






'''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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							:							Optional[Any]  							=		logging.get_logger(__name__)

__A							:							int  							=		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"vit"

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple=768				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     int=3072				,     __lowerCamelCase     :     Optional[Any]="gelu"				,     __lowerCamelCase     :     Optional[int]=0.0				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Optional[Any]=0.0_2				,     __lowerCamelCase     :     int=1E-12				,     __lowerCamelCase     :     Tuple=224				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Optional[int]=3				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     str=16				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     encoder_stride
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     Optional[int]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]     )




    @property
    def    a__				(					self     :     Optional[int]     )							->							float:

     '''simple docstring'''




     return 1E-4

