





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). '       ,   __snake_case       ,   )
class 					A__   (       __snake_case			):
      _UpperCAmelCase	:str						    =   RobertaConfig
      _UpperCAmelCase	:Any						    =   'roberta'





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   RobertaEmbeddings(A_   )
       self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '       ,   __snake_case       ,   )
class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   RobertaConfig
      _UpperCAmelCase	:List[Any]						    =   'roberta'





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )
       UpperCamelCase	:					Any       	=   config.num_labels
       UpperCamelCase	:					int       	=   config.num_hidden_layers

       UpperCamelCase	:					Union[str, Any]       	=   DeeRobertaModel(A_   )
       UpperCamelCase	:					str       	=   nn.Dropout(config.hidden_dropout_prob   )
       UpperCamelCase	:					List[str]       	=   nn.Linear(config.hidden_size       ,							self.config.num_labels   )





      @add_start_docstrings_to_model_forward(A_   )
      def        __UpperCamelCase( self       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=-1       ,							A_=False       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.num_layers
       try:
        UpperCamelCase	:					Any       	=   self.roberta(
            A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							position_ids=A_       ,							head_mask=A_       ,							inputs_embeds=A_       ,							)

        UpperCamelCase	:					Optional[int]       	=   outputs[1]

        UpperCamelCase	:					Tuple       	=   self.dropout(A_   )
        UpperCamelCase	:					str       	=   self.classifier(A_   )
        UpperCamelCase	:					Union[str, Any]       	=   (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
        UpperCamelCase	:					Optional[Any]       	=   e.message
        UpperCamelCase	:					Dict       	=   e.exit_layer
        UpperCamelCase	:					Optional[Any]       	=   outputs[0]

       if not self.training:
        UpperCamelCase	:					Optional[int]       	=   entropy(A_   )
        UpperCamelCase	:					str       	=   []
        UpperCamelCase	:					Dict       	=   []
       if labels is not None:
        if self.num_labels == 1:
         #  We are doing regression
         UpperCamelCase	:					List[Any]       	=   MSELoss()
         UpperCamelCase	:					Any       	=   loss_fct(logits.view(-1   )       ,							labels.view(-1   )   )
        else:
         UpperCamelCase	:					List[str]       	=   CrossEntropyLoss()
         UpperCamelCase	:					Tuple       	=   loss_fct(logits.view(-1       ,							self.num_labels   )       ,							labels.view(-1   )   )

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

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

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







import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
