
'''simple docstring'''







from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase					:    str       				=			logging.get_logger(__name__)

_lowercase					:    List[Any]       				=			{
    '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 					lowerCAmelCase__  (    lowerCamelCase_	):
					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							,				__SCREAMING_SNAKE_CASE=5_02_65							,				__SCREAMING_SNAKE_CASE=1_63_84							,				__SCREAMING_SNAKE_CASE=10_24							,				__SCREAMING_SNAKE_CASE=12							,				__SCREAMING_SNAKE_CASE=40_96							,				__SCREAMING_SNAKE_CASE=16							,				__SCREAMING_SNAKE_CASE=12							,				__SCREAMING_SNAKE_CASE=40_96							,				__SCREAMING_SNAKE_CASE=16							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE="gelu"							,				__SCREAMING_SNAKE_CASE=10_24							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.02							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=1							,				__SCREAMING_SNAKE_CASE=0							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE = 5_12							,				**__SCREAMING_SNAKE_CASE							,				):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			vocab_size
											lowercase_      :	Union[str, Any]						=			max_encoder_position_embeddings
											lowercase_      :	str						=			max_decoder_position_embeddings
											lowercase_      :	List[str]						=			d_model
											lowercase_      :	Union[str, Any]						=			encoder_ffn_dim
											lowercase_      :	Union[str, Any]						=			encoder_layers
											lowercase_      :	List[str]						=			encoder_attention_heads
											lowercase_      :	Dict						=			decoder_ffn_dim
											lowercase_      :	List[str]						=			decoder_layers
											lowercase_      :	Optional[int]						=			decoder_attention_heads
											lowercase_      :	Tuple						=			dropout
											lowercase_      :	Optional[int]						=			attention_dropout
											lowercase_      :	Dict						=			activation_dropout
											lowercase_      :	Optional[int]						=			activation_function
											lowercase_      :	Tuple						=			init_std
											lowercase_      :	Union[str, Any]						=			encoder_layerdrop
											lowercase_      :	List[Any]						=			decoder_layerdrop
											lowercase_      :	List[str]						=			classifier_dropout
											lowercase_      :	Optional[Any]						=			use_cache
											lowercase_      :	Union[str, Any]						=			encoder_layers
											lowercase_      :	Tuple						=			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							,				)





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								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:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**__snake_case							)	,				tensor_type=__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								return self.tokenizer.batch_decode(*__snake_case	,				**__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.tokenizer.decode(*__snake_case	,				**__snake_case							)




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
