




import warnings

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


lowerCamelCase    :		Dict			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}







class   A(						__lowercase       ):



 '''simple docstring'''


 UpperCamelCase						       =						"mvp"
 UpperCamelCase						       =						["past_key_values"]
 UpperCamelCase						       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

 def __init__(    self :  Optional[int]						,							A_ :  List[str]=50267						,							A_ :  Dict=1024						,							A_ :  Optional[Any]=12						,							A_ :  Tuple=4096						,							A_ :  Dict=16						,							A_ :  Tuple=12						,							A_ :  int=4096						,							A_ :  List[Any]=16						,							A_ :  str=0.0						,							A_ :  List[Any]=0.0						,							A_ :  List[Any]="gelu"						,							A_ :  Dict=1024						,							A_ :  List[str]=0.1						,							A_ :  Tuple=0.0						,							A_ :  Optional[int]=0.0						,							A_ :  Optional[Any]=0.02						,							A_ :  Optional[int]=0.0						,							A_ :  str=False						,							A_ :  List[str]=True						,							A_ :  Any=1						,							A_ :  List[str]=0						,							A_ :  Dict=2						,							A_ :  Any=True						,							A_ :  List[Any]=2						,							A_ :  str=2						,							A_ :  str=False						,							A_ :  List[Any]=100						,							A_ :  int=800						,							**A_ :  Optional[int]						,							)       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   encoder_ffn_dim
       lowerCamelCase_							=   encoder_layers
       lowerCamelCase_							=   encoder_attention_heads
       lowerCamelCase_							=   decoder_ffn_dim
       lowerCamelCase_							=   decoder_layers
       lowerCamelCase_							=   decoder_attention_heads
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   attention_dropout
       lowerCamelCase_							=   activation_dropout
       lowerCamelCase_							=   activation_function
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   encoder_layerdrop
       lowerCamelCase_							=   decoder_layerdrop
       lowerCamelCase_							=   classifier_dropout
       lowerCamelCase_							=   use_cache
       lowerCamelCase_							=   encoder_layers
       lowerCamelCase_							=   scale_embedding  # scale factor will be sqrt(d_model) if True
       lowerCamelCase_							=   use_prompt
       lowerCamelCase_							=   prompt_length
       lowerCamelCase_							=   prompt_mid_dim

       super().__init__(
           pad_token_id=_a						,							bos_token_id=_a						,							eos_token_id=_a						,							is_encoder_decoder=_a						,							decoder_start_token_id=_a						,							forced_eos_token_id=_a						,							**_a						,							)

       if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'						,							_a  ):
             lowerCamelCase_							=   self.bos_token_id
             warnings.warn(
                 f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                 'The config can simply be saved and uploaded again to be fixed.'  )









from ..utils import DummyObject, requires_backends







class   A(						metaclass=UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''transformers''', '''torch''', '''note_seq''']

 def __init__(    self :  Any						,							*A_ :  Any						,							**A_ :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       requires_backends(self						,							['transformers', 'torch', 'note_seq']  )

 @classmethod
 def     a__			(    cls :  Tuple						,							*A_ :  Dict						,							**A_ :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )





 @classmethod
 def     a__			(    cls :  int						,							*A_ :  Dict						,							**A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       requires_backends(cls						,							['transformers', 'torch', 'note_seq']  )



