



'''simple docstring'''
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). """						,					lowercase						,					)
class 							lowercase__	(							lowercase   ):
	lowercase__  				=			RobertaConfig
	lowercase__  				=			"""roberta"""





	def __init__( self					:				str ,lowerCamelCase__					:				Any ):







							'''simple docstring'''
							super().__init__(lowerCamelCase__ )

							_UpperCamelCase					:		List[str] 				=   RobertaEmbeddings(lowerCamelCase__ )
							self.init_weights()






@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """						,					lowercase						,					)
class 							lowercase__	(							lowercase   ):
	lowercase__  				=			RobertaConfig
	lowercase__  				=			"""roberta"""





	def __init__( self					:				Optional[Any] ,lowerCamelCase__					:				Any ):







							'''simple docstring'''
							super().__init__(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   config.num_labels
							_UpperCamelCase					:		List[str] 				=   config.num_hidden_layers

							_UpperCamelCase					:		int 				=   DeeRobertaModel(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   nn.Dropout(config.hidden_dropout_prob )
							_UpperCamelCase					:		Optional[Any] 				=   nn.Linear(config.hidden_size ,self.config.num_labels )





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







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   self.num_layers
							try:
													_UpperCamelCase					:		Tuple 				=   self.roberta(
													    lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,token_type_ids=lowerCamelCase__ ,position_ids=lowerCamelCase__ ,head_mask=lowerCamelCase__ ,inputs_embeds=lowerCamelCase__ ,)

													_UpperCamelCase					:		Tuple 				=   outputs[1]

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

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

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

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






'''simple docstring'''

class   __lowerCAmelCase						:  # Public class to implement a graph





      '''simple docstring'''






      def __init__(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= row
             lowercase__      			= col
             lowercase__      			= graph



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowercase__      			= [-1, 0, 1, -1, 1, -1, 0, 1]
             lowercase__      			= True  # Make those cells visited
             for k in range(8 ):
                    if self.is_safe(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase ):
                           self.diffs(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Dict ):  # And finally, count all islands.

             '''simple docstring'''







             lowercase__      			= [[False for j in range(self.COL )] for i in range(self.ROW )]
             lowercase__      			= 0
             for i in range(self.ROW ):
                    for j in range(self.COL ):
                           if visited[i][j] is False and self.graph[i][j] == 1:
                                  self.diffs(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                                  count += 1
             return count




