



'''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


UpperCamelCase__:     Optional[int]										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  Union[List[ControlNetModel], Tuple[ControlNetModel]]	)    ->	Dict:
								super().__init__()
								UpperCAmelCase				: List[str]											=	nn.ModuleList(__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  torch.FloatTensor	,    __snake_case    :  Union[torch.Tensor, float, int]	,    __snake_case    :  torch.Tensor	,    __snake_case    :  List[torch.tensor]	,    __snake_case    :  List[float]	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[torch.Tensor] = None	,    __snake_case    :  Optional[Dict[str, Any]] = None	,    __snake_case    :  bool = False	,    __snake_case    :  bool = True	,    )    ->	Union[ControlNetOutput, Tuple]:
								for i, (image, scale, controlnet) in enumerate(zip(__snake_case	,    __snake_case	,    self.nets	)	):
															UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	controlnet(
															    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    )

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

								return down_block_res_samples, mid_block_res_sample







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Union[str, os.PathLike]	,    __snake_case    :  bool = True	,    __snake_case    :  Callable = None	,    __snake_case    :  bool = False	,    __snake_case    :  Optional[str] = None	,    )    ->	Any:
								UpperCAmelCase				: List[str]											=	0
								UpperCAmelCase				: Dict											=	save_directory
								for controlnet in self.nets:
															controlnet.save_pretrained(
															    __snake_case	,    is_main_process=__snake_case	,    save_function=__snake_case	,    safe_serialization=__snake_case	,    variant=__snake_case	,    )

															idx += 1
															UpperCAmelCase				: List[str]											=	model_path_to_save + F"""_{idx}"""







	@classmethod
	def   A			(		cls    :  List[str]	,    __snake_case    :  Optional[Union[str, os.PathLike]]	,    **__snake_case    :  int	)    ->	Optional[Any]:
								UpperCAmelCase				: int											=	0
								UpperCAmelCase				: Union[str, Any]											=	[]

								# 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`, ...
								UpperCAmelCase				: int											=	pretrained_model_path
								while os.path.isdir(__snake_case	):
															UpperCAmelCase				: int											=	ControlNetModel.from_pretrained(__snake_case	,    **__snake_case	)
															controlnets.append(__snake_case	)

															idx += 1
															UpperCAmelCase				: Any											=	pretrained_model_path + F"""_{idx}"""

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

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

								return cls(__snake_case	)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:     Optional[Any]										=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





