code: 
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  class 					_UpperCamelCase (      lowercase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  ["""image_processor""", """tokenizer"""]
  							_A  				=  """AutoImageProcessor"""
  							_A  				=  """AutoTokenizer"""
  
  
  
  
  
  
  
  							def __init__(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				**SCREAMING_SNAKE_CASE_  :				Optional[int]		):
  													_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.'		,				_A		,				)
  																			_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 have to specify an image_processor.'		)
  													if tokenizer is None:
  																			raise ValueError('You have to specify a tokenizer.'		)
  
  													super().__init__(_A		,				_A		)
  													_a     =			self.image_processor
  
  
  
  
  
  
  
  							def __call__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Dict=None		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=None		,				SCREAMING_SNAKE_CASE_  :				Any=None		,				**SCREAMING_SNAKE_CASE_  :				str		):
  
  													if text is None and images is None:
  																			raise ValueError('You have to specify either text or images. Both cannot be none.'		)
  
  													if text is not None:
  																			_a     =			self.tokenizer(_A		,				return_tensors=_A		,				**_A		)
  
  													if images is not None:
  																			_a     =			self.image_processor(_A		,				return_tensors=_A		,				**_A		)
  
  													if text is not None and images is not None:
  																			_a     =			image_features.pixel_values
  																			return encoding
  													elif text is not None:
  																			return encoding
  													else:
  																			return BatchEncoding(data=dict(**_A		)		,				tensor_type=_A		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				*SCREAMING_SNAKE_CASE_  :				List[str]		,				**SCREAMING_SNAKE_CASE_  :				str		):
  													return self.tokenizer.batch_decode(*_A		,				**_A		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				*SCREAMING_SNAKE_CASE_  :				Tuple		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													return self.tokenizer.decode(*_A		,				**_A		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Tuple		):
  													_a     =			self.tokenizer.model_input_names
  													_a     =			self.image_processor.model_input_names
  													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													warnings.warn(
  													    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,				_A		,				)
  													return self.image_processor_class
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													warnings.warn(
  													    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,				_A		,				)
  													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_		,				)
  
  
  