




'''simple docstring'''





class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          _snake_case				= name
          _snake_case				= value
          _snake_case				= weight






     def __repr__(					self       )				->     str:
          return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






     def 							_lowercase      (					self       )				->     str:
          return self.value






     def 							_lowercase      (					self       )				->     Optional[int]:
          return self.name






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          return self.weight






     def 							_lowercase      (					self       )				->     Any:
          return self.value / self.weight







def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Optional[int]       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= []
     for i in range(len(_UpperCamelCase       )       ):
          menu.append(Things(name[i]   ,		value[i]   ,		weight[i]       )       )
     return menu





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Optional[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     _snake_case				= sorted(_UpperCamelCase   ,		key=_UpperCamelCase   ,		reverse=_UpperCamelCase       )
     _snake_case				= []
     _snake_case				,   _snake_case				= 0.0, 0.0
     for i in range(len(_UpperCamelCase       )       ):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]       )
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)





def       __a      (  )		->					Tuple:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''simple docstring'''





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


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		{
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""rwkv"""
     SCREAMING_SNAKE_CASE_				:       Any       							=		{"""max_position_embeddings""": """context_length"""}






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=50_277   ,_SCREAMING_SNAKE_CASE=1_024   ,_SCREAMING_SNAKE_CASE=4_096   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=6   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[str]:
          _snake_case				= vocab_size
          _snake_case				= context_length
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= attention_hidden_size if attention_hidden_size is not None else hidden_size
          _snake_case				= intermediate_size if intermediate_size is not None else 4 * hidden_size
          _snake_case				= layer_norm_epsilon
          _snake_case				= rescale_every
          _snake_case				= use_cache

          _snake_case				= bos_token_id
          _snake_case				= eos_token_id

          super().__init__(
              tie_word_embeddings=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )


