





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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				Optional[int]							=    'gptsan-japanese'
      A_						:				Any							=    [
          'past_key_values',
      ]
      A_						:				Any							=    {
          'hidden_size': 'd_model',
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }


      def __init__(self       :      Optional[int]				,			a__       :      Optional[Any]=3_6000				,			a__       :      Union[str, Any]=1280				,			a__       :      List[Any]=1024				,			a__       :      List[str]=8192				,			a__       :      Union[str, Any]=4096				,			a__       :      int=128				,			a__       :      Optional[int]=10				,			a__       :      Tuple=0				,			a__       :      List[str]=16				,			a__       :      Any=16				,			a__       :      Dict=128				,			a__       :      str=0.0				,			a__       :      Tuple=1E-5				,			a__       :      Any=False				,			a__       :      List[Any]=0.0				,			a__       :      Optional[Any]="float32"				,			a__       :      int=False				,			a__       :      Optional[int]=False				,			a__       :      List[str]=False				,			a__       :      Optional[int]=0.0_0_2				,			a__       :      List[Any]=False				,			a__       :      List[Any]=True				,			a__       :      Any=3_5998				,			a__       :      int=3_5995				,			a__       :      Union[str, Any]=3_5999				,			**a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   vocab_size
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   d_model
         __snake_case							       =   d_ff
         __snake_case							       =   d_ext
         __snake_case							       =   d_spout
         __snake_case							       =   num_switch_layers
         __snake_case							       =   num_ext_layers
         __snake_case							       =   num_switch_layers + num_ext_layers
         __snake_case							       =   num_heads
         __snake_case							       =   num_experts
         __snake_case							       =   expert_capacity
         __snake_case							       =   dropout_rate
         __snake_case							       =   layer_norm_epsilon
         __snake_case							       =   router_bias
         __snake_case							       =   router_jitter_noise
         __snake_case							       =   router_dtype
         __snake_case							       =   router_ignore_padding_tokens
         __snake_case							       =   output_hidden_states
         __snake_case							       =   output_attentions
         __snake_case							       =   initializer_factor
         __snake_case							       =   output_router_logits
         __snake_case							       =   use_cache

         super().__init__(
             separator_token_id=a__				,			pad_token_id=a__				,			eos_token_id=a__				,			**a__				,			)


def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float(moles / volume						) * nfactor						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (volume)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (pressure)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





