code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  class 							__snake_case    (				UpperCamelCase_       ):
  	__lowerCAmelCase						=     ['''image_processor''', '''tokenizer''']
  	__lowerCAmelCase						=     '''AutoImageProcessor'''
  	__lowerCAmelCase						=     '''AutoTokenizer'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					**UpperCamelCase_		)			->		Optional[Any]:
  		snake_case__  					=      None
  		if "feature_extractor" in kwargs:
  			warnings.warn(
  			    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
  			    ' instead.'			,					__UpperCamelCase			,					)
  			snake_case__  					=      kwargs.pop('feature_extractor'		)
  
  		snake_case__  					=      image_processor if image_processor is not None else feature_extractor
  		if image_processor is None:
  			raise ValueError('You need to specify an `image_processor`.'		)
  		if tokenizer is None:
  			raise ValueError('You need to specify a `tokenizer`.'		)
  
  		super().__init__(__UpperCamelCase			,					__UpperCamelCase		)
  		snake_case__  					=      self.image_processor
  		snake_case__  					=      False
  
  
  
  
  
  	def __call__(		self			,					*UpperCamelCase_			,					**UpperCamelCase_		)			->		Union[str, Any]:
  
  
  		if self._in_target_context_manager:
  			return self.current_processor(*__UpperCamelCase			,					**__UpperCamelCase		)
  
  		snake_case__  					=      kwargs.pop('images'			,					__UpperCamelCase		)
  		snake_case__  					=      kwargs.pop('text'			,					__UpperCamelCase		)
  		if len(__UpperCamelCase		) > 0:
  			snake_case__  					=      args[0]
  			snake_case__  					=      args[1:]
  
  		if images is None and text is None:
  			raise ValueError('You need to specify either an `images` or `text` input to process.'		)
  
  		if images is not None:
  			snake_case__  					=      self.image_processor(__UpperCamelCase			,					*__UpperCamelCase			,					**__UpperCamelCase		)
  		if text is not None:
  			snake_case__  					=      self.tokenizer(__UpperCamelCase			,					**__UpperCamelCase		)
  
  		if text is None:
  			return inputs
  		elif images is None:
  			return encodings
  		else:
  			snake_case__  					=      encodings['''input_ids''']
  			return inputs
  
  
  
  
  
  	def      _snake_case      (		self			,					*UpperCamelCase_			,					**UpperCamelCase_		)			->		List[str]:
  		return self.tokenizer.batch_decode(*__UpperCamelCase			,					**__UpperCamelCase		)
  
  
  
  
  
  	def      _snake_case      (		self			,					*UpperCamelCase_			,					**UpperCamelCase_		)			->		List[Any]:
  		return self.tokenizer.decode(*__UpperCamelCase			,					**__UpperCamelCase		)
  
  
  
  
  
  	@contextmanager
  	def      _snake_case      (		self		)			->		Tuple:
  		warnings.warn(
  		    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
  		    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
  		    'your images inputs, or in a separate call.'		)
  		snake_case__  					=      True
  		snake_case__  					=      self.tokenizer
  		yield
  		snake_case__  					=      self.image_processor
  		snake_case__  					=      False
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[str]:
  		warnings.warn(
  		    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,					__UpperCamelCase			,					)
  		return self.image_processor_class
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Tuple:
  		warnings.warn(
  		    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,					__UpperCamelCase			,					)
  		return self.image_processor
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  a__				: Optional[Any]									=    logging.get_logger(__name__)
  
  a__				: 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 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''layoutlmv2'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3_0522			,					UpperCamelCase_=768			,					UpperCamelCase_=12			,					UpperCamelCase_=12			,					UpperCamelCase_=3072			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=512			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-1_2			,					UpperCamelCase_=0			,					UpperCamelCase_=1024			,					UpperCamelCase_=128			,					UpperCamelCase_=32			,					UpperCamelCase_=True			,					UpperCamelCase_=256			,					UpperCamelCase_=64			,					UpperCamelCase_=True			,					UpperCamelCase_=[7, 7, 256]			,					UpperCamelCase_=128			,					UpperCamelCase_=128			,					UpperCamelCase_=True			,					UpperCamelCase_=True			,					UpperCamelCase_=False			,					UpperCamelCase_=None			,					**UpperCamelCase_			,					)			->		Tuple:
  		super().__init__(
  		    vocab_size=UpperCamelCase_			,					hidden_size=UpperCamelCase_			,					num_hidden_layers=UpperCamelCase_			,					num_attention_heads=UpperCamelCase_			,					intermediate_size=UpperCamelCase_			,					hidden_act=UpperCamelCase_			,					hidden_dropout_prob=UpperCamelCase_			,					attention_probs_dropout_prob=UpperCamelCase_			,					max_position_embeddings=UpperCamelCase_			,					type_vocab_size=UpperCamelCase_			,					initializer_range=UpperCamelCase_			,					layer_norm_eps=UpperCamelCase_			,					pad_token_id=UpperCamelCase_			,					**UpperCamelCase_			,					)
  		snake_case__  					=      max_ad_position_embeddings
  		snake_case__  					=      max_rel_pos
  		snake_case__  					=      rel_pos_bins
  		snake_case__  					=      fast_qkv
  		snake_case__  					=      max_rel_ad_pos
  		snake_case__  					=      rel_ad_pos_bins
  		snake_case__  					=      convert_sync_batchnorm
  		snake_case__  					=      image_feature_pool_shape
  		snake_case__  					=      coordinate_size
  		snake_case__  					=      shape_size
  		snake_case__  					=      has_relative_attention_bias
  		snake_case__  					=      has_spatial_attention_bias
  		snake_case__  					=      has_visual_segment_embedding
  		snake_case__  					=      (
  		    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  		)
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		self		)			->		Dict:
  		return {
  		    "MODEL.MASK_ON": True,
  		    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
  		    "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      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      detectrona.config.get_cfg()
  		for k, v in self.detectrona_config_args.items():
  			snake_case__  					=      k.split('.'		)
  			snake_case__  					=      detectrona_config
  			for attribute in attributes[:-1]:
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  			setattr(UpperCamelCase_			,					attributes[-1]			,					UpperCamelCase_		)
  
  		return detectrona_config
  
  
  
  