




"""simple docstring"""




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Dict:



       """simple docstring"""

       A__							=   torch.exp(lowercase_						)
       A__							=   torch.sum(lowercase_			,    dim=1						)  # sum of exp(x_i)
       A__							=   torch.sum(x * exp_x			,    dim=1						)  # sum of x_i * exp(x_i)
       return torch.log(lowercase_						) - B / A


class     UpperCamelCase_   (			nn.Module					):

    '''simple docstring'''






    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any])						->int:





           '''simple docstring'''

           super().__init__()
           A__							=   config.output_attentions
           A__							=   config.output_hidden_states
           A__							=   nn.ModuleList([BertLayer(UpperCAmelCase__) for _ in range(config.num_hidden_layers)])
           A__							=   nn.ModuleList([BertHighway(UpperCAmelCase__) for _ in range(config.num_hidden_layers)])

           A__							=   [-1 for _ in range(config.num_hidden_layers)]


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str)						->List[Any]:





           '''simple docstring'''

           if (type(UpperCAmelCase__) is float) or (type(UpperCAmelCase__) is int):
                  for i in range(len(self.early_exit_entropy)):
                         A__							=   x
           else:
                  A__							=   x


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[int])						->int:





           '''simple docstring'''

           A__							=   pooler.state_dict()
           for highway in self.highway:
                  for name, param in highway.pooler.state_dict().items():
                         param.copy_(loaded_model[name])





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Optional[Any]=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			)						->List[str]:





           '''simple docstring'''

           A__							=   ()
           A__							=   ()
           A__							=   ()
           for i, layer_module in enumerate(self.layer):
                  if self.output_hidden_states:
                         A__							=   all_hidden_states + (hidden_states,)

                  A__							=   layer_module(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			head_mask[i] ,			UpperCAmelCase__ ,			UpperCAmelCase__)
                  A__							=   layer_outputs[0]

                  if self.output_attentions:
                         A__							=   all_attentions + (layer_outputs[1],)

                  A__							=   (hidden_states,)
                  if self.output_hidden_states:
                         A__							=   current_outputs + (all_hidden_states,)
                  if self.output_attentions:
                         A__							=   current_outputs + (all_attentions,)

                  A__							=   self.highway[i](UpperCAmelCase__)
                  # logits, pooled_output

                  if not self.training:
                         A__							=   highway_exit[0]
                         A__							=   entropy(UpperCAmelCase__)
                         A__							=   highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                         A__							=   all_highway_exits + (highway_exit,)

                         if highway_entropy < self.early_exit_entropy[i]:
                                A__							=   (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                                raise HighwayException(UpperCAmelCase__ ,			i + 1)
                  else:
                         A__							=   all_highway_exits + (highway_exit,)

        # Add last layer
           if self.output_hidden_states:
                  A__							=   all_hidden_states + (hidden_states,)

           A__							=   (hidden_states,)
           if self.output_hidden_states:
                  A__							=   outputs + (all_hidden_states,)
           if self.output_attentions:
                  A__							=   outputs + (all_attentions,)

           A__							=   outputs + (all_highway_exits,)
           return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits


@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''		, UpperCAmelCase__		, )
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Optional[int])						->Optional[Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__)
           A__							=   config

           A__							=   BertEmbeddings(UpperCAmelCase__)
           A__							=   DeeBertEncoder(UpperCAmelCase__)
           A__							=   BertPooler(UpperCAmelCase__)

           self.init_weights()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           self.encoder.init_highway_pooler(self.pooler)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->int:





           '''simple docstring'''

           return self.embeddings.word_embeddings


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->int:





           '''simple docstring'''

           A__							=   value


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Tuple)						->List[Any]:





           '''simple docstring'''

           for layer, heads in heads_to_prune.items():
                  self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase__)





    @add_start_docstrings_to_model_forward(UpperCAmelCase__)
    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List[Any]=None ,			UpperCAmelCase__				:     List[str]=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Tuple=None ,			UpperCAmelCase__				:     List[str]=None ,			UpperCAmelCase__				:     Optional[int]=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Tuple=None ,			)						->str:





           '''simple docstring'''

           if input_ids is not None and inputs_embeds is not None:
                  raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''')
           elif input_ids is not None:
                  A__							=   input_ids.size()
           elif inputs_embeds is not None:
                  A__							=   inputs_embeds.size()[:-1]
           else:
                  raise ValueError('''You have to specify either input_ids or inputs_embeds''')

           A__							=   input_ids.device if input_ids is not None else inputs_embeds.device

           if attention_mask is None:
                  A__							=   torch.ones(UpperCAmelCase__ ,			device=UpperCAmelCase__)
           if encoder_attention_mask is None:
                  A__							=   torch.ones(UpperCAmelCase__ ,			device=UpperCAmelCase__)
           if token_type_ids is None:
                  A__							=   torch.zeros(UpperCAmelCase__ ,			dtype=torch.long ,			device=UpperCAmelCase__)

           # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
           # ourselves in which case we just need to make it broadcastable to all heads.
           A__							=   self.get_extended_attention_mask(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

           # If a 2D ou 3D attention mask is provided for the cross-attention
           # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
           if encoder_attention_mask.dim() == 3:
                  A__							=   encoder_attention_mask[:, None, :, :]
           if encoder_attention_mask.dim() == 2:
                  A__							=   encoder_attention_mask[:, None, None, :]

           A__							=   encoder_extended_attention_mask.to(
               dtype=next(self.parameters()).dtype)  # fp16 compatibility
           A__							=   (1.0 - encoder_extended_attention_mask) * -10000.0

           # Prepare head mask if needed
           # 1.0 in head_mask indicate we keep the head
           # attention_probs has shape bsz x n_heads x N x N
           # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
           # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
           A__							=   self.get_head_mask(UpperCAmelCase__ ,			self.config.num_hidden_layers)

           A__							=   self.embeddings(
               input_ids=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			inputs_embeds=UpperCAmelCase__)
           A__							=   self.encoder(
               UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			head_mask=UpperCAmelCase__ ,			encoder_hidden_states=UpperCAmelCase__ ,			encoder_attention_mask=UpperCAmelCase__ ,			)
           A__							=   encoder_outputs[0]
           A__							=   self.pooler(UpperCAmelCase__)

           A__							=   (
               sequence_output,
               pooled_output,
           ) + encoder_outputs[
               1:
           ]  # add hidden_states and attentions if they are here
           return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     int)						->Tuple:





           '''simple docstring'''

           A__							=   message
           A__							=   exit_layer  # start from 1!


class     UpperCamelCase_   (			nn.Module					):

    '''simple docstring'''






    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->Any:





           '''simple docstring'''

           super().__init__()
           A__							=   BertPooler(UpperCAmelCase__)
           A__							=   nn.Dropout(config.hidden_dropout_prob)
           A__							=   nn.Linear(config.hidden_size ,			config.num_labels)





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           A__							=   encoder_outputs[0]
           A__							=   self.pooler(UpperCAmelCase__)
           # "return" pooler_output

           # BertModel
           A__							=   (pooler_input, pooler_output) + encoder_outputs[1:]
           # "return" bmodel_output

           # Dropout and classification
           A__							=   bmodel_output[1]

           A__							=   self.dropout(UpperCAmelCase__)
           A__							=   self.classifier(UpperCAmelCase__)

           return logits, pooled_output


@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''		, UpperCAmelCase__		, )
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     List[str])						->Tuple:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__)
           A__							=   config.num_labels
           A__							=   config.num_hidden_layers

           A__							=   DeeBertModel(UpperCAmelCase__)
           A__							=   nn.Dropout(config.hidden_dropout_prob)
           A__							=   nn.Linear(config.hidden_size ,			self.config.num_labels)

           self.init_weights()





    @add_start_docstrings_to_model_forward(UpperCAmelCase__)
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[Any]=None ,			UpperCAmelCase__				:     int=None ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[int]=None ,			UpperCAmelCase__				:     Optional[Any]=None ,			UpperCAmelCase__				:     int=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Tuple=-1 ,			UpperCAmelCase__				:     Any=False ,			)						->str:





           '''simple docstring'''

           A__							=   self.num_layers
           try:
                  A__							=   self.bert(
                      UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__ ,			token_type_ids=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			head_mask=UpperCAmelCase__ ,			inputs_embeds=UpperCAmelCase__ ,			)
                  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

                  A__							=   outputs[1]

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

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

                  # work with highway exits
                  A__							=   []
                  for highway_exit in outputs[-1]:
                         A__							=   highway_exit[0]
                         if not self.training:
                                highway_logits_all.append(UpperCAmelCase__)
                                highway_entropy.append(highway_exit[2])
                         if self.num_labels == 1:
                                #  We are doing regression
                                A__							=   MSELoss()
                                A__							=   loss_fct(highway_logits.view(-1) ,			labels.view(-1))
                         else:
                                A__							=   CrossEntropyLoss()
                                A__							=   loss_fct(highway_logits.view(-1 ,			self.num_labels) ,			labels.view(-1))
                         highway_losses.append(UpperCAmelCase__)

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

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








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

           for dest in self.connections[node]:
                  current_probability += self.connections[node][dest]
                  if current_probability > random_value:
                         return dest
           return ""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(lowercase_			,    lowercase_			,    lowercase_						)

       A__							=   Counter(graph.get_nodes()						)
       A__							=   start

       for _ in range(lowercase_						):
              A__							=   graph.transition(lowercase_						)
              visited[node] += 1

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


