





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


__UpperCAmelCase			:  Optional[int]										=				logging.get_logger(__name__)

__UpperCAmelCase			:  Dict										=				{
    "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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""opt"""
       lowerCAmelCase__ 						=	["""past_key_values"""]




       def __init__(    self					:       Tuple				,							A					:       Dict=50_272				,							A					:       List[str]=768				,							A					:       int=12				,							A					:       int=3_072				,							A					:       Optional[Any]=2_048				,							A					:       Tuple=True				,							A					:       Optional[int]=False				,							A					:       Tuple=None				,							A					:       List[Any]=0.1				,							A					:       Tuple=0.0				,							A					:       List[str]=12				,							A					:       Any="relu"				,							A					:       Tuple=0.0				,							A					:       List[str]=0.02				,							A					:       int=True				,							A					:       Tuple=1				,							A					:       Union[str, Any]=2				,							A					:       Dict=2				,							A					:       Optional[int]=True				,							A					:       Tuple=True				,							**A					:       Any				,							):
         super().__init__(
             pad_token_id=A				,							bos_token_id=A				,							eos_token_id=A				,							**A				,							)
         __snake_case:		List[str]					    =		vocab_size
         __snake_case:		Optional[int]					    =		max_position_embeddings
         __snake_case:		List[str]					    =		num_attention_heads
         __snake_case:		Union[str, Any]					    =		word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
         __snake_case:		Union[str, Any]					    =		ffn_dim
         __snake_case:		Optional[int]					    =		hidden_size
         __snake_case:		Optional[int]					    =		num_hidden_layers
         __snake_case:		Optional[Any]					    =		dropout
         __snake_case:		Optional[int]					    =		attention_dropout
         __snake_case:		Optional[Any]					    =		activation_function
         __snake_case:		Any					    =		init_std
         __snake_case:		int					    =		layerdrop
         __snake_case:		int					    =		use_cache
         __snake_case:		Any					    =		do_layer_norm_before
         # We keep these variables at `True` for backward compatibility.
         __snake_case:		Any					    =		enable_bias
         __snake_case:		List[str]					    =		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:		Optional[int]					    =		_remove_final_layer_norm













from functools import lru_cache







@lru_cache
def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  if not isinstance(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__):
    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__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		str					    =		len(SCREAMING_SNAKE_CASE__)
  # Find m such that F_m >= n where F_i is the i_th fibonacci number.
  __snake_case:		Optional[int]					    =		0
  while True:
    if fibonacci(SCREAMING_SNAKE_CASE__) >= len_list:
      __snake_case:		str					    =		i
      break
    i += 1
  __snake_case:		Tuple					    =		0
  while fibb_k > 0:
    __snake_case:		str					    =		min(
        offset + fibonacci(fibb_k - 1)   ,   len_list - 1)  # Prevent out of range
    __snake_case:		List[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()






