



"""simple docstring"""

import warnings

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


lowercase__    :  Optional[Any]							    =	logging.get_logger(__name__)

lowercase__    :  Union[str, 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       (			lowerCAmelCase__):
 _lowerCAmelCase   :			int							=		"""xlnet"""
 _lowerCAmelCase   :			Union[str, Any]							=		["""mems"""]
 _lowerCAmelCase   :			str							=		{
     """n_token""": """vocab_size""",  # Backward compatibility
     """hidden_size""": """d_model""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }

 def __init__(     self		:       Tuple  ,     lowercase_		:       Optional[int]=32000  ,     lowercase_		:       Optional[int]=1024  ,     lowercase_		:       List[str]=24  ,     lowercase_		:       Tuple=16  ,     lowercase_		:       Tuple=4096  ,     lowercase_		:       Tuple="gelu"  ,     lowercase_		:       Dict=True  ,     lowercase_		:       Union[str, Any]="bi"  ,     lowercase_		:       List[Any]=0.02  ,     lowercase_		:       str=1E-12  ,     lowercase_		:       str=0.1  ,     lowercase_		:       int=512  ,     lowercase_		:       Union[str, Any]=None  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       str=False  ,     lowercase_		:       Tuple=False  ,     lowercase_		:       Optional[Any]=-1  ,     lowercase_		:       List[Any]=False  ,     lowercase_		:       int="last"  ,     lowercase_		:       Tuple=True  ,     lowercase_		:       Dict="tanh"  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Optional[Any]=5  ,     lowercase_		:       Union[str, Any]=5  ,     lowercase_		:       Optional[Any]=5  ,     lowercase_		:       Any=1  ,     lowercase_		:       int=2  ,     **lowercase_		:       Optional[Any]  ,     ):
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     int				=	d_model
     snake_case_ :     Dict				=	n_layer
     snake_case_ :     List[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})"				)
     snake_case_ :     List[Any]				=	d_model // n_head
     snake_case_ :     Any				=	ff_activation
     snake_case_ :     List[str]				=	d_inner
     snake_case_ :     Tuple				=	untie_r
     snake_case_ :     Any				=	attn_type

     snake_case_ :     str				=	initializer_range
     snake_case_ :     List[Any]				=	layer_norm_eps

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

     snake_case_ :     int				=	summary_type
     snake_case_ :     str				=	summary_use_proj
     snake_case_ :     List[str]				=	summary_activation
     snake_case_ :     Dict				=	summary_last_dropout
     snake_case_ :     Optional[Any]				=	start_n_top
     snake_case_ :     Dict				=	end_n_top

     snake_case_ :     Any				=	bos_token_id
     snake_case_ :     Tuple				=	pad_token_id
     snake_case_ :     Union[str, 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_  ,     )
         snake_case_ :     Optional[int]				=	kwargs['''use_cache''']

     snake_case_ :     int				=	use_mems_eval
     snake_case_ :     str				=	use_mems_train
     super().__init__(pad_token_id=lowercase_  ,     bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)

 @property
 def 						_snake_case					(     self		:       List[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 						_snake_case					(     self		:       Any  ,     lowercase_		:       Dict				):
     # 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"""

from __future__ import annotations




def __lowercase			(							_a		,			_a		,			_a		,			):
    if (stress, tangential_force, area).count(0    ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''    )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''    )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''    )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''    )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





