


'''simple docstring'''







def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       Dict    							=							[]
				__magic_name__					:       Tuple    							=							[]
				__magic_name__					:       Any    							=							{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				__magic_name__					:       Optional[Any]    							=							len(_UpperCamelCase		) if (len(_UpperCamelCase		) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8		)  , "Stack".center(_UpperCamelCase		)  , "Postfix".center(_UpperCamelCase		)  , sep=" | "  , )
				print("-" * (print_width * 3 + 7)		)

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(_UpperCamelCase		)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(_UpperCamelCase		)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()		)  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(_UpperCamelCase		) == 0:
																stack.append(_UpperCamelCase		)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(_UpperCamelCase		) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()		)  # pop stack & add to Postfix
																stack.append(_UpperCamelCase		)  # push x to stack

								print(
								    x.center(8		)  , ("".join(_UpperCamelCase		)).ljust(_UpperCamelCase		)  , ("".join(_UpperCamelCase		)).ljust(_UpperCamelCase		)  , sep=" | "  , )  # Output in tabular format

				while len(_UpperCamelCase		) > 0:  # while stack is not empty
								post_fix.append(stack.pop()		)  # pop stack & add to Postfix
								print(
								    " ".center(8		)  , ("".join(_UpperCamelCase		)).ljust(_UpperCamelCase		)  , ("".join(_UpperCamelCase		)).ljust(_UpperCamelCase		)  , sep=" | "  , )  # Output in tabular format

				return "".join(_UpperCamelCase		)  # return Postfix as str



def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       Optional[int]    							=							list(infix[::-1]		)  # reverse the infix equation

				for i in range(len(_UpperCamelCase		)		):
								if infix[i] == "(":
												__magic_name__					:       Optional[int]    							=							")"  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__					:       Tuple    							=							"("  # change ")" to "("

				return (infix_2_postfix("".join(_UpperCamelCase		)		))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							: Any										=			input("\nEnter an Infix Equation = ")  # Input an Infix equation
		_SCREAMING_SNAKE_CASE							: Optional[int]										=			''''''.join(Infix.split())  # Remove spaces from the input
		print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")





'''simple docstring'''





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy

@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       Dict       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          super().__init__(_SCREAMING_SNAKE_CASE       )

          _snake_case				= RobertaEmbeddings(_SCREAMING_SNAKE_CASE       )
          self.init_weights()







@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          super().__init__(_SCREAMING_SNAKE_CASE       )
          _snake_case				= config.num_labels
          _snake_case				= config.num_hidden_layers

          _snake_case				= DeeRobertaModel(_SCREAMING_SNAKE_CASE       )
          _snake_case				= nn.Dropout(config.hidden_dropout_prob       )
          _snake_case				= nn.Linear(config.hidden_size   ,self.config.num_labels       )






     @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE       )
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=-1   ,_SCREAMING_SNAKE_CASE=False   ,)				->     List[str]:
          _snake_case				= self.num_layers
          try:
               _snake_case				= self.roberta(
                   _SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE   ,token_type_ids=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,head_mask=_SCREAMING_SNAKE_CASE   ,inputs_embeds=_SCREAMING_SNAKE_CASE   ,)

               _snake_case				= outputs[1]

               _snake_case				= self.dropout(_SCREAMING_SNAKE_CASE       )
               _snake_case				= self.classifier(_SCREAMING_SNAKE_CASE       )
               _snake_case				= (logits,) + outputs[2:]  # add hidden states and attention if they are here
          except HighwayException as e:
               _snake_case				= e.message
               _snake_case				= e.exit_layer
               _snake_case				= outputs[0]

          if not self.training:
               _snake_case				= entropy(_SCREAMING_SNAKE_CASE       )
               _snake_case				= []
               _snake_case				= []
          if labels is not None:
               if self.num_labels == 1:
                    #  We are doing regression
                    _snake_case				= MSELoss()
                    _snake_case				= loss_fct(logits.view(-1       )   ,labels.view(-1       )       )
               else:
                    _snake_case				= CrossEntropyLoss()
                    _snake_case				= loss_fct(logits.view(-1   ,self.num_labels       )   ,labels.view(-1       )       )

               # work with highway exits
               _snake_case				= []
               for highway_exit in outputs[-1]:
                    _snake_case				= highway_exit[0]
                    if not self.training:
                         highway_logits_all.append(_SCREAMING_SNAKE_CASE       )
                         highway_entropy.append(highway_exit[2]       )
                    if self.num_labels == 1:
                         #  We are doing regression
                         _snake_case				= MSELoss()
                         _snake_case				= loss_fct(highway_logits.view(-1       )   ,labels.view(-1       )       )
                    else:
                         _snake_case				= CrossEntropyLoss()
                         _snake_case				= loss_fct(highway_logits.view(-1   ,self.num_labels       )   ,labels.view(-1       )       )
                    highway_losses.append(_SCREAMING_SNAKE_CASE       )

               if train_highway:
                    _snake_case				= (sum(highway_losses[:-1]       ),) + outputs
                    # exclude the final highway, of course
               else:
                    _snake_case				= (loss,) + outputs
          if not self.training:
               _snake_case				= outputs + ((original_entropy, highway_entropy), exit_layer)
               if output_layer >= 0:
                    _snake_case				= (
                        (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                    )  # use the highway of the last layer

          return outputs  # (loss), logits, (hidden_states), (attentions), entropy


