


'''simple docstring'''

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__				     =		logging.get_logger(__name__)






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Dict				)      ->     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 __UpperCamelCase   (			__lowerCamelCase		: Dict      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







					'''simple docstring'''






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






class 						__SCREAMING_SNAKE_CASE					(      lowercase_ ):
					UpperCAmelCase 					=				['''pixel_values''']






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






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = PILImageResampling.BILINEAR     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    int:
										_a						=		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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							)       ->    Tuple:
										_a						=		rescale(image=__UpperCamelCase     ,							scale=1 / 127.5     ,							data_format=__UpperCamelCase		)
										_a						=		image - 1
										return image






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = ChannelDimension.FIRST     ,							**__UpperCamelCase     ,							)       ->    List[str]:
										_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(__UpperCamelCase		)
										_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(__UpperCamelCase		)

										_a						=		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.
										_a						=		[to_numpy_array(__UpperCamelCase		) for image in images]

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

										if do_normalize:
															_a						=		[self.normalize(image=__UpperCamelCase		) for image in images]

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

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

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

										_a						=		{"input_ids": images}
										return BatchFeature(data=__UpperCamelCase     ,							tensor_type=__UpperCamelCase		)





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

