
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


lowercase__          =logging.get_logger(__name__)


if is_vision_available():
      import PIL



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Any     	=				["pixel_values"]
   def __init__(self			: Union[str, Any] ,    snake_case_			: bool = True ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: PILImageResampling = PILImageResampling.BICUBIC ,    snake_case_			: bool = True ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: bool = True ,    snake_case_			: Union[int, float] = 1 / 2_5_5 ,    snake_case_			: bool = True ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: bool = True ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(**snake_case_       )
    __a			:  Any		    =  size if size is not None else {'''shortest_edge''': 2_2_4}
    __a			:  Optional[Any]		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_       )
    __a			:  Union[str, Any]		    =  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
    __a			:  Optional[Any]		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_ ,    param_name='''crop_size'''       )

    __a			:  Tuple		    =  do_resize
    __a			:  Any		    =  size
    __a			:  List[Any]		    =  resample
    __a			:  Tuple		    =  do_center_crop
    __a			:  List[Any]		    =  crop_size
    __a			:  List[Any]		    =  do_rescale
    __a			:  str		    =  rescale_factor
    __a			:  List[Any]		    =  do_normalize
    __a			:  List[str]		    =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    __a			:  List[Any]		    =  image_std if image_std is not None else OPENAI_CLIP_STD
    __a			:  str		    =  do_convert_rgb
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: np.ndarray ,    snake_case_			: Dict[str, int] ,    snake_case_			: PILImageResampling = PILImageResampling.BICUBIC ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: Any ,    ):
    __a			:  int		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_       )
    if "shortest_edge" not in size:
     raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
    __a			:  Optional[int]		    =  get_resize_output_image_size(snake_case_ ,    size=size['''shortest_edge'''] ,    default_to_square=snake_case_       )
    return resize(snake_case_ ,    size=snake_case_ ,    resample=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: np.ndarray ,    snake_case_			: Dict[str, int] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: int ,    ):
    __a			:  Union[str, Any]		    =  get_size_dict(snake_case_       )
    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(snake_case_ ,    size=(size['''height'''], size['''width''']) ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: np.ndarray ,    snake_case_			: Union[int, float] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: int ,    ):
    return rescale(snake_case_ ,    scale=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: np.ndarray ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: Dict ,    ):
    return normalize(snake_case_ ,    mean=snake_case_ ,    std=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )







   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: ImageInput ,    snake_case_			: bool = None ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: PILImageResampling = None ,    snake_case_			: bool = None ,    snake_case_			: int = None ,    snake_case_			: bool = None ,    snake_case_			: float = None ,    snake_case_			: bool = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: bool = None ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    snake_case_			: Optional[ChannelDimension] = ChannelDimension.FIRST ,    **snake_case_			: List[str] ,    ):
    __a			:  List[Any]		    =  do_resize if do_resize is not None else self.do_resize
    __a			:  Any		    =  size if size is not None else self.size
    __a			:  Optional[Any]		    =  get_size_dict(snake_case_ ,    param_name='''size''' ,    default_to_square=snake_case_       )
    __a			:  Dict		    =  resample if resample is not None else self.resample
    __a			:  List[str]		    =  do_center_crop if do_center_crop is not None else self.do_center_crop
    __a			:  Dict		    =  crop_size if crop_size is not None else self.crop_size
    __a			:  Any		    =  get_size_dict(snake_case_ ,    param_name='''crop_size''' ,    default_to_square=snake_case_       )
    __a			:  Any		    =  do_rescale if do_rescale is not None else self.do_rescale
    __a			:  Tuple		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a			:  Any		    =  do_normalize if do_normalize is not None else self.do_normalize
    __a			:  int		    =  image_mean if image_mean is not None else self.image_mean
    __a			:  Tuple		    =  image_std if image_std is not None else self.image_std
    __a			:  Union[str, Any]		    =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __a			:  List[Any]		    =  make_list_of_images(snake_case_       )

    if not valid_images(snake_case_       ):
     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:
     __a			:  Optional[Any]		    =  [convert_to_rgb(snake_case_       ) for image in images]

    # All transformations expect numpy arrays.
    __a			:  List[str]		    =  [to_numpy_array(snake_case_       ) for image in images]

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

    if do_center_crop:
     __a			:  str		    =  [self.center_crop(image=snake_case_ ,    size=snake_case_       ) for image in images]

    if do_rescale:
     __a			:  Optional[Any]		    =  [self.rescale(image=snake_case_ ,    scale=snake_case_       ) for image in images]

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

    __a			:  List[str]		    =  [to_channel_dimension_format(snake_case_ ,    snake_case_       ) for image in images]

    __a			:  str		    =  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_ ,    tensor_type=snake_case_       )


from typing import TYPE_CHECKING

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


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
