





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__				,			)








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

