

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__A      		=     r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''



@add_start_docstrings(__lowercase					)
class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Dict										=    "rag"
      UpperCamelCase_      :		Tuple										=    True



      def __init__(      self				:    Optional[Any]      , A__				:    Any=None      , A__				:    Dict=True      , A__				:    Tuple=None      , A__				:    int=None      , A__				:    Optional[Any]=None      , A__				:    List[Any]=None      , A__				:    int=None      , A__				:    List[str]=" / "      , A__				:    str=" // "      , A__				:    Dict=5      , A__				:    Optional[int]=300      , A__				:    str=768      , A__				:    Any=8      , A__				:    str="wiki_dpr"      , A__				:    Optional[int]="train"      , A__				:    Any="compressed"      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]=None      , A__				:    List[str]=False      , A__				:    List[Any]=False      , A__				:    int=0.0      , A__				:    Any=True      , A__				:    Tuple=False      , A__				:    Tuple=False      , A__				:    List[Any]=False      , A__				:    Any=True      , A__				:    int=None      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            bos_token_id=A__      , pad_token_id=A__      , eos_token_id=A__      , decoder_start_token_id=A__      , forced_eos_token_id=A__      , is_encoder_decoder=A__      , prefix=A__      , vocab_size=A__      , **A__      , )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        _snake_case						=							kwargs.pop('''question_encoder'''		)
        _snake_case						=							question_encoder_config.pop('''model_type'''		)
        _snake_case						=							kwargs.pop('''generator'''		)
        _snake_case						=							decoder_config.pop('''model_type'''		)

        from ..auto.configuration_auto import AutoConfig

        _snake_case						=							AutoConfig.for_model(A__      , **A__		)
        _snake_case						=							AutoConfig.for_model(A__      , **A__		)

        _snake_case						=							reduce_loss
        _snake_case						=							label_smoothing
        _snake_case						=							exclude_bos_score
        _snake_case						=							do_marginalize

        _snake_case						=							title_sep
        _snake_case						=							doc_sep
        _snake_case						=							n_docs
        _snake_case						=							max_combined_length

        _snake_case						=							dataset
        _snake_case						=							dataset_split
        _snake_case						=							index_name

        _snake_case						=							retrieval_vector_size
        _snake_case						=							retrieval_batch_size
        _snake_case						=							passages_path
        _snake_case						=							index_path
        _snake_case						=							use_dummy_dataset

        _snake_case						=							output_retrieved

        _snake_case						=							do_deduplication

        _snake_case						=							use_cache

        if self.forced_eos_token_id is None:
          _snake_case						=							getattr(self.generator      , '''forced_eos_token_id'''      , A__		)



      @classmethod
      def 						UpperCamelCase_		(      cls				:    Tuple      , A__				:    PretrainedConfig      , A__				:    PretrainedConfig      , **A__				:    int		)   ->					PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()      , generator=generator_config.to_dict()      , **A__		)







      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Dict:
        _snake_case						=							copy.deepcopy(self.__dict__		)
        _snake_case						=							self.question_encoder.to_dict()
        _snake_case						=							self.generator.to_dict()
        _snake_case						=							self.__class__.model_type
        return output




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

