






"""simple docstring"""







from __future__ import annotations

from queue import Queue

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      a ^= b
      b ^= a
      a ^= b
      return a, b

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       1
      while (1 << j) < max_node:
            for i in range(1      ,       max_node + 1							):
                  UpperCAmelCase							=       parent[j - 1][parent[j - 1][i]]
            j += 1
      return parent

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      if level[u] < level[v]:
            UpperCAmelCase			,					UpperCAmelCase							=       swap(_snake_case      ,       _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]:
                  UpperCAmelCase							=       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]]:
                  UpperCAmelCase			,					UpperCAmelCase							=       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
      return parent[0][u]

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case = 1      ,       ):







      """simple docstring"""






      UpperCAmelCase							=       0
      UpperCAmelCase							=       Queue(maxsize=_snake_case							)
      q.put(_snake_case							)
      while q.qsize() != 0:
            UpperCAmelCase							=       q.get()
            for v in graph[u]:
                  if level[v] == -1:
                        UpperCAmelCase							=       level[u] + 1
                        q.put(_snake_case							)
                        UpperCAmelCase							=       u
      return level, parent

def _a (		):







      """simple docstring"""






      UpperCAmelCase							=       13
      # initializing with 0
      UpperCAmelCase							=       [[0 for _ in range(max_node + 10							)] for _ in range(20							)]
      # initializing with -1 which means every node is unvisited
      UpperCAmelCase							=       [-1 for _ in range(max_node + 10							)]
      UpperCAmelCase							=       {
          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: [],
      }
      UpperCAmelCase			,					UpperCAmelCase							=       breadth_first_search(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       1							)
      UpperCAmelCase							=       create_sparse(_snake_case      ,       _snake_case							)
      print("""LCA of node 1 and 3 is: """      ,       lowest_common_ancestor(1      ,       3      ,       _snake_case      ,       _snake_case							)							)
      print("""LCA of node 5 and 6 is: """      ,       lowest_common_ancestor(5      ,       6      ,       _snake_case      ,       _snake_case							)							)
      print("""LCA of node 7 and 11 is: """      ,       lowest_common_ancestor(7      ,       11      ,       _snake_case      ,       _snake_case							)							)
      print("""LCA of node 6 and 7 is: """      ,       lowest_common_ancestor(6      ,       7      ,       _snake_case      ,       _snake_case							)							)
      print("""LCA of node 4 and 12 is: """      ,       lowest_common_ancestor(4      ,       12      ,       _snake_case      ,       _snake_case							)							)
      print("""LCA of node 8 and 8 is: """      ,       lowest_common_ancestor(8      ,       8      ,       _snake_case      ,       _snake_case							)							)


if __name__ == "__main__":
  main()











"""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 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          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(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          if not valid_images(A					):
                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.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

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

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



