




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 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

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

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

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

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










from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

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




