






"""simple docstring"""


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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= torch.exp(__UpperCamelCase )
  __A									= torch.sum(__UpperCamelCase		,       dim=1 )  # sum of exp(x_i)
  __A									= torch.sum(x * exp_x		,       dim=1 )  # sum of x_i * exp(x_i)
  return torch.log(__UpperCamelCase ) - B / A


class 					snake_case		(							nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     super().__init__()
     __A									= config.output_attentions
     __A									= config.output_hidden_states
     __A									= nn.ModuleList([BertLayer(_lowerCamelCase  ) for _ in range(config.num_hidden_layers  )]  )
     __A									= nn.ModuleList([BertHighway(_lowerCamelCase  ) for _ in range(config.num_hidden_layers  )]  )

     __A									= [-1 for _ in range(config.num_hidden_layers  )]


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     if (type(_lowerCamelCase  ) is float) or (type(_lowerCamelCase  ) is int):
       for i in range(len(self.early_exit_entropy  )  ):
         __A									= x
     else:
       __A									= x


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A									= pooler.state_dict()
     for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
         param.copy_(loaded_model[name]  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Optional[int]=None,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[int]=None,       ):

     '''simple docstring'''
     __A									= ()
     __A									= ()
     __A									= ()
     for i, layer_module in enumerate(self.layer  ):
       if self.output_hidden_states:
         __A									= all_hidden_states + (hidden_states,)

       __A									= layer_module(
           _lowerCamelCase,       _lowerCamelCase,       head_mask[i],       _lowerCamelCase,       _lowerCamelCase  )
       __A									= layer_outputs[0]

       if self.output_attentions:
         __A									= all_attentions + (layer_outputs[1],)

       __A									= (hidden_states,)
       if self.output_hidden_states:
         __A									= current_outputs + (all_hidden_states,)
       if self.output_attentions:
         __A									= current_outputs + (all_attentions,)

       __A									= self.highway[i](_lowerCamelCase  )
       # logits, pooled_output

       if not self.training:
         __A									= highway_exit[0]
         __A									= entropy(_lowerCamelCase  )
         __A									= highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
         __A									= all_highway_exits + (highway_exit,)

         if highway_entropy < self.early_exit_entropy[i]:
           __A									= (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
           raise HighwayException(_lowerCamelCase,       i + 1  )
       else:
         __A									= all_highway_exits + (highway_exit,)

        # Add last layer
     if self.output_hidden_states:
       __A									= all_hidden_states + (hidden_states,)

     __A									= (hidden_states,)
     if self.output_hidden_states:
       __A									= outputs + (all_hidden_states,)
     if self.output_attentions:
       __A									= outputs + (all_attentions,)

     __A									= outputs + (all_highway_exits,)
     return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "   ,	_lowerCAmelCase   ,	)
class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Tuple,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     super().__init__(_lowerCamelCase  )
     __A									= config

     __A									= BertEmbeddings(_lowerCamelCase  )
     __A									= DeeBertEncoder(_lowerCamelCase  )
     __A									= BertPooler(_lowerCamelCase  )

     self.init_weights()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     self.encoder.init_highway_pooler(self.pooler  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return self.embeddings.word_embeddings


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     Tuple  ):

     '''simple docstring'''
     __A									= value


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     for layer, heads in heads_to_prune.items():
       self.encoder.layer[layer].attention.prune_heads(_lowerCamelCase  )






   @add_start_docstrings_to_model_forward(_lowerCamelCase  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]=None,       _lowerCamelCase :     List[str]=None,       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     str=None,       _lowerCamelCase :     int=None,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[int]=None,       _lowerCamelCase :     Optional[Any]=None,       ):

     '''simple docstring'''
     if input_ids is not None and inputs_embeds is not None:
       raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''  )
     elif input_ids is not None:
       __A									= input_ids.size()
     elif inputs_embeds is not None:
       __A									= inputs_embeds.size()[:-1]
     else:
       raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

     __A									= input_ids.device if input_ids is not None else inputs_embeds.device

     if attention_mask is None:
       __A									= torch.ones(_lowerCamelCase,       device=_lowerCamelCase  )
     if encoder_attention_mask is None:
       __A									= torch.ones(_lowerCamelCase,       device=_lowerCamelCase  )
     if token_type_ids is None:
       __A									= torch.zeros(_lowerCamelCase,       dtype=torch.long,       device=_lowerCamelCase  )

     # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
     # ourselves in which case we just need to make it broadcastable to all heads.
     __A									= self.get_extended_attention_mask(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase  )

     # If a 2D ou 3D attention mask is provided for the cross-attention
     # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
     if encoder_attention_mask.dim() == 3:
       __A									= encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.dim() == 2:
       __A									= encoder_attention_mask[:, None, None, :]

     __A									= encoder_extended_attention_mask.to(
         dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
     __A									= (1.0 - encoder_extended_attention_mask) * -1_00_00.0

     # Prepare head mask if needed
     # 1.0 in head_mask indicate we keep the head
     # attention_probs has shape bsz x n_heads x N x N
     # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
     # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
     __A									= self.get_head_mask(_lowerCamelCase,       self.config.num_hidden_layers  )

     __A									= self.embeddings(
         input_ids=_lowerCamelCase,       position_ids=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       inputs_embeds=_lowerCamelCase  )
     __A									= self.encoder(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       head_mask=_lowerCamelCase,       encoder_hidden_states=_lowerCamelCase,       encoder_attention_mask=_lowerCamelCase,       )
     __A									= encoder_outputs[0]
     __A									= self.pooler(_lowerCamelCase  )

     __A									= (
         sequence_output,
         pooled_output,
     ) + encoder_outputs[
         1:
     ]  # add hidden_states and attentions if they are here
     return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     List[str],       _lowerCamelCase :     Tuple  ):

     '''simple docstring'''
     __A									= message
     __A									= exit_layer  # start from 1!





class 					snake_case		(							nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     super().__init__()
     __A									= BertPooler(_lowerCamelCase  )
     __A									= nn.Dropout(config.hidden_dropout_prob  )
     __A									= nn.Linear(config.hidden_size,       config.num_labels  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     # Pooler
     __A									= encoder_outputs[0]
     __A									= self.pooler(_lowerCamelCase  )
     # "return" pooler_output

     # BertModel
     __A									= (pooler_input, pooler_output) + encoder_outputs[1:]
     # "return" bmodel_output

     # Dropout and classification
     __A									= bmodel_output[1]

     __A									= self.dropout(_lowerCamelCase  )
     __A									= self.classifier(_lowerCamelCase  )

     return logits, pooled_output





@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "   ,	_lowerCAmelCase   ,	)
class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Union[str, Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     super().__init__(_lowerCamelCase  )
     __A									= config.num_labels
     __A									= config.num_hidden_layers

     __A									= DeeBertModel(_lowerCamelCase  )
     __A									= nn.Dropout(config.hidden_dropout_prob  )
     __A									= nn.Linear(config.hidden_size,       self.config.num_labels  )

     self.init_weights()






   @add_start_docstrings_to_model_forward(_lowerCamelCase  )
   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Optional[int]=None,       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Optional[Any]=-1,       _lowerCamelCase :     int=False,       ):

     '''simple docstring'''
     __A									= self.num_layers
     try:
       __A									= self.bert(
           _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       position_ids=_lowerCamelCase,       head_mask=_lowerCamelCase,       inputs_embeds=_lowerCamelCase,       )
       # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

       __A									= outputs[1]

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

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

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

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

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












"""simple docstring"""


from __future__ import annotations

from typing import Any



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not postfix_notation:
    return 0

  __A									= {'''+''', '''-''', '''*''', '''/'''}
  __A									= []

  for token in postfix_notation:
    if token in operations:
      __A			,    __A									= stack.pop(), stack.pop()
      if token == "+":
        stack.append(a + b )
      elif token == "-":
        stack.append(a - b )
      elif token == "*":
        stack.append(a * b )
      else:
        if a * b < 0 and a % b != 0:
          stack.append(a // b + 1 )
        else:
          stack.append(a // b )
    else:
      stack.append(int(__UpperCamelCase ) )

  return stack.pop()


if __name__ == "__main__":
       import doctest

       doctest.testmod()




