



import warnings

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


a__    =	logging.get_logger(__name__)

a__    =	{
    """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						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		"""xlnet"""
   snake_case_					: Any          =		["""mems"""]
   snake_case_					: List[Any]          =		{
       """n_token""": """vocab_size""",  # Backward compatibility
       """hidden_size""": """d_model""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Tuple=3_2000				,		lowerCAmelCase							:			int=1024				,		lowerCAmelCase							:			str=24				,		lowerCAmelCase							:			Dict=16				,		lowerCAmelCase							:			Tuple=4096				,		lowerCAmelCase							:			Dict="gelu"				,		lowerCAmelCase							:			Union[str, Any]=True				,		lowerCAmelCase							:			List[str]="bi"				,		lowerCAmelCase							:			Union[str, Any]=0.02				,		lowerCAmelCase							:			List[str]=1E-12				,		lowerCAmelCase							:			Dict=0.1				,		lowerCAmelCase							:			List[str]=512				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			Tuple=True				,		lowerCAmelCase							:			List[Any]=False				,		lowerCAmelCase							:			str=False				,		lowerCAmelCase							:			int=-1				,		lowerCAmelCase							:			Optional[int]=False				,		lowerCAmelCase							:			List[str]="last"				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			Optional[int]="tanh"				,		lowerCAmelCase							:			List[str]=0.1				,		lowerCAmelCase							:			Optional[Any]=5				,		lowerCAmelCase							:			Dict=5				,		lowerCAmelCase							:			Optional[int]=5				,		lowerCAmelCase							:			Optional[int]=1				,		lowerCAmelCase							:			Any=2				,		**lowerCAmelCase							:			str				,		)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					vocab_size
    _snake_case    :  Optional[Any]					=					d_model
    _snake_case    :  Tuple					=					n_layer
    _snake_case    :  int					=					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})''')
    _snake_case    :  Union[str, Any]					=					d_model // n_head
    _snake_case    :  List[Any]					=					ff_activation
    _snake_case    :  Optional[Any]					=					d_inner
    _snake_case    :  Optional[Any]					=					untie_r
    _snake_case    :  List[Any]					=					attn_type

    _snake_case    :  Union[str, Any]					=					initializer_range
    _snake_case    :  Optional[int]					=					layer_norm_eps

    _snake_case    :  str					=					dropout
    _snake_case    :  Dict					=					mem_len
    _snake_case    :  Optional[Any]					=					reuse_len
    _snake_case    :  Optional[Any]					=					bi_data
    _snake_case    :  List[Any]					=					clamp_len
    _snake_case    :  str					=					same_length

    _snake_case    :  List[str]					=					summary_type
    _snake_case    :  List[Any]					=					summary_use_proj
    _snake_case    :  Union[str, Any]					=					summary_activation
    _snake_case    :  Any					=					summary_last_dropout
    _snake_case    :  Optional[Any]					=					start_n_top
    _snake_case    :  Dict					=					end_n_top

    _snake_case    :  Union[str, Any]					=					bos_token_id
    _snake_case    :  Optional[Any]					=					pad_token_id
    _snake_case    :  List[str]					=					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				,		)
     _snake_case    :  str					=					kwargs["""use_cache"""]

    _snake_case    :  Any					=					use_mems_eval
    _snake_case    :  int					=					use_mems_train
    super().__init__(pad_token_id=lowerCAmelCase				,		bos_token_id=lowerCAmelCase				,		eos_token_id=lowerCAmelCase				,		**lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			int)				->				int:

    """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      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			Union[str, Any])				->				Optional[int]:

    """simple docstring"""







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





from math import ceil, sqrt



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000_000				)  ->     int:
 _snake_case    :  Tuple					=					0

 for outer_width in range(3				,       (limit // 4) + 2				):
  if outer_width**2 > limit:
   _snake_case    :  int					=					max(ceil(sqrt(outer_width**2 - limit				)				)				,       1				)
  else:
   _snake_case    :  Any					=					1
  if (outer_width - hole_width_lower_bound) % 2:
   hole_width_lower_bound += 1

  answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

 return answer


if __name__ == "__main__":
  print(F'''{solution() = }''')
