

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_									=   logging.get_logger(__name__)
def      a      (			A__	:   Tuple      ,   A__	:   Union[str, Any]      ,   A__	:   List[str]     )    ->      Any:





      """simple docstring"""



      return [
          int(1000 * (box[0] / width)     ),
          int(1000 * (box[1] / height)     ),
          int(1000 * (box[2] / width)     ),
          int(1000 * (box[3] / height)     ),
      ]
def      a      (			A__	:   Optional[int]      ,   A__	:   List[Any]      ,   A__	:   int = None     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =tesseract_config if tesseract_config is not None else ''

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

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

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

      # finally, normalize the bounding boxes
      _lowercase	       =[]
      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  __lowerCAmelCase     (					snake_case__							):
  _a									=  ['''pixel_values''']




  def __init__(				self							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = PILImageResampling.BILINEAR							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = ""							,   **lowerCAmelCase							,   )  -> List[str]:
        '''simple docstring'''






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

        _lowercase	       =do_resize
        _lowercase	       =size
        _lowercase	       =resample
        _lowercase	       =apply_ocr
        _lowercase	       =ocr_lang
        _lowercase	       =tesseract_config




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = PILImageResampling.BILINEAR							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = ChannelDimension.FIRST							,   **lowerCAmelCase							,   )  -> Any:
        '''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(_A					)
        _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(_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.'					)

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

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

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

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

        _lowercase	       =BatchFeature(data={'pixel_values': images}							,   tensor_type=_A					)

        if apply_ocr:
              _lowercase	       =words_batch
              _lowercase	       =boxes_batch
        return data









def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




