



'''simple docstring'''





import copy

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


_lowerCAmelCase      						=	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(SCREAMING_SNAKE_CASE_     )
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      str				=      '''rag'''
  __lowercase							:      Any				=      True




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

      from ..auto.configuration_auto import AutoConfig

      lowerCAmelCase__ :  Optional[int]							      =					AutoConfig.for_model(__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					AutoConfig.for_model(__UpperCAmelCase  ,**__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					reduce_loss
      lowerCAmelCase__ :  Dict							      =					label_smoothing
      lowerCAmelCase__ :  List[Any]							      =					exclude_bos_score
      lowerCAmelCase__ :  Union[str, Any]							      =					do_marginalize

      lowerCAmelCase__ :  Tuple							      =					title_sep
      lowerCAmelCase__ :  Union[str, Any]							      =					doc_sep
      lowerCAmelCase__ :  Union[str, Any]							      =					n_docs
      lowerCAmelCase__ :  Union[str, Any]							      =					max_combined_length

      lowerCAmelCase__ :  str							      =					dataset
      lowerCAmelCase__ :  List[Any]							      =					dataset_split
      lowerCAmelCase__ :  Optional[Any]							      =					index_name

      lowerCAmelCase__ :  Dict							      =					retrieval_vector_size
      lowerCAmelCase__ :  Tuple							      =					retrieval_batch_size
      lowerCAmelCase__ :  Optional[Any]							      =					passages_path
      lowerCAmelCase__ :  Union[str, Any]							      =					index_path
      lowerCAmelCase__ :  Dict							      =					use_dummy_dataset

      lowerCAmelCase__ :  Optional[int]							      =					output_retrieved

      lowerCAmelCase__ :  int							      =					do_deduplication

      lowerCAmelCase__ :  Optional[Any]							      =					use_cache

      if self.forced_eos_token_id is None:
          lowerCAmelCase__ :  Tuple							      =					getattr(self.generator  ,"""forced_eos_token_id"""  ,__UpperCAmelCase      )




  @classmethod
  def 							UpperCAmelCase_	(    cls  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		PretrainedConfig:
      return cls(question_encoder=question_encoder_config.to_dict()  ,generator=generator_config.to_dict()  ,**__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Optional[int]							      =					copy.deepcopy(self.__dict__      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.question_encoder.to_dict()
      lowerCAmelCase__ :  List[Any]							      =					self.generator.to_dict()
      lowerCAmelCase__ :  Tuple							      =					self.__class__.model_type
      return output







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


