
import warnings

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


_A   =			logging.get_logger(__name__)

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







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		"mvp"
     UpperCAmelCase__    :							Dict									=		["past_key_values"]
     UpperCAmelCase__    :							int									=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(self				:   Optional[Any]    ,   _A				:   Tuple=5_0_2_6_7    ,   _A				:   Optional[Any]=1_0_2_4    ,   _A				:   Tuple=1_2    ,   _A				:   List[Any]=4_0_9_6    ,   _A				:   str=1_6    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Tuple=4_0_9_6    ,   _A				:   Any=1_6    ,   _A				:   Dict=0.0    ,   _A				:   int=0.0    ,   _A				:   Dict="gelu"    ,   _A				:   Union[str, Any]=1_0_2_4    ,   _A				:   List[str]=0.1    ,   _A				:   Union[str, Any]=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.02    ,   _A				:   Union[str, Any]=0.0    ,   _A				:   Optional[Any]=False    ,   _A				:   int=True    ,   _A				:   Optional[int]=1    ,   _A				:   Optional[Any]=0    ,   _A				:   Tuple=2    ,   _A				:   Union[str, Any]=True    ,   _A				:   List[str]=2    ,   _A				:   int=2    ,   _A				:   Union[str, Any]=False    ,   _A				:   int=1_0_0    ,   _A				:   Any=8_0_0    ,   **_A				:   Tuple    ,   )						->						Optional[Any]:
         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=_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								= 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."       )



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

