


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


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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Union[str, Any]						) ->   Optional[int]:





	"""simple docstring"""







	A								=				b.T
	A								=				np.sum(np.square(UpperCamelCase__						)							,					axis=1						)
	A								=				np.sum(np.square(UpperCamelCase__						)							,					axis=0						)
	A								=				np.matmul(UpperCamelCase__							,					UpperCamelCase__						)
	A								=				aa[:, None] - 2 * ab + ba[None, :]
	return d





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[Any]						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				x.reshape(-1							,					3						)
	A								=				squared_euclidean_distance(UpperCamelCase__							,					UpperCamelCase__						)
	return np.argmin(UpperCamelCase__							,					axis=1						)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['pixel_values']

				def __init__(						self				,   a__ = None				,   a__ = True				,   a__ = None				,   a__ = PILImageResampling.BILINEAR				,   a__ = True				,   a__ = True				,   **a__				,   )	->				None:
					super().__init__(**a__   )
					A								=				size if size is not None else {"""height""": 256, """width""": 256}
					A								=				get_size_dict(a__   )
					A								=				np.array(a__   ) if clusters is not None else None
					A								=				do_resize
					A								=				size
					A								=				resample
					A								=				do_normalize
					A								=				do_color_quantize

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__ = PILImageResampling.BILINEAR				,   a__ = None				,   **a__				,   )	->				np.ndarray:
					A								=				get_size_dict(a__   )
					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(
					    a__				,   size=(size["""height"""], size["""width"""])				,   resample=a__				,   data_format=a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   )	->				np.ndarray:
					A								=				rescale(image=a__				,   scale=1 / 127.5				,   data_format=a__   )
					A								=				image - 1
					return image

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = ChannelDimension.FIRST				,   **a__				,   )	->				PIL.Image.Image:
					A								=				do_resize if do_resize is not None else self.do_resize
					A								=				size if size is not None else self.size
					A								=				get_size_dict(a__   )
					A								=				resample if resample is not None else self.resample
					A								=				do_normalize if do_normalize is not None else self.do_normalize
					A								=				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					A								=				clusters if clusters is not None else self.clusters
					A								=				np.array(a__   )

					A								=				make_list_of_images(a__   )

					if not valid_images(a__   ):
						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.
					A								=				[to_numpy_array(a__   ) for image in images]

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

					if do_normalize:
						A								=				[self.normalize(image=a__   ) for image in images]

					if do_color_quantize:
						A								=				[to_channel_dimension_format(a__				,   ChannelDimension.LAST   ) for image in images]
						# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
						A								=				np.array(a__   )
						A								=				color_quantize(a__				,   a__   ).reshape(images.shape[:-1]   )

						# flatten to (batch_size, height*width)
						A								=				images.shape[0]
						A								=				images.reshape(a__				,   -1   )

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

					A								=				{"""input_ids""": images}
					return BatchFeature(data=a__				,   tensor_type=a__   )







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'upernet'

				def __init__(						self				,   a__=None				,   a__=512				,   a__=0.02				,   a__=[1, 2, 3, 6]				,   a__=True				,   a__=0.4				,   a__=384				,   a__=256				,   a__=1				,   a__=False				,   a__=255				,   **a__				,   )	->				Tuple:
					super().__init__(**a__   )

					if backbone_config is None:
						logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
						A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
					elif isinstance(a__				,   a__   ):
						A								=				backbone_config.get("""model_type"""   )
						A								=				CONFIG_MAPPING[backbone_model_type]
						A								=				config_class.from_dict(a__   )

					A								=				backbone_config
					A								=				hidden_size
					A								=				initializer_range
					A								=				pool_scales
					A								=				use_auxiliary_head
					A								=				auxiliary_loss_weight
					A								=				auxiliary_in_channels
					A								=				auxiliary_channels
					A								=				auxiliary_num_convs
					A								=				auxiliary_concat_input
					A								=				loss_ignore_index

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				copy.deepcopy(self.__dict__   )
					A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output



