






"""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

__lowerCamelCase							=  logging.get_logger(__name__)



def   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Union[str, Any]  )    ->							Dict:
						return [
						    int(1_0_0_0 * (box[0] / width)  ),
						    int(1_0_0_0 * (box[1] / height)  ),
						    int(1_0_0_0 * (box[2] / width)  ),
						    int(1_0_0_0 * (box[3] / height)  ),
						]



def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    List[str] = None  )    ->							int:
						__magic_name__:	List[str]							=       tesseract_config if tesseract_config is not None else """"""

						# apply OCR
						__magic_name__:	Dict							=       to_pil_image(__lowerCamelCase  )
						__magic_name__,			__magic_name__:	List[Any]							=       pil_image.size
						__magic_name__:	Dict							=       pytesseract.image_to_data(__lowerCamelCase      ,   lang=__lowerCamelCase      ,   output_type="""dict"""      ,   config=__lowerCamelCase  )
						__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Optional[Any]							=       data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

						# finally, normalize the bounding boxes
						__magic_name__:	int							=       []
						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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["pixel_values"]

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

											__magic_name__:	str							=       do_resize
											__magic_name__:	str							=       size
											__magic_name__:	Any							=       resample
											__magic_name__:	Optional[Any]							=       apply_ocr
											__magic_name__:	int							=       ocr_lang
											__magic_name__:	Any							=       tesseract_config

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      np.ndarray     ,		__snake_case  :      Dict[str, int]     ,		__snake_case  :      PILImageResampling = PILImageResampling.BILINEAR     ,		__snake_case  :      Optional[Union[str, ChannelDimension]] = None     ,		**__snake_case  :      List[Any]     ,		)			->							str:
											__magic_name__:	Dict							=       get_size_dict(__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()}'					)
											__magic_name__:	Optional[int]							=       (size["""height"""], size["""width"""])
											return resize(__snake_case     ,		size=__snake_case     ,		resample=__snake_case     ,		data_format=__snake_case     ,		**__snake_case					)






					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      ImageInput     ,		__snake_case  :      bool = None     ,		__snake_case  :      Dict[str, int] = None     ,		__snake_case  :      PILImageResampling = None     ,		__snake_case  :      bool = None     ,		__snake_case  :      Optional[str] = None     ,		__snake_case  :      Optional[str] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		__snake_case  :      ChannelDimension = ChannelDimension.FIRST     ,		**__snake_case  :      int     ,		)			->							Optional[int]:
											__magic_name__:	Optional[Any]							=       do_resize if do_resize is not None else self.do_resize
											__magic_name__:	Dict							=       size if size is not None else self.size
											__magic_name__:	int							=       get_size_dict(__snake_case					)
											__magic_name__:	List[str]							=       resample if resample is not None else self.resample
											__magic_name__:	Dict							=       apply_ocr if apply_ocr is not None else self.apply_ocr
											__magic_name__:	Optional[int]							=       ocr_lang if ocr_lang is not None else self.ocr_lang
											__magic_name__:	Dict							=       tesseract_config if tesseract_config is not None else self.tesseract_config

											__magic_name__:	Tuple							=       make_list_of_images(__snake_case					)

											if not valid_images(__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.
											__magic_name__:	Dict							=       [to_numpy_array(__snake_case					) for image in images]

											if apply_ocr:
																	requires_backends(self     ,		"""pytesseract"""					)
																	__magic_name__:	Any							=       []
																	__magic_name__:	Dict							=       []
																	for image in images:
																							__magic_name__,			__magic_name__:	List[str]							=       apply_tesseract(__snake_case     ,		__snake_case     ,		__snake_case					)
																							words_batch.append(__snake_case					)
																							boxes_batch.append(__snake_case					)

											if do_resize:
																	__magic_name__:	str							=       [self.resize(image=__snake_case     ,		size=__snake_case     ,		resample=__snake_case					) for image in images]

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											__magic_name__:	List[Any]							=       [flip_channel_order(__snake_case					) for image in images]
											__magic_name__:	str							=       [to_channel_dimension_format(__snake_case     ,		__snake_case					) for image in images]

											__magic_name__:	List[str]							=       BatchFeature(data={"""pixel_values""": images}     ,		tensor_type=__snake_case					)

											if apply_ocr:
																	__magic_name__:	str							=       words_batch
																	__magic_name__:	str							=       boxes_batch
											return data









from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

