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


lowercase_  : int								=					logging.get_logger(__name__)







def       A__   (						snake_case_			:		Any							,					snake_case_			:		List[str]   ):
 SCREAMING_SNAKE_CASE__: List[str]=   set()
 SCREAMING_SNAKE_CASE__: int=   []

 def parse_line(snake_case_			:		List[str]   ):
  for line in fp:
   if isinstance(snake_case_							,					snake_case_   ):
    SCREAMING_SNAKE_CASE__: Dict=   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(snake_case_   ) > 0:
     SCREAMING_SNAKE_CASE__: List[str]=   '''\n'''.join(snake_case_   )
     # Only keep the warnings specified in `targets`
     if any(F': {x}: ' in warning for x in targets   ):
      selected_warnings.add(snake_case_   )
     buffer.clear()
    continue
   else:
    SCREAMING_SNAKE_CASE__: int=   line.strip()
    buffer.append(snake_case_   )

 if from_gh:
  for filename in os.listdir(snake_case_   ):
   SCREAMING_SNAKE_CASE__: Optional[Any]=   os.path.join(snake_case_							,					snake_case_   )
   if not os.path.isdir(snake_case_   ):
    # read the file
    if filename != "warnings.txt":
     continue
    with open(snake_case_   ) as fp:
     parse_line(snake_case_   )
 else:
  try:
   with zipfile.ZipFile(snake_case_   ) as z:
    for filename in z.namelist():
     if not os.path.isdir(snake_case_   ):
      # read the file
      if filename != "warnings.txt":
       continue
      with z.open(snake_case_   ) as fp:
       parse_line(snake_case_   )
  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       A__   (						snake_case_			:		Dict							,					snake_case_			:		Any   ):
 SCREAMING_SNAKE_CASE__: List[str]=   set()

 SCREAMING_SNAKE_CASE__: Tuple=   [os.path.join(snake_case_							,					snake_case_   ) for p in os.listdir(snake_case_   ) if (p.endswith('''.zip'''   ) or from_gh)]
 for p in paths:
  selected_warnings.update(extract_warnings_from_single_artifact(snake_case_							,					snake_case_   )   )

 return selected_warnings


if __name__ == "__main__":







      def       A__   (						snake_case_			:		int   ):
       return values.split(''','''   )

      lowercase_  : Dict								=					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.',
      )

      lowercase_  : Tuple								=					parser.parse_args()

      lowercase_  : List[Any]								=					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
            lowercase_  : 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('=' * 8_0)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      lowercase_  : Optional[Any]								=					extract_warnings(args.output_dir, args.targets)
      lowercase_  : 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)








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d