






'''simple docstring'''







import copy

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


UpperCamelCase     :   List[str]	      =		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(a      )
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       'rag'
		_UpperCamelCase						=       True






		def __init__(   self ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=" / " ,_lowerCAmelCase=" // " ,_lowerCAmelCase=5 ,_lowerCAmelCase=3_00 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=8 ,_lowerCAmelCase="wiki_dpr" ,_lowerCAmelCase="train" ,_lowerCAmelCase="compressed" ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
								super().__init__(
								    bos_token_id=_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,decoder_start_token_id=_lowerCAmelCase ,forced_eos_token_id=_lowerCAmelCase ,is_encoder_decoder=_lowerCAmelCase ,prefix=_lowerCAmelCase ,vocab_size=_lowerCAmelCase ,**_lowerCAmelCase ,)
								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(_lowerCAmelCase ,**_lowerCAmelCase							)
								lowerCamelCase__								=	AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase							)

								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""" ,_lowerCAmelCase							)






		@classmethod
		def       UpperCamelCase_       (   cls ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							):
								return cls(question_encoder=question_encoder_config.to_dict() ,generator=generator_config.to_dict() ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	copy.deepcopy(self.__dict__							)
								lowerCamelCase__								=	self.question_encoder.to_dict()
								lowerCamelCase__								=	self.generator.to_dict()
								lowerCamelCase__								=	self.__class__.model_type
								return output








'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






