


import copy

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


a : Dict		=				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(lowerCAmelCase__				)
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"rag"
					__UpperCAmelCase            =				True






					def __init__(				self ,       snake_case_=None ,       snake_case_=True ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=None ,       snake_case_=" / " ,       snake_case_=" // " ,       snake_case_=5 ,       snake_case_=3_0_0 ,       snake_case_=7_6_8 ,       snake_case_=8 ,       snake_case_="wiki_dpr" ,       snake_case_="train" ,       snake_case_="compressed" ,       snake_case_=None ,       snake_case_=None ,       snake_case_=False ,       snake_case_=False ,       snake_case_=0.0 ,       snake_case_=True ,       snake_case_=False ,       snake_case_=False ,       snake_case_=False ,       snake_case_=True ,       snake_case_=None ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__(
								    bos_token_id=snake_case_ ,       pad_token_id=snake_case_ ,       eos_token_id=snake_case_ ,       decoder_start_token_id=snake_case_ ,       forced_eos_token_id=snake_case_ ,       is_encoder_decoder=snake_case_ ,       prefix=snake_case_ ,       vocab_size=snake_case_ ,       **snake_case_ ,       )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								__lowercase 					=   kwargs.pop('''question_encoder'''						)
								__lowercase 					=   question_encoder_config.pop('''model_type'''						)
								__lowercase 					=   kwargs.pop('''generator'''						)
								__lowercase 					=   decoder_config.pop('''model_type'''						)

								from ..auto.configuration_auto import AutoConfig

								__lowercase 					=   AutoConfig.for_model(snake_case_ ,       **snake_case_						)
								__lowercase 					=   AutoConfig.for_model(snake_case_ ,       **snake_case_						)

								__lowercase 					=   reduce_loss
								__lowercase 					=   label_smoothing
								__lowercase 					=   exclude_bos_score
								__lowercase 					=   do_marginalize

								__lowercase 					=   title_sep
								__lowercase 					=   doc_sep
								__lowercase 					=   n_docs
								__lowercase 					=   max_combined_length

								__lowercase 					=   dataset
								__lowercase 					=   dataset_split
								__lowercase 					=   index_name

								__lowercase 					=   retrieval_vector_size
								__lowercase 					=   retrieval_batch_size
								__lowercase 					=   passages_path
								__lowercase 					=   index_path
								__lowercase 					=   use_dummy_dataset

								__lowercase 					=   output_retrieved

								__lowercase 					=   do_deduplication

								__lowercase 					=   use_cache

								if self.forced_eos_token_id is None:
											__lowercase 					=   getattr(self.generator ,       '''forced_eos_token_id''' ,       snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_ ,       **snake_case_						)   ->			PretrainedConfig:







								'''simple docstring'''

								return cls(question_encoder=question_encoder_config.to_dict() ,       generator=generator_config.to_dict() ,       **snake_case_						)






					def A      (				self						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   copy.deepcopy(self.__dict__						)
								__lowercase 					=   self.question_encoder.to_dict()
								__lowercase 					=   self.generator.to_dict()
								__lowercase 					=   self.__class__.model_type
								return output






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)




class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["speech"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Tuple:







								'''simple docstring'''

								requires_backends(self ,       ['''speech''']						)


