





"""simple docstring"""


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


lowerCamelCase__      =      logging.get_logger(__name__)

lowerCamelCase__      =      {
    "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    __SCREAMING_SNAKE_CASE			(  _lowerCAmelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    "opt"
  SCREAMING_SNAKE_CASE__							:int		   =    ["past_key_values"]

  def __init__(						self      :				Optional[Any]    ,      __a      :				Optional[Any]=5_0272    ,      __a      :				List[str]=768    ,      __a      :				Optional[int]=12    ,      __a      :				Tuple=3072    ,      __a      :				Any=2048    ,      __a      :				Dict=True    ,      __a      :				List[str]=False    ,      __a      :				List[str]=None    ,      __a      :				Optional[int]=0.1    ,      __a      :				int=0.0    ,      __a      :				List[Any]=12    ,      __a      :				Any="relu"    ,      __a      :				Tuple=0.0    ,      __a      :				Optional[int]=0.02    ,      __a      :				str=True    ,      __a      :				Union[str, Any]=1    ,      __a      :				Optional[int]=2    ,      __a      :				Optional[int]=2    ,      __a      :				Any=True    ,      __a      :				Tuple=True    ,      **__a      :				Optional[Any]    ,      )						-> Tuple:
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,      bos_token_id=lowerCAmelCase__    ,      eos_token_id=lowerCAmelCase__    ,      **lowerCAmelCase__    ,      )
        _UpperCamelCase					:		Optional[int]  =				vocab_size
        _UpperCamelCase					:		List[str]  =				max_position_embeddings
        _UpperCamelCase					:		Optional[int]  =				num_attention_heads
        _UpperCamelCase					:		List[str]  =				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        _UpperCamelCase					:		List[Any]  =				ffn_dim
        _UpperCamelCase					:		List[str]  =				hidden_size
        _UpperCamelCase					:		int  =				num_hidden_layers
        _UpperCamelCase					:		Tuple  =				dropout
        _UpperCamelCase					:		int  =				attention_dropout
        _UpperCamelCase					:		Dict  =				activation_function
        _UpperCamelCase					:		Optional[int]  =				init_std
        _UpperCamelCase					:		List[Any]  =				layerdrop
        _UpperCamelCase					:		Union[str, Any]  =				use_cache
        _UpperCamelCase					:		Optional[Any]  =				do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        _UpperCamelCase					:		Dict  =				enable_bias
        _UpperCamelCase					:		Dict  =				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
        _UpperCamelCase					:		List[str]  =				_remove_final_layer_norm







"""simple docstring"""




from functools import lru_cache





@lru_cache
def        a_				(							_lowercase   ):
						if not isinstance(_lowercase       ,						_lowercase   ):
												raise TypeError('''k must be an integer.'''   )
						if k < 0:
												raise ValueError('''k integer must be greater or equal to zero.'''   )
						if k == 0:
												return 0
						elif k == 1:
												return 1
						else:
												return fibonacci(k - 1   ) + fibonacci(k - 2   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Any     =    len(_lowercase   )
						# Find m such that F_m >= n where F_i is the i_th fibonacci number.
						_UpperCamelCase			:					Optional[int]     =    0
						while True:
												if fibonacci(_lowercase   ) >= len_list:
																		_UpperCamelCase			:					List[Any]     =    i
																		break
												i += 1
						_UpperCamelCase			:					Tuple     =    0
						while fibb_k > 0:
												_UpperCamelCase			:					Union[str, Any]     =    min(
												    offset + fibonacci(fibb_k - 1   )       ,						len_list - 1   )  # Prevent out of range
												_UpperCamelCase			:					Any     =    arr[index_k]
												if item_k_1 == val:
																		return index_k
												elif val < item_k_1:
																		fibb_k -= 1
												elif val > item_k_1:
																		offset += fibonacci(fibb_k - 1   )
																		fibb_k -= 2
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



