






'''simple docstring'''



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

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


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

__UpperCamelCase      :       List[Any]      			=      """=======\n>>>>>>>\n"""

__UpperCamelCase      :       str      			=      [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

__UpperCamelCase      :       List[Any]      			=      [
    # (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	(				lowercase_:	Any    )  ->			Optional[int]:

   """simple docstring"""
   return ConvertCommand(args.tfds_path,					args.datasets_directory    )



class 							__SCREAMING_SNAKE_CASE       (							__snake_case     ):




      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase      )							->List[Any]:



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




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   *lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    get_logger('datasets-cli/converting'      )

         __a				     =    tfds_path
         __a				     =    datasets_directory






      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''


         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(__UpperCamelCase      )
         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(__UpperCamelCase ,   __UpperCamelCase      )
            __a				     =    os.path.join(__UpperCamelCase ,   __UpperCamelCase      )

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

            with open(__UpperCamelCase ,   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 lowerCamelCase      : e in out_line ,   __UpperCamelCase      )      )
                  out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__UpperCamelCase      ) + '\n'      )
                  out_lines.append(__UpperCamelCase      )
                  out_lines.append(__UpperCamelCase      )
                  continue
               else:
                  for pattern, replacement in TO_CONVERT:
                     __a				     =    re.sub(__UpperCamelCase ,   __UpperCamelCase ,   __UpperCamelCase      )

                # 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]+)' ,   __UpperCamelCase      )
                  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(__UpperCamelCase      )

            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(__UpperCamelCase ,   __UpperCamelCase      )
               __a				     =    os.path.join(__UpperCamelCase ,   __UpperCamelCase      )
               os.makedirs(__UpperCamelCase ,   exist_ok=__UpperCamelCase      )
               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(__UpperCamelCase      )

            if needs_manual_update:
               with_manual_update.append(__UpperCamelCase      )

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

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







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)