
'''simple docstring'''







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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    :							Dict      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Union[str, Any]:
	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__	(   __A			,				__A			,				__A = None	)       ->	Any:
	_snake_case   						=		tesseract_config if tesseract_config is not None else ''

	# apply OCR
	_snake_case   						=		to_pil_image(__A	)
	_snake_case						, _snake_case   						=		pil_image.size
	_snake_case   						=		pytesseract.image_to_data(__A			,				lang=__A			,				output_type='dict'			,				config=__A	)
	_snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case   						=		data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes





class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""pixel_values"""]


							def __init__(       self    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = PILImageResampling.BILINEAR    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = ""    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )
								_snake_case   						=		size if size is not None else {'height': 2_24, 'width': 2_24}
								_snake_case   						=		get_size_dict(lowerCAmelCase_  )

								_snake_case   						=		do_resize
								_snake_case   						=		size
								_snake_case   						=		resample
								_snake_case   						=		apply_ocr
								_snake_case   						=		ocr_lang
								_snake_case   						=		tesseract_config


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = PILImageResampling.BILINEAR    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		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()}'  )
								_snake_case   						=		(size['height'], size['width'])
								return resize(lowerCAmelCase_    ,   size=lowerCAmelCase_    ,   resample=lowerCAmelCase_    ,   data_format=lowerCAmelCase_    ,   **lowerCAmelCase_  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = ChannelDimension.FIRST    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		do_resize if do_resize is not None else self.do_resize
								_snake_case   						=		size if size is not None else self.size
								_snake_case   						=		get_size_dict(lowerCAmelCase_  )
								_snake_case   						=		resample if resample is not None else self.resample
								_snake_case   						=		apply_ocr if apply_ocr is not None else self.apply_ocr
								_snake_case   						=		ocr_lang if ocr_lang is not None else self.ocr_lang
								_snake_case   						=		tesseract_config if tesseract_config is not None else self.tesseract_config

								_snake_case   						=		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.'  )

								# All transformations expect numpy arrays.
								_snake_case   						=		[to_numpy_array(lowerCAmelCase_  ) for image in images]

								if apply_ocr:
									requires_backends(self    ,   'pytesseract'  )
									_snake_case   						=		[]
									_snake_case   						=		[]
									for image in images:
										_snake_case						, _snake_case   						=		apply_tesseract(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
										words_batch.append(lowerCAmelCase_  )
										boxes_batch.append(lowerCAmelCase_  )

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

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								_snake_case   						=		[flip_channel_order(lowerCAmelCase_  ) for image in images]
								_snake_case   						=		[to_channel_dimension_format(lowerCAmelCase_    ,   lowerCAmelCase_  ) for image in images]

								_snake_case   						=		BatchFeature(data={'pixel_values': images}    ,   tensor_type=lowerCAmelCase_  )

								if apply_ocr:
									_snake_case   						=		words_batch
									_snake_case   						=		boxes_batch
								return data



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 100	)       ->	int:
	_snake_case   						=		n * (n + 1) * (2 * n + 1) / 6
	_snake_case   						=		(n * (n + 1) / 2) ** 2
	return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

