


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


snake_case							: int      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :Optional[int]    ,__snake_case     :Union[List[ControlNetModel], Tuple[ControlNetModel]]   )      ->					Dict:
												super().__init__()
												a__    =  nn.ModuleList(__snake_case   )
					def 				lowerCamelCase__(   self     :int    ,__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   )   ):
																			a__   ,      a__    =  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:
																										a__   ,      a__    =  down_samples, mid_sample
																			else:
																										a__    =  [
																										    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 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union[str, os.PathLike]    ,__snake_case     :bool = True    ,__snake_case     :Callable = None    ,__snake_case     :bool = False    ,__snake_case     :Optional[str] = None    ,)      ->					List[str]:
												a__    =  0
												a__    =  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
																			a__    =  model_path_to_save + F'_{idx}'



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

												# 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__    =  pretrained_model_path
												while os.path.isdir(__snake_case   ):
																			a__    =  ControlNetModel.from_pretrained(__snake_case    ,**__snake_case   )
																			controlnets.append(__snake_case   )

																			idx += 1
																			a__    =  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   )








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




