





"""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

__A											=						logging.get_logger(__name__)
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Tuple:





  """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  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				to_pil_image(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				pil_image.size
  lowerCAmelCase__     :Dict											=				pytesseract.image_to_data(_SCREAMING_SNAKE_CASE	,  lang=_SCREAMING_SNAKE_CASE	,  output_type='dict'	,  config=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

  return words, normalized_boxes






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   ["""pixel_values"""]






       def __init__(       self       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = PILImageResampling.BILINEAR       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = 1 / 2_5_5       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = ""       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         lowerCAmelCase__     :Union[str, Any]											=				get_size_dict(__UpperCAmelCase )

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = PILImageResampling.BILINEAR       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				get_size_dict(__UpperCAmelCase )
         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()}" )
         lowerCAmelCase__     :Tuple											=				(size['height'], size['width'])
         return resize(__UpperCAmelCase       ,					size=__UpperCAmelCase       ,					resample=__UpperCAmelCase       ,					data_format=__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         return rescale(__UpperCAmelCase       ,					scale=__UpperCAmelCase       ,					data_format=__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         return normalize(__UpperCAmelCase       ,					mean=__UpperCAmelCase       ,					std=__UpperCAmelCase       ,					data_format=__UpperCAmelCase       ,					**__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = ChannelDimension.FIRST       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__     :Tuple											=				size if size is not None else self.size
         lowerCAmelCase__     :str											=				get_size_dict(__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				resample if resample is not None else self.resample
         lowerCAmelCase__     :int											=				do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__     :str											=				rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__     :str											=				do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__     :str											=				image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__     :List[Any]											=				image_std if image_std is not None else self.image_std
         lowerCAmelCase__     :List[str]											=				apply_ocr if apply_ocr is not None else self.apply_ocr
         lowerCAmelCase__     :Union[str, Any]											=				ocr_lang if ocr_lang is not None else self.ocr_lang
         lowerCAmelCase__     :Union[str, Any]											=				tesseract_config if tesseract_config is not None else self.tesseract_config

         lowerCAmelCase__     :str											=				make_list_of_images(__UpperCAmelCase )

         if not valid_images(__UpperCAmelCase ):
           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.
         lowerCAmelCase__     :Optional[Any]											=				[to_numpy_array(__UpperCAmelCase ) for image in images]

         # Tesseract OCR to get words + normalized bounding boxes
         if apply_ocr:
           requires_backends(self       ,					'pytesseract' )
           lowerCAmelCase__     :Union[str, Any]											=				[]
           lowerCAmelCase__     :Dict											=				[]
           for image in images:
             lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				apply_tesseract(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             words_batch.append(__UpperCAmelCase )
             boxes_batch.append(__UpperCAmelCase )

         if do_resize:
           lowerCAmelCase__     :Optional[int]											=				[self.resize(image=__UpperCAmelCase       ,					size=__UpperCAmelCase       ,					resample=__UpperCAmelCase ) for image in images]

         if do_rescale:
           lowerCAmelCase__     :List[Any]											=				[self.rescale(image=__UpperCAmelCase       ,					scale=__UpperCAmelCase ) for image in images]

         if do_normalize:
           lowerCAmelCase__     :List[Any]											=				[self.normalize(image=__UpperCAmelCase       ,					mean=__UpperCAmelCase       ,					std=__UpperCAmelCase ) for image in images]

         lowerCAmelCase__     :Tuple											=				[to_channel_dimension_format(__UpperCAmelCase       ,					__UpperCAmelCase ) for image in images]

         lowerCAmelCase__     :List[str]											=				BatchFeature(data={'pixel_values': images}       ,					tensor_type=__UpperCAmelCase )

         if apply_ocr:
           lowerCAmelCase__     :int											=				words_batch
           lowerCAmelCase__     :Tuple											=				boxes_batch
         return data





def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




