


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

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


__a     =			"""<<<<<<< This should probably be modified because it mentions: """

__a     =			"""=======
>>>>>>>
"""

__a     =			[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

__a     =			[
    # (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   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:
   return ConvertCommand(args.tfds_path  ,						args.datasets_directory   )







class   __lowercase				(  __snake_case  ):





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					ArgumentParser       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     parser.add_parser(
             """convert"""					,       help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""					,       )
         train_parser.add_argument(
             """--tfds_path"""					,       type=__lowerCamelCase					,       required=__lowerCamelCase					,       help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""					,       )
         train_parser.add_argument(
             """--datasets_directory"""					,       type=__lowerCamelCase					,       required=__lowerCamelCase					,       help="""Path to the HuggingFace Datasets folder."""       )
         train_parser.set_defaults(func=__lowerCamelCase       )





      def __init__(		self						:					Tuple					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					str					,       *__lowerCamelCase						:					Dict       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     get_logger("""datasets-cli/converting"""       )

         UpperCAmelCase							=     tfds_path
         UpperCAmelCase							=     datasets_directory





      def        _lowercase				(		self						:					List[str]       )    ->   Optional[int]:






         """simple docstring"""







         if os.path.isdir(self._tfds_path       ):
            UpperCAmelCase							=     os.path.abspath(self._tfds_path       )
         elif os.path.isfile(self._tfds_path       ):
            UpperCAmelCase							=     os.path.dirname(self._tfds_path       )
         else:
            raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""       )

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

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

         UpperCAmelCase							=     []
         UpperCAmelCase							=     []
         UpperCAmelCase							=     {}

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

         for f_name in file_names:
            self._logger.info(F"""Looking at file {f_name}"""       )
            UpperCAmelCase							=     os.path.join(__lowerCamelCase					,       __lowerCamelCase       )
            UpperCAmelCase							=     os.path.join(__lowerCamelCase					,       __lowerCamelCase       )

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

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

            UpperCAmelCase							=     []
            UpperCAmelCase							=     False
            UpperCAmelCase							=     False
            UpperCAmelCase							=     []
            for line in lines:
               UpperCAmelCase							=     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:
                  UpperCAmelCase							=     """import datasets\n"""
               elif "import tensorflow" in out_line:
                  # order is important here
                  UpperCAmelCase							=     """"""
                  continue
               elif "from absl import logging" in out_line:
                  UpperCAmelCase							=     """from datasets import logging\n"""
               elif "getLogger" in out_line:
                  UpperCAmelCase							=     out_line.replace("""getLogger"""					,       """get_logger"""       )
               elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
                  UpperCAmelCase							=     True
                  UpperCAmelCase							=     list(filter(lambda __lowerCamelCase       : e in out_line					,       __lowerCamelCase       )       )
                  out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowerCamelCase       ) + """\n"""       )
                  out_lines.append(__lowerCamelCase       )
                  out_lines.append(__lowerCamelCase       )
                  continue
               else:
                  for pattern, replacement in TO_CONVERT:
                     UpperCAmelCase							=     re.sub(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase       )

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

            if is_builder or "wmt" in f_name:
               # We create a new directory for each dataset
               UpperCAmelCase							=     f_name.replace(""".py"""					,       """"""       )
               UpperCAmelCase							=     os.path.join(__lowerCamelCase					,       __lowerCamelCase       )
               UpperCAmelCase							=     os.path.join(__lowerCamelCase					,       __lowerCamelCase       )
               os.makedirs(__lowerCamelCase					,       exist_ok=__lowerCamelCase       )
               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(__lowerCamelCase       )

            if needs_manual_update:
               with_manual_update.append(__lowerCamelCase       )

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

         for utils_file in utils_files:
            try:
               UpperCAmelCase							=     os.path.basename(__lowerCamelCase       )
               UpperCAmelCase							=     imports_to_builder_map[f_name.replace(""".py"""					,       """"""       )]
               self._logger.info(F"""Moving {dest_folder} to {utils_file}"""       )
               shutil.copy(__lowerCamelCase					,       __lowerCamelCase       )
            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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a     =			{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			[
     """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """IBertForMaskedLM""",
     """IBertForMultipleChoice""",
     """IBertForQuestionAnswering""",
     """IBertForSequenceClassification""",
     """IBertForTokenClassification""",
     """IBertModel""",
     """IBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



