

'''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


UpperCamelCase_    						=       logging.get_logger(__name__)






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






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Optional[int]						)				->  int:
						lowercase :							Dict    		=x.reshape(-1   , 3						)
						lowercase :							Tuple    		=squared_euclidean_distance(__magic_name__   , __magic_name__						)
						return np.argmin(__magic_name__   , axis=1						)







class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		Union[str, Any]		,					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__     :		Dict		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )
										lowercase :							str    		=size if size is not None else {'''height''': 256, '''width''': 256}
										lowercase :							Optional[int]    		=get_size_dict(UpperCAmelCase__   )
										lowercase :							List[str]    		=np.array(UpperCAmelCase__   ) if clusters is not None else None
										lowercase :							List[Any]    		=do_resize
										lowercase :							str    		=size
										lowercase :							Dict    		=resample
										lowercase :							str    		=do_normalize
										lowercase :							List[Any]    		=do_color_quantize







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Dict[str, int]		,					UpperCAmelCase__     :		PILImageResampling = PILImageResampling.BILINEAR		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Any		,					):
										'''simple docstring'''
										lowercase :							Optional[int]    		=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  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					):
										'''simple docstring'''
										lowercase :							List[Any]    		=rescale(image=UpperCAmelCase__		,					scale=1 / 1_27.5		,					data_format=UpperCAmelCase__   )
										lowercase :							Tuple    		=image - 1
										return image







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					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]		,					):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=do_resize if do_resize is not None else self.do_resize
										lowercase :							Optional[Any]    		=size if size is not None else self.size
										lowercase :							int    		=get_size_dict(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=resample if resample is not None else self.resample
										lowercase :							List[Any]    		=do_normalize if do_normalize is not None else self.do_normalize
										lowercase :							List[str]    		=do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										lowercase :							Tuple    		=clusters if clusters is not None else self.clusters
										lowercase :							Optional[int]    		=np.array(UpperCAmelCase__   )

										lowercase :							List[str]    		=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.
										lowercase :							Any    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_resize:
																lowercase :							Dict    		=[self.resize(image=UpperCAmelCase__		,					size=UpperCAmelCase__		,					resample=UpperCAmelCase__   ) for image in images]

										if do_normalize:
																lowercase :							List[Any]    		=[self.normalize(image=UpperCAmelCase__   ) for image in images]

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

																# flatten to (batch_size, height*width)
																lowercase :							Optional[int]    		=images.shape[0]
																lowercase :							Optional[Any]    		=images.reshape(UpperCAmelCase__		,					-1   )

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

										lowercase :							str    		={'''input_ids''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )








'''simple docstring'''





from collections import defaultdict






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  int:
						lowercase :							Optional[Any]    		=1
						lowercase :							Union[str, Any]    		=True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(__magic_name__						)
						if ret % 2 == 0:
												cuts.append(__magic_name__						)
						return ret






def 				_lowerCAmelCase			(   )				->  int:
						dfs(1						)


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       10, 9
							UpperCamelCase_    						=       defaultdict(list)
							UpperCamelCase_    						=       {}
							UpperCamelCase_    						=       []
							UpperCamelCase_    						=       0
							UpperCamelCase_    						=       [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)





