'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


UpperCamelCase__  :						str					=      [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

   for issue in open_issues:
      UpperCAmelCase__				:					int 					=	sorted(issue.get_comments()	,      key=lambda _A     : i.created_at	,      reverse=_A     )
      UpperCAmelCase__				:					int 					=	comments[0] if len(_A     ) > 0 else None
      if (
          last_comment is not None
          and last_comment.user.login == "github-actions[bot]"
          and (dt.utcnow() - issue.updated_at).days > 7
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Closes the issue after 7 days of inactivity since the Stalebot notification.
         issue.edit(state='''closed'''     )
      elif (
          "stale" in issue.get_labels()
          and last_comment is not None
          and last_comment.user.login != "github-actions[bot]"
      ):
         # Opens the issue if someone other than Stalebot commented.
         issue.edit(state='''open'''     )
         issue.remove_from_labels('''stale'''     )
      elif (
          (dt.utcnow() - issue.updated_at).days > 23
          and (dt.utcnow() - issue.created_at).days >= 30
          and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
      ):
         # Post a Stalebot notification after 23 days of inactivity.
         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/diffusers/blob/main/CONTRIBUTING.md) '''
             '''are likely to be ignored.'''     )
         issue.add_to_labels('''stale'''     )


if __name__ == "__main__":
  main()





'''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




