

'''simple docstring'''



import copy

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


UpperCAmelCase					:			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 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "rag"
 lowerCAmelCase__       = True
 def __init__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=True   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Tuple=None   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=" / "   ,				__SCREAMING_SNAKE_CASE   :			Dict=" // "   ,				__SCREAMING_SNAKE_CASE   :			Dict=5   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=300   ,				__SCREAMING_SNAKE_CASE   :			int=768   ,				__SCREAMING_SNAKE_CASE   :			int=8   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]="wiki_dpr"   ,				__SCREAMING_SNAKE_CASE   :			Dict="train"   ,				__SCREAMING_SNAKE_CASE   :			Any="compressed"   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Tuple=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=True   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			str=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=False   ,				__SCREAMING_SNAKE_CASE   :			Dict=True   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=None   ,				**__SCREAMING_SNAKE_CASE   :			List[str]   ,				)    ->					Optional[int]:


      """simple docstring"""




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

      from ..auto.configuration_auto import AutoConfig

      __SCREAMING_SNAKE_CASE      = AutoConfig.for_model(__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = AutoConfig.for_model(__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = reduce_loss
      __SCREAMING_SNAKE_CASE      = label_smoothing
      __SCREAMING_SNAKE_CASE      = exclude_bos_score
      __SCREAMING_SNAKE_CASE      = do_marginalize

      __SCREAMING_SNAKE_CASE      = title_sep
      __SCREAMING_SNAKE_CASE      = doc_sep
      __SCREAMING_SNAKE_CASE      = n_docs
      __SCREAMING_SNAKE_CASE      = max_combined_length

      __SCREAMING_SNAKE_CASE      = dataset
      __SCREAMING_SNAKE_CASE      = dataset_split
      __SCREAMING_SNAKE_CASE      = index_name

      __SCREAMING_SNAKE_CASE      = retrieval_vector_size
      __SCREAMING_SNAKE_CASE      = retrieval_batch_size
      __SCREAMING_SNAKE_CASE      = passages_path
      __SCREAMING_SNAKE_CASE      = index_path
      __SCREAMING_SNAKE_CASE      = use_dummy_dataset

      __SCREAMING_SNAKE_CASE      = output_retrieved

      __SCREAMING_SNAKE_CASE      = do_deduplication

      __SCREAMING_SNAKE_CASE      = use_cache

      if self.forced_eos_token_id is None:
           __SCREAMING_SNAKE_CASE      = getattr(self.generator   ,				"""forced_eos_token_id"""   ,				__SCREAMING_SNAKE_CASE     )
 @classmethod
 def  UpperCAmelCase__						(						cls   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			PretrainedConfig   ,				__SCREAMING_SNAKE_CASE   :			PretrainedConfig   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					PretrainedConfig:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					int:


      """simple docstring"""




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









'''simple docstring'''



from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 		lowerCAmelCase__  ( a					,   a   ):



 """simple docstring"""



 @register_to_config
 def __init__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			int = 768   ,				)    ->					int:


      """simple docstring"""




      super().__init__()

      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.zeros(1   ,				__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.ones(1   ,				__SCREAMING_SNAKE_CASE     )     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, torch.device]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[torch.dtype] = None   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.mean.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.std.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      return self
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds - self.mean) * 1.0 / self.std
      return embeds


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds * self.std) + self.mean
      return embeds






