





"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a						: List[Any]     	=			logging.get_logger(__name__)

a						: Optional[int]     	=			{'''vocab_file''': '''sentencepiece.bpe.model'''}

a						: Optional[Any]     	=			{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

a						: Optional[int]     	=			{
    '''camembert-base''': 512,
}

a						: Union[str, Any]     	=			'''▁'''


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					int      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					List[str]      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					str      					=["""input_ids""", """attention_mask"""]






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="</s>"      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__=["<s>NOTUSED", "</s>NOTUSED"]      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> None:
								# Mask token behave like a normal word, i.e. include the space before it
								a      :		List[Any]	=				AddedToken(lowerCAmelCase__      ,					lstrip=lowerCAmelCase__      ,					rstrip=lowerCAmelCase__      ) if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ) else mask_token

								a      :		Dict	=				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowerCAmelCase__      ,					eos_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					additional_special_tokens=lowerCAmelCase__      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**lowerCAmelCase__      ,					)
								a      :		str	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(lowerCAmelCase__      )      )
								a      :		List[str]	=				vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								a      :		List[str]	=				{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								a      :		List[str]	=				len(self.fairseq_tokens_to_ids      )
								a      :		List[Any]	=				len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
								a      :		Any	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:

								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								a      :		List[str]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__      )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__      )) + [1, 1] + ([0] * len(lowerCAmelCase__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.sep_token_id]
								a      :		Tuple	=				[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 + sep + token_ids_a + sep      ) * [0]






							@property
							def     __a   (   self      )	-> Union[str, Any]:
								return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )






							def     __a   (   self      )	-> Optional[int]:
								a      :		List[str]	=				{self.convert_ids_to_tokens(lowerCAmelCase__      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[str]:
								return self.sp_model.encode(lowerCAmelCase__      ,					out_type=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowerCAmelCase__      ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[Any]:
								a      :		Union[str, Any]	=				[]
								a      :		str	=				""
								a      :		Tuple	=				False
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										if not prev_is_special:
											out_string += " "
										out_string += self.sp_model.decode(lowerCAmelCase__      ) + token
										a      :		str	=				True
										a      :		Union[str, Any]	=				[]
									else:
										current_sub_tokens.append(lowerCAmelCase__      )
										a      :		Tuple	=				False
								out_string += self.sp_model.decode(lowerCAmelCase__      )
								return out_string.strip()






							def __getstate__(   self      )	-> Tuple:
								a      :		str	=				self.__dict__.copy()
								a      :		Union[str, Any]	=				None
								return state






							def __setstate__(   self      ,					lowerCAmelCase__      )	-> str:
								a      :		Union[str, Any]	=				d

								# for backward compatibility
								if not hasattr(self      ,					"sp_model_kwargs"      ):
									a      :		Any	=				{}

								a      :		Tuple	=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__      ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
									return
								a      :		int	=				os.path.join(
								    lowerCAmelCase__      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCAmelCase__      ) and os.path.isfile(self.vocab_file      ):
									copyfile(self.vocab_file      ,					lowerCAmelCase__      )
								elif not os.path.isfile(self.vocab_file      ):
									with open(lowerCAmelCase__      ,					"wb"      ) as fi:
										a      :		Optional[Any]	=				self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase__      )

								return (out_vocab_file,)









"""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


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

a						: Tuple     	=			'''=======
>>>>>>>
'''

a						: List[Any]     	=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

a						: 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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->Any:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path    ,       args.datasets_directory					)







class        __UpperCamelCase				(						a__						):






							@staticmethod
							def     __a   (   lowerCAmelCase__      )	-> int:
								a      :		Any	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      )	-> Tuple:
								a      :		List[str]	=				get_logger("datasets-cli/converting"      )

								a      :		Optional[int]	=				tfds_path
								a      :		str	=				datasets_directory






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

								a      :		str	=				os.path.abspath(self._datasets_directory      )

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

								a      :		str	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		str	=				{}

								if os.path.isdir(self._tfds_path      ):
									a      :		Dict	=				os.listdir(lowerCAmelCase__      )
								else:
									a      :		Dict	=				[os.path.basename(self._tfds_path      )]

								for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""      )
									a      :		Any	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Dict	=				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:
										a      :		Any	=				f.readlines()

									a      :		Tuple	=				[]
									a      :		Any	=				False
									a      :		Dict	=				False
									a      :		Dict	=				[]
									for line in lines:
										a      :		Tuple	=				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      :		Optional[Any]	=				""
											continue
										elif "from absl import logging" in out_line:
											a      :		Any	=				"from datasets import logging\n"
										elif "getLogger" in out_line:
											a      :		int	=				out_line.replace("getLogger"      ,					"get_logger"      )
										elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
											a      :		Optional[Any]	=				True
											a      :		Optional[Any]	=				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:
												a      :		int	=				re.sub(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )

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

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


