






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


__snake_case												=  logging.get_logger(__name__)







class 			UpperCAmelCase_						(					snake_case__ ):



      """simple docstring"""
      UpperCamelCase_				:					Optional[Any]           =["pixel_values"]
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = 1 / 255    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      str:
          super().__init__(**SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Optional[int]								=   size if size is not None else {"shortest_edge": 384}
          UpperCamelCase       :Union[str, Any]								=   get_size_dict(SCREAMING_SNAKE_CASE_    ,				default_to_square=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :int								=   do_resize
          UpperCamelCase       :Optional[int]								=   size
          # Default value set here for backwards compatibility where the value in config is None
          UpperCamelCase       :str								=   crop_pct if crop_pct is not None else 224 / 256
          UpperCamelCase       :Tuple								=   resample
          UpperCamelCase       :Union[str, Any]								=   do_rescale
          UpperCamelCase       :List[str]								=   rescale_factor
          UpperCamelCase       :Tuple								=   do_normalize
          UpperCamelCase       :Optional[Any]								=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase       :Optional[Any]								=   image_std if image_std is not None else IMAGENET_STANDARD_STD
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      int:
          UpperCamelCase       :Optional[int]								=   get_size_dict(SCREAMING_SNAKE_CASE_    ,				default_to_square=SCREAMING_SNAKE_CASE_ )
          if "shortest_edge" not in size:
              raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''' )
          UpperCamelCase       :Optional[Any]								=   size["shortest_edge"]

          if shortest_edge < 384:
              # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
              UpperCamelCase       :Dict								=   int(shortest_edge / crop_pct )
              UpperCamelCase       :Optional[Any]								=   get_resize_output_image_size(SCREAMING_SNAKE_CASE_    ,				size=SCREAMING_SNAKE_CASE_    ,				default_to_square=SCREAMING_SNAKE_CASE_ )
              UpperCamelCase       :int								=   resize(image=SCREAMING_SNAKE_CASE_    ,				size=SCREAMING_SNAKE_CASE_    ,				resample=SCREAMING_SNAKE_CASE_    ,				data_format=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
              # then crop to (shortest_edge, shortest_edge)
              return center_crop(image=SCREAMING_SNAKE_CASE_    ,				size=(shortest_edge, shortest_edge)    ,				data_format=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
          else:
              # warping (no cropping) when evaluated at 384 or larger
              return resize(
                  SCREAMING_SNAKE_CASE_    ,				size=(shortest_edge, shortest_edge)    ,				resample=SCREAMING_SNAKE_CASE_    ,				data_format=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      int:
          return rescale(SCREAMING_SNAKE_CASE_    ,				scale=SCREAMING_SNAKE_CASE_    ,				data_format=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Dict:
          return normalize(SCREAMING_SNAKE_CASE_    ,				mean=SCREAMING_SNAKE_CASE_    ,				std=SCREAMING_SNAKE_CASE_    ,				data_format=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      List[str]:
          UpperCamelCase       :Optional[int]								=   do_resize if do_resize is not None else self.do_resize
          UpperCamelCase       :str								=   crop_pct if crop_pct is not None else self.crop_pct
          UpperCamelCase       :Dict								=   resample if resample is not None else self.resample
          UpperCamelCase       :List[Any]								=   do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase       :Optional[Any]								=   rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase       :str								=   do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase       :Optional[int]								=   image_mean if image_mean is not None else self.image_mean
          UpperCamelCase       :Dict								=   image_std if image_std is not None else self.image_std

          UpperCamelCase       :Tuple								=   size if size is not None else self.size
          UpperCamelCase       :List[str]								=   get_size_dict(SCREAMING_SNAKE_CASE_    ,				default_to_square=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :Any								=   make_list_of_images(SCREAMING_SNAKE_CASE_ )

          if not valid_images(SCREAMING_SNAKE_CASE_ ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

          if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.''' )

          if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
              raise ValueError('''crop_pct must be specified if size < 384.''' )

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

          # All transformations expect numpy arrays.
          UpperCamelCase       :List[Any]								=   [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]

          if do_resize:
              UpperCamelCase       :List[Any]								=   [self.resize(image=SCREAMING_SNAKE_CASE_    ,				size=SCREAMING_SNAKE_CASE_    ,				crop_pct=SCREAMING_SNAKE_CASE_    ,				resample=SCREAMING_SNAKE_CASE_ ) for image in images]

          if do_rescale:
              UpperCamelCase       :Any								=   [self.rescale(image=SCREAMING_SNAKE_CASE_    ,				scale=SCREAMING_SNAKE_CASE_ ) for image in images]

          if do_normalize:
              UpperCamelCase       :Any								=   [self.normalize(image=SCREAMING_SNAKE_CASE_    ,				mean=SCREAMING_SNAKE_CASE_    ,				std=SCREAMING_SNAKE_CASE_ ) for image in images]

          UpperCamelCase       :Any								=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) for image in images]

          UpperCamelCase       :Optional[Any]								=   {"pixel_values": images}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_    ,				tensor_type=SCREAMING_SNAKE_CASE_ )






'''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


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       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						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               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						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    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     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # 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(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    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       )

