

import os
import warnings
from typing import List, Optional

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


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			question_encoder
							A__        =			generator
							A__        =			self.question_encoder


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							if os.path.isfile(_lowerCamelCase       ):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'       )
							os.makedirs(_lowerCamelCase      ,      exist_ok=_lowerCamelCase       )
							A__        =			os.path.join(_lowerCamelCase      ,      "question_encoder_tokenizer"       )
							A__        =			os.path.join(_lowerCamelCase      ,      "generator_tokenizer"       )
							self.question_encoder.save_pretrained(_lowerCamelCase       )
							self.generator.save_pretrained(_lowerCamelCase       )


			@classmethod
			def      UpperCamelCase		(    cls      ,      lowercase      ,      **lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							from ..auto.tokenization_auto import AutoTokenizer

							A__        =			kwargs.pop("config"      ,      _lowerCamelCase       )

							if config is None:
											A__        =			RagConfig.from_pretrained(_lowerCamelCase       )

							A__        =			AutoTokenizer.from_pretrained(
							    _lowerCamelCase      ,      config=config.question_encoder      ,      subfolder="question_encoder_tokenizer"       )
							A__        =			AutoTokenizer.from_pretrained(
							    _lowerCamelCase      ,      config=config.generator      ,      subfolder="generator_tokenizer"       )
							return cls(question_encoder=_lowerCamelCase      ,      generator=_lowerCamelCase       )


			def __call__(    self      ,      *lowercase      ,      **lowercase       )  ->						List[str]:
							'''simple docstring'''






							return self.current_tokenizer(*_lowerCamelCase      ,      **_lowerCamelCase       )


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






							return self.generator.batch_decode(*_lowerCamelCase      ,      **_lowerCamelCase       )


			def      UpperCamelCase		(    self      ,      *lowercase      ,      **lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							return self.generator.decode(*_lowerCamelCase      ,      **_lowerCamelCase       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			self.question_encoder


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			self.generator


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = "longest"      ,      lowercase = None      ,      lowercase = True      ,      **lowercase      ,      )  ->						BatchEncoding:
							'''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"      ,      _lowerCamelCase      ,      )
							if max_length is None:
											A__        =			self.current_tokenizer.model_max_length
							A__        =			self(
							    _lowerCamelCase      ,      add_special_tokens=_lowerCamelCase      ,      return_tensors=_lowerCamelCase      ,      max_length=_lowerCamelCase      ,      padding=_lowerCamelCase      ,      truncation=_lowerCamelCase      ,      **_lowerCamelCase      ,      )
							if tgt_texts is None:
											return model_inputs
							# Process tgt_texts
							if max_target_length is None:
											A__        =			self.current_tokenizer.model_max_length
							A__        =			self(
							    text_target=_lowerCamelCase      ,      add_special_tokens=_lowerCamelCase      ,      return_tensors=_lowerCamelCase      ,      padding=_lowerCamelCase      ,      max_length=_lowerCamelCase      ,      truncation=_lowerCamelCase      ,      **_lowerCamelCase      ,      )
							A__        =			labels["input_ids"]
							return model_inputs




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

				def is_too_big(SCREAMING_SNAKE_CASE_:							List[str]      ):
								return tok(SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								A__        =			new_src + " " + src
								A__        =			new_tgt + " " + tgt
								if is_too_big(SCREAMING_SNAKE_CASE_      ) or is_too_big(SCREAMING_SNAKE_CASE_      ):  # cant fit, finalize example
												finished_src.append(SCREAMING_SNAKE_CASE_      )
												finished_tgt.append(SCREAMING_SNAKE_CASE_      )
												A__    ,				A__        =			src, tgt
								else:  # can fit, keep adding
												A__    ,				A__        =			cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(SCREAMING_SNAKE_CASE_      )
								finished_tgt.append(SCREAMING_SNAKE_CASE_      )
				return finished_src, finished_tgt



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			Path(SCREAMING_SNAKE_CASE_      )
				save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				for split in ["train"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__    ,				A__        =			pack_examples(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								print(F'packed {split} split from {len(SCREAMING_SNAKE_CASE_      )} examples -> {len(SCREAMING_SNAKE_CASE_      )}.'      )
								Path(save_path / F'{split}.source'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
								Path(save_path / F'{split}.target'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
				for split in ["val", "test"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.source'      )
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.target'      )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

