





"""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

lowerCamelCase__				  =   logging.get_logger(__name__)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Optional[Any]:




      """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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	int						=       to_pil_image(lowercase_			)
      _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       pil_image.size
      _UpperCamelCase      :	Tuple						=       pytesseract.image_to_data(lowercase_		,lang=lowercase_		,output_type="dict"		,config=lowercase_			)
      _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       data["text"], data["left"], data["top"], data["width"], data["height"]

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

      # turn coordinates into (left, top, left+width, top+height) format
      _UpperCamelCase      :	str						=       []
      for x, y, w, h in zip(lowercase_		,lowercase_		,lowercase_		,lowercase_			):
            _UpperCamelCase      :	List[Any]						=       [x, y, x + w, y + h]
            actual_boxes.append(lowercase_			)

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

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

      return words, normalized_boxes



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					["pixel_values"]

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

        _UpperCamelCase      :	int						=       do_resize
        _UpperCamelCase      :	Union[str, Any]						=       size
        _UpperCamelCase      :	Optional[int]						=       resample
        _UpperCamelCase      :	Any						=       do_rescale
        _UpperCamelCase      :	List[str]						=       rescale_value
        _UpperCamelCase      :	Tuple						=       do_normalize
        _UpperCamelCase      :	Optional[Any]						=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCamelCase      :	Tuple						=       image_std if image_std is not None else IMAGENET_STANDARD_STD
        _UpperCamelCase      :	str						=       apply_ocr
        _UpperCamelCase      :	List[Any]						=       ocr_lang
        _UpperCamelCase      :	Dict						=       tesseract_config

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       np.ndarray					,   __a   :       Dict[str, int]					,   __a   :       PILImageResampling = PILImageResampling.BILINEAR					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       Optional[int]					,   )		->				np.ndarray:
        _UpperCamelCase      :	int						=       get_size_dict(__a	)
        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      :	Union[str, Any]						=       (size["height"], size["width"])
        return resize(__a					,   size=__a					,   resample=__a					,   data_format=__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       np.ndarray					,   __a   :       Union[int, float]					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       Optional[Any]					,   )		->				np.ndarray:
        return rescale(__a					,   scale=__a					,   data_format=__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       np.ndarray					,   __a   :       Union[float, Iterable[float]]					,   __a   :       Union[float, Iterable[float]]					,   __a   :       Optional[Union[str, ChannelDimension]] = None					,   **__a   :       Optional[Any]					,   )		->				np.ndarray:
        return normalize(__a					,   mean=__a					,   std=__a					,   data_format=__a					,   **__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       ImageInput					,   __a   :       bool = None					,   __a   :       Dict[str, int] = None					,   __a   :       str=None					,   __a   :       bool = None					,   __a   :       float = None					,   __a   :       bool = None					,   __a   :       Union[float, Iterable[float]] = None					,   __a   :       Union[float, Iterable[float]] = None					,   __a   :       bool = None					,   __a   :       Optional[str] = None					,   __a   :       Optional[str] = None					,   __a   :       Optional[Union[str, TensorType]] = None					,   __a   :       ChannelDimension = ChannelDimension.FIRST					,   **__a   :       Optional[int]					,   )		->				PIL.Image.Image:
        _UpperCamelCase      :	str						=       do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase      :	Optional[int]						=       size if size is not None else self.size
        _UpperCamelCase      :	Tuple						=       get_size_dict(__a	)
        _UpperCamelCase      :	Union[str, Any]						=       resample if resample is not None else self.resample
        _UpperCamelCase      :	Optional[Any]						=       do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase      :	Tuple						=       rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase      :	List[str]						=       do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase      :	Tuple						=       image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase      :	Any						=       image_std if image_std is not None else self.image_std
        _UpperCamelCase      :	Any						=       apply_ocr if apply_ocr is not None else self.apply_ocr
        _UpperCamelCase      :	Union[str, Any]						=       ocr_lang if ocr_lang is not None else self.ocr_lang
        _UpperCamelCase      :	Dict						=       tesseract_config if tesseract_config is not None else self.tesseract_config

        _UpperCamelCase      :	Union[str, Any]						=       make_list_of_images(__a	)

        if not valid_images(__a	):
              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      :	Any						=       [to_numpy_array(__a	) for image in images]

        # Tesseract OCR to get words + normalized bounding boxes
        if apply_ocr:
              requires_backends(self					,   "pytesseract"	)
              _UpperCamelCase      :	List[str]						=       []
              _UpperCamelCase      :	Any						=       []
              for image in images:
                    _UpperCamelCase,  _UpperCamelCase      :	int						=       apply_tesseract(__a					,   __a					,   __a	)
                    words_batch.append(__a	)
                    boxes_batch.append(__a	)

        if do_resize:
              _UpperCamelCase      :	List[str]						=       [self.resize(image=__a					,   size=__a					,   resample=__a	) for image in images]

        if do_rescale:
              _UpperCamelCase      :	Tuple						=       [self.rescale(image=__a					,   scale=__a	) for image in images]

        if do_normalize:
              _UpperCamelCase      :	Optional[int]						=       [self.normalize(image=__a					,   mean=__a					,   std=__a	) for image in images]

        _UpperCamelCase      :	Dict						=       [to_channel_dimension_format(__a					,   __a	) for image in images]

        _UpperCamelCase      :	Any						=       BatchFeature(data={"pixel_values": images}					,   tensor_type=__a	)

        if apply_ocr:
              _UpperCamelCase      :	Union[str, Any]						=       words_batch
              _UpperCamelCase      :	List[str]						=       boxes_batch
        return data







"""simple docstring"""


class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Optional[Any]					,   __a   :       list[int]	)		->				None:
        _UpperCamelCase      :	Tuple						=       len(__a	)
        _UpperCamelCase      :	Dict						=       [0] * len_array

        if len_array > 0:
              _UpperCamelCase      :	Optional[Any]						=       array[0]

        for i in range(1					,   __a	):
              _UpperCamelCase      :	Tuple						=       self.prefix_sum[i - 1] + array[i]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       int					,   __a   :       int	)		->				int:
        if start == 0:
              return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int	)		->				bool:
        _UpperCamelCase      :	int						=       {0}
        for sum_item in self.prefix_sum:
              if sum_item - target_sum in sums:
                    return True

              sums.add(__a	)

        return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
