



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


a_						:     Tuple	     =		logging.get_logger(__name__)

a_						:     str	     =		{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}





class    lowerCamelCase__  (						_UpperCAmelCase):
					"""simple docstring"""



					_A       =			'luke'







					def __init__(self				,			__a=5_02_67				,			__a=50_00_00				,			__a=7_68				,			__a=2_56				,			__a=12				,			__a=12				,			__a=30_72				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=5_12				,			__a=2				,			__a=0.02				,			__a=1E-12				,			__a=True				,			__a=None				,			__a=1				,			__a=0				,			__a=2				,			**__a				,			):






											'''simple docstring'''



											super().__init__(pad_token_id=lowerCamelCase_				,			bos_token_id=lowerCamelCase_				,			eos_token_id=lowerCamelCase_				,			**lowerCamelCase_    )

											lowerCamelCase								=		vocab_size
											lowerCamelCase								=		entity_vocab_size
											lowerCamelCase								=		hidden_size
											lowerCamelCase								=		entity_emb_size
											lowerCamelCase								=		num_hidden_layers
											lowerCamelCase								=		num_attention_heads
											lowerCamelCase								=		hidden_act
											lowerCamelCase								=		intermediate_size
											lowerCamelCase								=		hidden_dropout_prob
											lowerCamelCase								=		attention_probs_dropout_prob
											lowerCamelCase								=		max_position_embeddings
											lowerCamelCase								=		type_vocab_size
											lowerCamelCase								=		initializer_range
											lowerCamelCase								=		layer_norm_eps
											lowerCamelCase								=		use_entity_aware_attention
											lowerCamelCase								=		classifier_dropout




import warnings

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





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'CLIPImageProcessor'
     _A       =			('CLIPTokenizer', 'CLIPTokenizerFast')







     def __init__(self				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		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				,			)
                 lowerCamelCase								=		kwargs.pop("feature_extractor"    )

           lowerCamelCase								=		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__(__a				,			__a    )







     def __call__(self				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''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:
                 lowerCamelCase								=		self.tokenizer(__a				,			return_tensors=__a				,			**__a    )

           if images is not None:
                 lowerCamelCase								=		self.image_processor(__a				,			return_tensors=__a				,			**__a    )

           if text is not None and images is not None:
                 lowerCamelCase								=		image_features.pixel_values
                 return encoding
           elif text is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**__a    )				,			tensor_type=__a    )







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.tokenizer.model_input_names
           lowerCamelCase								=		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           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 						_a      (self    ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			__a				,			)
           return self.image_processor