


import copy

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


_snake_case					:		Union[str, Any]      =    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(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  "rag"
							__UpperCAmelCase   :							Any					  =  True

							def __init__(							self	:  List[Any]   ,     lowerCamelCase	:  List[Any]=None   ,     lowerCamelCase	:  Any=True   ,     lowerCamelCase	:  str=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=" / "   ,     lowerCamelCase	:  Tuple=" // "   ,     lowerCamelCase	:  Union[str, Any]=5   ,     lowerCamelCase	:  Optional[int]=300   ,     lowerCamelCase	:  Optional[Any]=768   ,     lowerCamelCase	:  Optional[Any]=8   ,     lowerCamelCase	:  Union[str, Any]="wiki_dpr"   ,     lowerCamelCase	:  int="train"   ,     lowerCamelCase	:  List[str]="compressed"   ,     lowerCamelCase	:  List[str]=None   ,     lowerCamelCase	:  Optional[int]=None   ,     lowerCamelCase	:  Tuple=False   ,     lowerCamelCase	:  Tuple=False   ,     lowerCamelCase	:  Dict=0.0   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  str=False   ,     lowerCamelCase	:  Optional[Any]=False   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  str=None   ,     **lowerCamelCase	:  List[str]   ,     )     ->   Union[str, Any]:
														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"
														__snake_case		:      List[str]										=       kwargs.pop("question_encoder"    )
														__snake_case		:      int										=       question_encoder_config.pop("model_type"    )
														__snake_case		:      Optional[int]										=       kwargs.pop("generator"    )
														__snake_case		:      int										=       decoder_config.pop("model_type"    )

														from ..auto.configuration_auto import AutoConfig

														__snake_case		:      Tuple										=       AutoConfig.for_model(lowerCamelCase   ,     **lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       AutoConfig.for_model(lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Any										=       reduce_loss
														__snake_case		:      Union[str, Any]										=       label_smoothing
														__snake_case		:      Optional[int]										=       exclude_bos_score
														__snake_case		:      Union[str, Any]										=       do_marginalize

														__snake_case		:      Optional[int]										=       title_sep
														__snake_case		:      Optional[int]										=       doc_sep
														__snake_case		:      Union[str, Any]										=       n_docs
														__snake_case		:      Optional[int]										=       max_combined_length

														__snake_case		:      Dict										=       dataset
														__snake_case		:      Optional[int]										=       dataset_split
														__snake_case		:      Tuple										=       index_name

														__snake_case		:      Optional[int]										=       retrieval_vector_size
														__snake_case		:      Optional[Any]										=       retrieval_batch_size
														__snake_case		:      Tuple										=       passages_path
														__snake_case		:      Tuple										=       index_path
														__snake_case		:      Optional[int]										=       use_dummy_dataset

														__snake_case		:      Optional[Any]										=       output_retrieved

														__snake_case		:      Any										=       do_deduplication

														__snake_case		:      Optional[int]										=       use_cache

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

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  PretrainedConfig   ,     lowerCamelCase	:  PretrainedConfig   ,     **lowerCamelCase	:  Optional[Any]    )     ->   PretrainedConfig:
														return cls(question_encoder=question_encoder_config.to_dict()   ,     generator=generator_config.to_dict()   ,     **lowerCamelCase    )






							def    __snake_case (							self	:  int    )     ->   Union[str, Any]:
														__snake_case		:      Dict										=       copy.deepcopy(self.__dict__    )
														__snake_case		:      Optional[Any]										=       self.question_encoder.to_dict()
														__snake_case		:      Optional[Any]										=       self.generator.to_dict()
														__snake_case		:      List[Any]										=       self.__class__.model_type
														return output





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

