




from __future__ import annotations

from math import pi

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      if (inductance, frequency, reactance).count(0					) != 1:
            raise ValueError('One and only one argument must be 0'					)
      if inductance < 0:
            raise ValueError('Inductance cannot be negative'					)
      if frequency < 0:
            raise ValueError('Frequency cannot be negative'					)
      if reactance < 0:
            raise ValueError('Inductive reactance cannot be negative'					)
      if inductance == 0:
            return {"inductance": reactance / (2 * pi * frequency)}
      elif frequency == 0:
            return {"frequency": reactance / (2 * pi * inductance)}
      elif reactance == 0:
            return {"reactance": 2 * pi * frequency * inductance}
      else:
            raise ValueError('Exactly one argument must be 0'					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()









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


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

lowerCamelCase    :		Tuple			   =       {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''transfo-xl'''
 UpperCamelCase						       =						['''mems''']
 UpperCamelCase						       =						{
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(    self :  Any						,							A_ :  Optional[Any]=267735						,							A_ :  Optional[Any]=[20000, 40000, 200000]						,							A_ :  Union[str, Any]=1024						,							A_ :  Optional[Any]=1024						,							A_ :  Optional[int]=16						,							A_ :  Any=64						,							A_ :  List[Any]=4096						,							A_ :  str=4						,							A_ :  int=False						,							A_ :  List[Any]=18						,							A_ :  Optional[int]=1600						,							A_ :  Union[str, Any]=1000						,							A_ :  Optional[Any]=True						,							A_ :  Optional[int]=True						,							A_ :  List[str]=0						,							A_ :  int=-1						,							A_ :  List[Any]=True						,							A_ :  List[Any]=0.1						,							A_ :  str=0.0						,							A_ :  Dict=True						,							A_ :  Dict="normal"						,							A_ :  Dict=0.01						,							A_ :  Optional[Any]=0.01						,							A_ :  Any=0.02						,							A_ :  int=1E-5						,							A_ :  List[str]=0						,							**A_ :  Optional[Any]						,							)       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   []
       self.cutoffs.extend(A_  )
       if proj_share_all_but_first:
             lowerCamelCase_							=   [False] + [True] * len(self.cutoffs  )
       else:
             lowerCamelCase_							=   [False] + [False] * len(self.cutoffs  )
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   d_embed
       lowerCamelCase_							=   d_head
       lowerCamelCase_							=   d_inner
       lowerCamelCase_							=   div_val
       lowerCamelCase_							=   pre_lnorm
       lowerCamelCase_							=   n_layer
       lowerCamelCase_							=   n_head
       lowerCamelCase_							=   mem_len
       lowerCamelCase_							=   same_length
       lowerCamelCase_							=   attn_type
       lowerCamelCase_							=   clamp_len
       lowerCamelCase_							=   sample_softmax
       lowerCamelCase_							=   adaptive
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   dropatt
       lowerCamelCase_							=   untie_r
       lowerCamelCase_							=   init
       lowerCamelCase_							=   init_range
       lowerCamelCase_							=   proj_init_std
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   layer_norm_epsilon
       super().__init__(eos_token_id=A_						,							**A_  )

 @property
 def     a__			(    self :  Tuple  )       ->     Any:

       """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 :  Dict						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






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



