code: 
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  _snake_case			:       int      =						logging.get_logger(__name__)
  
  _snake_case			:       List[str]      =						{
      "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
      "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
      # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
  }
  
  # soft dependency
  if is_detectrona_available():
  						import detectrona
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Any							      =				'''layoutlmv2'''
  
  
  
  
  
  						def __init__( self       :Optional[Any]		,						lowerCAmelCase__       :str=30522		,						lowerCAmelCase__       :int=768		,						lowerCAmelCase__       :Dict=12		,						lowerCAmelCase__       :str=12		,						lowerCAmelCase__       :Optional[Any]=3072		,						lowerCAmelCase__       :int="gelu"		,						lowerCAmelCase__       :int=0.1		,						lowerCAmelCase__       :Optional[Any]=0.1		,						lowerCAmelCase__       :Any=512		,						lowerCAmelCase__       :str=2		,						lowerCAmelCase__       :str=0.02		,						lowerCAmelCase__       :Optional[int]=1E-1_2		,						lowerCAmelCase__       :List[str]=0		,						lowerCAmelCase__       :str=1024		,						lowerCAmelCase__       :str=128		,						lowerCAmelCase__       :Optional[int]=32		,						lowerCAmelCase__       :Optional[Any]=True		,						lowerCAmelCase__       :Dict=256		,						lowerCAmelCase__       :Optional[int]=64		,						lowerCAmelCase__       :Union[str, Any]=True		,						lowerCAmelCase__       :Dict=[7, 7, 256]		,						lowerCAmelCase__       :str=128		,						lowerCAmelCase__       :Optional[Any]=128		,						lowerCAmelCase__       :int=True		,						lowerCAmelCase__       :List[str]=True		,						lowerCAmelCase__       :str=False		,						lowerCAmelCase__       :str=None		,						**lowerCAmelCase__       :Dict		,						)     ->str:
  										super().__init__(
  										    vocab_size=lowerCAmelCase__		,						hidden_size=lowerCAmelCase__		,						num_hidden_layers=lowerCAmelCase__		,						num_attention_heads=lowerCAmelCase__		,						intermediate_size=lowerCAmelCase__		,						hidden_act=lowerCAmelCase__		,						hidden_dropout_prob=lowerCAmelCase__		,						attention_probs_dropout_prob=lowerCAmelCase__		,						max_position_embeddings=lowerCAmelCase__		,						type_vocab_size=lowerCAmelCase__		,						initializer_range=lowerCAmelCase__		,						layer_norm_eps=lowerCAmelCase__		,						pad_token_id=lowerCAmelCase__		,						**lowerCAmelCase__		,						)
  										lowercase						    =			max_ad_position_embeddings
  										lowercase						    =			max_rel_pos
  										lowercase						    =			rel_pos_bins
  										lowercase						    =			fast_qkv
  										lowercase						    =			max_rel_ad_pos
  										lowercase						    =			rel_ad_pos_bins
  										lowercase						    =			convert_sync_batchnorm
  										lowercase						    =			image_feature_pool_shape
  										lowercase						    =			coordinate_size
  										lowercase						    =			shape_size
  										lowercase						    =			has_relative_attention_bias
  										lowercase						    =			has_spatial_attention_bias
  										lowercase						    =			has_visual_segment_embedding
  										lowercase						    =			(
  										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  										)
  
  
  
  
  
  						@classmethod
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]    )     ->Tuple:
  										return {
  										    "MODEL.MASK_ON": True,
  										    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
  										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
  										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
  										    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
  										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
  										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
  										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
  										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
  										    "MODEL.POST_NMS_TOPK_TEST": 1000,
  										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
  										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
  										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
  										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
  										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
  										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
  										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
  										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
  										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
  										    "MODEL.RESNETS.DEPTH": 101,
  										    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
  										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
  										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
  										    "MODEL.RESNETS.NUM_GROUPS": 32,
  										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
  										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
  										}
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple    )     ->Tuple:
  										lowercase						    =			detectrona.config.get_cfg()
  										for k, v in self.detectrona_config_args.items():
  														lowercase						    =			k.split("."    )
  														lowercase						    =			detectrona_config
  														for attribute in attributes[:-1]:
  																		lowercase						    =			getattr(lowerCAmelCase__		,						lowerCAmelCase__    )
  														setattr(lowerCAmelCase__		,						attributes[-1]		,						lowerCAmelCase__    )
  
  										return detectrona_config
  
style_context: 
  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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  _snake_case			:       Tuple      =						logging.get_logger(__name__)
  
  
  if is_vision_available():
  						import PIL
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']
  
  
  
  
  
  						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
  										super().__init__(**lowerCAmelCase__    )
  
  										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# The previous feature extractor size parameter was in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  
  										lowercase						    =			do_resize
  										lowercase						    =			size
  										lowercase						    =			resample
  										lowercase						    =			do_thumbnail
  										lowercase						    =			do_align_long_axis
  										lowercase						    =			do_pad
  										lowercase						    =			do_rescale
  										lowercase						    =			rescale_factor
  										lowercase						    =			do_normalize
  										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										if (output_width < output_height and input_width > input_height) or (
  										    output_width > output_height and input_width < input_height
  										):
  														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )
  
  										if data_format is not None:
  														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										return image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  
  										lowercase						    =			output_width - input_width
  										lowercase						    =			output_height - input_height
  
  										if random_padding:
  														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
  														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
  										else:
  														lowercase						    =			delta_height // 2
  														lowercase						    =			delta_width // 2
  
  										lowercase						    =			delta_height - pad_top
  										lowercase						    =			delta_width - pad_left
  
  										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
  										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
  										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
  										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
  										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
  										lowercase    ,  lowercase						    =			size["height"], size["width"]
  
  										# We always resize to the smallest of either the input or output size.
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
  
  										if height == input_height and width == input_width:
  														return image
  
  										if input_height > input_width:
  														lowercase						    =			int(input_width * height / input_height    )
  										elif input_width > input_height:
  														lowercase						    =			int(input_height * width / input_width    )
  
  										return resize(
  										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			min(size["height"]		,						size["width"]    )
  										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
  										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  										return resized_image
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
  										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
  										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
  										lowercase						    =			do_resize if do_resize is not None else self.do_resize
  										lowercase						    =			size if size is not None else self.size
  										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
  														# Previous feature extractor had size in (width, height) format
  														lowercase						    =			size[::-1]
  										lowercase						    =			get_size_dict(lowerCAmelCase__    )
  										lowercase						    =			resample if resample is not None else self.resample
  										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
  										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
  										lowercase						    =			do_pad if do_pad is not None else self.do_pad
  										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
  										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
  										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
  										lowercase						    =			image_mean if image_mean is not None else self.image_mean
  										lowercase						    =			image_std if image_std is not None else self.image_std
  
  										lowercase						    =			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_rescale and rescale_factor is None:
  														raise ValueError("Rescale factor must be specified if do_rescale is True."    )
  
  										if do_pad and size is None:
  														raise ValueError("Size must be specified if do_pad 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.
  										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]
  
  										if do_align_long_axis:
  														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_resize:
  														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]
  
  										if do_thumbnail:
  														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]
  
  										if do_pad:
  														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]
  
  										if do_rescale:
  														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]
  
  										if do_normalize:
  														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]
  
  										lowercase						    =			{"pixel_values": images}
  										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
  