





'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    create_all_state(1		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							[]		,							SCREAMING_SNAKE_CASE_     )
    return result


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       list[int]		,							SCREAMING_SNAKE_CASE_	:       list[list[int]]		,							):



    '''simple docstring'''





    if level == 0:
        total_list.append(current_list[:]     )
        return

    for i in range(SCREAMING_SNAKE_CASE_		,							total_number - level + 2     ):
        current_list.append(SCREAMING_SNAKE_CASE_     )
        create_all_state(i + 1		,							SCREAMING_SNAKE_CASE_		,							level - 1		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        current_list.pop()


def 						__a(SCREAMING_SNAKE_CASE_	:       list[list[int]]     ):



    '''simple docstring'''





    for i in total_list:
        print(*SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				4
       _SCREAMING_SNAKE_CASE									=				2
       _SCREAMING_SNAKE_CASE									=				generate_all_combinations(n, k)
       print_all_state(total_list)









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "gpt_bigcode"
 __lowerCamelCase    :				Optional[int]								=       ["past_key_values"]
 __lowerCamelCase    :				List[str]								=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__(						self      ,  _lowerCAmelCase=50257      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="gelu_pytorch_tanh"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   n_positions
     _lowerCAmelCase						      =   n_embd
     _lowerCAmelCase						      =   n_layer
     _lowerCAmelCase						      =   n_head
     _lowerCAmelCase						      =   n_inner
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   resid_pdrop
     _lowerCAmelCase						      =   embd_pdrop
     _lowerCAmelCase						      =   attn_pdrop
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scale_attn_weights
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   attention_softmax_in_fpaa
     _lowerCAmelCase						      =   scale_attention_softmax_in_fpaa
     _lowerCAmelCase						      =   multi_query

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)


