

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). "  ,  _SCREAMING_SNAKE_CASE  ,  )
class       __A (							_SCREAMING_SNAKE_CASE       ):
    lowerCAmelCase_							:						Any						 =							RobertaConfig
    lowerCAmelCase_							:						Any						 =							"roberta"






    def __init__(		self	:			Dict			, UpperCAmelCase_	:			int			):
        super().__init__(_lowerCAmelCase			)

        lowerCAmelCase :	Dict				=					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. "  ,  _SCREAMING_SNAKE_CASE  ,  )
class       __A (							_SCREAMING_SNAKE_CASE       ):
    lowerCAmelCase_							:						List[Any]						 =							RobertaConfig
    lowerCAmelCase_							:						Optional[Any]						 =							"roberta"






    def __init__(		self	:			Tuple			, UpperCAmelCase_	:			Optional[int]			):
        super().__init__(_lowerCAmelCase			)
        lowerCAmelCase :	Optional[int]				=					config.num_labels
        lowerCAmelCase :	Optional[int]				=					config.num_hidden_layers

        lowerCAmelCase :	str				=					DeeRobertaModel(_lowerCAmelCase			)
        lowerCAmelCase :	Dict				=					nn.Dropout(config.hidden_dropout_prob			)
        lowerCAmelCase :	Tuple				=					nn.Linear(config.hidden_size			, self.config.num_labels			)






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

            lowerCAmelCase :	List[str]				=					outputs[1]

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

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

            # work with highway exits
            lowerCAmelCase :	int				=					[]
            for highway_exit in outputs[-1]:
                lowerCAmelCase :	Tuple				=					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
                    lowerCAmelCase :	Optional[int]				=					MSELoss()
                    lowerCAmelCase :	int				=					loss_fct(highway_logits.view(-1			)			, labels.view(-1			)			)
                else:
                    lowerCAmelCase :	Optional[int]				=					CrossEntropyLoss()
                    lowerCAmelCase :	Union[str, Any]				=					loss_fct(highway_logits.view(-1			, self.num_labels			)			, labels.view(-1			)			)
                highway_losses.append(_lowerCAmelCase			)

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

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











class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3