





'''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


__SCREAMING_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)

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.getLogger()
def 	UpperCAmelCase_							(					)		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   argparse.ArgumentParser()
     parser.add_argument("-f"       )
     _UpperCAmelCase     							=   parser.parse_args()
     return args.f
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      int="eval"       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   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}'       )


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







class   A_	(  lowerCAmelCase_			):




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertGreaterEqual(result["eval_accuracy"]	,			0.7_5    )




 @slow
 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertLess(result["eval_perplexity"]	,			1_0_0    )




 @slow
 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_	,			split="test"    )
           self.assertGreaterEqual(result["test_rouge1"]	,			1_0    )
           self.assertGreaterEqual(result["test_rouge2"]	,			2    )
           self.assertGreaterEqual(result["test_rougeL"]	,			7    )
           self.assertGreaterEqual(result["test_rougeLsum"]	,			7    )




 @slow
 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertLess(result["eval_perplexity"]	,			4_2    )




 @slow
 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertGreaterEqual(result["eval_accuracy"]	,			0.4_2    )




 @slow
 def      lowercase	(					self       :    Tuple    ):
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      _UpperCAmelCase     							=   7 if get_gpu_count() > 1 else 2

      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertGreaterEqual(result["eval_accuracy"]	,			0.7_5    )
           self.assertGreaterEqual(result["eval_f1"]	,			0.3    )




 @slow
 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_auto_remove_tmp_dir()
      _UpperCAmelCase     							=   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()
           _UpperCAmelCase     							=   get_results(snake_case_    )
           self.assertGreaterEqual(result["eval_f1"]	,			3_0    )
           self.assertGreaterEqual(result["eval_exact"]	,			3_0    )











import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_UpperCAmelCase				:       Optional[int]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    lowerCamelCase__    :      str					 =		set()
    lowerCamelCase__    :      Any					 =		[]

    def parse_line(_UpperCAmelCase ):
        for line in fp:
            if isinstance(_UpperCAmelCase						,	_UpperCAmelCase ):
                lowerCamelCase__    :      Any					 =		line.decode('UTF-8' )
            if "warnings summary (final)" in line:
                continue
            # This means we are outside the body of a warning
            elif not line.startswith(' ' ):
                # process a single warning and move it to `selected_warnings`.
                if len(_UpperCAmelCase ) > 0:
                    lowerCamelCase__    :      str					 =		'\n'.join(_UpperCAmelCase )
                    # Only keep the warnings specified in `targets`
                    if any(F""": {x}: """ in warning for x in targets ):
                        selected_warnings.add(_UpperCAmelCase )
                    buffer.clear()
                continue
            else:
                lowerCamelCase__    :      List[str]					 =		line.strip()
                buffer.append(_UpperCAmelCase )

    if from_gh:
        for filename in os.listdir(_UpperCAmelCase ):
            lowerCamelCase__    :      Dict					 =		os.path.join(_UpperCAmelCase						,	_UpperCAmelCase )
            if not os.path.isdir(_UpperCAmelCase ):
                # read the file
                if filename != "warnings.txt":
                    continue
                with open(_UpperCAmelCase ) as fp:
                    parse_line(_UpperCAmelCase )
    else:
        try:
            with zipfile.ZipFile(_UpperCAmelCase ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_UpperCAmelCase ):
                        # read the file
                        if filename != "warnings.txt":
                            continue
                        with z.open(_UpperCAmelCase ) as fp:
                            parse_line(_UpperCAmelCase )
        except Exception:
            logger.warning(
                F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )

    return selected_warnings


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		set()

    lowerCamelCase__    :      Optional[int]					 =		[os.path.join(_UpperCAmelCase						,	_UpperCAmelCase ) for p in os.listdir(_UpperCAmelCase ) if (p.endswith('.zip' ) or from_gh)]
    for p in paths:
        selected_warnings.update(extract_warnings_from_single_artifact(_UpperCAmelCase						,	_UpperCAmelCase ) )

    return selected_warnings


if __name__ == "__main__":


    def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
        return values.split(',' )

    _UpperCAmelCase				:       List[str]	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
    parser.add_argument(
        """--output_dir""",
        type=str,
        required=True,
        help="""Where to store the downloaded artifacts and other result files.""",
    )
    parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
    # optional parameters
    parser.add_argument(
        """--targets""",
        default="""DeprecationWarning,UserWarning,FutureWarning""",
        type=list_str,
        help="""Comma-separated list of target warning(s) which we want to extract.""",
    )
    parser.add_argument(
        """--from_gh""",
        action="""store_true""",
        help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
    )

    _UpperCAmelCase				:       Union[str, Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       Dict	   =				args.from_gh
    if from_gh:
        # The artifacts have to be downloaded using `actions/download-artifact@v3`
        pass
    else:
        os.makedirs(args.output_dir, exist_ok=True)

        # get download links
        _UpperCAmelCase				:       Union[str, Any]	   =				get_artifacts_links(args.workflow_run_id, token=args.token)
        with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(artifacts, fp, ensure_ascii=False, indent=4)

        # download artifacts
        for idx, (name, url) in enumerate(artifacts.items()):
            print(name)
            print(url)
            print("""=""" * 80)
            download_artifact(name, url, args.output_dir, args.token)
            # Be gentle to GitHub
            time.sleep(1)

    # extract warnings from artifacts
    _UpperCAmelCase				:       Dict	   =				extract_warnings(args.output_dir, args.targets)
    _UpperCAmelCase				:       Optional[Any]	   =				sorted(selected_warnings)
    with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
        json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)





