
'''simple docstring'''





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


a__		: int  							=							logging.get_logger(__name__)

a__		: Any  							=							{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Optional[int]    					=       'splinter'






 def __init__(      self	:Union[str, Any]						,					_A	:int=30_522						,					_A	:List[str]=768						,					_A	:str=12						,					_A	:List[str]=12						,					_A	:Any=3_072						,					_A	:int="gelu"						,					_A	:List[Any]=0.1						,					_A	:str=0.1						,					_A	:Dict=512						,					_A	:Any=2						,					_A	:int=0.02						,					_A	:Optional[int]=1E-12						,					_A	:List[Any]=True						,					_A	:List[str]=0						,					_A	:Optional[int]=104						,					**_A	:int						,					)					->      Dict:







       '''simple docstring'''



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

       __A													=      vocab_size
       __A													=      max_position_embeddings
       __A													=      hidden_size
       __A													=      num_hidden_layers
       __A													=      num_attention_heads
       __A													=      intermediate_size
       __A													=      hidden_act
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      initializer_range
       __A													=      type_vocab_size
       __A													=      layer_norm_eps
       __A													=      use_cache
       __A													=      question_token_id







import warnings

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    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__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           if text is None and videos is None:
               raise ValueError("""You have to specify either text or videos. Both cannot be none."""			)

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



