
"""simple docstring"""



from typing import Dict, Iterable, 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, to_pil_image
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, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

UpperCamelCase__          =	logging.get_logger(__name__)
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    return [
        int(1000 * (box[0] / width) ),
        int(1000 * (box[1] / height) ),
        int(1000 * (box[2] / width) ),
        int(1000 * (box[3] / height) ),
    ]
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      List[Any]     							=					to_pil_image(_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pil_image.size
    UpperCAmelCase__	:      List[str]     							=					pytesseract.image_to_data(_snake_case    ,lang=_snake_case    ,output_type='dict'    ,config=_snake_case )
    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					data['text'], data['left'], data['top'], data['width'], data['height']

    # filter empty words and corresponding coordinates
    UpperCAmelCase__	:      Optional[Any]     							=					[idx for idx, word in enumerate(_snake_case ) if not word.strip()]
    UpperCAmelCase__	:      Dict     							=					[word for idx, word in enumerate(_snake_case ) if idx not in irrelevant_indices]
    UpperCAmelCase__	:      str     							=					[coord for idx, coord in enumerate(_snake_case ) if idx not in irrelevant_indices]
    UpperCAmelCase__	:      Union[str, Any]     							=					[coord for idx, coord in enumerate(_snake_case ) if idx not in irrelevant_indices]
    UpperCAmelCase__	:      int     							=					[coord for idx, coord in enumerate(_snake_case ) if idx not in irrelevant_indices]
    UpperCAmelCase__	:      str     							=					[coord for idx, coord in enumerate(_snake_case ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    UpperCAmelCase__	:      Any     							=					[]
    for x, y, w, h in zip(_snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
        UpperCAmelCase__	:      List[str]     							=					[x, y, x + w, y + h]
        actual_boxes.append(_snake_case )

    # finally, normalize the bounding boxes
    UpperCAmelCase__	:      List[str]     							=					[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(_snake_case    ,_snake_case    ,_snake_case ) )

    assert len(_snake_case ) == len(_snake_case ), "Not as many words as there are bounding boxes"

    return words, normalized_boxes





class 				a			(					lowercase      ):
      UpperCamelCase							:   List[Any]           =     ["""pixel_values"""]



      def __init__(   self     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = PILImageResampling.BILINEAR     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = 1 / 255     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = ""     ,	**UpperCamelCase_     ,	):
          super().__init__(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					size if size is not None else {'height': 224, 'width': 224}
          UpperCAmelCase__	:      List[Any]     							=					get_size_dict(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					do_resize
          UpperCAmelCase__	:      Optional[Any]     							=					size
          UpperCAmelCase__	:      Optional[int]     							=					resample
          UpperCAmelCase__	:      List[str]     							=					do_rescale
          UpperCAmelCase__	:      Union[str, Any]     							=					rescale_value
          UpperCAmelCase__	:      List[str]     							=					do_normalize
          UpperCAmelCase__	:      Optional[Any]     							=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase__	:      Any     							=					image_std if image_std is not None else IMAGENET_STANDARD_STD
          UpperCAmelCase__	:      Optional[Any]     							=					apply_ocr
          UpperCAmelCase__	:      Optional[Any]     							=					ocr_lang
          UpperCAmelCase__	:      Tuple     							=					tesseract_config



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = PILImageResampling.BILINEAR     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Optional[Any]     							=					get_size_dict(UpperCamelCase_      )
          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()}'''      )
          UpperCAmelCase__	:      List[Any]     							=					(size['height'], size['width'])
          return resize(UpperCamelCase_     ,	size=UpperCamelCase_     ,	resample=UpperCamelCase_     ,	data_format=UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          return rescale(UpperCamelCase_     ,	scale=UpperCamelCase_     ,	data_format=UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          return normalize(UpperCamelCase_     ,	mean=UpperCamelCase_     ,	std=UpperCamelCase_     ,	data_format=UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_=None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = ChannelDimension.FIRST     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      List[Any]     							=					do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__	:      List[str]     							=					size if size is not None else self.size
          UpperCAmelCase__	:      Optional[Any]     							=					get_size_dict(UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					resample if resample is not None else self.resample
          UpperCAmelCase__	:      Tuple     							=					do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__	:      Optional[Any]     							=					rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__	:      Optional[Any]     							=					do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__	:      Optional[Any]     							=					image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__	:      List[Any]     							=					image_std if image_std is not None else self.image_std
          UpperCAmelCase__	:      str     							=					apply_ocr if apply_ocr is not None else self.apply_ocr
          UpperCAmelCase__	:      Dict     							=					ocr_lang if ocr_lang is not None else self.ocr_lang
          UpperCAmelCase__	:      List[str]     							=					tesseract_config if tesseract_config is not None else self.tesseract_config

          UpperCAmelCase__	:      Any     							=					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:
              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.'      )

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('If do_normalize is True, image_mean and image_std must be specified.'      )

          # All transformations expect numpy arrays.
          UpperCAmelCase__	:      Optional[Any]     							=					[to_numpy_array(UpperCamelCase_      ) for image in images]

          # Tesseract OCR to get words + normalized bounding boxes
          if apply_ocr:
              requires_backends(self     ,	'pytesseract'      )
              UpperCAmelCase__	:      Any     							=					[]
              UpperCAmelCase__	:      str     							=					[]
              for image in images:
                  UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					apply_tesseract(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                  words_batch.append(UpperCamelCase_      )
                  boxes_batch.append(UpperCamelCase_      )

          if do_resize:
              UpperCAmelCase__	:      Optional[int]     							=					[self.resize(image=UpperCamelCase_     ,	size=UpperCamelCase_     ,	resample=UpperCamelCase_      ) for image in images]

          if do_rescale:
              UpperCAmelCase__	:      int     							=					[self.rescale(image=UpperCamelCase_     ,	scale=UpperCamelCase_      ) for image in images]

          if do_normalize:
              UpperCAmelCase__	:      Optional[Any]     							=					[self.normalize(image=UpperCamelCase_     ,	mean=UpperCamelCase_     ,	std=UpperCamelCase_      ) for image in images]

          UpperCAmelCase__	:      List[str]     							=					[to_channel_dimension_format(UpperCamelCase_     ,	UpperCamelCase_      ) for image in images]

          UpperCAmelCase__	:      int     							=					BatchFeature(data={'pixel_values': images}     ,	tensor_type=UpperCamelCase_      )

          if apply_ocr:
              UpperCAmelCase__	:      List[Any]     							=					words_batch
              UpperCAmelCase__	:      Any     							=					boxes_batch
          return data



"""simple docstring"""



from __future__ import annotations

import queue





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Optional[int]     							=					None




def 					lowerCamelCase   (     ):
    print('\n********Press N to stop entering at any point of time********\n' )
    UpperCAmelCase__	:      int     							=					input('Enter the value of the root node: ' ).strip().lower()
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    UpperCAmelCase__	:      Dict     							=					TreeNode(int(_snake_case ) )
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Tuple     							=					q.get()
        UpperCAmelCase__	:      List[Any]     							=					F'''Enter the left node of {node_found.data}: '''
        UpperCAmelCase__	:      Dict     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      Optional[int]     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Tuple     							=					left_node
        q.put(_snake_case )
        UpperCAmelCase__	:      int     							=					F'''Enter the right node of {node_found.data}: '''
        UpperCAmelCase__	:      int     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      int     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Any     							=					right_node
        q.put(_snake_case )
    raise
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    print(node.data    ,end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    in_order(node.left )
    print(node.data    ,end=',' )
    in_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data    ,end=',' )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      int     							=					q.get()
        print(node_dequeued.data    ,end=',' )
        if node_dequeued.left:
            q.put(node_dequeued.left )
        if node_dequeued.right:
            q.put(node_dequeued.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Optional[Any]     							=					[]
        while not q.empty():
            UpperCAmelCase__	:      Union[str, Any]     							=					q.get()
            print(node_dequeued.data    ,end=',' )
            if node_dequeued.left:
                list_.append(node_dequeued.left )
            if node_dequeued.right:
                list_.append(node_dequeued.right )
        print()
        for node in list_:
            q.put(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[int]     							=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data    ,end=',' )
            stack.append(_snake_case )
            UpperCAmelCase__	:      Optional[int]     							=					n.left
        # end of while means current node doesn't have left child
        UpperCAmelCase__	:      Union[str, Any]     							=					stack.pop()
        # start to traverse its right child
        UpperCAmelCase__	:      Dict     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[Any]     							=					node
    while n or stack:
        while n:
            stack.append(_snake_case )
            UpperCAmelCase__	:      int     							=					n.left
        UpperCAmelCase__	:      Tuple     							=					stack.pop()
        print(n.data    ,end=',' )
        UpperCAmelCase__	:      Tuple     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    UpperCAmelCase__	:      Union[str, Any]     							=					node
    stacka.append(_snake_case )
    while stacka:  # to find the reversed order of post order, store it in stack2
        UpperCAmelCase__	:      Tuple     							=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(_snake_case )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data    ,end=',' )
def 					lowerCamelCase   (     _snake_case = ""    ,_snake_case=50    ,_snake_case="*" ):
    if not s:
        return "\n" + width * char
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					divmod(width - len(_snake_case ) - 2    ,2 )
    return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(prompt('Binary Tree Traversals'))

    UpperCamelCase__          =	build_tree()
    print(prompt('Pre Order Traversal'))
    pre_order(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal'))
    in_order(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal'))
    post_order(node)
    print(prompt() + '\n')

    print(prompt('Level Order Traversal'))
    level_order(node)
    print(prompt() + '\n')

    print(prompt('Actual Level Order Traversal'))
    level_order_actual(node)
    print('*' * 50 + '\n')

    print(prompt('Pre Order Traversal - Iteration Version'))
    pre_order_iter(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal - Iteration Version'))
    in_order_iter(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal - Iteration Version'))
    post_order_iter(node)
    print(prompt())

