
'''simple docstring'''







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

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


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

_lowercase :  int    							=       "=======\n>>>>>>>\n"

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

_lowercase :  Optional[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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Namespace							):



						"""simple docstring"""





						return ConvertCommand(args.tfds_path      ,      args.datasets_directory							)






class  lowerCAmelCase__   (  lowerCamelCase_     ):






					@staticmethod
					def 	_snake_case    (   __SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							*__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       get_logger('''datasets-cli/converting'''		)

											lowercase_	:     List[Any]													=       tfds_path
											lowercase_	:     Tuple													=       datasets_directory






					def 	_snake_case    (   self		):


											"""simple docstring"""





											if os.path.isdir(self._tfds_path		):
																	lowercase_	:     Any													=       os.path.abspath(self._tfds_path		)
											elif os.path.isfile(self._tfds_path		):
																	lowercase_	:     Optional[Any]													=       os.path.dirname(self._tfds_path		)
											else:
																	raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''		)

											lowercase_	:     List[Any]													=       os.path.abspath(self._datasets_directory		)

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

											lowercase_	:     Optional[Any]													=       []
											lowercase_	:     Optional[Any]													=       []
											lowercase_	:     Union[str, Any]													=       {}

											if os.path.isdir(self._tfds_path		):
																	lowercase_	:     int													=       os.listdir(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     Any													=       [os.path.basename(self._tfds_path		)]

											for f_name in file_names:
																	self._logger.info(F'''Looking at file {f_name}'''		)
																	lowercase_	:     int													=       os.path.join(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Any													=       os.path.join(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

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

																	with open(__SCREAMING_SNAKE_CASE				,							encoding='''utf-8'''		) as f:
																							lowercase_	:     List[str]													=       f.readlines()

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

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

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

																	if needs_manual_update:
																							with_manual_update.append(__SCREAMING_SNAKE_CASE		)

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

											for utils_file in utils_files:
																	try:
																							lowercase_	:     Union[str, Any]													=       os.path.basename(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     int													=       imports_to_builder_map[f_name.replace('''.py'''				,							''''''		)]
																							self._logger.info(F'''Moving {dest_folder} to {utils_file}'''		)
																							shutil.copy(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	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\'.'''		)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



