



"""simple docstring"""






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__snake_case							:   Union[str, Any]      =    [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm_flax
	import run_flax_glue
	import run_flax_ner
	import run_mlm_flax
	import run_qa
	import run_summarization_flax
	import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__snake_case							:   Union[str, Any]      =    logging.getLogger()


def 			_lowercase				(  )						->   List[str]:
			__lowerCAmelCase      :  Tuple												=       argparse.ArgumentParser()
			parser.add_argument("-f"	)
			__lowerCAmelCase      :  List[str]												=       parser.parse_args()
			return args.f


def 			_lowercase				(  __snake_case					,__snake_case="eval"	)						->   Tuple:
			__lowerCAmelCase      :  Union[str, Any]												=       os.path.join(__lowercase					,F"""{split}_results.json"""	)
			if os.path.exists(__lowercase	):
						with open(__lowercase					,"r"	) as f:
									return json.load(__lowercase	)
			raise ValueError(F"""can\'t find {path}"""	)


__snake_case							:   List[str]      =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 			A__   (  lowerCAmelCase_							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  List[Any]												=       F"""\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_flax_glue.main()
							__lowerCAmelCase      :  Union[str, Any]												=       get_results(snake_case_)
							self.assertGreaterEqual(result["eval_accuracy"]       ,			0.75)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  int												=       F"""\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_clm_flax.main()
							__lowerCAmelCase      :  Optional[Any]												=       get_results(snake_case_)
							self.assertLess(result["eval_perplexity"]       ,			100)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  Union[str, Any]												=       F"""\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_summarization_flax.main()
							__lowerCAmelCase      :  Union[str, Any]												=       get_results(snake_case_       ,			split="test")
							self.assertGreaterEqual(result["test_rouge1"]       ,			10)
							self.assertGreaterEqual(result["test_rouge2"]       ,			2)
							self.assertGreaterEqual(result["test_rougeL"]       ,			7)
							self.assertGreaterEqual(result["test_rougeLsum"]       ,			7)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  int												=       F"""\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_mlm_flax.main()
							__lowerCAmelCase      :  Optional[Any]												=       get_results(snake_case_)
							self.assertLess(result["eval_perplexity"]       ,			42)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  int												=       F"""\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_ta_mlm_flax.main()
							__lowerCAmelCase      :  Tuple												=       get_results(snake_case_)
							self.assertGreaterEqual(result["eval_accuracy"]       ,			0.42)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       7 if get_gpu_count() > 1 else 2

				__lowerCAmelCase      :  str												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  List[Any]												=       F"""\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_flax_ner.main()
							__lowerCAmelCase      :  Optional[Any]												=       get_results(snake_case_)
							self.assertGreaterEqual(result["eval_accuracy"]       ,			0.75)
							self.assertGreaterEqual(result["eval_f1"]       ,			0.3)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  str												=       F"""\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

				with patch.object(snake_case_       ,			"argv"       ,			snake_case_):
							run_qa.main()
							__lowerCAmelCase      :  str												=       get_results(snake_case_)
							self.assertGreaterEqual(result["eval_f1"]       ,			30)
							self.assertGreaterEqual(result["eval_exact"]       ,			30)






'''simple docstring'''




import os
from datetime import datetime as dt

from github import Github


__SCREAMING_SNAKE_CASE     :str       =				[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Github(os.environ["GITHUB_TOKEN"]       )
     _UpperCAmelCase     							=   g.get_repo("huggingface/accelerate"       )
     _UpperCAmelCase     							=   repo.get_issues(state="open"       )

     for issue in open_issues:
          _UpperCAmelCase     							=   sorted([comment for comment in issue.get_comments()]			,     key=lambda __lowercase       : i.created_at			,     reverse=__lowercase       )
          _UpperCAmelCase     							=   comments[0] if len(__lowercase       ) > 0 else None
          _UpperCAmelCase     							=   dt.utcnow()
          _UpperCAmelCase     							=   (current_time - issue.updated_at).days
          _UpperCAmelCase     							=   (current_time - issue.created_at).days
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and days_since_updated > 7
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Close issue since it has been 7 days of inactivity since bot mention.
               issue.edit(state="closed"       )
          elif (
              days_since_updated > 23
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Add stale comment
               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/accelerate/blob/main/CONTRIBUTING.md) "
                   "are likely to be ignored."       )


if __name__ == "__main__":
  main()




