

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

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


A_			:					List[Any]    			=     '<<<<<<< This should probably be modified because it mentions: '

A_			:					Optional[Any]    			=     '=======\n>>>>>>>\n'

A_			:					List[str]    			=     [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_			:					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 		UpperCamelCase   (lowercase_:					Namespace   )	-> List[str]:
	return ConvertCommand(args.tfds_path       , args.datasets_directory   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@staticmethod
					def     __A     (					A__     ):
						A__				:       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       ,			A__       ,			A__       ,			*A__     ):
						A__				:       Any	  =						get_logger("""datasets-cli/converting"""     )

						A__				:       List[str]	  =						tfds_path
						A__				:       Optional[int]	  =						datasets_directory






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

						A__				:       List[str]	  =						os.path.abspath(self._datasets_directory     )

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

						A__				:       List[str]	  =						[]
						A__				:       Tuple	  =						[]
						A__				:       Optional[Any]	  =						{}

						if os.path.isdir(self._tfds_path     ):
							A__				:       Union[str, Any]	  =						os.listdir(A__     )
						else:
							A__				:       List[Any]	  =						[os.path.basename(self._tfds_path     )]

						for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}"""     )
							A__				:       Optional[Any]	  =						os.path.join(A__       ,			A__     )
							A__				:       List[str]	  =						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:
								A__				:       Optional[int]	  =						f.readlines()

							A__				:       str	  =						[]
							A__				:       Dict	  =						False
							A__				:       Tuple	  =						False
							A__				:       Tuple	  =						[]
							for line in lines:
								A__				:       List[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:
									A__				:       Optional[int]	  =						"""import datasets\n"""
								elif "import tensorflow" in out_line:
									# order is important here
									A__				:       Dict	  =						""""""
									continue
								elif "from absl import logging" in out_line:
									A__				:       List[Any]	  =						"""from datasets import logging\n"""
								elif "getLogger" in out_line:
									A__				:       str	  =						out_line.replace("""getLogger"""       ,			"""get_logger"""     )
								elif any(expression in out_line for expression in TO_HIGHLIGHT     ):
									A__				:       Dict	  =						True
									A__				:       Tuple	  =						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:
										A__				:       List[Any]	  =						re.sub(A__       ,			A__       ,			A__     )

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

							if is_builder or "wmt" in f_name:
								# We create a new directory for each dataset
								A__				:       Union[str, Any]	  =						f_name.replace(""".py"""       ,			""""""     )
								A__				:       Optional[Any]	  =						os.path.join(A__       ,			A__     )
								A__				:       Optional[Any]	  =						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:
								A__				:       Optional[Any]	  =						os.path.basename(A__     )
								A__				:       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'."""     )






from typing import TYPE_CHECKING

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


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



