
import warnings

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


UpperCAmelCase      =   logging.get_logger(__name__)

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







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

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



 def __init__(     self		,				_UpperCAmelCase=5_02_67		,				_UpperCAmelCase=10_24		,				_UpperCAmelCase=12		,				_UpperCAmelCase=40_96		,				_UpperCAmelCase=16		,				_UpperCAmelCase=12		,				_UpperCAmelCase=40_96		,				_UpperCAmelCase=16		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=10_24		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=1		,				_UpperCAmelCase=0		,				_UpperCAmelCase=2		,				_UpperCAmelCase=True		,				_UpperCAmelCase=2		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=8_00		,				**_UpperCAmelCase		,				):
       snake_case_	=				vocab_size
       snake_case_	=				max_position_embeddings
       snake_case_	=				d_model
       snake_case_	=				encoder_ffn_dim
       snake_case_	=				encoder_layers
       snake_case_	=				encoder_attention_heads
       snake_case_	=				decoder_ffn_dim
       snake_case_	=				decoder_layers
       snake_case_	=				decoder_attention_heads
       snake_case_	=				dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				activation_function
       snake_case_	=				init_std
       snake_case_	=				encoder_layerdrop
       snake_case_	=				decoder_layerdrop
       snake_case_	=				classifier_dropout
       snake_case_	=				use_cache
       snake_case_	=				encoder_layers
       snake_case_	=				scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case_	=				use_prompt
       snake_case_	=				prompt_length
       snake_case_	=				prompt_mid_dim

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

       if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''		,				_UpperCAmelCase ):
             snake_case_	=				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.''' )



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


if __name__ == "__main__":
   print(solution())