


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,
)


SCREAMING_SNAKE_CASE__					:     Dict       =     logging.getLogger(__name__)


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def        __snake_case(  self							,					A_							,					A_							,					A_=None							,					A_=None    ):
       _UpperCAmelCase		:							Any							=     self.layer[current_layer](A_							,					A_							,					head_mask[current_layer]    )

       _UpperCAmelCase		:							Optional[int]							=     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.'''							,	A							,	)
class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_    ):
       super().__init__(A_    )

       _UpperCAmelCase		:							Tuple							=     BertEncoderWithPabee(A_    )

       self.init_weights()
       _UpperCAmelCase		:							Union[str, Any]							=     0
       _UpperCAmelCase		:							Dict							=     0
       _UpperCAmelCase		:							str							=     0

       _UpperCAmelCase		:							Tuple							=     0


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Optional[int]							=     threshold


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Optional[int]							=     patience


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     0
       _UpperCAmelCase		:							Dict							=     0


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.inference_layers_num / self.inference_instances_num
       _UpperCAmelCase		:							int							=     (
           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(A_    )




     @add_start_docstrings_to_model_forward(A_    )
     def        __snake_case(  self							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=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:
         _UpperCAmelCase		:							Tuple							=     input_ids.size()
       elif inputs_embeds is not None:
         _UpperCAmelCase		:							str							=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

       _UpperCAmelCase		:							Any							=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _UpperCAmelCase		:							Dict							=     torch.ones(A_							,					device=A_    )
       if token_type_ids is None:
         _UpperCAmelCase		:							Optional[Any]							=     torch.zeros(A_							,					dtype=torch.long							,					device=A_    )

       # 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.
       _UpperCAmelCase		:							torch.Tensor							=     self.get_extended_attention_mask(A_							,					A_							,					A_    )

       # 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:
         _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							Optional[int]							=     encoder_hidden_states.size()
         _UpperCAmelCase		:							Optional[int]							=     (encoder_batch_size, encoder_sequence_length)
         if encoder_attention_mask is None:
           _UpperCAmelCase		:							str							=     torch.ones(A_							,					device=A_    )
         _UpperCAmelCase		:							int							=     self.invert_attention_mask(A_    )
       else:
         _UpperCAmelCase		:							Tuple							=     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]
       _UpperCAmelCase		:							Optional[int]							=     self.get_head_mask(A_							,					self.config.num_hidden_layers    )

       _UpperCAmelCase		:							str							=     self.embeddings(
           input_ids=A_							,					position_ids=A_							,					token_type_ids=A_							,					inputs_embeds=A_    )
       _UpperCAmelCase		:							Optional[Any]							=     embedding_output

       if self.training:
         _UpperCAmelCase		:							List[str]							=     []
         for i in range(self.config.num_hidden_layers    ):
           _UpperCAmelCase		:							str							=     self.encoder.adaptive_forward(
               A_							,					current_layer=A_							,					attention_mask=A_							,					head_mask=A_    )

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

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

           _UpperCAmelCase		:							str							=     logits
           if patient_counter == self.patience:
             break
         _UpperCAmelCase		:							str							=     [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
    the pooled output) e.g. for GLUE tasks. '''							,	A							,	)
class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_    ):
       super().__init__(A_    )
       _UpperCAmelCase		:							Optional[Any]							=     config.num_labels

       _UpperCAmelCase		:							Any							=     BertModelWithPabee(A_    )
       _UpperCAmelCase		:							Dict							=     nn.Dropout(config.hidden_dropout_prob    )
       _UpperCAmelCase		:							List[Any]							=     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(A_    )
     def        __snake_case(  self							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					A_=None							,					):
       _UpperCAmelCase		:							Optional[int]							=     self.bert(
           input_ids=A_							,					attention_mask=A_							,					token_type_ids=A_							,					position_ids=A_							,					head_mask=A_							,					inputs_embeds=A_							,					output_dropout=self.dropout							,					output_layers=self.classifiers							,					regression=self.num_labels == 1							,					)

       _UpperCAmelCase		:							List[Any]							=     (logits[-1],)

       if labels is not None:
         _UpperCAmelCase		:							int							=     None
         _UpperCAmelCase		:							Optional[int]							=     0
         for ix, logits_item in enumerate(A_    ):
           if self.num_labels == 1:
             #  We are doing regression
             _UpperCAmelCase		:							Union[str, Any]							=     MSELoss()
             _UpperCAmelCase		:							Tuple							=     loss_fct(logits_item.view(-1    )							,					labels.view(-1    )    )
           else:
             _UpperCAmelCase		:							List[Any]							=     CrossEntropyLoss()
             _UpperCAmelCase		:							List[Any]							=     loss_fct(logits_item.view(-1							,					self.num_labels    )							,					labels.view(-1    )    )
           if total_loss is None:
             _UpperCAmelCase		:							List[str]							=     loss
           else:
             total_loss += loss * (ix + 1)
           total_weights += ix + 1
         _UpperCAmelCase		:							List[Any]							=     (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    a__    (		snake_case__					:							Dict     ):
  _UpperCAmelCase		:							str							=     [False] * len(snake_case__     )
  _UpperCAmelCase		:							str							=     [-1] * len(snake_case__     )

  def dfs(snake_case__					:							Dict						,   snake_case__					:							Optional[Any]     ):
    _UpperCAmelCase		:							str							=     True
    _UpperCAmelCase		:							Optional[Any]							=     c
    for u in graph[v]:
      if not visited[u]:
        dfs(snake_case__						,   1 - c     )

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

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

  return True


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





print(check_bipartite_dfs(graph))





