import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


SCREAMING_SNAKE_CASE :	List[Any]							     =		"""<<<<<<< This should probably be modified because it mentions: """

SCREAMING_SNAKE_CASE :	Dict							     =		"""=======
>>>>>>>
"""

SCREAMING_SNAKE_CASE :	Any							     =		[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"""tfds\.core""", R"""datasets"""),
    (R"""tf\.io\.gfile\.GFile""", R"""open"""),
    (R"""tf\.([\w\d]+)""", R"""datasets.Value('\1')"""),
    (R"""tfds\.features\.Text\(\)""", R"""datasets.Value('string')"""),
    (R"""tfds\.features\.Text\(""", R"""datasets.Value('string'),"""),
    (R"""features\s*=\s*tfds.features.FeaturesDict\(""", R"""features=datasets.Features("""),
    (R"""tfds\.features\.FeaturesDict\(""", R"""dict("""),
    (R"""The TensorFlow Datasets Authors""", R"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (R"""tfds\.""", R"""datasets."""),
    (R"""dl_manager\.manual_dir""", R"""self.config.data_dir"""),
    (R"""self\.builder_config""", R"""self.config"""),
]



def   __A		(			_A					):



 """simple docstring"""


 return ConvertCommand(args.tfds_path							,				args.datasets_directory					)






class    A_ (       a_ ):

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      ArgumentParser       ):
   __a       =  parser.add_parser(
       "convert"     ,							help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."     ,							)
   train_parser.add_argument(
       "--tfds_path"     ,							type=__SCREAMING_SNAKE_CASE     ,							required=__SCREAMING_SNAKE_CASE     ,							help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."     ,							)
   train_parser.add_argument(
       "--datasets_directory"     ,							type=__SCREAMING_SNAKE_CASE     ,							required=__SCREAMING_SNAKE_CASE     ,							help="Path to the HuggingFace Datasets folder."       )
   train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE       )

  def __init__(		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      str     ,							*__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  get_logger("datasets-cli/converting"       )

   __a       =  tfds_path
   __a       =  datasets_directory

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if os.path.isdir(self._tfds_path       ):
    __a       =  os.path.abspath(self._tfds_path       )
   elif os.path.isfile(self._tfds_path       ):
    __a       =  os.path.dirname(self._tfds_path       )
   else:
    raise ValueError("--tfds_path is neither a directory nor a file. Please check path."       )

   __a       =  os.path.abspath(self._datasets_directory       )

   self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""       )

   __a       =  []
   __a       =  []
   __a       =  {}

   if os.path.isdir(self._tfds_path       ):
    __a       =  os.listdir(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  [os.path.basename(self._tfds_path       )]

   for f_name in file_names:
    self._logger.info(f"""Looking at file {f_name}"""       )
    __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

    if not os.path.isfile(__SCREAMING_SNAKE_CASE       ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
     self._logger.info("Skipping file"       )
     continue

    with open(__SCREAMING_SNAKE_CASE     ,							encoding="utf-8"       ) as f:
     __a       =  f.readlines()

    __a       =  []
    __a       =  False
    __a       =  False
    __a       =  []
    for line in lines:
     __a       =  line

     # Convert imports
     if "import tensorflow.compat.v2 as tf" in out_line:
      continue
     elif "@tfds.core" in out_line:
      continue
     elif "builder=self" in out_line:
      continue
     elif "import tensorflow_datasets.public_api as tfds" in out_line:
      __a       =  "import datasets\n"
     elif "import tensorflow" in out_line:
      # order is important here
      __a       =  ""
      continue
     elif "from absl import logging" in out_line:
      __a       =  "from datasets import logging\n"
     elif "getLogger" in out_line:
      __a       =  out_line.replace("getLogger"     ,							"get_logger"       )
     elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
      __a       =  True
      __a       =  list(filter(lambda __SCREAMING_SNAKE_CASE       : e in out_line     ,							__SCREAMING_SNAKE_CASE       )       )
      out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__SCREAMING_SNAKE_CASE       ) + "\n"       )
      out_lines.append(__SCREAMING_SNAKE_CASE       )
      out_lines.append(__SCREAMING_SNAKE_CASE       )
      continue
     else:
      for pattern, replacement in TO_CONVERT:
       __a       =  re.sub(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

                # Take care of saving utilities (to later move them together with main script)
     if "tensorflow_datasets" in out_line:
      __a       =  re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"     ,							__SCREAMING_SNAKE_CASE       )
      tfds_imports.extend(imp.strip() for imp in match.group(1       ).split(","       )       )
      __a       =  "from . import " + match.group(1       )

     # Check we have not forget anything
     if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
      raise ValueError(f"""Error converting {out_line.strip()}"""       )

     if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
      __a       =  True
     out_lines.append(__SCREAMING_SNAKE_CASE       )

    if is_builder or "wmt" in f_name:
     # We create a new directory for each dataset
     __a       =  f_name.replace(".py"     ,							""       )
     __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
     __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
     os.makedirs(__SCREAMING_SNAKE_CASE     ,							exist_ok=__SCREAMING_SNAKE_CASE       )
     self._logger.info(f"""Adding directory {output_dir}"""       )
     imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}       )
    else:
     # Utilities will be moved at the end
     utils_files.append(__SCREAMING_SNAKE_CASE       )

    if needs_manual_update:
     with_manual_update.append(__SCREAMING_SNAKE_CASE       )

    with open(__SCREAMING_SNAKE_CASE     ,							"w"     ,							encoding="utf-8"       ) as f:
     f.writelines(__SCREAMING_SNAKE_CASE       )
    self._logger.info(f"""Converted in {output_file}"""       )

   for utils_file in utils_files:
    try:
     __a       =  os.path.basename(__SCREAMING_SNAKE_CASE       )
     __a       =  imports_to_builder_map[f_name.replace(".py"     ,							""       )]
     self._logger.info(f"""Moving {dest_folder} to {utils_file}"""       )
     shutil.copy(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    except KeyError:
     self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually."""       )

   if with_manual_update:
    for file_path in with_manual_update:
     self._logger.warning(
         f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""       )





from datetime import datetime as dt
import os

from github import Github


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def   __A		(			):



 """simple docstring"""


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

 for issue in open_issues:
  __a       =  sorted([comment for comment in issue.get_comments()]							,				key=lambda _A					: i.created_at							,				reverse=_A					)
  __a       =  comments[0] if len(_A					) > 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()					)
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="closed"					)
  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()					)
  ):
   # print(f"Would add stale comment to {issue.number}")
   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/transformers/blob/main/CONTRIBUTING.md) "
       "are likely to be ignored."					)


if __name__ == "__main__":
  main()


