

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


A_ :				Tuple						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






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






					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(A__					, A__					, self.nets    )    ):
							A__     ,     A__							:					List[str]       =	controlnet(
							    A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, A__					, )

							# merge samples
							if i == 0:
								A__     ,     A__							:					List[Any]       =	down_samples, mid_sample
							else:
								A__							:					List[str]       =	[
								    samples_prev + samples_curr
								    for samples_prev, samples_curr in zip(A__					, A__    )
								]
								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					, ):
						A__							:					Optional[int]       =	0
						A__							:					Union[str, Any]       =	save_directory
						for controlnet in self.nets:
							controlnet.save_pretrained(
							    A__					, is_main_process=A__					, save_function=A__					, safe_serialization=A__					, variant=A__					, )

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






					@classmethod
					def 						__A    (				cls					, A__					, **A__    ):
						A__							:					Union[str, Any]       =	0
						A__							:					Dict       =	[]

						# 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`, ...
						A__							:					Union[str, Any]       =	pretrained_model_path
						while os.path.isdir(A__    ):
							A__							:					Any       =	ControlNetModel.from_pretrained(A__					, **A__    )
							controlnets.append(A__    )

							idx += 1
							A__							:					List[str]       =	pretrained_model_path + F"""_{idx}"""

						logger.info(F"""{len(A__    )} controlnets loaded from {pretrained_model_path}."""    )

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

						return cls(A__    )






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



