


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

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


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

_lowercase       :      str	=	"=======\n>>>>>>>\n"

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

_lowercase       :      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   _lowerCAmelCase				(   UpperCamelCase__:     Namespace						) ->   Any:





	"""simple docstring"""







	return ConvertCommand(args.tfds_path							,					args.datasets_directory						)






class       _UpperCamelCase    (					lowerCAmelCase__				):

				"""simple docstring"""







				@staticmethod
				def      _UpperCAmelCase					(						a__   )	->				Any:
					A								=				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				,   a__				,   a__				,   *a__   )	->				List[Any]:
					A								=				get_logger("""datasets-cli/converting"""   )

					A								=				tfds_path
					A								=				datasets_directory

				def      _UpperCAmelCase					(						self   )	->				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(_lowerCamelCase   )
					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(_lowerCamelCase				,   _lowerCamelCase   )
						A								=				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:
							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 a__   : 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:
									A								=				re.sub(_lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase   )

                # 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]+)"""				,   _lowerCamelCase   )
								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(_lowerCamelCase   )

						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(_lowerCamelCase				,   _lowerCamelCase   )
							A								=				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:
							A								=				os.path.basename(_lowerCamelCase   )
							A								=				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


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



