



'''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


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						:




    def __init__(			self  :       Any  ,     a  :       List[str]  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    str           =     question_encoder
       lowercase_	:    List[str]           =     generator
       lowercase_	:    Dict           =     self.question_encoder




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       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_			)
       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 				lowerCAmelCase__				(			cls  :       Dict  ,     a  :       Tuple  ,     **a  :       Dict			):






       '''simple docstring'''






       from ..auto.tokenization_auto import AutoTokenizer

       lowercase_	:    List[str]           =     kwargs.pop("config"  ,     lowerCAmelCase_			)

       if config is None:
          lowercase_	:    Dict           =     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  :       int  ,     *a  :       int  ,     **a  :       Tuple			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       int  ,     *a  :       Optional[int]  ,     **a  :       List[str]			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     *a  :       List[Any]  ,     **a  :       Union[str, Any]			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.question_encoder




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     self.generator





    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]  ,     a  :       Optional[Any] = None  ,     a  :       int = None  ,     a  :       List[Any] = None  ,     a  :       List[str] = "longest"  ,     a  :       Union[str, Any] = None  ,     a  :       Optional[Any] = True  ,     **a  :       Optional[int]  ,     ):






       '''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_	:    Any           =     self.current_tokenizer.model_max_length
       lowercase_	:    Dict           =     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_	:    List[str]           =     self.current_tokenizer.model_max_length
       lowercase_	:    List[str]           =     self(
           text_target=lowerCAmelCase_  ,     add_special_tokens=lowerCAmelCase_  ,     return_tensors=lowerCAmelCase_  ,     padding=lowerCAmelCase_  ,     max_length=lowerCAmelCase_  ,     truncation=lowerCAmelCase_  ,     **lowerCAmelCase_  ,     )
       lowercase_	:    Union[str, Any]           =     labels["input_ids"]
       return model_inputs












'''simple docstring'''
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    'pipelines_utils',
    '0.22.0',
    'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
    standard_warn=False,
    stacklevel=3,
)




