code: 
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''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 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""layoutlmv2"""
  
     def __init__(    self:	int   ,       snake_case__:	List[Any]=30_522   ,       snake_case__:	Optional[int]=768   ,       snake_case__:	Union[str, Any]=12   ,       snake_case__:	Union[str, Any]=12   ,       snake_case__:	Optional[int]=3_072   ,       snake_case__:	Optional[int]="gelu"   ,       snake_case__:	Optional[Any]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	str=512   ,       snake_case__:	str=2   ,       snake_case__:	str=0.02   ,       snake_case__:	str=1E-12   ,       snake_case__:	str=0   ,       snake_case__:	List[str]=1_024   ,       snake_case__:	Any=128   ,       snake_case__:	Any=32   ,       snake_case__:	List[Any]=True   ,       snake_case__:	int=256   ,       snake_case__:	Tuple=64   ,       snake_case__:	Optional[int]=True   ,       snake_case__:	Union[str, Any]=[7, 7, 256]   ,       snake_case__:	Dict=128   ,       snake_case__:	int=128   ,       snake_case__:	Any=True   ,       snake_case__:	Any=True   ,       snake_case__:	Tuple=False   ,       snake_case__:	Optional[Any]=None   ,       **snake_case__:	str   ,       )  ->		Any:
  
  
         """simple docstring"""
  
  
  
         super().__init__(
             vocab_size=snake_case__   ,       hidden_size=snake_case__   ,       num_hidden_layers=snake_case__   ,       num_attention_heads=snake_case__   ,       intermediate_size=snake_case__   ,       hidden_act=snake_case__   ,       hidden_dropout_prob=snake_case__   ,       attention_probs_dropout_prob=snake_case__   ,       max_position_embeddings=snake_case__   ,       type_vocab_size=snake_case__   ,       initializer_range=snake_case__   ,       layer_norm_eps=snake_case__   ,       pad_token_id=snake_case__   ,       **snake_case__   ,       )
         _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:	Tuple	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         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": 2_000,
             "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
             "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
             "MODEL.POST_NMS_TOPK_TEST": 1_000,
             "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:	List[Any]	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _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(snake_case__   ,       snake_case__	)
             setattr(snake_case__   ,       attributes[-1]   ,       snake_case__	)
  
         return detectrona_config
  
style_context: 
  
  
  
  
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""led"""
     __lowerCAmelCase      =						{
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }
  
     def __init__(    self:	int   ,       snake_case__:	str=50_265   ,       snake_case__:	Dict=16_384   ,       snake_case__:	str=1_024   ,       snake_case__:	Tuple=12   ,       snake_case__:	int=4_096   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	int=12   ,       snake_case__:	Union[str, Any]=4_096   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	str=0.0   ,       snake_case__:	Optional[Any]=0.0   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]="gelu"   ,       snake_case__:	Optional[int]=1_024   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=0.0   ,       snake_case__:	List[str]=0.0   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=2   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	Tuple=0   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	Union[List[int], int] = 512   ,       **snake_case__:	int   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	vocab_size
         _lowercase     =	max_encoder_position_embeddings
         _lowercase     =	max_decoder_position_embeddings
         _lowercase     =	d_model
         _lowercase     =	encoder_ffn_dim
         _lowercase     =	encoder_layers
         _lowercase     =	encoder_attention_heads
         _lowercase     =	decoder_ffn_dim
         _lowercase     =	decoder_layers
         _lowercase     =	decoder_attention_heads
         _lowercase     =	dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	activation_function
         _lowercase     =	init_std
         _lowercase     =	encoder_layerdrop
         _lowercase     =	decoder_layerdrop
         _lowercase     =	classifier_dropout
         _lowercase     =	use_cache
         _lowercase     =	encoder_layers
         _lowercase     =	attention_window
  
         super().__init__(
             pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       is_encoder_decoder=snake_case__   ,       decoder_start_token_id=snake_case__   ,       **snake_case__   ,       )
  