





"""simple docstring"""





import copy

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


lowerCAmelCase__     			=			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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"rag"
						UpperCAmelCase_ =			True


						def __init__(self   ,							__a=None   ,							__a=True   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=" / "   ,							__a=" // "   ,							__a=5   ,							__a=3_00   ,							__a=7_68   ,							__a=8   ,							__a="wiki_dpr"   ,							__a="train"   ,							__a="compressed"   ,							__a=None   ,							__a=None   ,							__a=False   ,							__a=False   ,							__a=0.0   ,							__a=True   ,							__a=False   ,							__a=False   ,							__a=False   ,							__a=True   ,							__a=None   ,							**__a   ,							) ->   str:
								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"
								UpperCamelCase   =	kwargs.pop("question_encoder"		)
								UpperCamelCase   =	question_encoder_config.pop("model_type"		)
								UpperCamelCase   =	kwargs.pop("generator"		)
								UpperCamelCase   =	decoder_config.pop("model_type"		)

								from ..auto.configuration_auto import AutoConfig

								UpperCamelCase   =	AutoConfig.for_model(__a   ,							**__a		)
								UpperCamelCase   =	AutoConfig.for_model(__a   ,							**__a		)

								UpperCamelCase   =	reduce_loss
								UpperCamelCase   =	label_smoothing
								UpperCamelCase   =	exclude_bos_score
								UpperCamelCase   =	do_marginalize

								UpperCamelCase   =	title_sep
								UpperCamelCase   =	doc_sep
								UpperCamelCase   =	n_docs
								UpperCamelCase   =	max_combined_length

								UpperCamelCase   =	dataset
								UpperCamelCase   =	dataset_split
								UpperCamelCase   =	index_name

								UpperCamelCase   =	retrieval_vector_size
								UpperCamelCase   =	retrieval_batch_size
								UpperCamelCase   =	passages_path
								UpperCamelCase   =	index_path
								UpperCamelCase   =	use_dummy_dataset

								UpperCamelCase   =	output_retrieved

								UpperCamelCase   =	do_deduplication

								UpperCamelCase   =	use_cache

								if self.forced_eos_token_id is None:
										UpperCamelCase   =	getattr(self.generator   ,							"forced_eos_token_id"   ,							__a		)


						@classmethod
						def     snake_case_   (cls   ,							__a   ,							__a   ,							**__a		) ->   PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict()   ,							generator=generator_config.to_dict()   ,							**__a		)




						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	copy.deepcopy(self.__dict__		)
								UpperCamelCase   =	self.question_encoder.to_dict()
								UpperCamelCase   =	self.generator.to_dict()
								UpperCamelCase   =	self.__class__.model_type
								return output













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__     			=			{'''tokenization_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

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






