



import os
from collections import deque

import torch
from torch.utils.data import Dataset



class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				A_     :       Dict=""				,				A_     :       Tuple="train"):
									assert os.path.isdir(A_)

									lowerCAmelCase_			:     Optional[int]									= []
									lowerCAmelCase_			:     Dict									= os.listdir(A_)
									for story_filename in story_filenames_list:
											if "summary" in story_filename:
													continue
											lowerCAmelCase_			:     str									= os.path.join(A_				,				A_)
											if not os.path.isfile(A_):
													continue
											self.documents.append(A_)




							def __len__(       self     :       Optional[int]):
									return len(self.documents)







							def __getitem__(       self     :       Optional[int]				,				A_     :       Tuple):
									lowerCAmelCase_			:     List[str]									= self.documents[idx]
									lowerCAmelCase_			:     Optional[Any]									= document_path.split('''/''')[-1]
									with open(A_				,				encoding='''utf-8''') as source:
											lowerCAmelCase_			:     Any									= source.read()
											lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= process_story(A_)
									return document_name, story_lines, summary_lines





def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Tuple									= list(filter(lambda __UpperCamelCase : len(__UpperCamelCase ) != 0     ,[line.strip() for line in raw_story.split('''\n''' )] ) )

		# for some unknown reason some lines miss a period, add it
		lowerCAmelCase_			:     Union[str, Any]									= [_add_missing_period(__UpperCamelCase ) for line in nonempty_lines]

		# gather article lines
		lowerCAmelCase_			:     Any									= []
		lowerCAmelCase_			:     Any									= deque(__UpperCamelCase )
		while True:
				try:
						lowerCAmelCase_			:     Any									= lines.popleft()
						if element.startswith('''@highlight''' ):
								break
						story_lines.append(__UpperCamelCase )
				except IndexError:
						# if "@highlight" is absent from the file we pop
						# all elements until there is None, raising an exception.
						return story_lines, []

    # gather summary lines
		lowerCAmelCase_			:     str									= list(filter(lambda __UpperCamelCase : not t.startswith('''@highlight''' )     ,__UpperCamelCase ) )

		return story_lines, summary_lines


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Any									= ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
		if line.startswith('''@highlight''' ):
				return line
		if line[-1] in END_TOKENS:
				return line
		return line + "."


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		Union[str, Any] ):
		if len(__UpperCamelCase ) > block_size:
				return sequence[:block_size]
		else:
				sequence.extend([pad_token_id] * (block_size - len(__UpperCamelCase )) )
				return sequence


def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Union[str, Any]									= torch.ones_like(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= sequence == pad_token_id
		lowerCAmelCase_			:     int									= 0
		return mask


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[Any] ):
		lowerCAmelCase_			:     Dict									= [tokenizer.encode(__UpperCamelCase ) for line in story_lines]
		lowerCAmelCase_			:     Optional[Any]									= [token for sentence in story_lines_token_ids for token in sentence]
		lowerCAmelCase_			:     Tuple									= [tokenizer.encode(__UpperCamelCase ) for line in summary_lines]
		lowerCAmelCase_			:     Optional[Any]									= [token for sentence in summary_lines_token_ids for token in sentence]

		return story_token_ids, summary_token_ids





def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Tuple ):
		lowerCAmelCase_			:     Union[str, Any]									= []
		for sequence in batch:
				lowerCAmelCase_			:     str									= -1
				lowerCAmelCase_			:     List[str]									= []
				for s in sequence:
						if s == separator_token_id:
								sentence_num += 1
						embeddings.append(sentence_num % 2 )
				batch_embeddings.append(__UpperCamelCase )
		return torch.tensor(__UpperCamelCase )










from datetime import datetime as dt
import os

from github import Github


A__  :  List[str]								=					[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Union[str, Any]									= Github(os.environ['''GITHUB_TOKEN'''] )
		lowerCAmelCase_			:     Tuple									= g.get_repo('''huggingface/transformers''' )
		lowerCAmelCase_			:     int									= repo.get_issues(state='''open''' )

		for issue in open_issues:
				lowerCAmelCase_			:     Optional[Any]									= sorted([comment for comment in issue.get_comments()]     ,key=lambda __UpperCamelCase : i.created_at     ,reverse=__UpperCamelCase )
				lowerCAmelCase_			:     Tuple									= 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()





