

"""simple docstring"""




import warnings

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


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

_SCREAMING_SNAKE_CASE					:  Any             =      {
    '''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 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


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






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Optional[int]=3_2000     ,       __lowerCamelCase						:		str=1024     ,       __lowerCamelCase						:		List[Any]=24     ,       __lowerCamelCase						:		str=16     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		int="gelu"     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		str="bi"     ,       __lowerCamelCase						:		int=0.02     ,       __lowerCamelCase						:		str=1e-12     ,       __lowerCamelCase						:		Optional[Any]=0.1     ,       __lowerCamelCase						:		Optional[int]=512     ,       __lowerCamelCase						:		Dict=None     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		Tuple=False     ,       __lowerCamelCase						:		List[str]=False     ,       __lowerCamelCase						:		Tuple=-1     ,       __lowerCamelCase						:		Optional[int]=False     ,       __lowerCamelCase						:		str="last"     ,       __lowerCamelCase						:		str=True     ,       __lowerCamelCase						:		int="tanh"     ,       __lowerCamelCase						:		Union[str, Any]=0.1     ,       __lowerCamelCase						:		List[str]=5     ,       __lowerCamelCase						:		Union[str, Any]=5     ,       __lowerCamelCase						:		Optional[Any]=5     ,       __lowerCamelCase						:		str=1     ,       __lowerCamelCase						:		List[str]=2     ,       **__lowerCamelCase						:		List[Any]     ,       )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   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})''' )
       SCREAMING_SNAKE_CASE__   =   d_model // n_head
       SCREAMING_SNAKE_CASE__   =   ff_activation
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   attn_type

       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   reuse_len
       SCREAMING_SNAKE_CASE__   =   bi_data
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   same_length

       SCREAMING_SNAKE_CASE__   =   summary_type
       SCREAMING_SNAKE_CASE__   =   summary_use_proj
       SCREAMING_SNAKE_CASE__   =   summary_activation
       SCREAMING_SNAKE_CASE__   =   summary_last_dropout
       SCREAMING_SNAKE_CASE__   =   start_n_top
       SCREAMING_SNAKE_CASE__   =   end_n_top

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   pad_token_id
       SCREAMING_SNAKE_CASE__   =   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.'''     ,       __lowerCamelCase     ,       )
            SCREAMING_SNAKE_CASE__   =   kwargs['''use_cache''']

       SCREAMING_SNAKE_CASE__   =   use_mems_eval
       SCREAMING_SNAKE_CASE__   =   use_mems_train
       super().__init__(pad_token_id=__lowerCamelCase     ,       bos_token_id=__lowerCamelCase     ,       eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:
       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 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		int )		->							Optional[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


