


'''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


__lowercase	:						Any       =	logging.get_logger(__name__)





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a							):

						'''simple docstring'''





						super().__init__()
						__a			:       str					    =  nn.ModuleList(__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = False						,				__a = True						,				):

						'''simple docstring'''





						for i, (image, scale, controlnet) in enumerate(zip(__a						,				__a						,				self.nets							)							):
											__a    ,				__a			:       Any					    =  controlnet(
											    __a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				)

											# merge samples
											if i == 0:
																__a    ,				__a			:       Optional[int]					    =  down_samples, mid_sample
											else:
																__a			:       Dict					    =  [
																    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       __UpperCAmelCase							(   self						,				__a						,				__a = True						,				__a = None						,				__a = False						,				__a = None						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  0
						__a			:       List[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			:       List[str]					    =  model_path_to_save + f"""_{idx}"""





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a						,				**__a							):

						'''simple docstring'''





						__a			:       List[str]					    =  0
						__a			:       str					    =  []

						# 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			:       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							)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






