




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


__A    	=			logging.get_logger(__name__)

__A    	=			{
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}



class 						lowercase_					(						UpperCamelCase__  ):
      UpperCamelCase_    :	int		   =       "opt"
      UpperCamelCase_    :	List[str]		   =       ["past_key_values"]



      def __init__(    self   :       Union[str, Any]    ,      A__   :       Any=50272    ,      A__   :       Optional[int]=768    ,      A__   :       List[str]=12    ,      A__   :       str=3072    ,      A__   :       str=2048    ,      A__   :       Optional[int]=True    ,      A__   :       List[str]=False    ,      A__   :       Union[str, Any]=None    ,      A__   :       int=0.1    ,      A__   :       Any=0.0    ,      A__   :       int=12    ,      A__   :       Optional[int]="relu"    ,      A__   :       Any=0.0    ,      A__   :       List[str]=0.02    ,      A__   :       Tuple=True    ,      A__   :       Any=1    ,      A__   :       int=2    ,      A__   :       Optional[int]=2    ,      A__   :       Any=True    ,      A__   :       int=True    ,      **A__   :       Any    ,      )					->  Any:
        super().__init__(
            pad_token_id=snake_case_    ,      bos_token_id=snake_case_    ,      eos_token_id=snake_case_    ,      **snake_case_    ,      )
        _snake_case		=							vocab_size
        _snake_case		=							max_position_embeddings
        _snake_case		=							num_attention_heads
        _snake_case		=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        _snake_case		=							ffn_dim
        _snake_case		=							hidden_size
        _snake_case		=							num_hidden_layers
        _snake_case		=							dropout
        _snake_case		=							attention_dropout
        _snake_case		=							activation_function
        _snake_case		=							init_std
        _snake_case		=							layerdrop
        _snake_case		=							use_cache
        _snake_case		=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        _snake_case		=							enable_bias
        _snake_case		=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        _snake_case		=							_remove_final_layer_norm




import numpy as np



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							np.exp(_UpperCamelCase  )

  # Add up the all the exponentials
  _snake_case		=							np.sum(_UpperCamelCase  )

  # Divide every exponent by the sum of all exponents
  _snake_case		=							exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

