

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_big_bird import BigBirdTokenizer
else:
	A_	:			List[str]  	=      None

A_	:			Union[str, Any]  	=      logging.get_logger(__name__)
A_	:			int  	=      {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:			List[str]  	=      {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

A_	:			List[Any]  	=      {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}


A_	:			Any  	=      '▁'



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       int  							=	PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       Tuple  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       int  							=	BigBirdTokenizer
					UpperCAmelCase__:       List[Any]  							=	['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:       List[int]  							=	[]






					def __init__(			self   ,	A__=None   ,	A__=None   ,	A__="<unk>"   ,	A__="<s>"   ,	A__="</s>"   ,	A__="<pad>"   ,	A__="[SEP]"   ,	A__="[MASK]"   ,	A__="[CLS]"   ,	**A__   ,	):
						A__							:   List[str]					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else bos_token
						A__							:   Dict					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else eos_token
						A__							:   Union[str, Any]					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else unk_token
						A__							:   Union[str, Any]					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else pad_token
						A__							:   Optional[Any]					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else cls_token
						A__							:   Union[str, Any]					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__							:   str					       =		AddedToken(A__   ,	lstrip=A__   ,	rstrip=A__      ) if isinstance(A__   ,	A__      ) else mask_token

						super().__init__(
						    A__   ,	tokenizer_file=A__   ,	bos_token=A__   ,	eos_token=A__   ,	unk_token=A__   ,	sep_token=A__   ,	pad_token=A__   ,	cls_token=A__   ,	mask_token=A__   ,	**A__   ,	)

						A__							:   Optional[int]					       =		vocab_file
						A__							:   int					       =		False if not self.vocab_file else True






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   Optional[int]					       =		[self.sep_token_id]
						A__							:   Dict					       =		[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   ,	A__   ,	A__ = None   ,	A__ = False      ):

						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 None:
							return [1] + ([0] * len(A__      )) + [1]
						return [1] + ([0] * len(A__      )) + [1] + ([0] * len(A__      )) + [1]






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   Tuple					       =		[self.sep_token_id]
						A__							:   str					       =		[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   ,	A__   ,	A__ = None      ):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""      )

						if not os.path.isdir(A__      ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
							return
						A__							:   List[str]					       =		os.path.join(
						    A__   ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

						return (out_vocab_file,)






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

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


A_	:			Optional[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[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 						UpperCamelCase (lowercase_:			Namespace )					->				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__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

						A__							:   Optional[Any]					       =		tfds_path
						A__							:   Dict					       =		datasets_directory






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

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

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

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

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[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(A__   ,	A__      )
							A__							:   Optional[Any]					       =		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__							:   Dict					       =		f.readlines()

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

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



