


import heapq


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:list[list]				     =						[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(a     , [-1 * len(a ), (key, value)] )

		# chosen_vertices = set of chosen vertices
		SCREAMING_SNAKE_CASE_				:List[str]				     =						set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				SCREAMING_SNAKE_CASE_				:List[str]				     =						heapq.heappop(a )[1][0]
				chosen_vertices.add(a )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								SCREAMING_SNAKE_CASE_				:str				     =						elem[1][1].index(a )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(a )
		return chosen_vertices


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		SCREAMING_SNAKE_CASE__     					=			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
		print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')










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


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)


def     lowercase	(    a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						x.reshape(-1     , 3 )
		SCREAMING_SNAKE_CASE_				:str				     =						squared_euclidean_distance(a     , a )
		return np.argmin(a     , axis=1 )



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			List[str]       			=  ["""pixel_values"""]






		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Union[str, Any]   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						size if size is not None else {"height": 2_56, "width": 2_56}
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						np.array(UpperCAmelCase) if clusters is not None else None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_color_quantize






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     List[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				: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 _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						rescale(image=UpperCAmelCase   ,  scale=1 / 127.5   ,  data_format=UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image - 1
				return image







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				:Any				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						clusters if clusters is not None else self.clusters
				SCREAMING_SNAKE_CASE_				:Tuple				     =						np.array(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Dict				     =						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.
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

				if do_resize:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[self.resize(image=UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:int				     =						[self.normalize(image=UpperCAmelCase) for image in images]

				if do_color_quantize:
						SCREAMING_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)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.array(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						color_quantize(UpperCAmelCase   ,  UpperCAmelCase).reshape(images.shape[:-1])

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

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

				SCREAMING_SNAKE_CASE_				:str				     =						{"input_ids": images}
				return BatchFeature(data=UpperCAmelCase   ,  tensor_type=UpperCAmelCase)






