'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCamelCase__  :						Optional[Any]					=      TypeVar('T')







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   return (position - 1) // 2







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   return (2 * position) + 1







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   return (2 * position) + 2


class 					_lowercase						(   Generic[T] ):
      '''simple docstring'''


      def __init__(					self				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					list[tuple[T, int]] 					=	[]
         UpperCAmelCase__				:					dict[T, int] 					=	{}
         UpperCAmelCase__				:					int 					=	0




      def __len__(					self				)      ->      int:



         '''simple docstring'''



         return self.elements




      def __repr__(					self				)      ->      str:



         '''simple docstring'''



         return str(self.heap				)




      def 		lowerCAmelCase__       (					self				)      ->      bool:



         '''simple docstring'''



         return self.elements == 0




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         self.heap.append((elem, weight)				)
         UpperCAmelCase__				:					Optional[Any] 					=	self.elements
         self.elements += 1
         self._bubble_up(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      T:



         '''simple docstring'''



         if self.elements > 1:
            self._swap_nodes(0       ,self.elements - 1				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					Any 					=	self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
            UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	self.heap[0]
            self._bubble_down(lowerCamelCase_				)
         return elem




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.position_map[elem]
         UpperCAmelCase__				:					Dict 					=	(elem, weight)
         if position > 0:
            UpperCAmelCase__				:					Union[str, Any] 					=	get_parent_position(lowerCamelCase_				)
            UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	self.heap[parent_position]
            if parent_weight > weight:
               self._bubble_up(lowerCamelCase_				)
            else:
               self._bubble_down(lowerCamelCase_				)
         else:
            self._bubble_down(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.position_map[elem]
         if curr_pos == 0:
            return None
         UpperCAmelCase__				:					Optional[Any] 					=	get_parent_position(lowerCamelCase_				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	self.heap[curr_pos]
         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[int] 					=	self.heap[parent_position]
         if parent_weight > weight:
            self._swap_nodes(lowerCamelCase_       ,lowerCamelCase_				)
            return self._bubble_up(lowerCamelCase_				)
         return None




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.position_map[elem]
         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[int] 					=	self.heap[curr_pos]
         UpperCAmelCase__				:					int 					=	get_child_left_position(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	get_child_right_position(lowerCamelCase_				)
         if child_left_position < self.elements and child_right_position < self.elements:
            UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[int] 					=	self.heap[child_left_position]
            UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	self.heap[child_right_position]
            if child_right_weight < child_left_weight and child_right_weight < weight:
               self._swap_nodes(lowerCamelCase_       ,lowerCamelCase_				)
               return self._bubble_down(lowerCamelCase_				)
         if child_left_position < self.elements:
            UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[int] 					=	self.heap[child_left_position]
            if child_left_weight < weight:
               self._swap_nodes(lowerCamelCase_       ,lowerCamelCase_				)
               return self._bubble_down(lowerCamelCase_				)
         else:
            return None
         if child_right_position < self.elements:
            UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	self.heap[child_right_position]
            if child_right_weight < weight:
               self._swap_nodes(lowerCamelCase_       ,lowerCamelCase_				)
               return self._bubble_down(lowerCamelCase_				)
         return None




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.heap[nodea_pos][0]
         UpperCAmelCase__				:					Union[str, Any] 					=	self.heap[nodea_pos][0]
         UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         UpperCAmelCase__				:					str 					=	nodea_pos
         UpperCAmelCase__				:					Union[str, Any] 					=	nodea_pos




class 					_lowercase						(   Generic[T] ):
      '''simple docstring'''


      def __init__(					self				)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					dict[T, dict[T, int]] 					=	{}
         UpperCAmelCase__				:					int 					=	0




      def __repr__(					self				)      ->      str:



         '''simple docstring'''



         return str(self.connections				)




      def __len__(					self				)      ->      int:



         '''simple docstring'''



         return self.nodes




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         if node not in self.connections:
            UpperCAmelCase__				:					Tuple 					=	{}
            self.nodes += 1




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      None:



         '''simple docstring'''



         self.add_node(lowerCamelCase_				)
         self.add_node(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	weight
         UpperCAmelCase__				:					str 					=	weight







def __UpperCamelCase( _A       :					GraphUndirectedWeighted[T]	,      ):
   '''simple docstring'''
   UpperCAmelCase__				:					dict[T, int] 					=	{node: maxsize for node in graph.connections}
   UpperCAmelCase__				:					dict[T, T | None] 					=	{node: None for node in graph.connections}

   UpperCAmelCase__				:					MinPriorityQueue[T] 					=	MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_A	,      _A     )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCAmelCase__				:					Union[str, Any] 					=	priority_queue.extract_min()
   UpperCAmelCase__				:					Optional[Any] 					=	0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCAmelCase__				:					List[str] 					=	dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_A	,      dist[neighbour]     )
         UpperCAmelCase__				:					List[Any] 					=	node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCAmelCase__				:					List[str] 					=	priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCAmelCase__				:					Any 					=	dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_A	,      dist[neighbour]     )
            UpperCAmelCase__				:					List[Any] 					=	node
   return dist, parent





'''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). '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= RobertaConfig
      UpperCAmelCase_   :   Tuple   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	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. '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= RobertaConfig
      UpperCAmelCase_   :   Optional[Any]   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	config.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	config.num_hidden_layers

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




      @add_start_docstrings_to_model_forward(lowerCamelCase_				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=-1       ,lowerCamelCase_=False       ,)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_layers
         try:
            UpperCAmelCase__				:					Union[str, Any] 					=	self.roberta(
                lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,position_ids=lowerCamelCase_       ,head_mask=lowerCamelCase_       ,inputs_embeds=lowerCamelCase_       ,)

            UpperCAmelCase__				:					List[Any] 					=	outputs[1]

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

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

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

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




