



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_						=       logging.get_logger(__name__)
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Tuple       						=["""pixel_values"""]




				def __init__(  self   :  str,   __lowercase   :  bool = True,   __lowercase   :  int = 32,   __lowercase   :  Optional[int]=PILImageResampling.BILINEAR,   __lowercase   :  bool = True,   **__lowercase   :  Optional[int],   ):
							lowercase__   					=			do_resize
							lowercase__   					=			do_rescale
							lowercase__   					=			size_divisor
							lowercase__   					=			resample
							super().__init__(**__lowercase  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  np.ndarray,   __lowercase   :  int,   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[ChannelDimension] = None,   **__lowercase   :  List[Any]  ):
							lowercase__					,    lowercase__   					=			get_image_size(__lowercase  )
							# Rounds the height and width down to the closest multiple of size_divisor
							lowercase__   					=			height // size_divisor * size_divisor
							lowercase__   					=			width // size_divisor * size_divisor
							lowercase__   					=			resize(__lowercase,   (new_h, new_w),   resample=__lowercase,   data_format=__lowercase,   **__lowercase  )
							return image




				def 							A__ (  self   :  List[Any],   __lowercase   :  np.ndarray,   __lowercase   :  float,   __lowercase   :  Optional[ChannelDimension] = None,   **__lowercase   :  Union[str, Any]  ):
							return rescale(image=__lowercase,   scale=__lowercase,   data_format=__lowercase,   **__lowercase  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]],   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[int] = None,   __lowercase   :  str=None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[Union[TensorType, str]] = None,   __lowercase   :  ChannelDimension = ChannelDimension.FIRST,   **__lowercase   :  Dict,   ):
							lowercase__   					=			do_resize if do_resize is not None else self.do_resize
							lowercase__   					=			do_rescale if do_rescale is not None else self.do_rescale
							lowercase__   					=			size_divisor if size_divisor is not None else self.size_divisor
							lowercase__   					=			resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
										raise ValueError("size_divisor is required for resizing"  )

							lowercase__   					=			make_list_of_images(__lowercase  )

							if not valid_images(__lowercase  ):
										raise ValueError("Invalid image(s)"  )

							# All transformations expect numpy arrays.
							lowercase__   					=			[to_numpy_array(__lowercase  ) for img in images]

							if do_resize:
										lowercase__   					=			[self.resize(__lowercase,   size_divisor=__lowercase,   resample=__lowercase  ) for image in images]

							if do_rescale:
										lowercase__   					=			[self.rescale(__lowercase,   scale=1 / 255  ) for image in images]

							lowercase__   					=			[to_channel_dimension_format(__lowercase,   __lowercase  ) for image in images]

							lowercase__   					=			{"pixel_values": images}
							return BatchFeature(data=__lowercase,   tensor_type=__lowercase  )









from graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [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],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

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

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




