




"""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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__         =      {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

UpperCamelCase__         =      {
    "camembert-base": 512,
}

UpperCamelCase__         =      "▁"
class       a__   ( _SCREAMING_SNAKE_CASE				):
						_a							:   Optional[Any] =   VOCAB_FILES_NAMES
						_a							:   int =   PRETRAINED_VOCAB_FILES_MAP
						_a							:   Any =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:   Optional[int] =   ["""input_ids""", """attention_mask"""]






						def __init__(       self		,      _A		,      _A="<s>"		,      _A="</s>"		,      _A="</s>"		,      _A="<s>"		,      _A="<unk>"		,      _A="<pad>"		,      _A="<mask>"		,      _A=["<s>NOTUSED", "</s>NOTUSED"]		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	AddedToken(_A		,      lstrip=_A		,      rstrip=_A      ) if isinstance(_A		,      _A      ) else mask_token

											__lowerCAmelCase           =	{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=_A		,      eos_token=_A		,      unk_token=_A		,      sep_token=_A		,      cls_token=_A		,      pad_token=_A		,      mask_token=_A		,      additional_special_tokens=_A		,      sp_model_kwargs=self.sp_model_kwargs		,      **_A		,      )
											__lowerCAmelCase           =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
											self.sp_model.Load(str(_A      )      )
											__lowerCAmelCase           =	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>
											__lowerCAmelCase           =	{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
											__lowerCAmelCase           =	len(self.fairseq_tokens_to_ids      )
											__lowerCAmelCase           =	len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
											__lowerCAmelCase           =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






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




											"""simple docstring"""



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






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




											"""simple docstring"""



											if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_A		,      token_ids_a=_A		,      already_has_special_tokens=_A      )

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






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




											"""simple docstring"""



											__lowerCAmelCase           =	[self.sep_token_id]
											__lowerCAmelCase           =	[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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
											vocab.update(self.added_tokens_encoder      )
											return vocab






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return self.sp_model.encode(_A		,      out_type=_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
											elif self.sp_model.PieceToId(_A      ) == 0:
																# Convert sentence piece unk token to fairseq unk token index
																return self.unk_token_id
											return self.fairseq_offset + self.sp_model.PieceToId(_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											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     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[]
											__lowerCAmelCase           =	""
											__lowerCAmelCase           =	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(_A      ) + token
																					__lowerCAmelCase           =	True
																					__lowerCAmelCase           =	[]
																else:
																					current_sub_tokens.append(_A      )
																					__lowerCAmelCase           =	False
											out_string += self.sp_model.decode(_A      )
											return out_string.strip()






						def __getstate__(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.__dict__.copy()
											__lowerCAmelCase           =	None
											return state






						def __setstate__(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	d

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

											__lowerCAmelCase           =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
											self.sp_model.Load(self.vocab_file      )






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




											"""simple docstring"""



											if not os.path.isdir(_A      ):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																return
											__lowerCAmelCase           =	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      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file		,      _A      )
											elif not os.path.isfile(self.vocab_file      ):
																with open(_A		,      "wb"      ) as fi:
																					__lowerCAmelCase           =	self.sp_model.serialized_model_proto()
																					fi.write(_A      )

											return (out_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)




