

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

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


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

_a:    Union[str, Any] 							=			"""=======
>>>>>>>
"""

_a:    int 							=			[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_a:    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       __lowerCAmelCase      (						A    ):
	return ConvertCommand(args.tfds_path       ,     args.datasets_directory    )




class 						__UpperCamelCase    (					lowercase						):




	@staticmethod
	def       __A					(		lowerCAmelCase	:						ArgumentParser							):


		'''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	:						Any		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	*lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			tfds_path
		UpperCAmelCase_       				=			datasets_directory




	def       __A					(		self	:						Union[str, Any]							):


		'''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:    List[str] 							=			{
    """configuration_encodec""": [
        """ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EncodecConfig""",
    ],
    """feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """EncodecModel""",
				    """EncodecPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

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