


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


_snake_case     :		Tuple						=				logging.get_logger(__name__)







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			int							 =   b.T
							__snake_case		:			Tuple							 =   np.sum(np.square(_lowerCamelCase	)							,	axis=1	)
							__snake_case		:			str							 =   np.sum(np.square(_lowerCamelCase	)							,	axis=0	)
							__snake_case		:			int							 =   np.matmul(_lowerCamelCase							,	_lowerCamelCase	)
							__snake_case		:			Tuple							 =   aa[:, None] - 2 * ab + ba[None, :]
							return d







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			Optional[Any]							 =   x.reshape(-1							,	3	)
							__snake_case		:			str							 =   squared_euclidean_distance(_lowerCamelCase							,	_lowerCamelCase	)
							return np.argmin(_lowerCamelCase							,	axis=1	)

class 				a		(__lowercase      ):




							"""simple docstring"""


							__UpperCAmelCase    :							List[str]       =  ["pixel_values"]

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

							def     __snake_case					(							self       :  Optional[Any]    ,	lowerCamelCase       :  np.ndarray    ,	lowerCamelCase       :  Dict[str, int]    ,	lowerCamelCase       :  PILImageResampling = PILImageResampling.BILINEAR    ,	lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = None    ,	**lowerCamelCase       :  int    ,	)	->    np.ndarray:
														__snake_case		:			List[str]							 =   get_size_dict(lowerCamelCase				)
														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(
														    lowerCamelCase    ,	size=(size["height"], size["width"])    ,	resample=lowerCamelCase    ,	data_format=lowerCamelCase    ,	**lowerCamelCase				)

							def     __snake_case					(							self       :  Optional[Any]    ,	lowerCamelCase       :  np.ndarray    ,	lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = None    ,	)	->    np.ndarray:
														__snake_case		:			Tuple							 =   rescale(image=lowerCamelCase    ,	scale=1 / 1_27.5    ,	data_format=lowerCamelCase				)
														__snake_case		:			Any							 =   image - 1
														return image






							def     __snake_case					(							self       :  str    ,	lowerCamelCase       :  ImageInput    ,	lowerCamelCase       :  bool = None    ,	lowerCamelCase       :  Dict[str, int] = None    ,	lowerCamelCase       :  PILImageResampling = None    ,	lowerCamelCase       :  bool = None    ,	lowerCamelCase       :  Optional[bool] = None    ,	lowerCamelCase       :  Optional[Union[List[List[int]], np.ndarray]] = None    ,	lowerCamelCase       :  Optional[Union[str, TensorType]] = None    ,	lowerCamelCase       :  Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST    ,	**lowerCamelCase       :  int    ,	)	->    PIL.Image.Image:
														__snake_case		:			Tuple							 =   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		:			List[Any]							 =   get_size_dict(lowerCamelCase				)
														__snake_case		:			Optional[Any]							 =   resample if resample is not None else self.resample
														__snake_case		:			List[Any]							 =   do_normalize if do_normalize is not None else self.do_normalize
														__snake_case		:			Optional[int]							 =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
														__snake_case		:			List[str]							 =   clusters if clusters is not None else self.clusters
														__snake_case		:			List[Any]							 =   np.array(lowerCamelCase				)

														__snake_case		:			str							 =   make_list_of_images(lowerCamelCase				)

														if not valid_images(lowerCamelCase				):
																					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		:			str							 =   [to_numpy_array(lowerCamelCase				) for image in images]

														if do_resize:
																					__snake_case		:			Optional[Any]							 =   [self.resize(image=lowerCamelCase    ,	size=lowerCamelCase    ,	resample=lowerCamelCase				) for image in images]

														if do_normalize:
																					__snake_case		:			Optional[Any]							 =   [self.normalize(image=lowerCamelCase				) for image in images]

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

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

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

														__snake_case		:			int							 =   {"input_ids": images}
														return BatchFeature(data=lowerCamelCase    ,	tensor_type=lowerCamelCase				)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :					List[str]													=       {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					Optional[Any]													=       [
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

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