


"""simple docstring"""



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class _lowerCAmelCase    (				tf.keras.layers.Layer     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						)     ->		int:

       '''simple docstring'''




       super().__init__()
       snake_case   :				Optional[int]				 =   pad_token_id
       snake_case   :				Any				 =   max_length
       snake_case   :				Tuple				 =   vocab
       snake_case   :				Optional[int]				 =   merges
       snake_case   :				List[str]				 =   BytePairTokenizer(UpperCamelCase__						,     UpperCamelCase__						,     sequence_length=UpperCamelCase__						)







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   [" ".join(UpperCamelCase__						) for m in tokenizer.bpe_ranks.keys()]
       snake_case   :				Any				 =   tokenizer.get_vocab()
       return cls(UpperCamelCase__						,     UpperCamelCase__						,     *UpperCamelCase__						,     **UpperCamelCase__						)







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   GPTaTokenizer.from_pretrained(UpperCamelCase__						,     *UpperCamelCase__						,     **UpperCamelCase__						)
       return cls.from_tokenizer(UpperCamelCase__						,     *UpperCamelCase__						,     **UpperCamelCase__						)







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




       return cls(**UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       return {
           "vocab": self.vocab,
           "merges": self.merges,
           "max_length": self.max_length,
           "pad_token_id": self.pad_token_id,
       }







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		str:

       '''simple docstring'''




       snake_case   :				Tuple				 =   self.tf_tokenizer(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   tf.ones_like(UpperCamelCase__						)

       if self.pad_token_id is not None:
           # pad the tokens up to max length
           snake_case   :				Optional[int]				 =   max_length if max_length is not None else self.max_length

           if max_length is not None:
               snake_case ,snake_case   :				Tuple				 =   pad_model_inputs(
                   UpperCamelCase__						,     max_seq_length=UpperCamelCase__						,     pad_value=self.pad_token_id						)

       return {"attention_mask": attention_mask, "input_ids": input_ids}









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    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
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   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]			):
            snake_case   :				int				 =   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)





