



import warnings

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


__lowerCamelCase         =   logging.get_logger(__name__)

__lowerCamelCase         =   {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}


class   snake_case_ (snake_case__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""xlnet"""
       _lowerCamelCase			    =				["""mems"""]
       _lowerCamelCase			    =				{
           """n_token""": """vocab_size""",  # Backward compatibility
           """hidden_size""": """d_model""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }



       def __init__(     self     ,lowercase=32000     ,lowercase=1024     ,lowercase=24     ,lowercase=16     ,lowercase=4096     ,lowercase="gelu"     ,lowercase=True     ,lowercase="bi"     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=0.1     ,lowercase=512     ,lowercase=None     ,lowercase=True     ,lowercase=False     ,lowercase=False     ,lowercase=-1     ,lowercase=False     ,lowercase="last"     ,lowercase=True     ,lowercase="tanh"     ,lowercase=0.1     ,lowercase=5     ,lowercase=5     ,lowercase=5     ,lowercase=1     ,lowercase=2     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		vocab_size
             UpperCAmelCase_  :	Any				      =		d_model
             UpperCAmelCase_  :	Tuple				      =		n_layer
             UpperCAmelCase_  :	Dict				      =		n_head
             if d_model % n_head != 0:
                   raise ValueError(F"""\'d_model % n_head\' ({d_model % n_head}) should be equal to 0""")
             if "d_head" in kwargs:
                   if kwargs["d_head"] != d_model // n_head:
                         raise ValueError(
                             F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})""")
             UpperCAmelCase_  :	Any				      =		d_model // n_head
             UpperCAmelCase_  :	Union[str, Any]				      =		ff_activation
             UpperCAmelCase_  :	Dict				      =		d_inner
             UpperCAmelCase_  :	Optional[int]				      =		untie_r
             UpperCAmelCase_  :	Any				      =		attn_type

             UpperCAmelCase_  :	Tuple				      =		initializer_range
             UpperCAmelCase_  :	List[Any]				      =		layer_norm_eps

             UpperCAmelCase_  :	str				      =		dropout
             UpperCAmelCase_  :	Tuple				      =		mem_len
             UpperCAmelCase_  :	Optional[Any]				      =		reuse_len
             UpperCAmelCase_  :	Any				      =		bi_data
             UpperCAmelCase_  :	Optional[int]				      =		clamp_len
             UpperCAmelCase_  :	int				      =		same_length

             UpperCAmelCase_  :	int				      =		summary_type
             UpperCAmelCase_  :	Tuple				      =		summary_use_proj
             UpperCAmelCase_  :	Dict				      =		summary_activation
             UpperCAmelCase_  :	Optional[int]				      =		summary_last_dropout
             UpperCAmelCase_  :	str				      =		start_n_top
             UpperCAmelCase_  :	List[str]				      =		end_n_top

             UpperCAmelCase_  :	Union[str, Any]				      =		bos_token_id
             UpperCAmelCase_  :	List[Any]				      =		pad_token_id
             UpperCAmelCase_  :	Any				      =		eos_token_id

             if "use_cache" in kwargs:
                   warnings.warn(
                       "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
                       " instead."     ,lowercase_     ,)
                   UpperCAmelCase_  :	Optional[int]				      =		kwargs["use_cache"]

             UpperCAmelCase_  :	Dict				      =		use_mems_eval
             UpperCAmelCase_  :	Optional[int]				      =		use_mems_train
             super().__init__(pad_token_id=lowercase_     ,bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_)



       @property
       def  A_   (     self):

             """simple docstring"""





             logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
             return -1



       @max_position_embeddings.setter
       def  A_   (     self     ,lowercase):

             """simple docstring"""





             raise NotImplementedError(
                 F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
