





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

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


__UpperCAmelCase       :   Optional[int]				       =							"<<<<<<< This should probably be modified because it mentions: "

__UpperCAmelCase       :   List[str]				       =							"=======\n>>>>>>>\n"

__UpperCAmelCase       :   Tuple				       =							[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

__UpperCAmelCase       :   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__							(    SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  return ConvertCommand(args.tfds_path					,					args.datasets_directory)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @staticmethod
       def 							UpperCAmelCase__ (  A :    ArgumentParser		):
         __snake_case:						Optional[Any]   =						parser.add_parser(
             """convert"""							,				help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""							,				)
         train_parser.add_argument(
             """--tfds_path"""							,				type=A							,				required=A							,				help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""							,				)
         train_parser.add_argument(
             """--datasets_directory"""							,				type=A							,				required=A							,				help="""Path to the HuggingFace Datasets folder."""		)
         train_parser.set_defaults(func=A		)




       def __init__(  self :    Any							,				A :    str							,				A :    str							,				*A :    List[str]		):
         __snake_case:						Dict   =						get_logger("""datasets-cli/converting"""		)

         __snake_case:						Dict   =						tfds_path
         __snake_case:						Tuple   =						datasets_directory






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

         __snake_case:						Union[str, Any]   =						os.path.abspath(self._datasets_directory		)

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

         __snake_case:						Optional[int]   =						[]
         __snake_case:						str   =						[]
         __snake_case:						Tuple   =						{}

         if os.path.isdir(self._tfds_path		):
           __snake_case:						Optional[int]   =						os.listdir(A		)
         else:
           __snake_case:						Tuple   =						[os.path.basename(self._tfds_path		)]

         for f_name in file_names:
           self._logger.info(f'''Looking at file {f_name}'''		)
           __snake_case:						Optional[Any]   =						os.path.join(A							,				A		)
           __snake_case:						Dict   =						os.path.join(A							,				A		)

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

           with open(A							,				encoding="""utf-8"""		) as f:
             __snake_case:						List[str]   =						f.readlines()

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

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

           if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             __snake_case:						Union[str, Any]   =						f_name.replace(""".py"""							,				""""""		)
             __snake_case:						Union[str, Any]   =						os.path.join(A							,				A		)
             __snake_case:						int   =						os.path.join(A							,				A		)
             os.makedirs(A							,				exist_ok=A		)
             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(A		)

           if needs_manual_update:
             with_manual_update.append(A		)

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

         for utils_file in utils_files:
           try:
             __snake_case:						List[str]   =						os.path.basename(A		)
             __snake_case:						List[str]   =						imports_to_builder_map[f_name.replace(""".py"""							,				""""""		)]
             self._logger.info(f'''Moving {dest_folder} to {utils_file}'''		)
             shutil.copy(A							,				A		)
           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\'.'''		)













import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__UpperCAmelCase       :   Tuple				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				*A :    Union[str, Any]							,				**A :    Optional[int]		):
         warnings.warn(
             """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
             """ Please use PerceiverImageProcessor instead."""							,				A							,				)
         super().__init__(*A							,				**A		)






