






'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


A:      Union[str, Any]  				=				[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]






def 	_UpperCAmelCase    (  )			->			Dict:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			Github(os.environ['GITHUB_TOKEN']							)
						lowercase_      :   str        =			g.get_repo('huggingface/diffusers'							)
						lowercase_      :   Optional[int]        =			repo.get_issues(state='open'							)

						for issue in open_issues:
												lowercase_      :   str        =			sorted(issue.get_comments()						,			key=lambda a							: i.created_at						,			reverse=_A							)
												lowercase_      :   str        =			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 >= 3_0
												    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 > 2_3
												    and (dt.utcnow() - issue.created_at).days >= 3_0
												    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'''

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
