'''simple docstring'''




import copy

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


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





@add_start_docstrings(_UpperCAmelCase)
class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''rag'''
			UpperCamelCase__				=       True







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

										from ..auto.configuration_auto import AutoConfig

										lowercase_					:			Optional[int]										=       AutoConfig.for_model(lowercase_				,			**lowercase_    )
										lowercase_					:			Any										=       AutoConfig.for_model(lowercase_				,			**lowercase_    )

										lowercase_					:			Dict										=       reduce_loss
										lowercase_					:			Union[str, Any]										=       label_smoothing
										lowercase_					:			int										=       exclude_bos_score
										lowercase_					:			List[Any]										=       do_marginalize

										lowercase_					:			int										=       title_sep
										lowercase_					:			Any										=       doc_sep
										lowercase_					:			Dict										=       n_docs
										lowercase_					:			Optional[int]										=       max_combined_length

										lowercase_					:			List[str]										=       dataset
										lowercase_					:			Optional[Any]										=       dataset_split
										lowercase_					:			Dict										=       index_name

										lowercase_					:			List[Any]										=       retrieval_vector_size
										lowercase_					:			str										=       retrieval_batch_size
										lowercase_					:			Any										=       passages_path
										lowercase_					:			Union[str, Any]										=       index_path
										lowercase_					:			str										=       use_dummy_dataset

										lowercase_					:			List[Any]										=       output_retrieved

										lowercase_					:			int										=       do_deduplication

										lowercase_					:			List[str]										=       use_cache

										if self.forced_eos_token_id is None:
																	lowercase_					:			Optional[Any]										=       getattr(self.generator				,			"""forced_eos_token_id"""				,			lowercase_    )







			@classmethod
			def 	SCREAMING_SNAKE_CASE_     (	cls				:  Tuple				,			lowercase_				:  PretrainedConfig				,			lowercase_				:  PretrainedConfig				,			**lowercase_				:  int    ):
										return cls(question_encoder=question_encoder_config.to_dict()				,			generator=generator_config.to_dict()				,			**lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			int										=       copy.deepcopy(self.__dict__    )
										lowercase_					:			List[Any]										=       self.question_encoder.to_dict()
										lowercase_					:			Optional[int]										=       self.generator.to_dict()
										lowercase_					:			str										=       self.__class__.model_type
										return output






'''simple docstring'''




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase						: Dict						=						{"configuration_mmbt": ["MMBTConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase						: Dict						=						["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						_lowercase						: Optional[int]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





