"""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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase     					=     logging.get_logger(__name__)
class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[str]						      =							["""pixel_values"""]







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = 1 / 2_5_5       ,     _SCREAMING_SNAKE_CASE = True       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	None:
							super().__init__(**_SCREAMING_SNAKE_CASE )
							a_							:			Union[str, Any]     		=					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
							a_							:			List[Any]     		=					get_size_dict(_SCREAMING_SNAKE_CASE )
							a_							:			Tuple     		=					do_resize
							a_							:			List[str]     		=					do_rescale
							a_							:			Dict     		=					do_normalize
							a_							:			Optional[Any]     		=					size
							a_							:			int     		=					resample
							a_							:			Dict     		=					rescale_factor
							a_							:			List[Any]     		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							a_							:			Optional[int]     		=					image_std if image_std is not None else IMAGENET_STANDARD_STD







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	np.ndarray:
							a_							:			Any     		=					get_size_dict(_SCREAMING_SNAKE_CASE )
							if "height" not in size or "width" not in size:
													raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
							return resize(
							    _SCREAMING_SNAKE_CASE       ,     size=(size["height"], size["width"])       ,     resample=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							return rescale(_SCREAMING_SNAKE_CASE       ,     scale=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	np.ndarray:
							return normalize(_SCREAMING_SNAKE_CASE       ,     mean=_SCREAMING_SNAKE_CASE       ,     std=_SCREAMING_SNAKE_CASE       ,     data_format=_SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Tuple:
							a_							:			str     		=					do_resize if do_resize is not None else self.do_resize
							a_							:			List[str]     		=					do_rescale if do_rescale is not None else self.do_rescale
							a_							:			Tuple     		=					do_normalize if do_normalize is not None else self.do_normalize
							a_							:			Optional[Any]     		=					resample if resample is not None else self.resample
							a_							:			Tuple     		=					rescale_factor if rescale_factor is not None else self.rescale_factor
							a_							:			Optional[int]     		=					image_mean if image_mean is not None else self.image_mean
							a_							:			List[Any]     		=					image_std if image_std is not None else self.image_std

							a_							:			List[str]     		=					size if size is not None else self.size
							a_							:			Optional[Any]     		=					get_size_dict(_SCREAMING_SNAKE_CASE )

							a_							:			Union[str, Any]     		=					make_list_of_images(_SCREAMING_SNAKE_CASE )

							if not valid_images(_SCREAMING_SNAKE_CASE ):
													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:
													raise ValueError("Size must be specified if do_resize is True." )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True." )

							# All transformations expect numpy arrays.
							a_							:			Union[str, Any]     		=					[to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]

							if do_resize:
													a_							:			int     		=					[self.resize(image=_SCREAMING_SNAKE_CASE       ,     size=_SCREAMING_SNAKE_CASE       ,     resample=_SCREAMING_SNAKE_CASE ) for image in images]

							if do_rescale:
													a_							:			Dict     		=					[self.rescale(image=_SCREAMING_SNAKE_CASE       ,     scale=_SCREAMING_SNAKE_CASE ) for image in images]

							if do_normalize:
													a_							:			Dict     		=					[self.normalize(image=_SCREAMING_SNAKE_CASE       ,     mean=_SCREAMING_SNAKE_CASE       ,     std=_SCREAMING_SNAKE_CASE ) for image in images]

							a_							:			str     		=					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE ) for image in images]

							a_							:			Optional[Any]     		=					{"pixel_values": images}
							return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,     tensor_type=_SCREAMING_SNAKE_CASE )





"""simple docstring"""






from __future__ import annotations

from queue import Queue

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int  )				-> tuple[int, int]:
						a ^= b
						b ^= a
						a ^= b
						return a, b

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> list[list[int]]:
						a_							:			List[str]     		=					1
						while (1 << j) < max_node:
												for i in range(1		,			max_node + 1  ):
																		a_							:			Union[str, Any]     		=					parent[j - 1][parent[j - 1][i]]
												j += 1
						return parent

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]  )				-> int:
						# u must be deeper in the tree than v
						if level[u] < level[v]:
												a_		,					a_							:			Tuple     		=					swap(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						# making depth of u same as depth of v
						for i in range(18		,			-1		,			-1  ):
												if level[u] - (1 << i) >= level[v]:
																		a_							:			Union[str, Any]     		=					parent[i][u]
    # at the same depth if u==v that mean lca is found
						if u == v:
												return u
						# moving both nodes upwards till lca in found
						for i in range(18		,			-1		,			-1  ):
												if parent[i][u] not in [0, parent[i][v]]:
																		a_		,					a_							:			Optional[Any]     		=					parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
						return parent[0][u]

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:list[int]		,			_SCREAMING_SNAKE_CASE	:list[list[int]]		,			_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:dict[int, list[int]]		,			_SCREAMING_SNAKE_CASE	:int = 1		,			)				-> tuple[list[int], list[list[int]]]:
						a_							:			List[Any]     		=					0
						a_							:			Queue[int]     		=					Queue(maxsize=_SCREAMING_SNAKE_CASE  )
						q.put(_SCREAMING_SNAKE_CASE  )
						while q.qsize() != 0:
												a_							:			List[str]     		=					q.get()
												for v in graph[u]:
																		if level[v] == -1:
																								a_							:			Tuple     		=					level[u] + 1
																								q.put(_SCREAMING_SNAKE_CASE  )
																								a_							:			Optional[int]     		=					u
						return level, parent

def  lowerCAmelCase_      ()				-> None:
						a_							:			List[Any]     		=					13
						# initializing with 0
						a_							:			Dict     		=					[[0 for _ in range(max_node + 10  )] for _ in range(20  )]
						# initializing with -1 which means every node is unvisited
						a_							:			Optional[int]     		=					[-1 for _ in range(max_node + 10  )]
						a_							:			dict[int, list[int]]     		=					{
						    1: [2, 3, 4],
						    2: [5],
						    3: [6, 7],
						    4: [8],
						    5: [9, 10],
						    6: [11],
						    7: [],
						    8: [12, 13],
						    9: [],
						    10: [],
						    11: [],
						    12: [],
						    13: [],
						}
						a_		,					a_							:			Any     		=					breadth_first_search(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			1  )
						a_							:			Any     		=					create_sparse(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						print("LCA of node 1 and 3 is: "		,			lowest_common_ancestor(1		,			3		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 5 and 6 is: "		,			lowest_common_ancestor(5		,			6		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 7 and 11 is: "		,			lowest_common_ancestor(7		,			11		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 6 and 7 is: "		,			lowest_common_ancestor(6		,			7		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 4 and 12 is: "		,			lowest_common_ancestor(4		,			12		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )
						print("LCA of node 8 and 8 is: "		,			lowest_common_ancestor(8		,			8		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )  )


if __name__ == "__main__":
				main()




