



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, normalize, rescale, resize, to_channel_dimension_format
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_vision_available, logging


if is_vision_available():
   import PIL


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					['''pixel_values''']




     def __init__(					self			,    a__ = True			,    a__ = None			,    a__ = PIL.Image.BICUBIC			,    a__ = True			,    a__ = None			,    a__ = 1 / 2_5_5			,    a__ = True			,    a__ = True			,    a__ = None			,    a__ = None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
         A__					=       get_size_dict(a__)
         A__					=       crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A__					=       get_size_dict(a__			,    param_name='''crop_size''')

         A__					=       do_resize
         A__					=       size
         A__					=       resample
         A__					=       do_center_crop
         A__					=       crop_size
         A__					=       do_rescale
         A__					=       rescale_factor
         A__					=       do_normalize
         A__					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A__					=       image_std if image_std is not None else IMAGENET_STANDARD_STD




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = PIL.Image.BICUBIC			,    a__ = None			,    **a__			,    ):
         A__					=       get_size_dict(a__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
         return resize(
             a__			,    size=(size['''height'''], size['''width'''])			,    resample=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    **a__			,    ):
         A__					=       get_size_dict(a__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
         return center_crop(a__			,    size=(size['''height'''], size['''width'''])			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    **a__			,    ):
         return rescale(a__			,    scale=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__ = None			,    **a__			,    ):
         return normalize(a__			,    mean=a__			,    std=a__			,    data_format=a__			,    **a__)




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None			,    a__=None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = ChannelDimension.FIRST			,    **a__			,    ):
         A__					=       do_resize if do_resize is not None else self.do_resize
         A__					=       resample if resample is not None else self.resample
         A__					=       do_center_crop if do_center_crop is not None else self.do_center_crop
         A__					=       do_rescale if do_rescale is not None else self.do_rescale
         A__					=       rescale_factor if rescale_factor is not None else self.rescale_factor
         A__					=       do_normalize if do_normalize is not None else self.do_normalize
         A__					=       image_mean if image_mean is not None else self.image_mean
         A__					=       image_std if image_std is not None else self.image_std

         A__					=       size if size is not None else self.size
         A__					=       get_size_dict(a__)
         A__					=       crop_size if crop_size is not None else self.crop_size
         A__					=       get_size_dict(a__			,    param_name='''crop_size''')

         A__					=       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 or resample is None:
             raise ValueError('''Size and resample 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.
         A__					=       [to_numpy_array(a__) for image in images]

         if do_resize:
             A__					=       [self.resize(image=a__			,    size=a__			,    resample=a__) for image in images]

         if do_center_crop:
             A__					=       [self.center_crop(image=a__			,    size=a__) for image in images]

         if do_rescale:
             A__					=       [self.rescale(image=a__			,    scale=a__) for image in images]

         if do_normalize:
             A__					=       [self.normalize(image=a__			,    mean=a__			,    std=a__) for image in images]

         A__					=       [to_channel_dimension_format(a__			,    a__) for image in images]

         A__					=       {'''pixel_values''': images}
         return BatchFeature(data=a__			,    tensor_type=a__)





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__)
