





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_albert import AlbertTokenizer
else:
    _UpperCAmelCase				:       str	   =				None

_UpperCAmelCase				:       List[Any]	   =				logging.get_logger(__name__)
_UpperCAmelCase				:       List[str]	   =				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase				:       Optional[int]	   =				{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase				:       Optional[Any]	   =				{
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

_UpperCAmelCase				:       Optional[int]	   =				"""▁"""

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		VOCAB_FILES_NAMES
 UpperCAmelCase__													=		PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__													=		AlbertTokenizer
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      Optional[Any]=True			,     UpperCAmelCase  :      Optional[Any]=True			,     UpperCAmelCase  :      List[Any]=False			,     UpperCAmelCase  :      Tuple="[CLS]"			,     UpperCAmelCase  :      str="[SEP]"			,     UpperCAmelCase  :      Any="<unk>"			,     UpperCAmelCase  :      Any="[SEP]"			,     UpperCAmelCase  :      List[Any]="<pad>"			,     UpperCAmelCase  :      List[str]="[CLS]"			,     UpperCAmelCase  :      Optional[int]="[MASK]"			,     **UpperCAmelCase  :      int			,     )			->	Dict:
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     lowerCamelCase__    :      Any					 =		(
         AddedToken(UpperCAmelCase			,     lstrip=UpperCAmelCase			,     rstrip=UpperCAmelCase			,     normalized=UpperCAmelCase	)
         if isinstance(UpperCAmelCase			,     UpperCAmelCase	)
         else mask_token
     )

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

     lowerCamelCase__    :      Union[str, Any]					 =		do_lower_case
     lowerCamelCase__    :      List[Any]					 =		remove_space
     lowerCamelCase__    :      Optional[Any]					 =		keep_accents
     lowerCamelCase__    :      Tuple					 =		vocab_file
     lowerCamelCase__    :      Tuple					 =		False if not self.vocab_file else True
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      Optional[Any]					 =		[self.sep_token_id]
     lowerCamelCase__    :      List[Any]					 =		[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  :      List[Any]			,     UpperCAmelCase  :      List[int]			,     UpperCAmelCase  :      Optional[List[int]] = None	)			->	List[int]:
     lowerCamelCase__    :      List[str]					 =		[self.sep_token_id]
     lowerCamelCase__    :      int					 =		[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			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[str] = None	)			->	Tuple[str]:
     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(UpperCAmelCase	):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
         return
     lowerCamelCase__    :      List[str]					 =		os.path.join(
         UpperCAmelCase			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

     return (out_vocab_file,)









'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)