
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       _lowerCAmelCase  (		lowerCAmelCase__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[str]       						=RobertaConfig
 SCREAMING_SNAKE_CASE_				:					List[str]       						="roberta"

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






      """simple docstring"""



      super().__init__(_lowerCamelCase   )

      UpperCamelCase						    =					RobertaEmbeddings(_lowerCamelCase   )
      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. "			,	lowerCAmelCase__			,	)
class       _lowerCAmelCase  (		lowerCAmelCase__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[str]       						=RobertaConfig
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						="roberta"

 def __init__(						self			:			str					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      super().__init__(_lowerCamelCase   )
      UpperCamelCase						    =					config.num_labels
      UpperCamelCase						    =					config.num_hidden_layers

      UpperCamelCase						    =					DeeRobertaModel(_lowerCamelCase   )
      UpperCamelCase						    =					nn.Dropout(config.hidden_dropout_prob   )
      UpperCamelCase						    =					nn.Linear(config.hidden_size					, self.config.num_labels   )






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






      """simple docstring"""



      UpperCamelCase						    =					self.num_layers
      try:
           UpperCamelCase						    =					self.roberta(
               _lowerCamelCase					, attention_mask=_lowerCamelCase					, token_type_ids=_lowerCamelCase					, position_ids=_lowerCamelCase					, head_mask=_lowerCamelCase					, inputs_embeds=_lowerCamelCase					, )

           UpperCamelCase						    =					outputs[1]

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

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

           # work with highway exits
           UpperCamelCase						    =					[]
           for highway_exit in outputs[-1]:
                UpperCamelCase						    =					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
                     UpperCamelCase						    =					MSELoss()
                     UpperCamelCase						    =					loss_fct(highway_logits.view(-1   )					, labels.view(-1   )   )
                else:
                     UpperCamelCase						    =					CrossEntropyLoss()
                     UpperCamelCase						    =					loss_fct(highway_logits.view(-1					, self.num_labels   )					, labels.view(-1   )   )
                highway_losses.append(_lowerCamelCase   )

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

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





"""simple docstring"""
def 					a__						(						)	-> Union[str, Any]:
      UpperCAmelCase__			:							Dict							=    []
      UpperCAmelCase__			:							Tuple							=    1

      while len(lowerCAmelCase    ) < 1E6:
            constant.append(str(lowerCAmelCase    )    )
            i += 1

      UpperCAmelCase__			:							Any							=    """""".join(lowerCAmelCase    )

      return (
          int(constant[0]    )
          * int(constant[9]    )
          * int(constant[99]    )
          * int(constant[9_99]    )
          * int(constant[99_99]    )
          * int(constant[9_99_99]    )
          * int(constant[99_99_99]    )
      )


if __name__ == "__main__":
    print(solution())

