



'''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__    :   List[Any]           =					logging.get_logger(__name__)





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      List[Any]				)    ->  int:
      return [
          int(10_00 * (box[0] / width)				),
          int(10_00 * (box[1] / height)				),
          int(10_00 * (box[2] / width)				),
          int(10_00 * (box[3] / height)				),
      ]





def   a_   (		_UpperCAmelCase    :      np.ndarray ,_UpperCAmelCase    :      Optional[str] ,_UpperCAmelCase    :      Optional[str]				)    ->  str:
      __snake_case :   Tuple													=    to_pil_image(_UpperCAmelCase				)
      __snake_case							,     __snake_case :   str													=    pil_image.size
      __snake_case :   Optional[Any]													=    pytesseract.image_to_data(_UpperCAmelCase ,lang=_UpperCAmelCase ,output_type='dict' ,config=_UpperCAmelCase				)
      __snake_case							,     __snake_case							,     __snake_case							,     __snake_case							,     __snake_case :   List[Any]													=    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

      # finally, normalize the bounding boxes
      __snake_case :   int													=    []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)				)

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

      return words, normalized_boxes





class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''pixel_values''']
 def __init__(   self						:							List[Any]      ,     __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						:							Union[str, Any]      ,     )      ->   None:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   Optional[int]													=    size if size is not None else {'height': 224, 'width': 224}
       __snake_case :   Optional[int]													=    get_size_dict(__a  )

       __snake_case :   Any													=    do_resize
       __snake_case :   Union[str, Any]													=    size
       __snake_case :   Optional[Any]													=    resample
       __snake_case :   Optional[int]													=    do_rescale
       __snake_case :   Dict													=    rescale_value
       __snake_case :   Union[str, Any]													=    do_normalize
       __snake_case :   Dict													=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __snake_case :   Optional[int]													=    image_std if image_std is not None else IMAGENET_STANDARD_STD
       __snake_case :   Optional[int]													=    apply_ocr
       __snake_case :   Optional[Any]													=    ocr_lang
       __snake_case :   Tuple													=    tesseract_config
 def A_   (   self						:							Tuple      ,     __a						:							np.ndarray      ,     __a						:							Dict[str, int]      ,     __a						:							PILImageResampling = PILImageResampling.BILINEAR      ,     __a						:							Optional[Union[str, ChannelDimension]] = None      ,     **__a						:							Any      ,     )      ->   np.ndarray:







       '''simple docstring'''
       __snake_case :   Any													=    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()}'''  )
       __snake_case :   Union[str, Any]													=    (size['height'], size['width'])
       return resize(__a      ,     size=__a      ,     resample=__a      ,     data_format=__a      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							np.ndarray      ,     __a						:							Union[int, float]      ,     __a						:							Optional[Union[str, ChannelDimension]] = None      ,     **__a						:							List[str]      ,     )      ->   np.ndarray:







       '''simple docstring'''
       return rescale(__a      ,     scale=__a      ,     data_format=__a      ,     **__a  )
 def A_   (   self						:							Dict      ,     __a						:							np.ndarray      ,     __a						:							Union[float, Iterable[float]]      ,     __a						:							Union[float, Iterable[float]]      ,     __a						:							Optional[Union[str, ChannelDimension]] = None      ,     **__a						:							Any      ,     )      ->   np.ndarray:







       '''simple docstring'''
       return normalize(__a      ,     mean=__a      ,     std=__a      ,     data_format=__a      ,     **__a  )





 def A_   (   self						:							Dict      ,     __a						:							ImageInput      ,     __a						:							bool = None      ,     __a						:							Dict[str, int] = None      ,     __a						:							int=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[Any]      ,     )      ->   PIL.Image.Image:







       '''simple docstring'''
       __snake_case :   int													=    do_resize if do_resize is not None else self.do_resize
       __snake_case :   Tuple													=    size if size is not None else self.size
       __snake_case :   List[str]													=    get_size_dict(__a  )
       __snake_case :   Union[str, Any]													=    resample if resample is not None else self.resample
       __snake_case :   Dict													=    do_rescale if do_rescale is not None else self.do_rescale
       __snake_case :   Union[str, Any]													=    rescale_factor if rescale_factor is not None else self.rescale_factor
       __snake_case :   int													=    do_normalize if do_normalize is not None else self.do_normalize
       __snake_case :   Union[str, Any]													=    image_mean if image_mean is not None else self.image_mean
       __snake_case :   Optional[Any]													=    image_std if image_std is not None else self.image_std
       __snake_case :   Tuple													=    apply_ocr if apply_ocr is not None else self.apply_ocr
       __snake_case :   Optional[Any]													=    ocr_lang if ocr_lang is not None else self.ocr_lang
       __snake_case :   Optional[Any]													=    tesseract_config if tesseract_config is not None else self.tesseract_config

       __snake_case :   Optional[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.
       __snake_case :   Optional[Any]													=    [to_numpy_array(__a  ) for image in images]

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

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

       if do_rescale:
             __snake_case :   List[str]													=    [self.rescale(image=__a      ,     scale=__a  ) for image in images]

       if do_normalize:
             __snake_case :   str													=    [self.normalize(image=__a      ,     mean=__a      ,     std=__a  ) for image in images]

       __snake_case :   Optional[int]													=    [to_channel_dimension_format(__a      ,     __a  ) for image in images]

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

       if apply_ocr:
             __snake_case :   int													=    words_batch
             __snake_case :   Dict													=    boxes_batch
       return data








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



