     )
		elif "\n".join(a_     ) != content:
				return True





def __A						(   a_ :							bool = False     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= [os.path.join(a_		,		a_     ) for f in os.listdir(a_     ) if f.endswith('''.py'''     )]
		SCREAMING_SNAKE_CASE       :     List[Any]				= [sort_auto_mapping(a_		,		overwrite=a_     ) for fname in fnames]

		if not overwrite and any(a_     ):
				SCREAMING_SNAKE_CASE       :     Tuple				= [f for f, d in zip(a_		,		a_     ) if d]
				raise ValueError(
				    F"The following files have auto mappings that need sorting: {', '.join(a_     )}. Run `make style` to fix"
				    ''' this.'''     )


if __name__ == "__main__":
					lowerCamelCase__ :       List[Any]						     =			argparse.ArgumentParser()
					parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
					lowerCamelCase__ :       Any						     =			parser.parse_args()

					sort_all_auto_mappings(not args.check_only)







"""simple docstring"""






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


lowerCamelCase__ :       List[Any]						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# 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       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

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

			return cls(lowerCamelCase_					)





