






'''simple docstring'''





import warnings

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


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)

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







class  _lowercase   (   _A						):
      _a							:						List[str]										=							'mvp'
      _a							:						Union[str, Any]										=							['past_key_values']
      _a							:						int										=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(     self				,			a=5_0_2_6_7				,			a=1_0_2_4				,			a=1_2				,			a=4_0_9_6				,			a=1_6				,			a=1_2				,			a=4_0_9_6				,			a=1_6				,			a=0.0				,			a=0.0				,			a="gelu"				,			a=1_0_2_4				,			a=0.1				,			a=0.0				,			a=0.0				,			a=0.02				,			a=0.0				,			a=False				,			a=True				,			a=1				,			a=0				,			a=2				,			a=True				,			a=2				,			a=2				,			a=False				,			a=1_0_0				,			a=8_0_0				,			**a				,			):
       snake_case__  :    Optional[int]							       =vocab_size
       snake_case__  :    List[str]							       =max_position_embeddings
       snake_case__  :    Any							       =d_model
       snake_case__  :    Union[str, Any]							       =encoder_ffn_dim
       snake_case__  :    str							       =encoder_layers
       snake_case__  :    str							       =encoder_attention_heads
       snake_case__  :    Tuple							       =decoder_ffn_dim
       snake_case__  :    int							       =decoder_layers
       snake_case__  :    Dict							       =decoder_attention_heads
       snake_case__  :    Optional[Any]							       =dropout
       snake_case__  :    Optional[Any]							       =attention_dropout
       snake_case__  :    Any							       =activation_dropout
       snake_case__  :    List[Any]							       =activation_function
       snake_case__  :    Tuple							       =init_std
       snake_case__  :    List[Any]							       =encoder_layerdrop
       snake_case__  :    Union[str, Any]							       =decoder_layerdrop
       snake_case__  :    int							       =classifier_dropout
       snake_case__  :    Tuple							       =use_cache
       snake_case__  :    str							       =encoder_layers
       snake_case__  :    str							       =scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case__  :    Any							       =use_prompt
       snake_case__  :    Dict							       =prompt_length
       snake_case__  :    Tuple							       =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   ):
        snake_case__  :    int							       =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."""   )













__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_93_44,
    "knot": 1.8_52,
}

__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 0.2_77_77_77_78,
    "mph": 0.6_21_37_11_92,
    "knot": 0.5_39_95_68_03,
}







def 			A__							(      _a  :					float		,  _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
  snake_case__  :    Tuple							       =(
      f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
      f"Valid values are: {', '.join(_a	)}"
  )
  raise ValueError(_a	)
 return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]		,  3	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





