
"""simple docstring"""



def    _UpperCAmelCase (					__lowerCamelCase  :			Any						)	->   list[list[int]]:
  _snake_case   	=			[]
  if len(_SCREAMING_SNAKE_CASE						) == 1:
    return [nums.copy()]
  for _ in range(len(_SCREAMING_SNAKE_CASE						)						):
    _snake_case   	=			nums.pop(0						)
    _snake_case   	=			permute(_SCREAMING_SNAKE_CASE						)
    for perm in permutations:
      perm.append(_SCREAMING_SNAKE_CASE						)
    result.extend(_SCREAMING_SNAKE_CASE						)
    nums.append(_SCREAMING_SNAKE_CASE						)
  return result
def    _UpperCAmelCase (					__lowerCamelCase  :			int						)	->   Optional[Any]:




  def backtrack(__lowerCamelCase  :			Dict						):
    if start == len(_SCREAMING_SNAKE_CASE						) - 1:
      output.append(nums[:]						)
    else:
      for i in range(_SCREAMING_SNAKE_CASE					,   len(_SCREAMING_SNAKE_CASE						)						):
        _snake_case	,   _snake_case   	=			nums[i], nums[start]
        backtrack(start + 1						)
        _snake_case	,   _snake_case   	=			nums[i], nums[start]  # backtrack

  _snake_case   	=			[]
  backtrack(0						)
  return output


if __name__ == "__main__":
 import doctest

 # use res to print the data in permute2 function
 UpperCAmelCase__        =					permutea([1, 2, 3])
 print(res)
 doctest.testmod()










from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _A     =							vocab_size
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							hidden_act
         _A     =							intermediate_size
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							max_relative_position
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							layer_norm_eps
         _A     =							classifier_dropout
         _A     =							use_cache





