code: 
  
  
  import warnings
  from typing import List, Optional, Union
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  ["image_processor", "tokenizer"]
  							_A  				=  "LayoutLMv2ImageProcessor"
  							_A  				=  ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
  
  
  
  
  
  
  
  							def __init__(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													_a     =			None
  													if "feature_extractor" in kwargs:
  																			warnings.warn(
  																			    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
  																			    ' instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  																			_a     =			kwargs.pop('feature_extractor'		)
  
  													_a     =			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__(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def __call__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				SCREAMING_SNAKE_CASE_  :				Union[List[List[int]], List[List[List[int]]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[List[int], List[List[int]]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Union[bool, str, PaddingStrategy] = False		,				SCREAMING_SNAKE_CASE_  :				Union[bool, str, TruncationStrategy] = False		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				int = 0		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[bool] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[bool] = None		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = False		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, TensorType]] = None		,				**SCREAMING_SNAKE_CASE_  :				int		,				):
  													# verify input
  													if self.image_processor.apply_ocr and (boxes is not None):
  																			raise ValueError(
  																			    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)
  
  													if self.image_processor.apply_ocr and (word_labels is not None):
  																			raise ValueError(
  																			    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)
  
  													if return_overflowing_tokens is True and return_offsets_mapping is False:
  																			raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)
  
  													# first, apply the image processor
  													_a     =			self.image_processor(images=SCREAMING_SNAKE_CASE_		,				return_tensors=SCREAMING_SNAKE_CASE_		)
  
  													# second, apply the tokenizer
  													if text is not None and self.image_processor.apply_ocr and text_pair is None:
  																			if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
  																									_a     =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
  																			_a     =			features['words']
  
  													_a     =			self.tokenizer(
  													    text=text if text is not None else features['words']		,				text_pair=text_pair if text_pair is not None else None		,				boxes=boxes if boxes is not None else features['boxes']		,				word_labels=SCREAMING_SNAKE_CASE_		,				add_special_tokens=SCREAMING_SNAKE_CASE_		,				padding=SCREAMING_SNAKE_CASE_		,				truncation=SCREAMING_SNAKE_CASE_		,				max_length=SCREAMING_SNAKE_CASE_		,				stride=SCREAMING_SNAKE_CASE_		,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_		,				return_token_type_ids=SCREAMING_SNAKE_CASE_		,				return_attention_mask=SCREAMING_SNAKE_CASE_		,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_		,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_		,				return_offsets_mapping=SCREAMING_SNAKE_CASE_		,				return_length=SCREAMING_SNAKE_CASE_		,				verbose=SCREAMING_SNAKE_CASE_		,				return_tensors=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)
  
  													# add pixel values
  													_a     =			features.pop('pixel_values'		)
  													if return_overflowing_tokens is True:
  																			_a     =			self.get_overflowing_images(SCREAMING_SNAKE_CASE_		,				encoded_inputs['overflow_to_sample_mapping']		)
  													_a     =			images
  
  													return encoded_inputs
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		):
  													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
  													_a     =			[]
  													for sample_idx in overflow_to_sample_mapping:
  																			images_with_overflow.append(images[sample_idx]		)
  
  													if len(SCREAMING_SNAKE_CASE_		) != len(SCREAMING_SNAKE_CASE_		):
  																			raise ValueError(
  																			    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
  																			    f""" {len(SCREAMING_SNAKE_CASE_		)} and {len(SCREAMING_SNAKE_CASE_		)}"""		)
  
  													return images_with_overflow
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				*SCREAMING_SNAKE_CASE_  :				List[Any]		,				**SCREAMING_SNAKE_CASE_  :				Tuple		):
  													return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				*SCREAMING_SNAKE_CASE_  :				Dict		,				**SCREAMING_SNAKE_CASE_  :				List[str]		):
  													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		):
  													warnings.warn(
  													    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													return self.image_processor_class
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													warnings.warn(
  													    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													return self.image_processor
  
  
  
style_context: 
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  lowercase_								=    {
      '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 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  "led"
  							_A  				=  {
  							    "num_attention_heads": "encoder_attention_heads",
  							    "hidden_size": "d_model",
  							    "attention_probs_dropout_prob": "attention_dropout",
  							    "initializer_range": "init_std",
  							}
  
  
  
  
  
  
  
  							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]=5_0_2_6_5		,				SCREAMING_SNAKE_CASE_  :				str=1_6_3_8_4		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_2		,				SCREAMING_SNAKE_CASE_  :				Any=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=1_6		,				SCREAMING_SNAKE_CASE_  :				str=1_2		,				SCREAMING_SNAKE_CASE_  :				List[Any]=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_6		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=0.0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				List[Any]=True		,				SCREAMING_SNAKE_CASE_  :				str=True		,				SCREAMING_SNAKE_CASE_  :				List[str]="gelu"		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.1		,				SCREAMING_SNAKE_CASE_  :				Dict=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.02		,				SCREAMING_SNAKE_CASE_  :				str=2		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=1		,				SCREAMING_SNAKE_CASE_  :				str=0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=2		,				SCREAMING_SNAKE_CASE_  :				Union[List[int], int] = 5_1_2		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													_a     =			vocab_size
  													_a     =			max_encoder_position_embeddings
  													_a     =			max_decoder_position_embeddings
  													_a     =			d_model
  													_a     =			encoder_ffn_dim
  													_a     =			encoder_layers
  													_a     =			encoder_attention_heads
  													_a     =			decoder_ffn_dim
  													_a     =			decoder_layers
  													_a     =			decoder_attention_heads
  													_a     =			dropout
  													_a     =			attention_dropout
  													_a     =			activation_dropout
  													_a     =			activation_function
  													_a     =			init_std
  													_a     =			encoder_layerdrop
  													_a     =			decoder_layerdrop
  													_a     =			classifier_dropout
  													_a     =			use_cache
  													_a     =			encoder_layers
  													_a     =			attention_window
  
  													super().__init__(
  													    pad_token_id=SCREAMING_SNAKE_CASE_		,				bos_token_id=SCREAMING_SNAKE_CASE_		,				eos_token_id=SCREAMING_SNAKE_CASE_		,				is_encoder_decoder=SCREAMING_SNAKE_CASE_		,				decoder_start_token_id=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)
  
  
  