
"""simple docstring"""




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


_A     :			Any  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			List[Any]    )					->							Any:
       lowercase       :							List[Any]									=				set()
       lowercase       :							List[str]									=				[]

       def parse_line(__snake_case							:			Dict    ):
              for line in fp:
                     if isinstance(__snake_case	,					__snake_case    ):
                            lowercase       :							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:
                                   lowercase       :							Dict									=				"\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:
                            lowercase       :							Dict									=				line.strip()
                            buffer.append(__snake_case    )

       if from_gh:
              for filename in os.listdir(__snake_case    ):
                     lowercase       :							Dict									=				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     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Union[str, Any]    )					->							Dict:
       lowercase       :							Optional[Any]									=				set()

       lowercase       :							List[str]									=				[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     __magic_name__      ( __snake_case							:			int    )					->							int:
          return values.split(","    )

   _A     :			Any  						=			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.""",
   )

   _A     :			Tuple  						=			parser.parse_args()

   _A     :			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
      _A     :			str  						=			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
   _A     :			Tuple  						=			extract_warnings(args.output_dir, args.targets)
   _A     :			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)




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


