




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

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


lowercase__							:	Any											=		"<<<<<<< This should probably be modified because it mentions: "

lowercase__							:	Any											=		"=======\n>>>>>>>\n"

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

lowercase__							:	Union[str, 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__       (						_A				):







	'''simple docstring'''





	return ConvertCommand(args.tfds_path	,      args.datasets_directory				)






class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






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






						def __init__(		self				:	str			,      __lowercase				:	str			,      __lowercase				:	str			,      *__lowercase				:	Optional[int]		):






							"""simple docstring"""






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

							snake_case_						       =	tfds_path
							snake_case_						       =	datasets_directory






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






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

							snake_case_						       =	os.path.abspath(self._datasets_directory		)

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

							snake_case_						       =	[]
							snake_case_						       =	[]
							snake_case_						       =	{}

							if os.path.isdir(self._tfds_path		):
								snake_case_						       =	os.listdir(__lowercase		)
							else:
								snake_case_						       =	[os.path.basename(self._tfds_path		)]

							for f_name in file_names:
								self._logger.info(f"Looking at file {f_name}"		)
								snake_case_						       =	os.path.join(__lowercase			,      __lowercase		)
								snake_case_						       =	os.path.join(__lowercase			,      __lowercase		)

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

								with open(__lowercase			,      encoding="utf-8"		) as f:
									snake_case_						       =	f.readlines()

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

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

								if is_builder or "wmt" in f_name:
									# We create a new directory for each dataset
									snake_case_						       =	f_name.replace(".py"			,      ""		)
									snake_case_						       =	os.path.join(__lowercase			,      __lowercase		)
									snake_case_						       =	os.path.join(__lowercase			,      __lowercase		)
									os.makedirs(__lowercase			,      exist_ok=__lowercase		)
									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(__lowercase		)

								if needs_manual_update:
									with_manual_update.append(__lowercase		)

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

							for utils_file in utils_files:
								try:
									snake_case_						       =	os.path.basename(__lowercase		)
									snake_case_						       =	imports_to_builder_map[f_name.replace(".py"			,      ""		)]
									self._logger.info(f"Moving {dest_folder} to {utils_file}"		)
									shutil.copy(__lowercase			,      __lowercase		)
								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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

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