'''simple docstring'''






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


__a									=   logging.get_logger(__name__)





class        A__				(			UpperCamelCase  ):





   """simple docstring"""





   UpperCamelCase_			:		Optional[int]       							=	['''pixel_values''']




   def __init__(					self	:						List[str]		,	lowerCAmelCase__	:						bool = True		,	lowerCAmelCase__	:						Dict[str, int] = None		,	lowerCAmelCase__	:						PILImageResampling = PILImageResampling.BICUBIC		,	lowerCAmelCase__	:						bool = True		,	lowerCAmelCase__	:						Dict[str, int] = None		,	lowerCAmelCase__	:						bool = True		,	lowerCAmelCase__	:						Union[int, float] = 1 / 2_5_5		,	lowerCAmelCase__	:						bool = True		,	lowerCAmelCase__	:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,	lowerCAmelCase__	:						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,	**lowerCAmelCase__	:						Optional[Any]		,	)			->       List[Any]:







      """simple docstring"""




      super().__init__(**lowerCamelCase__     )
      _UpperCAmelCase				:      Optional[int]				=  size if size is not None else {'shortest_edge': 2_2_4}
      _UpperCAmelCase				:      Optional[int]				=  get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
      _UpperCAmelCase				:      Tuple				=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
      _UpperCAmelCase				:      Tuple				=  get_size_dict(lowerCamelCase__		,	param_name="crop_size"     )

      _UpperCAmelCase				:      List[Any]				=  do_resize
      _UpperCAmelCase				:      Optional[Any]				=  size
      _UpperCAmelCase				:      Optional[int]				=  resample
      _UpperCAmelCase				:      List[str]				=  do_center_crop
      _UpperCAmelCase				:      Optional[int]				=  crop_size
      _UpperCAmelCase				:      List[str]				=  do_rescale
      _UpperCAmelCase				:      Any				=  rescale_factor
      _UpperCAmelCase				:      Any				=  do_normalize
      _UpperCAmelCase				:      int				=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _UpperCAmelCase				:      Union[str, Any]				=  image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def      _lowerCAmelCase						(					self	:						int		,	lowerCAmelCase__	:						np.ndarray		,	lowerCAmelCase__	:						Dict[str, int]		,	lowerCAmelCase__	:						PILImageResampling = PILImageResampling.BICUBIC		,	lowerCAmelCase__	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase__	:						Union[str, Any]		,	)			->       str:







      """simple docstring"""




      _UpperCAmelCase				:      Any				=  get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
      # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
      if "shortest_edge" in size:
         _UpperCAmelCase				:      Union[str, Any]				=  int((2_5_6 / 2_2_4) * size["shortest_edge"]     )
         _UpperCAmelCase				:      Tuple				=  get_resize_output_image_size(lowerCamelCase__		,	size=lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
         _UpperCAmelCase				:      int				=  {'height': output_size[0], 'width': output_size[1]}
      if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             F"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""     )
      return resize(
          lowerCamelCase__		,	size=(size_dict["height"], size_dict["width"])		,	resample=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )




   def      _lowerCAmelCase						(					self	:						Optional[Any]		,	lowerCAmelCase__	:						np.ndarray		,	lowerCAmelCase__	:						Dict[str, int]		,	lowerCAmelCase__	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase__	:						List[Any]		,	)			->       Optional[int]:







      """simple docstring"""




      _UpperCAmelCase				:      int				=  get_size_dict(lowerCamelCase__     )
      if "height" not in size or "width" not in size:
         raise ValueError(F"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""     )
      return center_crop(lowerCamelCase__		,	size=(size["height"], size["width"])		,	data_format=lowerCamelCase__		,	**lowerCamelCase__     )




   def      _lowerCAmelCase						(					self	:						Tuple		,	lowerCAmelCase__	:						np.ndarray		,	lowerCAmelCase__	:						Union[int, float]		,	lowerCAmelCase__	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase__	:						Union[str, Any]		,	)			->       Optional[int]:







      """simple docstring"""




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




   def      _lowerCAmelCase						(					self	:						List[Any]		,	lowerCAmelCase__	:						np.ndarray		,	lowerCAmelCase__	:						Union[float, List[float]]		,	lowerCAmelCase__	:						Union[float, List[float]]		,	lowerCAmelCase__	:						Optional[Union[str, ChannelDimension]] = None		,	**lowerCAmelCase__	:						int		,	)			->       str:







      """simple docstring"""




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




   def      _lowerCAmelCase						(					self	:						Any		,	lowerCAmelCase__	:						ImageInput		,	lowerCAmelCase__	:						Optional[bool] = None		,	lowerCAmelCase__	:						Optional[Dict[str, int]] = None		,	lowerCAmelCase__	:						PILImageResampling = None		,	lowerCAmelCase__	:						Optional[bool] = None		,	lowerCAmelCase__	:						Optional[Dict[str, int]] = None		,	lowerCAmelCase__	:						Optional[bool] = None		,	lowerCAmelCase__	:						Optional[float] = None		,	lowerCAmelCase__	:						Optional[bool] = None		,	lowerCAmelCase__	:						Optional[Union[float, Iterable[float]]] = None		,	lowerCAmelCase__	:						Optional[Union[float, Iterable[float]]] = None		,	lowerCAmelCase__	:						Optional[TensorType] = None		,	lowerCAmelCase__	:						ChannelDimension = ChannelDimension.FIRST		,	**lowerCAmelCase__	:						List[str]		,	)			->       Optional[int]:







      """simple docstring"""




      _UpperCAmelCase				:      Optional[int]				=  do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase				:      Tuple				=  resample if resample is not None else self.resample
      _UpperCAmelCase				:      int				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      _UpperCAmelCase				:      int				=  do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase				:      Optional[Any]				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase				:      List[Any]				=  do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase				:      int				=  image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase				:      Tuple				=  image_std if image_std is not None else self.image_std

      _UpperCAmelCase				:      Dict				=  size if size is not None else self.size
      _UpperCAmelCase				:      Any				=  get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__     )
      _UpperCAmelCase				:      Union[str, Any]				=  crop_size if crop_size is not None else self.crop_size
      _UpperCAmelCase				:      Any				=  get_size_dict(lowerCamelCase__		,	param_name="crop_size"     )

      _UpperCAmelCase				:      Optional[int]				=  make_list_of_images(lowerCamelCase__     )

      if not valid_images(lowerCamelCase__     ):
         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."     )

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

      if do_resize:
         _UpperCAmelCase				:      Union[str, Any]				=  [self.resize(lowerCamelCase__		,	lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

      if do_center_crop:
         _UpperCAmelCase				:      Optional[Any]				=  [self.center_crop(lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

      if do_rescale:
         _UpperCAmelCase				:      str				=  [self.rescale(lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

      if do_normalize:
         _UpperCAmelCase				:      List[Any]				=  [self.normalize(lowerCamelCase__		,	lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

      _UpperCAmelCase				:      Optional[int]				=  [to_channel_dimension_format(lowerCamelCase__		,	lowerCamelCase__     ) for image in images]

      _UpperCAmelCase				:      Tuple				=  {'pixel_values': images}
      return BatchFeature(data=lowerCamelCase__		,	tensor_type=lowerCamelCase__     )




'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_					:      Tuple    			=    {
    '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:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    '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

					snake_case_					:      int    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


