



from collections.abc import Callable

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Optional[int]       ,       a_							: Callable | None = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     list													=	[]
					# Stores indexes of each item for supporting updates and deletion.
					__UpperCAmelCase    :     dict													=	{}
					# Stores current size of heap.
					__UpperCAmelCase    :     int													=	0
					# Stores function used to evaluate the score of an item on which basis ordering
					# will be done.
					__UpperCAmelCase    :     Dict													=	key or (lambda a_ : x)




			def  snake_case__     (   self							: Optional[int]       ,       a_							: int ):
					'''simple docstring'''





					return int((i - 1) / 2 ) if i > 0 else None




			def  snake_case__     (   self							: List[Any]       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	int(2 * i + 1 )
					return left if 0 < left < self.size else None




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	int(2 * i + 2 )
					return right if 0 < right < self.size else None




			def  snake_case__     (   self							: List[str]       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	(
					    self.pos_map[self.arr[j][0]],
					    self.pos_map[self.arr[i][0]],
					)
					# Then swap the items in the list.
					__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	self.arr[j], self.arr[i]




			def  snake_case__     (   self							: Tuple       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					return self.arr[i][1] < self.arr[j][1]




			def  snake_case__     (   self							: Optional[int]       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self._left(a_ )
					__UpperCAmelCase    :     str													=	self._right(a_ )
					__UpperCAmelCase    :     Any													=	i

					if left is not None and not self._cmp(a_       ,       a_ ):
							__UpperCAmelCase    :     List[str]													=	left
					if right is not None and not self._cmp(a_       ,       a_ ):
							__UpperCAmelCase    :     Tuple													=	right

					return valid_parent




			def  snake_case__     (   self							: str       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self._parent(a_ )
					while parent is not None and not self._cmp(a_       ,       a_ ):
							self._swap(a_       ,       a_ )
							__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	parent, self._parent(a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self._get_valid_parent(a_ )
					while valid_parent != index:
							self._swap(a_       ,       a_ )
							__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	valid_parent, self._get_valid_parent(a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					if item not in self.pos_map:
							return
					__UpperCAmelCase    :     Tuple													=	self.pos_map[item]
					__UpperCAmelCase    :     Any													=	[item, self.key(a_ )]
					# Make sure heap is right in both up and down direction.
					# Ideally only one of them will make any change.
					self._heapify_up(a_ )
					self._heapify_down(a_ )




			def  snake_case__     (   self							: Any       ,       a_							: int ):
					'''simple docstring'''





					if item not in self.pos_map:
							return
					__UpperCAmelCase    :     List[str]													=	self.pos_map[item]
					del self.pos_map[item]
					__UpperCAmelCase    :     Tuple													=	self.arr[self.size - 1]
					__UpperCAmelCase    :     Any													=	index
					self.size -= 1
					# Make sure heap is right in both up and down direction. Ideally only one
					# of them will make any change- so no performance loss in calling both.
					if self.size > index:
							self._heapify_up(a_ )
							self._heapify_down(a_ )




			def  snake_case__     (   self							: Any       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	len(self.arr )
					if arr_len == self.size:
							self.arr.append([item, self.key(a_ )] )
					else:
							__UpperCAmelCase    :     Optional[int]													=	[item, self.key(a_ )]
					__UpperCAmelCase    :     Dict													=	self.size
					self.size += 1
					self._heapify_up(self.size - 1 )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					return self.arr[0] if self.size else None




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.get_top()
					if top_item_tuple:
							self.delete_item(top_item_tuple[0] )
					return top_item_tuple

def        a						(      ):







		'''simple docstring'''







if __name__ == "__main__":
				import doctest

				doctest.testmod()










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


__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	b.T
		__UpperCAmelCase    :     Any													=	np.sum(np.square(_UpperCAmelCase						) ,   axis=1						)
		__UpperCAmelCase    :     int													=	np.sum(np.square(_UpperCAmelCase						) ,   axis=0						)
		__UpperCAmelCase    :     Optional[int]													=	np.matmul(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	aa[:, None] - 2 * ab + ba[None, :]
		return d

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	x.reshape(-1 ,   3						)
		__UpperCAmelCase    :     Optional[int]													=	squared_euclidean_distance(_UpperCAmelCase ,   _UpperCAmelCase						)
		return np.argmin(_UpperCAmelCase ,   axis=1						)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""pixel_values"""]




			def __init__(   self							: str       ,       a_							: Optional[Union[List[List[int]], np.ndarray]] = None       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = PILImageResampling.BILINEAR       ,       a_							: bool = True       ,       a_							: bool = True       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )
					__UpperCAmelCase    :     Optional[int]													=	size if size is not None else {'''height''': 2_56, '''width''': 2_56}
					__UpperCAmelCase    :     List[str]													=	get_size_dict(a_ )
					__UpperCAmelCase    :     str													=	np.array(a_ ) if clusters is not None else None
					__UpperCAmelCase    :     Dict													=	do_resize
					__UpperCAmelCase    :     Tuple													=	size
					__UpperCAmelCase    :     Union[str, Any]													=	resample
					__UpperCAmelCase    :     Tuple													=	do_normalize
					__UpperCAmelCase    :     Optional[int]													=	do_color_quantize




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: PILImageResampling = PILImageResampling.BILINEAR       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	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  snake_case__     (   self							: Tuple       ,       a_							: np.ndarray       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	rescale(image=a_       ,       scale=1 / 1_2_7.5       ,       data_format=a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	image - 1
					return image




			def  snake_case__     (   self							: int       ,       a_							: ImageInput       ,       a_							: bool = None       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = None       ,       a_							: bool = None       ,       a_							: Optional[bool] = None       ,       a_							: Optional[Union[List[List[int]], np.ndarray]] = None       ,       a_							: Optional[Union[str, TensorType]] = None       ,       a_							: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,       **a_							: Any       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase    :     List[str]													=	size if size is not None else self.size
					__UpperCAmelCase    :     Any													=	get_size_dict(a_ )
					__UpperCAmelCase    :     Optional[int]													=	resample if resample is not None else self.resample
					__UpperCAmelCase    :     List[str]													=	do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase    :     int													=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					__UpperCAmelCase    :     Optional[int]													=	clusters if clusters is not None else self.clusters
					__UpperCAmelCase    :     Any													=	np.array(a_ )

					__UpperCAmelCase    :     Optional[int]													=	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.
					__UpperCAmelCase    :     List[Any]													=	[to_numpy_array(a_ ) for image in images]

					if do_resize:
							__UpperCAmelCase    :     List[str]													=	[self.resize(image=a_       ,       size=a_       ,       resample=a_ ) for image in images]

					if do_normalize:
							__UpperCAmelCase    :     Dict													=	[self.normalize(image=a_ ) for image in images]

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

							# flatten to (batch_size, height*width)
							__UpperCAmelCase    :     Any													=	images.shape[0]
							__UpperCAmelCase    :     Any													=	images.reshape(a_       ,       -1 )

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

					__UpperCAmelCase    :     int													=	{'''input_ids''': images}
					return BatchFeature(data=a_       ,       tensor_type=a_ )





