
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			None
     A__      =			None
     A__      =			None
     A__      =			None
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __init__(     self  ,     snake_case__=1  ,     snake_case__=0  ,     snake_case__=2  ,     snake_case__=512  ,     snake_case__="cls"  ,     snake_case__=False  ,     snake_case__=True  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(pad_token_id=snake_case__  ,     bos_token_id=snake_case__  ,     eos_token_id=snake_case__  ,     **snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = project_dim
       _SCREAMING_SNAKE_CASE   :				str							    = pooler_fn
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = learn_encoder
       _SCREAMING_SNAKE_CASE   :				Tuple							    = use_attention_mask
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			[r"""pooler""", r"""logit_scale"""]
     A__      =			[r"""position_ids""", r"""predictions.decoder.bias"""]
     A__      =			"""roberta"""
     A__      =			RobertaSeriesConfig
     def __init__(     self  ,     snake_case__				):




       """simple docstring"""


       super().__init__(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = XLMRobertaModel(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = nn.Linear(config.hidden_size  ,     config.project_dim				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(snake_case__  ,     "has_pre_transformation"  ,     snake_case__				)
       if self.has_pre_transformation:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = nn.Linear(config.hidden_size  ,     config.project_dim				)
         _SCREAMING_SNAKE_CASE   :				Any							    = nn.LayerNorm(config.hidden_size  ,     eps=config.layer_norm_eps				)
       self.post_init()




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = return_dict if return_dict is not None else self.config.use_return_dict

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.base_model(
           input_ids=snake_case__  ,     attention_mask=snake_case__  ,     token_type_ids=snake_case__  ,     position_ids=snake_case__  ,     head_mask=snake_case__  ,     inputs_embeds=snake_case__  ,     encoder_hidden_states=snake_case__  ,     encoder_attention_mask=snake_case__  ,     output_attentions=snake_case__  ,     output_hidden_states=True if self.has_pre_transformation else output_hidden_states  ,     return_dict=snake_case__  ,     )

       if self.has_pre_transformation:
         _SCREAMING_SNAKE_CASE   :				int							    = outputs["hidden_states"][-2]
         _SCREAMING_SNAKE_CASE   :				List[str]							    = self.pre_LN(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = self.transformation_pre(snake_case__				)

         return TransformationModelOutput(
             projection_state=snake_case__  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )
       else:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = self.transformation(outputs.last_hidden_state				)
         return TransformationModelOutput(
             projection_state=snake_case__  ,     last_hidden_state=outputs.last_hidden_state  ,     hidden_states=outputs.hidden_states  ,     attentions=outputs.attentions  ,     )





"""simple docstring"""
import operator as op





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  _SCREAMING_SNAKE_CASE   :				str							    = lambda lowerCamelCase__,						lowerCamelCase__				: int(x / y				)  # noqa: E731 integer division operation
  _SCREAMING_SNAKE_CASE   :				Any							    = {
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8				),						"Action".center(1_2				),						"Stack",						sep=" | "				)
  print("-" * (3_0 + len(lowerCamelCase__				))				)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(lowerCamelCase__				)  # append x to stack
      # output in tabular format
      print(x.rjust(8				),						("push(" + x + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)
    else:
      _SCREAMING_SNAKE_CASE   :				Dict							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      _SCREAMING_SNAKE_CASE   :				Any							    = stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8				),						("pop(" + a + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | "				)

      stack.append(
          str(opr[x](int(lowerCamelCase__				),						int(lowerCamelCase__				)				)				)				)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8				),						("push(" + a + x + b + ")").ljust(1_2				),						",".join(lowerCamelCase__				),						sep=" | ",						)

  return int(stack[0]				)


if __name__ == "__main__":
      lowercase_       :						int     					=	input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
      print('''\n\tResult = ''', solve(Postfix))



