



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

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


SCREAMING_SNAKE_CASE__       =      '''<<<<<<< This should probably be modified because it mentions: '''

SCREAMING_SNAKE_CASE__       =      '''=======
>>>>>>>
'''

SCREAMING_SNAKE_CASE__       =      [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

SCREAMING_SNAKE_CASE__       =      [
    # (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 A						(       __UpperCamelCase					)						->      Optional[Any]:
			return ConvertCommand(args.tfds_path ,	args.datasets_directory					)






class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@staticmethod
					def     _a							(     _snake_case					:       ArgumentParser					):






								"""simple docstring"""


								A__       =    parser.add_parser(
								    'convert'				,					help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'				,					)
								train_parser.add_argument(
								    '--tfds_path'				,					type=_snake_case				,					required=_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=_snake_case				,					required=_snake_case				,					help='Path to the HuggingFace Datasets folder.'					)
								train_parser.set_defaults(func=_snake_case					)



					def __init__(     self					:       str				,					_snake_case					:       str				,					_snake_case					:       str				,					*_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    get_logger('datasets-cli/converting'					)

								A__       =    tfds_path
								A__       =    datasets_directory



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								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(_snake_case					)
								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(_snake_case				,					_snake_case					)
											A__       =    os.path.join(_snake_case				,					_snake_case					)

											if not os.path.isfile(_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(_snake_case				,					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 _snake_case					: e in out_line				,					_snake_case					)					)
																	out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_snake_case					) + '\n'					)
																	out_lines.append(_snake_case					)
																	out_lines.append(_snake_case					)
																	continue
														else:
																	for pattern, replacement in TO_CONVERT:
																				A__       =    re.sub(_snake_case				,					_snake_case				,					_snake_case					)

                # 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]+)'				,					_snake_case					)
																	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(_snake_case					)

											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(_snake_case				,					_snake_case					)
														A__       =    os.path.join(_snake_case				,					_snake_case					)
														os.makedirs(_snake_case				,					exist_ok=_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(_snake_case					)

											if needs_manual_update:
														with_manual_update.append(_snake_case					)

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

								for utils_file in utils_files:
											try:
														A__       =    os.path.basename(_snake_case					)
														A__       =    imports_to_builder_map[f_name.replace('.py'				,					''					)]
														self._logger.info(F'''Moving {dest_folder} to {utils_file}'''					)
														shutil.copy(_snake_case				,					_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\'.'''					)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




