

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)


if is_vision_available():
  import PIL







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Tuple			       =["""pixel_values"""]


  def __init__(			self    : str				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Union[int, float] = 1 / 2_5_5				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : bool = True				,				**UpperCAmelCase__    : Dict				,				)					->None:
        """simple docstring"""







        super().__init__(**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      int       =   size if size is not None else {"""shortest_edge""": 2_2_4}
        SCREAMING_SNAKE_CASE :      List[str]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
        SCREAMING_SNAKE_CASE :      List[str]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__				,				param_name="""crop_size"""		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   do_resize
        SCREAMING_SNAKE_CASE :      List[str]       =   size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   resample
        SCREAMING_SNAKE_CASE :      Dict       =   do_center_crop
        SCREAMING_SNAKE_CASE :      str       =   crop_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   do_rescale
        SCREAMING_SNAKE_CASE :      Any       =   rescale_factor
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   do_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE :      List[Any]       =   image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE :      List[str]       =   do_convert_rgb


  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Dict[str, int]				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : Optional[int]				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)
        if "shortest_edge" not in size:
              raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   get_resize_output_image_size(UpperCAmelCase__				,				size=size["""shortest_edge"""]				,				default_to_square=UpperCAmelCase__		)
        return resize(UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Dict[str, int]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : Dict				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   get_size_dict(UpperCAmelCase__		)
        if "height" not in size or "width" not in size:
              raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"		)
        return center_crop(UpperCAmelCase__				,				size=(size["""height"""], size["""width"""])				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[int, float]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : int				,				)					->List[str]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : Any				,				)					->np.ndarray:
        """simple docstring"""







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




  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : ImageInput				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : PILImageResampling = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : int = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : float = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[ChannelDimension] = ChannelDimension.FIRST				,				**UpperCAmelCase__    : Dict				,				)					->PIL.Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE :      Dict       =   size if size is not None else self.size
        SCREAMING_SNAKE_CASE :      List[Any]       =   get_size_dict(UpperCAmelCase__				,				param_name="""size"""				,				default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE :      List[str]       =   do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE :      Dict       =   crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   get_size_dict(UpperCAmelCase__				,				param_name="""crop_size"""				,				default_to_square=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      int       =   do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE :      Any       =   rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE :      Optional[int]       =   do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE :      str       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE :      int       =   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_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop 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("""Image mean and std must be specified if do_normalize is True."""		)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              SCREAMING_SNAKE_CASE :      List[Any]       =   [convert_to_rgb(UpperCAmelCase__		) for image in images]

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE :      str       =   [to_numpy_array(UpperCAmelCase__		) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE :      Any       =   [self.resize(image=UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__		) for image in images]

        if do_center_crop:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [self.center_crop(image=UpperCAmelCase__				,				size=UpperCAmelCase__		) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE :      Dict       =   [self.rescale(image=UpperCAmelCase__				,				scale=UpperCAmelCase__		) for image in images]

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

        SCREAMING_SNAKE_CASE :      Tuple       =   [to_channel_dimension_format(UpperCAmelCase__				,				UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   {"""pixel_values""": images}
        return BatchFeature(data=UpperCAmelCase__				,				tensor_type=UpperCAmelCase__		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

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




