





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


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)

UpperCAmelCase						:   str			  =     {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Tuple					    =	'''gpt_bigcode'''
				UpperCamelCase    :  str					    =	['''past_key_values''']
				UpperCamelCase    :  List[str]					    =	{
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}



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

									__A	:						Union[str, Any]       =				bos_token_id
									__A	:						str       =				eos_token_id

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











from __future__ import annotations







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					if not nums:
										return 0
					__A	:						Optional[int]       =				nums[0]
					__A	:						str       =				0
					for num in nums[1:]:
										__A    ,				__A	:						Tuple       =				(
										    max_excluding + num,
										    max(a					,						a  ),
										)
					return max(a					,						a  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




