


import warnings

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







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		["image_processor", "tokenizer"]
		UpperCamelCase     :					Optional[Any]			       =		"ViTImageProcessor"
		UpperCamelCase     :					Dict			       =		("CLIPTokenizer", "CLIPTokenizerFast")







		def __init__( self		,      A=None		,      A=None		,      **A	)  -> Union[str, Any]:







					'''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=None		,      **A	)  -> List[str]:







					'''simple docstring'''







					if text is None and visual_prompt is None and images is None:
								raise ValueError("""You have to specify either text, visual prompt or images."""	)

					if text is not None and visual_prompt is not None:
								raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""	)

					if text is not None:
								lowerCamelCase    		=  self.tokenizer(A		,      return_tensors=A		,      **A	)

					if visual_prompt is not None:
								lowerCamelCase    		=  self.image_processor(A		,      return_tensors=A		,      **A	)

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

					if visual_prompt is not None and images is not None:
								lowerCamelCase    		=  {
								    """pixel_values""": image_features.pixel_values,
								    """conditional_pixel_values""": prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								lowerCamelCase    		=  image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								lowerCamelCase    		=  {
								    """conditional_pixel_values""": prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**A	)		,      tensor_type=A	)







		def 					__A   ( self		,      *A		,      **A	)  -> Any:







					'''simple docstring'''







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







		def 					__A   ( self		,      *A		,      **A	)  -> Optional[Any]:







					'''simple docstring'''







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







		@property
		def 					__A   ( self	)  -> Tuple:







					'''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	)  -> Optional[int]:







					'''simple docstring'''







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





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


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      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-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__(pad_token_id=A		,      **A	)

					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_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    		=  position_embedding_type
					lowerCamelCase    		=  classifier_dropout
					lowerCamelCase    		=  channel_shrink_ratio
					lowerCamelCase    		=  max_ad_position_embeddings

