



'''simple docstring'''






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


A_							: Tuple						 =	logging.get_logger(__name__)


if is_vision_available():
   import PIL



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  ["""pixel_values"""]







 def __init__(							self  ,a_ = True  ,a_ = None  ,a_ = PILImageResampling.BICUBIC  ,a_ = True  ,a_ = None  ,a_ = True  ,a_ = 1 / 255  ,a_ = True  ,a_ = None  ,a_ = None  ,a_ = True  ,**a_  ,)		->   None:
        super().__init__(**a_							)
        _UpperCAmelCase  :						str			     = size if size is not None else {"""shortest_edge""": 224}
        _UpperCAmelCase  :						Any			     = get_size_dict(a_  ,default_to_square=a_							)
        _UpperCAmelCase  :						List[str]			     = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        _UpperCAmelCase  :						Dict			     = get_size_dict(a_  ,default_to_square=a_  ,param_name="""crop_size"""							)

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







 def _snake_case (							self  ,a_  ,a_  ,a_ = PILImageResampling.BICUBIC  ,a_ = None  ,**a_  ,)		->   np.ndarray:
        _UpperCAmelCase  :						Optional[int]			     = get_size_dict(a_  ,default_to_square=a_							)
        if "shortest_edge" not in size:
               raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
        _UpperCAmelCase  :						Optional[int]			     = get_resize_output_image_size(a_  ,size=size["""shortest_edge"""]  ,default_to_square=a_							)
        return resize(a_  ,size=a_  ,resample=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   np.ndarray:
        _UpperCAmelCase  :						List[str]			     = get_size_dict(a_							)
        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(a_  ,size=(size["""height"""], size["""width"""])  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   Dict:
        return rescale(a_  ,scale=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_ = None  ,**a_  ,)		->   np.ndarray:
        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_ = None  ,a_ = ChannelDimension.FIRST  ,**a_  ,)		->   PIL.Image.Image:
        _UpperCAmelCase  :						Union[str, Any]			     = do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase  :						Union[str, Any]			     = size if size is not None else self.size
        _UpperCAmelCase  :						Any			     = get_size_dict(a_  ,param_name="""size"""  ,default_to_square=a_							)
        _UpperCAmelCase  :						Optional[int]			     = 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  :						List[Any]			     = crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase  :						List[Any]			     = get_size_dict(a_  ,param_name="""crop_size"""  ,default_to_square=a_							)
        _UpperCAmelCase  :						Dict			     = do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase  :						Optional[int]			     = rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase  :						Tuple			     = do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase  :						Optional[Any]			     = image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase  :						Union[str, Any]			     = image_std if image_std is not None else self.image_std
        _UpperCAmelCase  :						Union[str, Any]			     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCAmelCase  :						List[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_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:
               _UpperCAmelCase  :						int			     = [convert_to_rgb(a_							) for image in images]

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

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

        if do_center_crop:
               _UpperCAmelCase  :						Optional[int]			     = [self.center_crop(image=a_  ,size=a_							) for image in images]

        if do_rescale:
               _UpperCAmelCase  :						Optional[int]			     = [self.rescale(image=a_  ,scale=a_							) for image in images]

        if do_normalize:
               _UpperCAmelCase  :						Any			     = [self.normalize(image=a_  ,mean=a_  ,std=a_							) for image in images]

        _UpperCAmelCase  :						Union[str, Any]			     = [to_channel_dimension_format(a_  ,a_							) for image in images]

        _UpperCAmelCase  :						Optional[int]			     = {"""pixel_values""": images}
        return BatchFeature(data=a_  ,tensor_type=a_							)








'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



