



"""simple docstring"""







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


UpperCamelCase     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     "gpt_bigcode"
			A__	:					Any							  =     ["past_key_values"]
			A__	:					Dict							  =     {
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50257						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__="gelu_pytorch_tanh"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=50256						,	SCREAMING_SNAKE_CASE__=50256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				A__											=					vocab_size
				A__											=					n_positions
				A__											=					n_embd
				A__											=					n_layer
				A__											=					n_head
				A__											=					n_inner
				A__											=					activation_function
				A__											=					resid_pdrop
				A__											=					embd_pdrop
				A__											=					attn_pdrop
				A__											=					layer_norm_epsilon
				A__											=					initializer_range
				A__											=					scale_attn_weights
				A__											=					use_cache
				A__											=					attention_softmax_in_fpaa
				A__											=					scale_attention_softmax_in_fpaa
				A__											=					multi_query

				A__											=					bos_token_id
				A__											=					eos_token_id

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









'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






