
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

__a         =   logging.get_logger(__name__)




def 							a		(				snake_case__:   List[str]		,  snake_case__:   int		,  snake_case__:   str   ):


				'''simple docstring'''
				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 							a		(				snake_case__:   np.ndarray		,  snake_case__:   Optional[str]		,  snake_case__:   Optional[str] = None   ):


				'''simple docstring'''
				lowercase_	    =   tesseract_config if tesseract_config is not None else ''''''

				# apply OCR
				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 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      ['pixel_values']
	def __init__(			self    :     int			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = ""			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   resample
					lowercase_	    =   apply_ocr
					lowercase_	    =   ocr_lang
					lowercase_	    =   tesseract_config
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     ImageInput			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				SCREAMING_SNAKE_CASE_    :     ChannelDimension = ChannelDimension.FIRST			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	PIL.Image.Image:
					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(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   resample if resample is not None else self.resample
					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(SCREAMING_SNAKE_CASE_			)

					if not valid_images(SCREAMING_SNAKE_CASE_			):
									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.
					lowercase_	    =   [to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

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

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

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

					lowercase_	    =   BatchFeature(data={'''pixel_values''': images}			,				tensor_type=SCREAMING_SNAKE_CASE_			)

					if apply_ocr:
									lowercase_	    =   words_batch
									lowercase_	    =   boxes_batch
					return data






import unittest

from knapsack import knapsack as k
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   0
					lowercase_	    =   [0]
					lowercase_	    =   [0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)

					lowercase_	    =   [6_0]
					lowercase_	    =   [1_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				0			)
	def 		_lowercase    (			self    :     int			)			->	str:
					lowercase_	    =   3
					lowercase_	    =   [1, 2, 3]
					lowercase_	    =   [3, 2, 1]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				5			)




	def 		_lowercase    (			self    :     str			)			->	Tuple:
					lowercase_	    =   5_0
					lowercase_	    =   [6_0, 1_0_0, 1_2_0]
					lowercase_	    =   [1_0, 2_0, 3_0]
					lowercase_	    =   len(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)			,				2_2_0			)


if __name__ == "__main__":
						unittest.main()




