




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 :






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






       """simple docstring"""







       lowercase :       List[Any]       =			question_encoder
       lowercase :       Any       =			generator
       lowercase :       Optional[int]       =			self.question_encoder






  def  __a      (  self		:     str				,						_A		:     str				)		->   Union[str, 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				)
       lowercase :       Union[str, Any]       =			os.path.join(_lowerCAmelCase				,						'''question_encoder_tokenizer'''				)
       lowercase :       Tuple       =			os.path.join(_lowerCAmelCase				,						'''generator_tokenizer'''				)
       self.question_encoder.save_pretrained(_lowerCAmelCase				)
       self.generator.save_pretrained(_lowerCAmelCase				)






  @classmethod
  def  __a      (  cls		:     Union[str, Any]				,						_A		:     Tuple				,						**_A		:     List[str]				)		->   Tuple:






       """simple docstring"""







       from ..auto.tokenization_auto import AutoTokenizer

       lowercase :       List[Any]       =			kwargs.pop('''config'''				,						_lowerCAmelCase				)

       if config is None:
            lowercase :       Any       =			RagConfig.from_pretrained(_lowerCAmelCase				)

       lowercase :       Optional[Any]       =			AutoTokenizer.from_pretrained(
           _lowerCAmelCase				,						config=config.question_encoder				,						subfolder='''question_encoder_tokenizer'''				)
       lowercase :       Optional[int]       =			AutoTokenizer.from_pretrained(
           _lowerCAmelCase				,						config=config.generator				,						subfolder='''generator_tokenizer'''				)
       return cls(question_encoder=_lowerCAmelCase				,						generator=_lowerCAmelCase				)






  def __call__(  self		:     List[str]				,						*_A		:     str				,						**_A		:     Union[str, Any]				)		->   Any:






       """simple docstring"""







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






  def  __a      (  self		:     Union[str, Any]				,						*_A		:     Optional[int]				,						**_A		:     Optional[int]				)		->   Union[str, Any]:






       """simple docstring"""







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






  def  __a      (  self		:     int				,						*_A		:     Tuple				,						**_A		:     List[str]				)		->   Optional[int]:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[int]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Any       =			self.question_encoder






  def  __a      (  self		:     Optional[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Dict       =			self.generator






  def  __a      (  self		:     Any				,						_A		:     List[str]				,						_A		:     Optional[List[str]] = None				,						_A		:     Optional[int] = None				,						_A		:     Optional[int] = None				,						_A		:     str = "longest"				,						_A		:     str = None				,						_A		:     bool = True				,						**_A		:     List[str]				,						)		->   str:






       """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:
            lowercase :       str       =			self.current_tokenizer.model_max_length
       lowercase :       Any       =			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:
            lowercase :       str       =			self.current_tokenizer.model_max_length
       lowercase :       Optional[Any]       =			self(
           text_target=_lowerCAmelCase				,						add_special_tokens=_lowerCAmelCase				,						return_tensors=_lowerCAmelCase				,						padding=_lowerCAmelCase				,						max_length=_lowerCAmelCase				,						truncation=_lowerCAmelCase				,						**_lowerCAmelCase				,						)
       lowercase :       Optional[int]       =			labels["""input_ids"""]
       return model_inputs





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)