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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	int          =	"mpnet"

       def __init__(							self					:     Dict			,			_lowerCAmelCase					:     str=3_05_27			,			_lowerCAmelCase					:     str=7_68			,			_lowerCAmelCase					:     Union[str, Any]=12			,			_lowerCAmelCase					:     Optional[int]=12			,			_lowerCAmelCase					:     Any=30_72			,			_lowerCAmelCase					:     str="gelu"			,			_lowerCAmelCase					:     Optional[Any]=0.1			,			_lowerCAmelCase					:     Dict=0.1			,			_lowerCAmelCase					:     Optional[int]=5_12			,			_lowerCAmelCase					:     List[Any]=0.02			,			_lowerCAmelCase					:     Tuple=1e-12			,			_lowerCAmelCase					:     Dict=32			,			_lowerCAmelCase					:     Dict=1			,			_lowerCAmelCase					:     Any=0			,			_lowerCAmelCase					:     Dict=2			,			**_lowerCAmelCase					:     List[str]			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			bos_token_id=_lowerCAmelCase			,			eos_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				List[str] 			=    vocab_size
           __snake_case       :				Optional[Any] 			=    hidden_size
           __snake_case       :				Any 			=    num_hidden_layers
           __snake_case       :				Optional[Any] 			=    num_attention_heads
           __snake_case       :				Tuple 			=    hidden_act
           __snake_case       :				Dict 			=    intermediate_size
           __snake_case       :				List[str] 			=    hidden_dropout_prob
           __snake_case       :				Union[str, Any] 			=    attention_probs_dropout_prob
           __snake_case       :				Tuple 			=    max_position_embeddings
           __snake_case       :				Optional[int] 			=    initializer_range
           __snake_case       :				Any 			=    layer_norm_eps
           __snake_case       :				List[str] 			=    relative_attention_num_buckets




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



