
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						:      List[str]									= logging.get_logger(__name__)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




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







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

															# merge samples
															if i == 0:
																						__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      down_samples, mid_sample
															else:
																						__UpperCamelCase			:     Tuple         =      [
																						    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      _lowerCamelCase       (      self	:Dict  ,     a	:Union[str, os.PathLike]  ,     a	:bool = True  ,     a	:Callable = None  ,     a	:bool = False  ,     a	:Optional[str] = None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      0
								__UpperCamelCase			:     str         =      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
															__UpperCamelCase			:     List[Any]         =      model_path_to_save + f'_{idx}'







	@classmethod
	def      _lowerCamelCase       (      cls	:Union[str, Any]  ,     a	:Optional[Union[str, os.PathLike]]  ,     **a	:List[Any]		)	->				Any:
								__UpperCamelCase			:     Dict         =      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			:     Optional[int]         =      pretrained_model_path
								while os.path.isdir(a		):
															__UpperCamelCase			:     Optional[int]         =      ControlNetModel.from_pretrained(a  ,     **a		)
															controlnets.append(a		)

															idx += 1
															__UpperCamelCase			:     Optional[int]         =      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


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)