






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  Any        =							logging.get_logger()





@dataclass
class 					A__     :

     '''simple docstring'''






     snake_case__					=					42
     snake_case__					=					field(default_factory=__snake_case )
     snake_case__					=					field(default_factory=__snake_case )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Tensor   ,						_SCREAMING_SNAKE_CASE      :    Tensor ):






           """simple docstring"""




           UpperCamelCase					       =  len(list(m.modules() ) ) == 1 or isinstance(_SCREAMING_SNAKE_CASE   ,						nn.Convad ) or isinstance(_SCREAMING_SNAKE_CASE   ,						nn.BatchNormad )
           if has_not_submodules:
                 self.traced.append(_SCREAMING_SNAKE_CASE )







     def __call__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Tensor ):






           """simple docstring"""




           for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook ) )
           self.module(_SCREAMING_SNAKE_CASE )
           [x.remove() for x in self.handles]
           return self







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           return list(filter(lambda _SCREAMING_SNAKE_CASE : len(list(x.state_dict().keys() ) ) > 0   ,						self.traced ) )





@dataclass
class 					A__     :

     '''simple docstring'''






     snake_case__					=					42
     snake_case__					=					42
     snake_case__					=					1
     snake_case__					=					field(default_factory=__snake_case )
     snake_case__					=					field(default_factory=__snake_case )
     snake_case__					=					True







     def __call__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Tensor ):






           """simple docstring"""




           UpperCamelCase					       =  Tracker(self.dest )(_SCREAMING_SNAKE_CASE ).parametrized
           UpperCamelCase					       =  Tracker(self.src )(_SCREAMING_SNAKE_CASE ).parametrized

           UpperCamelCase					       =  list(filter(lambda _SCREAMING_SNAKE_CASE : type(_SCREAMING_SNAKE_CASE ) not in self.src_skip   ,						_SCREAMING_SNAKE_CASE ) )
           UpperCamelCase					       =  list(filter(lambda _SCREAMING_SNAKE_CASE : type(_SCREAMING_SNAKE_CASE ) not in self.dest_skip   ,						_SCREAMING_SNAKE_CASE ) )

           if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ) and self.raise_if_mismatch:
                 raise Exception(
                     f'Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE )} operations while'
                     f' destination module has {len(_SCREAMING_SNAKE_CASE )}.' )

           for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 dest_m.load_state_dict(src_m.state_dict() )
                 if self.verbose == 1:
                       print(f'Transfered from={src_m} to={dest_m}' )





class 					A__     (     nn.Module ):

     '''simple docstring'''






     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    nn.Module ):






           """simple docstring"""




           super().__init__()

           UpperCamelCase					       =  []
           # - get the stem
           feature_blocks.append(('conv1', model.stem) )
           # - get all the feature blocks
           for k, v in model.trunk_output.named_children():
                 assert k.startswith('block' ), f'Unexpected layer name {k}'
                 UpperCamelCase					       =  len(_SCREAMING_SNAKE_CASE ) + 1
                 feature_blocks.append((f'res{block_index}', v) )

           UpperCamelCase					       =  nn.ModuleDict(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Tensor ):






           """simple docstring"""




           return get_trunk_forward_outputs(
               _SCREAMING_SNAKE_CASE   ,						out_feat_keys=_SCREAMING_SNAKE_CASE   ,						feature_blocks=self._feature_blocks   ,						)





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  x.split('-' )
           return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )







     def __getitem__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if x not in self:
                 UpperCamelCase					       =  self.convert_name_to_timm(_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE   ,						pretrained=_SCREAMING_SNAKE_CASE ).eval(), None) )

           else:
                 UpperCamelCase					       =  super().__getitem__(_SCREAMING_SNAKE_CASE )

           return val





class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __getitem__( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           if "seer" in x and "in1k" not in x:
                 UpperCamelCase					       =  RegNetModel
           else:
                 UpperCamelCase					       =  RegNetForImageClassification
           return val

def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			List[str]:
      """simple docstring"""

      for from_key, to_key in keys:
            UpperCamelCase					       =  from_state_dict[from_key].clone()
            print(F'Copied key={from_key} to={to_key}')
      return to_state_dict
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase = True       ,							) ->			Optional[Any]:
      """simple docstring"""

      print(F'Converting {name}...')
      with torch.no_grad():
            UpperCamelCase       ,  UpperCamelCase					       =  from_model_func()
            UpperCamelCase					       =  our_model_func(_UpperCamelCase).eval()
            UpperCamelCase					       =  ModuleTransfer(src=_UpperCamelCase       ,							dest=_UpperCamelCase       ,							raise_if_mismatch=_UpperCamelCase)
            UpperCamelCase					       =  torch.randn((1, 3, 2_24, 2_24))
            module_transfer(_UpperCamelCase)

      if from_state_dict is not None:
            UpperCamelCase					       =  []
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  UpperCamelCase					       =  [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
            UpperCamelCase					       =  manually_copy_vissl_head(_UpperCamelCase       ,							our_model.state_dict()       ,							_UpperCamelCase)
            our_model.load_state_dict(_UpperCamelCase)

      UpperCamelCase					       =  our_model(_UpperCamelCase       ,							output_hidden_states=_UpperCamelCase)
      UpperCamelCase					       =  (
          our_outputs.logits if isinstance(_UpperCamelCase       ,							_UpperCamelCase) else our_outputs.last_hidden_state
      )

      UpperCamelCase					       =  from_model(_UpperCamelCase)
      UpperCamelCase					       =  from_output[-1] if type(_UpperCamelCase) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            UpperCamelCase					       =  our_outputs.hidden_states[-1]

      assert torch.allclose(_UpperCamelCase       ,							_UpperCamelCase), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name       ,							commit_message='Add model'       ,							use_temp_dir=_UpperCamelCase       ,							)

            UpperCamelCase					       =  2_24 if 'seer' not in name else 3_84
            # we can use the convnext one
            UpperCamelCase					       =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,							size=_UpperCamelCase)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name       ,							commit_message='Add image processor'       ,							use_temp_dir=_UpperCamelCase       ,							)

            print(F'Pushed {name}')
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase = None       ,							_UpperCamelCase = True) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  'imagenet-1k-id2label.json'
      UpperCamelCase					       =  10_00
      UpperCamelCase					       =  (1, num_labels)

      UpperCamelCase					       =  'huggingface/label-files'
      UpperCamelCase					       =  num_labels
      UpperCamelCase					       =  json.load(open(cached_download(hf_hub_url(_UpperCamelCase       ,							_UpperCamelCase       ,							repo_type='dataset'))       ,							'r'))
      UpperCamelCase					       =  {int(_UpperCamelCase): v for k, v in idalabel.items()}

      UpperCamelCase					       =  idalabel
      UpperCamelCase					       =  {v: k for k, v in idalabel.items()}

      UpperCamelCase					       =  partial(_UpperCamelCase       ,							num_labels=_UpperCamelCase       ,							idalabel=_UpperCamelCase       ,							labelaid=_UpperCamelCase)

      UpperCamelCase					       =  {
          'regnet-x-002': ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]       ,							hidden_sizes=[24, 56, 1_52, 3_68]       ,							groups_width=8       ,							layer_type='x'),
          'regnet-x-004': ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]       ,							hidden_sizes=[32, 64, 1_60, 3_84]       ,							groups_width=16       ,							layer_type='x'),
          'regnet-x-006': ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]       ,							hidden_sizes=[48, 96, 2_40, 5_28]       ,							groups_width=24       ,							layer_type='x'),
          'regnet-x-008': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]       ,							hidden_sizes=[64, 1_28, 2_88, 6_72]       ,							groups_width=16       ,							layer_type='x'),
          'regnet-x-016': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]       ,							hidden_sizes=[72, 1_68, 4_08, 9_12]       ,							groups_width=24       ,							layer_type='x'),
          'regnet-x-032': ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]       ,							hidden_sizes=[96, 1_92, 4_32, 10_08]       ,							groups_width=48       ,							layer_type='x'),
          'regnet-x-040': ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]       ,							hidden_sizes=[80, 2_40, 5_60, 13_60]       ,							groups_width=40       ,							layer_type='x'),
          'regnet-x-064': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]       ,							hidden_sizes=[1_68, 3_92, 7_84, 16_24]       ,							groups_width=56       ,							layer_type='x'),
          'regnet-x-080': ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]       ,							hidden_sizes=[80, 2_40, 7_20, 19_20]       ,							groups_width=1_20       ,							layer_type='x'),
          'regnet-x-120': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]       ,							hidden_sizes=[2_24, 4_48, 8_96, 22_40]       ,							groups_width=1_12       ,							layer_type='x'),
          'regnet-x-160': ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]       ,							hidden_sizes=[2_56, 5_12, 8_96, 20_48]       ,							groups_width=1_28       ,							layer_type='x'),
          'regnet-x-320': ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]       ,							hidden_sizes=[3_36, 6_72, 13_44, 25_20]       ,							groups_width=1_68       ,							layer_type='x'),
          # y variant
          'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]       ,							hidden_sizes=[24, 56, 1_52, 3_68]       ,							groups_width=8),
          'regnet-y-004': ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]       ,							hidden_sizes=[48, 1_04, 2_08, 4_40]       ,							groups_width=8),
          'regnet-y-006': ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]       ,							hidden_sizes=[48, 1_12, 2_56, 6_08]       ,							groups_width=16),
          'regnet-y-008': ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]       ,							hidden_sizes=[64, 1_28, 3_20, 7_68]       ,							groups_width=16),
          'regnet-y-016': ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]       ,							hidden_sizes=[48, 1_20, 3_36, 8_88]       ,							groups_width=24),
          'regnet-y-032': ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]       ,							hidden_sizes=[72, 2_16, 5_76, 15_12]       ,							groups_width=24),
          'regnet-y-040': ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]       ,							hidden_sizes=[1_28, 1_92, 5_12, 10_88]       ,							groups_width=64),
          'regnet-y-064': ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]       ,							hidden_sizes=[1_44, 2_88, 5_76, 12_96]       ,							groups_width=72),
          'regnet-y-080': ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]       ,							hidden_sizes=[1_68, 4_48, 8_96, 20_16]       ,							groups_width=56),
          'regnet-y-120': ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]       ,							hidden_sizes=[2_24, 4_48, 8_96, 22_40]       ,							groups_width=1_12),
          'regnet-y-160': ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]       ,							hidden_sizes=[2_24, 4_48, 12_32, 30_24]       ,							groups_width=1_12),
          'regnet-y-320': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]       ,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]       ,							groups_width=2_32),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]       ,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]       ,							groups_width=2_32),
          'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]       ,							hidden_sizes=[3_28, 9_84, 19_68, 49_20]       ,							groups_width=3_28),
          'regnet-y-1280-seer': RegNetConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[5_28, 10_56, 29_04, 73_92]       ,							groups_width=2_64),
          'regnet-y-2560-seer': RegNetConfig(
              depths=[3, 7, 16, 1]       ,							hidden_sizes=[6_40, 16_96, 25_44, 50_88]       ,							groups_width=6_40),
          'regnet-y-10b-seer': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]       ,							groups_width=10_10),
          # finetuned on imagenet
          'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]       ,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]       ,							groups_width=2_32),
          'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]       ,							hidden_sizes=[3_28, 9_84, 19_68, 49_20]       ,							groups_width=3_28),
          'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[5_28, 10_56, 29_04, 73_92]       ,							groups_width=2_64),
          'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]       ,							hidden_sizes=[6_40, 16_96, 25_44, 50_88]       ,							groups_width=6_40),
          'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]       ,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]       ,							groups_width=10_10),
      }

      UpperCamelCase					       =  NameToOurModelFuncMap()
      UpperCamelCase					       =  NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(_UpperCamelCase       ,							_UpperCamelCase) -> Tuple[nn.Module, Dict]:
            UpperCamelCase					       =  torch.hub.load_state_dict_from_url(_UpperCamelCase       ,							model_dir=str(_UpperCamelCase)       ,							map_location='cpu')
            UpperCamelCase					       =  model_func()
            # check if we have a head, if yes add it
            UpperCamelCase					       =  files['classy_state_dict']['base_model']['model']
            UpperCamelCase					       =  model_state_dict['trunk']
            model.load_state_dict(_UpperCamelCase)
            return model.eval(), model_state_dict["heads"]

      # pretrained
      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,							lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27       ,							group_width=10_10       ,							w_a=17_44       ,							w_a=6_2_0.8_3       ,							w_m=2.5_2)))       ,							)

      # IN1K finetuned
      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'       ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf())       ,							)

      UpperCamelCase					       =  partial(
          _UpperCamelCase       ,							'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,							lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27       ,							group_width=10_10       ,							w_a=17_44       ,							w_a=6_2_0.8_3       ,							w_m=2.5_2)))       ,							)

      if model_name:
            convert_weight_and_push(
                _UpperCamelCase       ,							names_to_from_model_map[model_name]       ,							names_to_ours_model_map[model_name]       ,							names_to_config[model_name]       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      _UpperCamelCase       ,							names_to_from_model_map[model_name]       ,							names_to_ours_model_map[model_name]       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							)
      return config, expected_shape


if __name__ == "__main__":
       __magic_name__   :  Union[str, Any]        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default=None,
           type=str,
           help=(
               '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
               ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=Path,
           required=True,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''',
           default=True,
           type=bool,
           required=False,
           help='''If True, push model and image processor to the hub.''',
       )

       __magic_name__   :  Tuple        =							parser.parse_args()

       __magic_name__   :  Path        =							args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)











from datetime import datetime as dt
import os

from github import Github


__magic_name__   :  Dict        =							[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  Github(os.environ['GITHUB_TOKEN'])
      UpperCamelCase					       =  g.get_repo('huggingface/transformers')
      UpperCamelCase					       =  repo.get_issues(state='open')

      for issue in open_issues:
            UpperCamelCase					       =  sorted([comment for comment in issue.get_comments()]       ,							key=lambda _UpperCamelCase: i.created_at       ,							reverse=_UpperCamelCase)
            UpperCamelCase					       =  comments[0] if len(_UpperCamelCase) > 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())
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed')
            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())
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.')


if __name__ == "__main__":
       main()



