




import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {"""vocab_file""": """vocab.json"""}

UpperCamelCase__         =      {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

UpperCamelCase__         =      {"""mgp-str""": 27}
class       a__   ( snake_case__				):
						_a							:   int =   VOCAB_FILES_NAMES
						_a							:   List[Any] =   PRETRAINED_VOCAB_FILES_MAP
						_a							:   int =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






						def __init__(       self		,      _A		,      _A="[GO]"		,      _A="[GO]"		,      _A="[s]"		,      _A="[GO]"		,      **_A      ):




											"""simple docstring"""



											super().__init__(
											    unk_token=_A		,      bos_token=_A		,      eos_token=_A		,      pad_token=_A		,      **_A		,      )

											with open(_A		,      encoding="utf-8"      ) as vocab_handle:
																__lowerCAmelCase           =	json.load(_A      )
											__lowerCAmelCase           =	{v: k for k, v in self.vocab.items()}






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.vocab      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return dict(self.vocab		,      **self.added_tokens_encoder      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[]
											for s in text:
																char_tokens.extend(_A      )
											return char_tokens






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return self.vocab.get(_A		,      self.vocab.get(self.unk_token      )      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return self.decoder.get(_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



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

											with open(_A		,      "w"		,      encoding="utf-8"      ) as f:
																f.write(json.dumps(self.vocab		,      indent=2		,      sort_keys=_A		,      ensure_ascii=_A      ) + "\n"      )

											return (vocab_file,)










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					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
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	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]					):
															__lowerCAmelCase           =	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)




