





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any






@dataclass
class     lowercase__       :

   '''simple docstring'''


   a						:							int
   a						:							Node | None   		=		None
   a						:							Node | None   		=		None




def 		lowerCAmelCase_    (  )      ->	Optional[Any]:
    UpperCamelCase__							:				str          =      Node(1      )
    UpperCamelCase__							:				Union[str, Any]          =      Node(2      )
    UpperCamelCase__							:				List[Any]          =      Node(3      )
    UpperCamelCase__							:				List[Any]          =      Node(4      )
    UpperCamelCase__							:				Tuple          =      Node(5      )
    return tree




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any      )      ->	Optional[Any]:
    return [root.data, *preorder(root.left      ), *preorder(root.right      )] if root else []




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]      )      ->	Dict:
    return postorder(root.left      ) + postorder(root.right      ) + [root.data] if root else []




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]      )      ->	str:
    return [*inorder(root.left      ), root.data, *inorder(root.right      )] if root else []




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]      )      ->	str:
    return (max(height(root.left      )		,   height(root.right      )      ) + 1) if root else 0




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]      )      ->	Any:
    UpperCamelCase__							:				Optional[int]          =      []

    if root is None:
        return output

    UpperCamelCase__							:				Tuple          =      deque([root]      )

    while process_queue:
        UpperCamelCase__							:				int          =      process_queue.popleft()
        output.append(node.data      )

        if node.left:
            process_queue.append(node.left      )
        if node.right:
            process_queue.append(node.right      )
    return output




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]		,   __UpperCAmelCase:			List[Any]      )      ->	Dict:
    UpperCamelCase__							:				List[str]          =      []

    def populate_output(__UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Tuple      ) -> None:
        if not root:
            return
        if level == 1:
            output.append(root.data      )
        elif level > 1:
            populate_output(root.left		,   level - 1      )
            populate_output(root.right		,   level - 1      )

    populate_output(UpperCamelCase__		,   UpperCamelCase__      )
    return output




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			Tuple      )      ->	Optional[Any]:
    UpperCamelCase__							:				Optional[int]          =      []

    def populate_output(__UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Union[str, Any]      ) -> None:
        if root is None:
            return
        if level == 1:
            output.append(root.data      )
        elif level > 1:
            populate_output(root.right		,   level - 1      )
            populate_output(root.left		,   level - 1      )

    populate_output(UpperCamelCase__		,   UpperCamelCase__      )
    return output




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]      )      ->	str:





    if root is None:
        return []

    UpperCamelCase__							:				Optional[int]          =      []

    UpperCamelCase__							:				Any          =      0
    UpperCamelCase__							:				Tuple          =      height(UpperCamelCase__      )

    for h in range(1		,   height_tree + 1      ):
        if not flag:
            output.append(get_nodes_from_left_to_right(UpperCamelCase__		,   UpperCamelCase__      )      )
            UpperCamelCase__							:				str          =      1
        else:
            output.append(get_nodes_from_right_to_left(UpperCamelCase__		,   UpperCamelCase__      )      )
            UpperCamelCase__							:				List[Any]          =      0

    return output




def 		lowerCAmelCase_    (  )      ->	Optional[int]:  # Main function for testing.
    UpperCamelCase__							:				List[Any]          =      make_tree()
    print(f"In-order Traversal: {inorder(UpperCamelCase__      )}"      )
    print(f"Pre-order Traversal: {preorder(UpperCamelCase__      )}"      )
    print(f"Post-order Traversal: {postorder(UpperCamelCase__      )}"		,   '''\n'''      )

    print(f"Height of Tree: {height(UpperCamelCase__      )}"		,   '''\n'''      )

    print('''Complete Level Order Traversal: '''      )
    print(level_order(UpperCamelCase__      )		,   '''\n'''      )

    print('''Level-wise order Traversal: '''      )

    for level in range(1		,   height(UpperCamelCase__      ) + 1      ):
        print(f"Level {level}:"		,   get_nodes_from_left_to_right(UpperCamelCase__		,   level=UpperCamelCase__      )      )

    print('''\nZigZag order Traversal: '''      )
    print(zigzag(UpperCamelCase__      )      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  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 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


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""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    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





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


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													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   )





