
'''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


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Optional[int]							,_a      :     Tuple							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   question_encoder
     _a				:	Any			=   generator
     _a				:	Optional[int]			=   self.question_encoder


  def   __lowercase		(		self      :     int							,_a      :     Optional[int]       ):


     '''simple docstring'''






     if os.path.isfile(_a       ):
        raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""       )
     os.makedirs(_a							,exist_ok=_a       )
     _a				:	Union[str, Any]			=   os.path.join(_a							,'question_encoder_tokenizer'       )
     _a				:	List[str]			=   os.path.join(_a							,'generator_tokenizer'       )
     self.question_encoder.save_pretrained(_a       )
     self.generator.save_pretrained(_a       )


  @classmethod
  def   __lowercase		(		cls      :     Optional[Any]							,_a      :     List[str]							,**_a      :     Optional[int]       ):


     '''simple docstring'''






     from ..auto.tokenization_auto import AutoTokenizer

     _a				:	int			=   kwargs.pop('config'							,_a       )

     if config is None:
        _a				:	Dict			=   RagConfig.from_pretrained(_a       )

     _a				:	List[str]			=   AutoTokenizer.from_pretrained(
         _a							,config=config.question_encoder							,subfolder='question_encoder_tokenizer'       )
     _a				:	Optional[int]			=   AutoTokenizer.from_pretrained(
         _a							,config=config.generator							,subfolder='generator_tokenizer'       )
     return cls(question_encoder=_a							,generator=_a       )


  def __call__(		self      :     int							,*_a      :     List[Any]							,**_a      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     int							,*_a      :     Optional[int]							,**_a      :     Union[str, Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Union[str, Any]							,*_a      :     int							,**_a      :     Optional[int]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	int			=   self.question_encoder


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.generator


  def   __lowercase		(		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      :     Any							,):


     '''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'							,_a							,)
     if max_length is None:
        _a				:	List[str]			=   self.current_tokenizer.model_max_length
     _a				:	Optional[Any]			=   self(
         _a							,add_special_tokens=_a							,return_tensors=_a							,max_length=_a							,padding=_a							,truncation=_a							,**_a							,)
     if tgt_texts is None:
        return model_inputs
     # Process tgt_texts
     if max_target_length is None:
        _a				:	Optional[int]			=   self.current_tokenizer.model_max_length
     _a				:	str			=   self(
         text_target=_a							,add_special_tokens=_a							,return_tensors=_a							,padding=_a							,max_length=_a							,truncation=_a							,**_a							,)
     _a				:	Dict			=   labels['input_ids']
     return model_inputs





'''simple docstring'''







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



