




"""simple docstring"""

from datetime import datetime as dt
import os

from github import Github


a					=			[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]






def 						_snake_case     (					)    ->					int:






      '''simple docstring'''


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

      for issue in open_issues:
            _A					   =   sorted([comment for comment in issue.get_comments()]				,  key=lambda _snake_case							: i.created_at				,  reverse=_UpperCAmelCase							)
            _A					   =   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()








import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase				:       Optional[Any]	   =				logging.getLogger(__name__)

@dataclass(frozen=__UpperCamelCase			)
class    lowerCAmelCase							:
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None


@dataclass(frozen=__UpperCamelCase			)
class    lowerCAmelCase							:
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class    lowerCAmelCase							(					__UpperCamelCase			):
     UpperCAmelCase__													=		42
     def __init__(      self  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      List[str]=False			,     UpperCAmelCase  :      bool = False			,     )			->	List[str]:
         lowerCamelCase__    :      int					 =		hans_processors[task]()

         lowerCamelCase__    :      Optional[Any]					 =		os.path.join(
             UpperCAmelCase			,     'cached_{}_{}_{}_{}'.format(
                 'dev' if evaluate else 'train'			,     tokenizer.__class__.__name__			,     str(UpperCAmelCase	)			,     UpperCAmelCase			,     )			,     )
         lowerCamelCase__    :      int					 =		processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		label_list[2], label_list[1]
         lowerCamelCase__    :      List[str]					 =		label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         lowerCamelCase__    :      str					 =		cached_features_file + '.lock'
         with FileLock(UpperCAmelCase	):
             if os.path.exists(UpperCAmelCase	) and not overwrite_cache:
                 logger.info(F"""Loading features from cached file {cached_features_file}"""	)
                 lowerCamelCase__    :      int					 =		torch.load(UpperCAmelCase	)
             else:
                 logger.info(F"""Creating features from dataset file at {data_dir}"""	)

                 lowerCamelCase__    :      str					 =		(
                     processor.get_dev_examples(UpperCAmelCase	) if evaluate else processor.get_train_examples(UpperCAmelCase	)
                 )

                 logger.info('Training examples: %s'			,     len(UpperCAmelCase	)	)
                 lowerCamelCase__    :      Dict					 =		hans_convert_examples_to_features(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
                 logger.info('Saving features into cached file %s'			,     UpperCAmelCase	)
                 torch.save(self.features			,     UpperCAmelCase	)
     def __len__(      self  :      Optional[int]	)			->	Optional[Any]:
         return len(self.features	)
     def __getitem__(      self  :      Tuple			,     UpperCAmelCase  :      Dict	)			->	InputFeatures:
         return self.features[i]







     def      A_	(      self  :      int	)			->	int:
         return self.label_list


if is_tf_available():
    import tensorflow as tf

    class    lowerCAmelCase							:
     UpperCAmelCase__													=		42
     def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int] = 128			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      bool = False			,     )			->	Union[str, Any]:
         lowerCamelCase__    :      Any					 =		hans_processors[task]()
         lowerCamelCase__    :      Optional[Any]					 =		processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		label_list[2], label_list[1]
         lowerCamelCase__    :      Optional[int]					 =		label_list

         lowerCamelCase__    :      int					 =		processor.get_dev_examples(UpperCAmelCase	) if evaluate else processor.get_train_examples(UpperCAmelCase	)
         lowerCamelCase__    :      Union[str, Any]					 =		hans_convert_examples_to_features(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

         def gen():
             for ex_index, ex in tqdm.tqdm(enumerate(self.features	)			,     desc='convert examples to features'	):
                 if ex_index % 10000 == 0:
                     logger.info('Writing example %d of %d' % (ex_index, len(UpperCAmelCase	))	)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

         lowerCamelCase__    :      Optional[int]					 =		tf.data.Dataset.from_generator(
             UpperCAmelCase			,     (
                 {
                     'example_id': tf.intaa,
                     'input_ids': tf.intaa,
                     'attention_mask': tf.intaa,
                     'token_type_ids': tf.intaa,
                 },
                 tf.intaa,
             )			,     (
                 {
                     'example_id': tf.TensorShape([]	),
                     'input_ids': tf.TensorShape([None, None]	),
                     'attention_mask': tf.TensorShape([None, None]	),
                     'token_type_ids': tf.TensorShape([None, None]	),
                 },
                 tf.TensorShape([]	),
             )			,     )
     def      A_	(      self  :      Any	)			->	Any:
         return self.dataset
     def __len__(      self  :      Tuple	)			->	int:
         return len(self.features	)
     def __getitem__(      self  :      List[str]			,     UpperCAmelCase  :      Any	)			->	InputFeatures:
         return self.features[i]







     def      A_	(      self  :      Dict	)			->	str:
         return self.label_list

class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      int			,     UpperCAmelCase  :      List[Any]	)			->	int:
     return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase			,     'heuristics_train_set.txt'	)	)			,     'train'	)
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      int	)			->	List[Any]:
     return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase			,     'heuristics_evaluation_set.txt'	)	)			,     'dev'	)
 def      A_	(      self  :      Any	)			->	List[Any]:
     return ["contradiction", "entailment", "neutral"]







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[str]	)			->	List[str]:
     lowerCamelCase__    :      List[str]					 =		[]
     for i, line in enumerate(UpperCAmelCase	):
         if i == 0:
             continue
         lowerCamelCase__    :      Tuple					 =		'%s-%s' % (set_type, line[0])
         lowerCamelCase__    :      str					 =		line[5]
         lowerCamelCase__    :      Dict					 =		line[6]
         lowerCamelCase__    :      int					 =		line[7][2:] if line[7].startswith('ex'	) else line[7]
         lowerCamelCase__    :      Dict					 =		line[0]
         examples.append(InputExample(guid=UpperCAmelCase			,     text_a=UpperCAmelCase			,     text_b=UpperCAmelCase			,     label=UpperCAmelCase			,     pairID=UpperCAmelCase	)	)
     return examples


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	)			->     Optional[int]:
    lowerCamelCase__    :      int					 =		{label: i for i, label in enumerate(_UpperCAmelCase )}

    lowerCamelCase__    :      List[Any]					 =		[]
    for ex_index, example in tqdm.tqdm(enumerate(_UpperCAmelCase )						,	desc='convert examples to features' ):
        if ex_index % 1_0000 == 0:
            logger.info('Writing example %d' % (ex_index) )

        lowerCamelCase__    :      List[Any]					 =		tokenizer(
            example.text_a						,	example.text_b						,	add_special_tokens=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	padding='max_length'						,	truncation=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	)

        lowerCamelCase__    :      List[str]					 =		label_map[example.label] if example.label in label_map else 0

        lowerCamelCase__    :      Optional[int]					 =		int(example.pairID )

        features.append(InputFeatures(**_UpperCAmelCase						,	label=_UpperCAmelCase						,	pairID=_UpperCAmelCase ) )

    for i, example in enumerate(examples[:5] ):
        logger.info('*** Example ***' )
        logger.info(F"""guid: {example}""" )
        logger.info(F"""features: {features[i]}""" )

    return features


_UpperCAmelCase				:       str	   =				{
    """hans""": 3,
}


_UpperCAmelCase				:       List[Any]	   =				{
    """hans""": HansProcessor,
}





