'''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

lowercase__  :      List[str]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      return [
          int(1000 * (box[0] / width)					),
          int(1000 * (box[1] / height)					),
          int(1000 * (box[2] / width)					),
          int(1000 * (box[3] / height)					),
      ]



def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      UpperCAmelCase_            =  to_pil_image(lowerCamelCase_					)
      UpperCAmelCase_            =  pil_image.size
      UpperCAmelCase_            =  pytesseract.image_to_data(lowerCamelCase_							,    lang=lowerCamelCase_							,    output_type='''dict'''							,    config=lowerCamelCase_					)
      UpperCAmelCase_            =  data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

      # filter empty words and corresponding coordinates
      UpperCAmelCase_            =  [idx for idx, word in enumerate(lowerCamelCase_					) if not word.strip()]
      UpperCAmelCase_            =  [word for idx, word in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
      UpperCAmelCase_            =  [coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
      UpperCAmelCase_            =  [coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
      UpperCAmelCase_            =  [coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
      UpperCAmelCase_            =  [coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCAmelCase_            =  []
      for x, y, w, h in zip(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_					):
            UpperCAmelCase_            =  [x, y, x + w, y + h]
            actual_boxes.append(lowerCamelCase_					)

      # finally, normalize the bounding boxes
      UpperCAmelCase_            =  []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_					)					)

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

      return words, normalized_boxes







class lowerCamelCase							(	lowercase_					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''pixel_values''']






  def __init__(      self			:       Optional[Any]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       float = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[float, Iterable[float]] = None  ,							UpperCAmelCase__			:       Union[float, Iterable[float]] = None  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[str] = None  ,							UpperCAmelCase__			:       Optional[str] = ""  ,							**UpperCAmelCase__			:       int  ,							)				->List[str]:
        super().__init__(**lowerCamelCase_  )
        UpperCAmelCase_            =  size if size is not None else {"""height""": 224, """width""": 224}
        UpperCAmelCase_            =  get_size_dict(lowerCamelCase_  )

        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  resample
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_value
        UpperCAmelCase_            =  do_normalize
        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
        UpperCAmelCase_            =  apply_ocr
        UpperCAmelCase_            =  ocr_lang
        UpperCAmelCase_            =  tesseract_config






  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Union[str, Any]  ,							)				->List[Any]:
        UpperCAmelCase_            =  get_size_dict(lowerCamelCase_  )
        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_            =  (size["""height"""], size["""width"""])
        return resize(lowerCamelCase_  ,							size=lowerCamelCase_  ,							resample=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[int, float]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[str]  ,							)				->Optional[int]:
        return rescale(lowerCamelCase_  ,							scale=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_  )






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[float, Iterable[float]]  ,							UpperCAmelCase__			:       Union[float, Iterable[float]]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Optional[int]  ,							)				->int:
        return normalize(lowerCamelCase_  ,							mean=lowerCamelCase_  ,							std=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       Optional[int]=None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Union[float, Iterable[float]] = None  ,							UpperCAmelCase__			:       Union[float, Iterable[float]] = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[str] = None  ,							UpperCAmelCase__			:       Optional[str] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       List[str]  ,							)				->str:
        UpperCAmelCase_            =  do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase_            =  size if size is not None else self.size
        UpperCAmelCase_            =  get_size_dict(lowerCamelCase_  )
        UpperCAmelCase_            =  resample if resample is not None else self.resample
        UpperCAmelCase_            =  do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase_            =  rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase_            =  do_normalize if do_normalize is not None else self.do_normalize
        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_            =  apply_ocr if apply_ocr is not None else self.apply_ocr
        UpperCAmelCase_            =  ocr_lang if ocr_lang is not None else self.ocr_lang
        UpperCAmelCase_            =  tesseract_config if tesseract_config is not None else self.tesseract_config

        UpperCAmelCase_            =  make_list_of_images(lowerCamelCase_  )

        if not valid_images(lowerCamelCase_  ):
              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_            =  [to_numpy_array(lowerCamelCase_  ) for image in images]

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

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

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

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

        UpperCAmelCase_            =  [to_channel_dimension_format(lowerCamelCase_  ,							lowerCamelCase_  ) for image in images]

        UpperCAmelCase_            =  BatchFeature(data={'''pixel_values''': images}  ,							tensor_type=lowerCamelCase_  )

        if apply_ocr:
              UpperCAmelCase_            =  words_batch
              UpperCAmelCase_            =  boxes_batch
        return data






'''simple docstring'''
from __future__ import annotations



def  __lowerCamelCase       (      _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  position
      UpperCAmelCase_            =  [
          (y + 1, x + 2),
          (y - 1, x + 2),
          (y + 1, x - 2),
          (y - 1, x - 2),
          (y + 2, x + 1),
          (y + 2, x - 1),
          (y - 2, x + 1),
          (y - 2, x - 1),
      ]
      UpperCAmelCase_            =  []

      for position in positions:
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position
            if 0 <= y_test < n and 0 <= x_test < n:
                  permissible_positions.append(_UpperCamelCase					)

      return permissible_positions



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]					):


      '''simple docstring'''


      return not any(elem == 0 for row in board for elem in row					)



def  __lowerCamelCase       (      _UpperCamelCase  :  list[list[int]]							,    _UpperCamelCase  :  tuple[int, int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if is_complete(_UpperCamelCase					):
            return True

      for position in get_valid_pos(_UpperCamelCase							,    len(_UpperCamelCase					)					):
            UpperCAmelCase_      ,		UpperCAmelCase_            =  position

            if board[y][x] == 0:
                  UpperCAmelCase_            =  curr + 1
                  if open_knight_tour_helper(_UpperCamelCase							,    _UpperCamelCase							,    curr + 1					):
                        return True
                  UpperCAmelCase_            =  0

      return False



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  [[0 for i in range(_UpperCamelCase					)] for j in range(_UpperCamelCase					)]

      for i in range(_UpperCamelCase					):
            for j in range(_UpperCamelCase					):
                  UpperCAmelCase_            =  1
                  if open_knight_tour_helper(_UpperCamelCase							,    (i, j)							,    1					):
                        return board
                  UpperCAmelCase_            =  0

      UpperCAmelCase_            =  F"""Open Kight Tour cannot be performed on a board of size {n}"""
      raise ValueError(_UpperCamelCase					)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





