






"""simple docstring"""







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a			:    Dict				 =		logging.get_logger(__name__)


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Dict=	question_encoder
												lowercase__    :				int=	generator
												lowercase__    :				List[str]=	self.question_encoder




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if os.path.isfile(snake_case__      ):
																		raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''      )
												os.makedirs(snake_case__	,  exist_ok=snake_case__      )
												lowercase__    :				int=	os.path.join(snake_case__	,  "question_encoder_tokenizer"      )
												lowercase__    :				Any=	os.path.join(snake_case__	,  "generator_tokenizer"      )
												self.question_encoder.save_pretrained(snake_case__      )
												self.generator.save_pretrained(snake_case__      )




						@classmethod
						def 		UpperCAmelCase_	(       cls	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												lowercase__    :				Dict=	kwargs.pop("config"	,  snake_case__      )

												if config is None:
																		lowercase__    :				Union[str, Any]=	RagConfig.from_pretrained(snake_case__      )

												lowercase__    :				Tuple=	AutoTokenizer.from_pretrained(
												    snake_case__	,  config=config.question_encoder	,  subfolder="question_encoder_tokenizer"      )
												lowercase__    :				List[Any]=	AutoTokenizer.from_pretrained(
												    snake_case__	,  config=config.generator	,  subfolder="generator_tokenizer"      )
												return cls(question_encoder=snake_case__	,  generator=snake_case__      )




						def __call__(       self	,  *snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												return self.current_tokenizer(*snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  *snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												return self.generator.batch_decode(*snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  *snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												return self.generator.decode(*snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.question_encoder




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	self.generator




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = None	,  snake_case__ = "longest"	,  snake_case__ = None	,  snake_case__ = True	,  **snake_case__	,  ):
												'''simple docstring'''
												warnings.warn(
												    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
												    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
												    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
												    "details"	,  snake_case__	,  )
												if max_length is None:
																		lowercase__    :				List[str]=	self.current_tokenizer.model_max_length
												lowercase__    :				List[str]=	self(
												    snake_case__	,  add_special_tokens=snake_case__	,  return_tensors=snake_case__	,  max_length=snake_case__	,  padding=snake_case__	,  truncation=snake_case__	,  **snake_case__	,  )
												if tgt_texts is None:
																		return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																		lowercase__    :				Dict=	self.current_tokenizer.model_max_length
												lowercase__    :				List[Any]=	self(
												    text_target=snake_case__	,  add_special_tokens=snake_case__	,  return_tensors=snake_case__	,  padding=snake_case__	,  max_length=snake_case__	,  truncation=snake_case__	,  **snake_case__	,  )
												lowercase__    :				str=	labels["input_ids"]
												return model_inputs














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				str=	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
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				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]						):
																		lowercase__    :				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)






