
import copy

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


lowercase   =	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(UpperCAmelCase							)
class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     '''rag'''
					SCREAMING_SNAKE_CASE           =     True

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  List[str]=None ,       __UpperCamelCase       :  List[Any]=True ,       __UpperCamelCase       :  int=None ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  Any=None ,       __UpperCamelCase       :  List[Any]=None ,       __UpperCamelCase       :  str=None ,       __UpperCamelCase       :  Optional[int]=" / " ,       __UpperCamelCase       :  Dict=" // " ,       __UpperCamelCase       :  List[str]=5 ,       __UpperCamelCase       :  Tuple=3_0_0 ,       __UpperCamelCase       :  Optional[int]=7_6_8 ,       __UpperCamelCase       :  Tuple=8 ,       __UpperCamelCase       :  Tuple="wiki_dpr" ,       __UpperCamelCase       :  Tuple="train" ,       __UpperCamelCase       :  Tuple="compressed" ,       __UpperCamelCase       :  Optional[Any]=None ,       __UpperCamelCase       :  Optional[Any]=None ,       __UpperCamelCase       :  Any=False ,       __UpperCamelCase       :  Union[str, Any]=False ,       __UpperCamelCase       :  Tuple=0.0 ,       __UpperCamelCase       :  Any=True ,       __UpperCamelCase       :  Tuple=False ,       __UpperCamelCase       :  Tuple=False ,       __UpperCamelCase       :  Optional[Any]=False ,       __UpperCamelCase       :  Optional[int]=True ,       __UpperCamelCase       :  Dict=None ,       **__UpperCamelCase       :  List[str] ,       ):
												super().__init__(
												    bos_token_id=__UpperCamelCase ,       pad_token_id=__UpperCamelCase ,       eos_token_id=__UpperCamelCase ,       decoder_start_token_id=__UpperCamelCase ,       forced_eos_token_id=__UpperCamelCase ,       is_encoder_decoder=__UpperCamelCase ,       prefix=__UpperCamelCase ,       vocab_size=__UpperCamelCase ,       **__UpperCamelCase ,       )
												assert (
												    "question_encoder" in kwargs and "generator" in kwargs
												), "Config has to be initialized with question_encoder and generator config"
												lowerCamelCase_					=       kwargs.pop("""question_encoder""" )
												lowerCamelCase_					=       question_encoder_config.pop("""model_type""" )
												lowerCamelCase_					=       kwargs.pop("""generator""" )
												lowerCamelCase_					=       decoder_config.pop("""model_type""" )

												from ..auto.configuration_auto import AutoConfig

												lowerCamelCase_					=       AutoConfig.for_model(__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       AutoConfig.for_model(__UpperCamelCase ,       **__UpperCamelCase )

												lowerCamelCase_					=       reduce_loss
												lowerCamelCase_					=       label_smoothing
												lowerCamelCase_					=       exclude_bos_score
												lowerCamelCase_					=       do_marginalize

												lowerCamelCase_					=       title_sep
												lowerCamelCase_					=       doc_sep
												lowerCamelCase_					=       n_docs
												lowerCamelCase_					=       max_combined_length

												lowerCamelCase_					=       dataset
												lowerCamelCase_					=       dataset_split
												lowerCamelCase_					=       index_name

												lowerCamelCase_					=       retrieval_vector_size
												lowerCamelCase_					=       retrieval_batch_size
												lowerCamelCase_					=       passages_path
												lowerCamelCase_					=       index_path
												lowerCamelCase_					=       use_dummy_dataset

												lowerCamelCase_					=       output_retrieved

												lowerCamelCase_					=       do_deduplication

												lowerCamelCase_					=       use_cache

												if self.forced_eos_token_id is None:
																			lowerCamelCase_					=       getattr(self.generator ,       """forced_eos_token_id""" ,       __UpperCamelCase )

					@classmethod
					def  lowercase__   (						cls       :  Tuple ,       __UpperCamelCase       :  PretrainedConfig ,       __UpperCamelCase       :  PretrainedConfig ,       **__UpperCamelCase       :  List[str] ):
												return cls(question_encoder=question_encoder_config.to_dict() ,       generator=generator_config.to_dict() ,       **__UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       copy.deepcopy(self.__dict__ )
												lowerCamelCase_					=       self.question_encoder.to_dict()
												lowerCamelCase_					=       self.generator.to_dict()
												lowerCamelCase_					=       self.__class__.model_type
												return output







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowercase   =	{}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





