





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									=				"=======\n>>>>>>>\n"

__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      lowerCAmelCase_ (       __a		)     ->		Optional[Any]:







  """simple docstring"""





  return ConvertCommand(args.tfds_path	,    args.datasets_directory		)






class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_					(UpperCAmelCase_				:   ArgumentParser)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =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				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   str       , *UpperCAmelCase_				:   List[str])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      str			  =get_logger("datasets-cli/converting")

   lowerCamelCase__:      Tuple			  =tfds_path
   lowerCamelCase__:      Union[str, Any]			  =datasets_directory







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Union[str, Any]:



   '''simple docstring'''



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

   lowerCamelCase__:      Any			  =os.path.abspath(self._datasets_directory)

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

   lowerCamelCase__:      List[str]			  =[]
   lowerCamelCase__:      Optional[int]			  =[]
   lowerCamelCase__:      int			  ={}

   if os.path.isdir(self._tfds_path):
     lowerCamelCase__:      Tuple			  =os.listdir(UpperCAmelCase_)
   else:
     lowerCamelCase__:      int			  =[os.path.basename(self._tfds_path)]

   for f_name in file_names:
     self._logger.info(F"""Looking at file {f_name}""")
     lowerCamelCase__:      Tuple			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
     lowerCamelCase__:      List[str]			  =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:
       lowerCamelCase__:      Union[str, Any]			  =f.readlines()

     lowerCamelCase__:      int			  =[]
     lowerCamelCase__:      Any			  =False
     lowerCamelCase__:      int			  =False
     lowerCamelCase__:      int			  =[]
     for line in lines:
       lowerCamelCase__:      List[Any]			  =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:
         lowerCamelCase__:      List[Any]			  ="import datasets\n"
       elif "import tensorflow" in out_line:
         # order is important here
         lowerCamelCase__:      Union[str, Any]			  =""
         continue
       elif "from absl import logging" in out_line:
         lowerCamelCase__:      Tuple			  ="from datasets import logging\n"
       elif "getLogger" in out_line:
         lowerCamelCase__:      List[str]			  =out_line.replace("getLogger"       , "get_logger")
       elif any(expression in out_line for expression in TO_HIGHLIGHT):
         lowerCamelCase__:      str			  =True
         lowerCamelCase__:      List[Any]			  =list(filter(lambda UpperCAmelCase_: 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:
           lowerCamelCase__:      Dict			  =re.sub(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)

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

     if is_builder or "wmt" in f_name:
       # We create a new directory for each dataset
       lowerCamelCase__:      Tuple			  =f_name.replace(".py"       , "")
       lowerCamelCase__:      Optional[int]			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
       lowerCamelCase__:      Optional[Any]			  =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:
       lowerCamelCase__:      Union[str, Any]			  =os.path.basename(UpperCAmelCase_)
       lowerCamelCase__:      Optional[Any]			  =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'.""")








from typing import TYPE_CHECKING

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


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

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

