


"""simple docstring"""






import warnings

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


_lowerCAmelCase							:    str					   =	logging.get_logger(__name__)

_lowerCAmelCase							:    Optional[int]					   =	{
    '''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 							A_      (       __lowerCamelCase ):
  lowerCAmelCase__      			=	'xlnet'
  lowerCAmelCase__      			=	['mems']
  lowerCAmelCase__      			=	{
      '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:						Union[str, Any]=32_000							,__lowerCAmelCase:						List[str]=1_024							,__lowerCAmelCase:						Tuple=24							,__lowerCAmelCase:						Optional[Any]=16							,__lowerCAmelCase:						Optional[int]=4_096							,__lowerCAmelCase:						int="gelu"							,__lowerCAmelCase:						str=True							,__lowerCAmelCase:						Dict="bi"							,__lowerCAmelCase:						Union[str, Any]=0.02							,__lowerCAmelCase:						Union[str, Any]=1e-12							,__lowerCAmelCase:						Union[str, Any]=0.1							,__lowerCAmelCase:						Optional[int]=512							,__lowerCAmelCase:						Optional[Any]=None							,__lowerCAmelCase:						int=True							,__lowerCAmelCase:						Optional[int]=False							,__lowerCAmelCase:						Dict=False							,__lowerCAmelCase:						Dict=-1							,__lowerCAmelCase:						Any=False							,__lowerCAmelCase:						Any="last"							,__lowerCAmelCase:						Dict=True							,__lowerCAmelCase:						Optional[Any]="tanh"							,__lowerCAmelCase:						Tuple=0.1							,__lowerCAmelCase:						List[str]=5							,__lowerCAmelCase:						Optional[Any]=5							,__lowerCAmelCase:						List[str]=5							,__lowerCAmelCase:						int=1							,__lowerCAmelCase:						List[Any]=2							,**__lowerCAmelCase:						Union[str, Any]							,):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     vocab_size
     _lowerCamelCase   :				Dict							=     d_model
     _lowerCamelCase   :				Optional[Any]							=     n_layer
     _lowerCamelCase   :				List[str]							=     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})"""    )
     _lowerCamelCase   :				Dict							=     d_model // n_head
     _lowerCamelCase   :				Dict							=     ff_activation
     _lowerCamelCase   :				int							=     d_inner
     _lowerCamelCase   :				Union[str, Any]							=     untie_r
     _lowerCamelCase   :				Optional[Any]							=     attn_type

     _lowerCamelCase   :				Tuple							=     initializer_range
     _lowerCamelCase   :				Optional[int]							=     layer_norm_eps

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

     _lowerCamelCase   :				List[Any]							=     summary_type
     _lowerCamelCase   :				Tuple							=     summary_use_proj
     _lowerCamelCase   :				int							=     summary_activation
     _lowerCamelCase   :				str							=     summary_last_dropout
     _lowerCamelCase   :				Tuple							=     start_n_top
     _lowerCamelCase   :				str							=     end_n_top

     _lowerCamelCase   :				int							=     bos_token_id
     _lowerCamelCase   :				str							=     pad_token_id
     _lowerCamelCase   :				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__							,)
        _lowerCamelCase   :				str							=     kwargs["use_cache"]

     _lowerCamelCase   :				Any							=     use_mems_eval
     _lowerCamelCase   :				Union[str, Any]							=     use_mems_train
     super().__init__(pad_token_id=lowerCamelCase__							,bos_token_id=lowerCamelCase__							,eos_token_id=lowerCamelCase__							,**lowerCamelCase__    )


  @property
  def 							_lowercase					(			self:						List[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 							_lowercase					(			self:						int							,__lowerCAmelCase:						Tuple    ):







     '''simple docstring'''







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


__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

      A_							       =		Stack()
      A_							       =		Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            elif i == ")":
                  # RULE 4
                  A_							       =		operator_stack.peek()
                  operator_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()

                  A_							       =		operators[opr](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





