import warnings

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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{
    '''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_       (		__A       ):
   __A	:		int         =				"xlnet"
   __A	:		List[Any]         =				["mems"]
   __A	:		List[str]         =				{
       "n_token": "vocab_size",  # Backward compatibility
       "hidden_size": "d_model",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(      self				:     Dict	,						lowercase_				:     List[Any]=3_20_00	,						lowercase_				:     Any=10_24	,						lowercase_				:     Dict=24	,						lowercase_				:     Any=16	,						lowercase_				:     List[Any]=40_96	,						lowercase_				:     Union[str, Any]="gelu"	,						lowercase_				:     Dict=True	,						lowercase_				:     Dict="bi"	,						lowercase_				:     Optional[int]=0.02	,						lowercase_				:     Optional[Any]=1E-12	,						lowercase_				:     Union[str, Any]=0.1	,						lowercase_				:     Tuple=5_12	,						lowercase_				:     Union[str, Any]=None	,						lowercase_				:     str=True	,						lowercase_				:     List[Any]=False	,						lowercase_				:     Union[str, Any]=False	,						lowercase_				:     List[str]=-1	,						lowercase_				:     Optional[int]=False	,						lowercase_				:     str="last"	,						lowercase_				:     List[Any]=True	,						lowercase_				:     Any="tanh"	,						lowercase_				:     Union[str, Any]=0.1	,						lowercase_				:     int=5	,						lowercase_				:     str=5	,						lowercase_				:     str=5	,						lowercase_				:     Optional[int]=1	,						lowercase_				:     Dict=2	,						**lowercase_				:     Union[str, Any]	,						)					->						int:
       lowercase__ :   Optional[int]    					=				vocab_size
       lowercase__ :   List[str]    					=				d_model
       lowercase__ :   Union[str, Any]    					=				n_layer
       lowercase__ :   Any    					=				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})'''  )
       lowercase__ :   List[Any]    					=				d_model // n_head
       lowercase__ :   List[str]    					=				ff_activation
       lowercase__ :   Optional[Any]    					=				d_inner
       lowercase__ :   int    					=				untie_r
       lowercase__ :   int    					=				attn_type

       lowercase__ :   Dict    					=				initializer_range
       lowercase__ :   List[str]    					=				layer_norm_eps

       lowercase__ :   List[Any]    					=				dropout
       lowercase__ :   Optional[Any]    					=				mem_len
       lowercase__ :   Any    					=				reuse_len
       lowercase__ :   List[str]    					=				bi_data
       lowercase__ :   str    					=				clamp_len
       lowercase__ :   Tuple    					=				same_length

       lowercase__ :   List[Any]    					=				summary_type
       lowercase__ :   str    					=				summary_use_proj
       lowercase__ :   List[str]    					=				summary_activation
       lowercase__ :   List[str]    					=				summary_last_dropout
       lowercase__ :   str    					=				start_n_top
       lowercase__ :   List[str]    					=				end_n_top

       lowercase__ :   List[Any]    					=				bos_token_id
       lowercase__ :   Tuple    					=				pad_token_id
       lowercase__ :   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_	,						)
           lowercase__ :   Tuple    					=				kwargs["use_cache"]

       lowercase__ :   str    					=				use_mems_eval
       lowercase__ :   List[str]    					=				use_mems_train
       super().__init__(pad_token_id=lowercase_	,						bos_token_id=lowercase_	,						eos_token_id=lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Union[str, Any]:
       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				:     Dict	,						lowercase_				:     List[Any]  )					->						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.'''  )







"""simple docstring"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





