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


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

@require_torch
class    snake_case       (   unittest.TestCase    ):




  def 						__lowercase(   self  :						List[str]  ,			a_  :						Any					)->							str:




       """simple docstring"""
       return FSMTTokenizer.from_pretrained(a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		FSMTForConditionalGeneration.from_pretrained(a_					).to(a_					)
       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 						__lowercase(   self  :						int  ,			a_  :						Optional[int]  ,			a_  :						str					)->							List[str]:




       """simple docstring"""
       # 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
       SCREAMING_SNAKE_CASE__					:					Any      =		F'''facebook/wmt19-{pair}'''
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_tokenizer(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_model(a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		bleu_data[pair]['src']
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bleu_data[pair]['tgt']

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





'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


_lowercase          =						[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]


def A ():
						_lowerCAmelCase    		=					Github(os.environ["""GITHUB_TOKEN"""]						)
						_lowerCAmelCase    		=					g.get_repo("""huggingface/diffusers"""						)
						_lowerCAmelCase    		=					repo.get_issues(state="""open"""						)

						for issue in open_issues:
												_lowerCAmelCase    		=					sorted(issue.get_comments()					,      key=lambda __lowerCamelCase						: i.created_at					,      reverse=__lowerCamelCase						)
												_lowerCAmelCase    		=					comments[0] if len(__lowerCamelCase						) > 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()
