



'''simple docstring'''





import copy

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


_UpperCamelCase											=						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(SCREAMING_SNAKE_CASE_     )
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""rag"""
  a_												=True




  def __init__( self      :	Any			,	_a      :	Tuple=None			,	_a      :	Union[str, Any]=True			,	_a      :	Tuple=None			,	_a      :	Optional[Any]=None			,	_a      :	str=None			,	_a      :	Union[str, Any]=None			,	_a      :	Union[str, Any]=None			,	_a      :	Union[str, Any]=" / "			,	_a      :	Tuple=" // "			,	_a      :	Optional[int]=5			,	_a      :	Tuple=300			,	_a      :	Optional[Any]=768			,	_a      :	Dict=8			,	_a      :	Union[str, Any]="wiki_dpr"			,	_a      :	str="train"			,	_a      :	List[Any]="compressed"			,	_a      :	Dict=None			,	_a      :	Optional[Any]=None			,	_a      :	Tuple=False			,	_a      :	Optional[Any]=False			,	_a      :	Tuple=0.0			,	_a      :	Union[str, Any]=True			,	_a      :	List[str]=False			,	_a      :	int=False			,	_a      :	Optional[int]=False			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=None			,	**_a      :	Union[str, Any]			,	)					->					Optional[int]:
    super().__init__(
        bos_token_id=_a			,	pad_token_id=_a			,	eos_token_id=_a			,	decoder_start_token_id=_a			,	forced_eos_token_id=_a			,	is_encoder_decoder=_a			,	prefix=_a			,	vocab_size=_a			,	**_a			,	)
    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       :       Tuple           = question_encoder_config.pop('model_type'   )
    __lowerCamelCase       :       Optional[Any]           = kwargs.pop('generator'   )
    __lowerCamelCase       :       Tuple           = decoder_config.pop('model_type'   )

    from ..auto.configuration_auto import AutoConfig

    __lowerCamelCase       :       int           = AutoConfig.for_model(_a			,	**_a   )
    __lowerCamelCase       :       Dict           = AutoConfig.for_model(_a			,	**_a   )

    __lowerCamelCase       :       List[str]           = reduce_loss
    __lowerCamelCase       :       Dict           = label_smoothing
    __lowerCamelCase       :       List[str]           = exclude_bos_score
    __lowerCamelCase       :       Optional[Any]           = do_marginalize

    __lowerCamelCase       :       Tuple           = title_sep
    __lowerCamelCase       :       Optional[Any]           = doc_sep
    __lowerCamelCase       :       Dict           = n_docs
    __lowerCamelCase       :       str           = max_combined_length

    __lowerCamelCase       :       Optional[Any]           = dataset
    __lowerCamelCase       :       List[Any]           = dataset_split
    __lowerCamelCase       :       Optional[Any]           = index_name

    __lowerCamelCase       :       Tuple           = retrieval_vector_size
    __lowerCamelCase       :       Optional[int]           = retrieval_batch_size
    __lowerCamelCase       :       List[Any]           = passages_path
    __lowerCamelCase       :       Union[str, Any]           = index_path
    __lowerCamelCase       :       str           = use_dummy_dataset

    __lowerCamelCase       :       Optional[Any]           = output_retrieved

    __lowerCamelCase       :       List[Any]           = do_deduplication

    __lowerCamelCase       :       Union[str, Any]           = use_cache

    if self.forced_eos_token_id is None:
      __lowerCamelCase       :       List[str]           = getattr(self.generator			,	'forced_eos_token_id'			,	_a   )




  @classmethod
  def 						_lowercase     ( cls      :	int			,	_a      :	PretrainedConfig			,	_a      :	PretrainedConfig			,	**_a      :	List[str]   )					->					PretrainedConfig:
    return cls(question_encoder=question_encoder_config.to_dict()			,	generator=generator_config.to_dict()			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       List[Any]           = copy.deepcopy(self.__dict__   )
    __lowerCamelCase       :       Union[str, Any]           = self.question_encoder.to_dict()
    __lowerCamelCase       :       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


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""xlm-roberta"""




  def __init__( self      :	Any			,	_a      :	Optional[Any]=3_0522			,	_a      :	Optional[Any]=768			,	_a      :	int=12			,	_a      :	Tuple=12			,	_a      :	str=3072			,	_a      :	List[Any]="gelu"			,	_a      :	int=0.1			,	_a      :	Optional[int]=0.1			,	_a      :	Optional[int]=512			,	_a      :	List[Any]=2			,	_a      :	Optional[Any]=0.02			,	_a      :	str=1e-12			,	_a      :	str=1			,	_a      :	str=0			,	_a      :	Optional[Any]=2			,	_a      :	Optional[int]="absolute"			,	_a      :	int=True			,	_a      :	Tuple=None			,	**_a      :	Any			,	)					->					Dict:
    super().__init__(pad_token_id=_a			,	bos_token_id=_a			,	eos_token_id=_a			,	**_a   )

    __lowerCamelCase       :       int           = vocab_size
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       int           = num_hidden_layers
    __lowerCamelCase       :       Optional[int]           = num_attention_heads
    __lowerCamelCase       :       List[str]           = hidden_act
    __lowerCamelCase       :       Any           = intermediate_size
    __lowerCamelCase       :       int           = hidden_dropout_prob
    __lowerCamelCase       :       List[Any]           = attention_probs_dropout_prob
    __lowerCamelCase       :       Optional[int]           = max_position_embeddings
    __lowerCamelCase       :       Tuple           = type_vocab_size
    __lowerCamelCase       :       Optional[int]           = initializer_range
    __lowerCamelCase       :       Optional[int]           = layer_norm_eps
    __lowerCamelCase       :       str           = position_embedding_type
    __lowerCamelCase       :       List[Any]           = use_cache
    __lowerCamelCase       :       int           = classifier_dropout






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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






