





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

SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Tuple:
		return [
		    int(1_000 * (box[0] / width)							),
		    int(1_000 * (box[1] / height)							),
		    int(1_000 * (box[2] / width)							),
		    int(1_000 * (box[3] / height)							),
		]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Union[str, Any]:
		__lowercase											=     to_pil_image(snake_case							)
		__lowercase						,      __lowercase											=     pil_image.size
		__lowercase											=     pytesseract.image_to_data(snake_case						,   lang=snake_case						,   output_type='dict'						,   config=snake_case							)
		__lowercase						,      __lowercase						,      __lowercase						,      __lowercase						,      __lowercase											=     data['text'], data['left'], data['top'], data['width'], data['height']

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

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

		# finally, normalize the bounding boxes
		__lowercase											=     []
		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 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		['''pixel_values''']




	def __init__(						self							:     Union[str, Any]					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Dict[str, int] = None					,      __lowerCamelCase							:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     float = 1 / 255					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Union[float, Iterable[float]] = None					,      __lowerCamelCase							:     Union[float, Iterable[float]] = None					,      __lowerCamelCase							:     bool = True					,      __lowerCamelCase							:     Optional[str] = None					,      __lowerCamelCase							:     Optional[str] = ""					,      **__lowerCamelCase							:     str					,      )       -> None:

			'''simple docstring'''




			super().__init__(**__lowerCamelCase   )
			__lowercase											=     size if size is not None else {'height': 224, 'width': 224}
			__lowercase											=     get_size_dict(__lowerCamelCase   )

			__lowercase											=     do_resize
			__lowercase											=     size
			__lowercase											=     resample
			__lowercase											=     do_rescale
			__lowercase											=     rescale_value
			__lowercase											=     do_normalize
			__lowercase											=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowercase											=     image_std if image_std is not None else IMAGENET_STANDARD_STD
			__lowercase											=     apply_ocr
			__lowercase											=     ocr_lang
			__lowercase											=     tesseract_config




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Dict[str, int]					,      __lowerCamelCase							:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     Union[str, Any]					,      )       -> np.ndarray:

			'''simple docstring'''




			__lowercase											=     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()}"   )
			__lowercase											=     (size['height'], size['width'])
			return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Union[int, float]					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     Tuple					,      )       -> np.ndarray:

			'''simple docstring'''




			return rescale(__lowerCamelCase					,      scale=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     np.ndarray					,      __lowerCamelCase							:     Union[float, Iterable[float]]					,      __lowerCamelCase							:     Union[float, Iterable[float]]					,      __lowerCamelCase							:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase							:     str					,      )       -> np.ndarray:

			'''simple docstring'''




			return normalize(__lowerCamelCase					,      mean=__lowerCamelCase					,      std=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase   )






	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     ImageInput					,      __lowerCamelCase							:     bool = None					,      __lowerCamelCase							:     Dict[str, int] = None					,      __lowerCamelCase							:     str=None					,      __lowerCamelCase							:     bool = None					,      __lowerCamelCase							:     float = None					,      __lowerCamelCase							:     bool = None					,      __lowerCamelCase							:     Union[float, Iterable[float]] = None					,      __lowerCamelCase							:     Union[float, Iterable[float]] = None					,      __lowerCamelCase							:     bool = None					,      __lowerCamelCase							:     Optional[str] = None					,      __lowerCamelCase							:     Optional[str] = None					,      __lowerCamelCase							:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase							:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase							:     Optional[Any]					,      )       -> PIL.Image.Image:

			'''simple docstring'''




			__lowercase											=     do_resize if do_resize is not None else self.do_resize
			__lowercase											=     size if size is not None else self.size
			__lowercase											=     get_size_dict(__lowerCamelCase   )
			__lowercase											=     resample if resample is not None else self.resample
			__lowercase											=     do_rescale if do_rescale is not None else self.do_rescale
			__lowercase											=     rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowercase											=     do_normalize if do_normalize is not None else self.do_normalize
			__lowercase											=     image_mean if image_mean is not None else self.image_mean
			__lowercase											=     image_std if image_std is not None else self.image_std
			__lowercase											=     apply_ocr if apply_ocr is not None else self.apply_ocr
			__lowercase											=     ocr_lang if ocr_lang is not None else self.ocr_lang
			__lowercase											=     tesseract_config if tesseract_config is not None else self.tesseract_config

			__lowercase											=     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.
			__lowercase											=     [to_numpy_array(__lowerCamelCase   ) for image in images]

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

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

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

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

			__lowercase											=     [to_channel_dimension_format(__lowerCamelCase					,      __lowerCamelCase   ) for image in images]

			__lowercase											=     BatchFeature(data={'pixel_values': images}					,      tensor_type=__lowerCamelCase   )

			if apply_ocr:
					__lowercase											=     words_batch
					__lowercase											=     boxes_batch
			return data











from __future__ import annotations



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[tuple[int, int]]:
		__lowercase						,      __lowercase											=     position
		__lowercase											=     [
		    (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),
		]
		__lowercase											=     []

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

		return permissible_positions



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		return not any(elem == 0 for row in board for elem in row							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:

		if is_complete(snake_case							):
				return True

		for position in get_valid_pos(snake_case						,   len(snake_case							)							):
				__lowercase						,      __lowercase											=     position

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

		return False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    list[list[int]]:
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()




