






from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .multicontrolnet import MultiControlNetModel
	from .pipeline_controlnet import StableDiffusionControlNetPipeline
	from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
	from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
	from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline







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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__UpperCAmelCase							:   Optional[Any]							=  logging.get_logger(__name__)





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   b.T
				snake_case__      :Optional[Any]         =   np.sum(np.square(__snake_case      )	,	axis=1      )
				snake_case__      :Tuple         =   np.sum(np.square(__snake_case      )	,	axis=0      )
				snake_case__      :Union[str, Any]         =   np.matmul(__snake_case	,	__snake_case      )
				snake_case__      :Union[str, Any]         =   aa[:, None] - 2 * ab + ba[None, :]
				return d





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							int      )     -> Any:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   x.reshape(-1	,	3      )
				snake_case__      :List[str]         =   squared_euclidean_distance(__snake_case	,	__snake_case      )
				return np.argmin(__snake_case	,	axis=1      )


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = None					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :List[Any]         =   size if size is not None else {"height": 256, "width": 256}
					snake_case__      :str         =   get_size_dict(UpperCamelCase       )
					snake_case__      :Dict         =   np.array(UpperCamelCase       ) if clusters is not None else None
					snake_case__      :str         =   do_resize
					snake_case__      :List[str]         =   size
					snake_case__      :List[Any]         =   resample
					snake_case__      :Union[str, Any]         =   do_normalize
					snake_case__      :int         =   do_color_quantize






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :List[str]         =   get_size_dict(UpperCamelCase       )
					if "height" not in size or "width" not in size:
									raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'       )
					return resize(
					    UpperCamelCase					,size=(size["height"], size["width"])					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,)						->    np.ndarray:
					snake_case__      :Tuple         =   rescale(image=UpperCamelCase					,scale=1 / 127.5					,data_format=UpperCamelCase       )
					snake_case__      :List[Any]         =   image - 1
					return image






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Optional[int]         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :int         =   size if size is not None else self.size
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase       )
					snake_case__      :str         =   resample if resample is not None else self.resample
					snake_case__      :Dict         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :Tuple         =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					snake_case__      :List[Any]         =   clusters if clusters is not None else self.clusters
					snake_case__      :str         =   np.array(UpperCamelCase       )

					snake_case__      :int         =   make_list_of_images(UpperCamelCase       )

					if not valid_images(UpperCamelCase       ):
									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_color_quantize and clusters is None:
									raise ValueError("Clusters must be specified if do_color_quantize is True."       )

					# All transformations expect numpy arrays.
					snake_case__      :Union[str, Any]         =   [to_numpy_array(UpperCamelCase       ) for image in images]

					if do_resize:
									snake_case__      :int         =   [self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       ) for image in images]

					if do_normalize:
									snake_case__      :Any         =   [self.normalize(image=UpperCamelCase       ) for image in images]

					if do_color_quantize:
									snake_case__      :Optional[Any]         =   [to_channel_dimension_format(UpperCamelCase					,ChannelDimension.LAST       ) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									snake_case__      :Union[str, Any]         =   np.array(UpperCamelCase       )
									snake_case__      :Optional[int]         =   color_quantize(UpperCamelCase					,UpperCamelCase       ).reshape(images.shape[:-1]       )

									# flatten to (batch_size, height*width)
									snake_case__      :List[Any]         =   images.shape[0]
									snake_case__      :str         =   images.reshape(UpperCamelCase					,-1       )

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									snake_case__      :Any         =   list(UpperCamelCase       )
					else:
									snake_case__      :List[str]         =   [to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       ) for image in images]

					snake_case__      :List[str]         =   {"input_ids": images}
					return BatchFeature(data=UpperCamelCase					,tensor_type=UpperCamelCase       )