
import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__magic_name__            =  logging.getLogger(__name__)




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	lowerCamelCase=None ):
          snake_case__						=				self.layer[current_layer](lowerCamelCase		,	lowerCamelCase		,	head_mask[current_layer] )

          snake_case__						=				layer_outputs[0]

          return hidden_states





@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'       ,					__UpperCamelCase       ,					)
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__(lowerCamelCase )

          snake_case__						=				BertEncoderWithPabee(lowerCamelCase )

          self.init_weights()
          snake_case__						=				0
          snake_case__						=				0
          snake_case__						=				0

          snake_case__						=				0
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				threshold
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				patience
      def A_			(			self ):
          snake_case__						=				0
          snake_case__						=				0
      def A_			(			self ):
          snake_case__						=				self.inference_layers_num / self.inference_instances_num
          snake_case__						=				(
              F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
              F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
          )
          print(lowerCamelCase )



      @add_start_docstrings_to_model_forward(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=False		,	):

          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:
              snake_case__						=				input_ids.size()
          elif inputs_embeds is not None:
              snake_case__						=				inputs_embeds.size()[:-1]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds" )

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

          if attention_mask is None:
              snake_case__						=				torch.ones(lowerCamelCase		,	device=lowerCamelCase )
          if token_type_ids is None:
              snake_case__						=				torch.zeros(lowerCamelCase		,	dtype=torch.long		,	device=lowerCamelCase )

          # 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.
          snake_case__						=				self.get_extended_attention_mask(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )

          # 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 self.config.is_decoder and encoder_hidden_states is not None:
              snake_case__						,					snake_case__						,					snake_case__						=				encoder_hidden_states.size()
              snake_case__						=				(encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                  snake_case__						=				torch.ones(lowerCamelCase		,	device=lowerCamelCase )
              snake_case__						=				self.invert_attention_mask(lowerCamelCase )
          else:
              snake_case__						=				None

          # 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]
          snake_case__						=				self.get_head_mask(lowerCamelCase		,	self.config.num_hidden_layers )

          snake_case__						=				self.embeddings(
              input_ids=lowerCamelCase		,	position_ids=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	inputs_embeds=lowerCamelCase )
          snake_case__						=				embedding_output

          if self.training:
              snake_case__						=				[]
              for i in range(self.config.num_hidden_layers ):
                  snake_case__						=				self.encoder.adaptive_forward(
                      lowerCamelCase		,	current_layer=lowerCamelCase		,	attention_mask=lowerCamelCase		,	head_mask=lowerCamelCase )

                  snake_case__						=				self.pooler(lowerCamelCase )
                  snake_case__						=				output_layers[i](output_dropout(lowerCamelCase ) )
                  res.append(lowerCamelCase )
          elif self.patience == 0:  # Use all layers for inference
              snake_case__						=				self.encoder(
                  lowerCamelCase		,	attention_mask=lowerCamelCase		,	head_mask=lowerCamelCase		,	encoder_hidden_states=lowerCamelCase		,	encoder_attention_mask=lowerCamelCase		,	)
              snake_case__						=				self.pooler(encoder_outputs[0] )
              snake_case__						=				[output_layers[self.config.num_hidden_layers - 1](lowerCamelCase )]
          else:
              snake_case__						=				0
              snake_case__						=				None
              snake_case__						=				0
              for i in range(self.config.num_hidden_layers ):
                  calculated_layer_num += 1
                  snake_case__						=				self.encoder.adaptive_forward(
                      lowerCamelCase		,	current_layer=lowerCamelCase		,	attention_mask=lowerCamelCase		,	head_mask=lowerCamelCase )

                  snake_case__						=				self.pooler(lowerCamelCase )
                  snake_case__						=				output_layers[i](lowerCamelCase )
                  if regression:
                      snake_case__						=				logits.detach()
                      if patient_result is not None:
                          snake_case__						=				patient_result.detach()
                      if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
                          patient_counter += 1
                      else:
                          snake_case__						=				0
                  else:
                      snake_case__						=				logits.detach().argmax(dim=1 )
                      if patient_result is not None:
                          snake_case__						=				patient_result.detach().argmax(dim=1 )
                      if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase ) ):
                          patient_counter += 1
                      else:
                          snake_case__						=				0

                  snake_case__						=				logits
                  if patient_counter == self.patience:
                      break
              snake_case__						=				[patient_result]
              self.inference_layers_num += calculated_layer_num
              self.inference_instances_num += 1

          return res





@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '       ,					__UpperCamelCase       ,					)
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase ):
          super().__init__(lowerCamelCase )
          snake_case__						=				config.num_labels

          snake_case__						=				BertModelWithPabee(lowerCamelCase )
          snake_case__						=				nn.Dropout(config.hidden_dropout_prob )
          snake_case__						=				nn.ModuleList(
              [nn.Linear(config.hidden_size		,	self.config.num_labels ) for _ in range(config.num_hidden_layers )] )

          self.init_weights()



      @add_start_docstrings_to_model_forward(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase=None		,	):
          snake_case__						=				self.bert(
              input_ids=lowerCamelCase		,	attention_mask=lowerCamelCase		,	token_type_ids=lowerCamelCase		,	position_ids=lowerCamelCase		,	head_mask=lowerCamelCase		,	inputs_embeds=lowerCamelCase		,	output_dropout=self.dropout		,	output_layers=self.classifiers		,	regression=self.num_labels == 1		,	)

          snake_case__						=				(logits[-1],)

          if labels is not None:
              snake_case__						=				None
              snake_case__						=				0
              for ix, logits_item in enumerate(lowerCamelCase ):
                  if self.num_labels == 1:
                      #  We are doing regression
                      snake_case__						=				MSELoss()
                      snake_case__						=				loss_fct(logits_item.view(-1 )		,	labels.view(-1 ) )
                  else:
                      snake_case__						=				CrossEntropyLoss()
                      snake_case__						=				loss_fct(logits_item.view(-1		,	self.num_labels )		,	labels.view(-1 ) )
                  if total_loss is None:
                      snake_case__						=				loss
                  else:
                      total_loss += loss * (ix + 1)
                  total_weights += ix + 1
              snake_case__						=				(total_loss / total_weights,) + outputs

          return outputs





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[False] * len(__lowerCAmelCase							)
    snake_case__						=				[-1] * len(__lowerCAmelCase							)

    def dfs(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				True
        snake_case__						=				c
        for u in graph[v]:
            if not visited[u]:
                dfs(__lowerCAmelCase    ,		1 - c							)

    for i in range(len(__lowerCAmelCase							)							):
        if not visited[i]:
            dfs(__lowerCAmelCase    ,		0							)

    for i in range(len(__lowerCAmelCase							)							):
        for j in graph[i]:
            if color[i] == color[j]:
                return False

    return True


# Adjacency list of graph
__magic_name__            =  {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))



