


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). "						,       a__						,       )
class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							RobertaConfig
       _a   							=							"roberta"



       def __init__(						self				:				Any		,				__a				:				Any							)					->Tuple:
          super().__init__(__a							)

          lowerCamelCase_						:   str		= 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. "						,       a__						,       )
class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							RobertaConfig
       _a   							=							"roberta"



       def __init__(						self				:				Optional[Any]		,				__a				:				Optional[int]							)					->List[str]:
          super().__init__(__a							)
          lowerCamelCase_						:   Any		= config.num_labels
          lowerCamelCase_						:   str		= config.num_hidden_layers

          lowerCamelCase_						:   Tuple		= DeeRobertaModel(__a							)
          lowerCamelCase_						:   Union[str, Any]		= nn.Dropout(config.hidden_dropout_prob							)
          lowerCamelCase_						:   Dict		= nn.Linear(config.hidden_size		,				self.config.num_labels							)




       @add_start_docstrings_to_model_forward(__a							)
       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				Tuple=None		,				__a				:				int=None		,				__a				:				Dict=None		,				__a				:				List[str]=None		,				__a				:				Dict=None		,				__a				:				List[str]=None		,				__a				:				str=None		,				__a				:				str=-1		,				__a				:				Tuple=False		,				)					->Optional[Any]:
          lowerCamelCase_						:   Tuple		= self.num_layers
          try:
             lowerCamelCase_						:   Any		= self.roberta(
                 __a		,				attention_mask=__a		,				token_type_ids=__a		,				position_ids=__a		,				head_mask=__a		,				inputs_embeds=__a		,				)

             lowerCamelCase_						:   List[str]		= outputs[1]

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

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

             # work with highway exits
             lowerCamelCase_						:   List[str]		= []
             for highway_exit in outputs[-1]:
                lowerCamelCase_						:   Dict		= 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
                   lowerCamelCase_						:   Union[str, Any]		= MSELoss()
                   lowerCamelCase_						:   List[str]		= loss_fct(highway_logits.view(-1							)		,				labels.view(-1							)							)
                else:
                   lowerCamelCase_						:   List[Any]		= CrossEntropyLoss()
                   lowerCamelCase_						:   List[Any]		= loss_fct(highway_logits.view(-1		,				self.num_labels							)		,				labels.view(-1							)							)
                highway_losses.append(__a							)

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

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









def    __lowerCamelCase   (      A__						: int						,						A__						: int					)     ->    int:
   return int((input_a, input_a).count(0					) == 0					)


def    __lowerCamelCase   (      )     ->    None:
   assert and_gate(0						,						0					) == 0
   assert and_gate(0						,						1					) == 0
   assert and_gate(1						,						0					) == 0
   assert and_gate(1						,						1					) == 1


if __name__ == "__main__":
      test_and_gate()
      print(and_gate(1, 0))
      print(and_gate(0, 0))
      print(and_gate(0, 1))
      print(and_gate(1, 1))





