






'''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							:							Union[str, Any]  							=		logging.get_logger(__name__)
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     question_encoder
     lowerCamelCase__  =     generator
     lowerCamelCase__  =     self.question_encoder

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Any     )							->							str:

     '''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     )
     lowerCamelCase__  =     os.path.join(__lowerCamelCase				,     "question_encoder_tokenizer"     )
     lowerCamelCase__  =     os.path.join(__lowerCamelCase				,     "generator_tokenizer"     )
     self.question_encoder.save_pretrained(__lowerCamelCase     )
     self.generator.save_pretrained(__lowerCamelCase     )

    @classmethod
    def    a__				(					cls     :     Optional[Any]				,     __lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     List[Any]     )							->							Dict:

     '''simple docstring'''




     from ..auto.tokenization_auto import AutoTokenizer

     lowerCamelCase__  =     kwargs.pop("config"				,     __lowerCamelCase     )

     if config is None:
      lowerCamelCase__  =     RagConfig.from_pretrained(__lowerCamelCase     )

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

    def __call__(					self     :     List[str]				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     List[str]     )							->							str:

     '''simple docstring'''




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

    def    a__				(					self     :     str				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     Any     )							->							Any:

     '''simple docstring'''




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

    def    a__				(					self     :     int				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     Dict     )							->							Dict:

     '''simple docstring'''




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

    def    a__				(					self     :     str     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.question_encoder

    def    a__				(					self     :     List[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     self.generator




    def    a__				(					self     :     int				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Optional[List[str]] = None				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     str = "longest"				,     __lowerCamelCase     :     str = None				,     __lowerCamelCase     :     bool = True				,     **__lowerCamelCase     :     Dict				,     )							->							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:
      lowerCamelCase__  =     self.current_tokenizer.model_max_length
     lowerCamelCase__  =     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:
      lowerCamelCase__  =     self.current_tokenizer.model_max_length
     lowerCamelCase__  =     self(
         text_target=__lowerCamelCase				,     add_special_tokens=__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     padding=__lowerCamelCase				,     max_length=__lowerCamelCase				,     truncation=__lowerCamelCase				,     **__lowerCamelCase				,     )
     lowerCamelCase__  =     labels["input_ids"]
     return model_inputs









'''simple docstring'''





from __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

 lowerCamelCase__  =     0
 lowerCamelCase__  =     [0] * len(lowercase__)
 for i in index:
  if weight[i] <= capacity:
   lowerCamelCase__  =     1
   max_value += value[i]
   capacity -= weight[i]
  else:
   lowerCamelCase__  =     capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

