





import os
from datetime import datetime as dt

from github import Github


a__:      List[Any]									=		[
    'good first issue',
    'feature request',
    'wip',
]


def 	UpperCamelCase__(       )->Optional[Any]:
       A__											=						Github(os.environ['''GITHUB_TOKEN''']  )
       A__											=						g.get_repo('''huggingface/accelerate'''  )
       A__											=						repo.get_issues(state='''open'''  )

       for issue in open_issues:
              A__											=						sorted([comment for comment in issue.get_comments()] ,     key=lambda UpperCamelCase__  : i.created_at ,     reverse=UpperCamelCase__  )
              A__											=						comments[0] if len(UpperCamelCase__  ) > 0 else None
              A__											=						dt.utcnow()
              A__											=						(current_time - issue.updated_at).days
              A__											=						(current_time - issue.created_at).days
              if (
                  last_comment is not None
                  and last_comment.user.login == "github-actions[bot]"
                  and days_since_updated > 7
                  and days_since_creation >= 30
                  and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
              ):
                     # Close issue since it has been 7 days of inactivity since bot mention.
                     issue.edit(state='''closed'''  )
              elif (
                  days_since_updated > 23
                  and days_since_creation >= 30
                  and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
              ):
                     # Add stale comment
                     issue.create_comment(
                         '''This issue has been automatically marked as stale because it has not had '''
                         '''recent activity. If you think this still needs to be addressed '''
                         '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                         '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
                         '''are likely to be ignored.'''  )


if __name__ == "__main__":
       main()












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,
)


a__:      int									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None ):
        A__											=						self.layer[current_layer](__lowerCamelCase,__lowerCamelCase,head_mask[current_layer] )

        A__											=						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 )

        A__											=						BertEncoderWithPabee(__lowerCamelCase )

        self.init_weights()
        A__											=						0
        A__											=						0
        A__											=						0

        A__											=						0



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						threshold



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						patience



 def 				UpperCamelCase			(							self ):
        A__											=						0
        A__											=						0



 def 				UpperCamelCase			(							self ):
        A__											=						self.inference_layers_num / self.inference_instances_num
        A__											=						(
            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 				UpperCamelCase			(							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:
               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(__lowerCamelCase,device=__lowerCamelCase )
        if token_type_ids is None:
               A__											=						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.
        A__											=						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:
               A__					,	A__					,	A__											=						encoder_hidden_states.size()
               A__											=						(encoder_batch_size, encoder_sequence_length)
               if encoder_attention_mask is None:
                      A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
               A__											=						self.invert_attention_mask(__lowerCamelCase )
        else:
               A__											=						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]
        A__											=						self.get_head_mask(__lowerCamelCase,self.config.num_hidden_layers )

        A__											=						self.embeddings(
            input_ids=__lowerCamelCase,position_ids=__lowerCamelCase,token_type_ids=__lowerCamelCase,inputs_embeds=__lowerCamelCase )
        A__											=						embedding_output

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

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

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

                      A__											=						logits
                      if patient_counter == self.patience:
                             break
               A__											=						[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. '''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )
        A__											=						config.num_labels

        A__											=						BertModelWithPabee(__lowerCamelCase )
        A__											=						nn.Dropout(config.hidden_dropout_prob )
        A__											=						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 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        A__											=						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,)

        A__											=						(logits[-1],)

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

        return outputs





