






'''simple docstring'''



import copy

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


__UpperCamelCase      :       List[str]      			=      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(_lowerCAmelCase     )
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="rag"
      __a  					=True




      def __init__(  self ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=" / " ,   lowerCamelCase=" // " ,   lowerCamelCase=5 ,   lowerCamelCase=300 ,   lowerCamelCase=768 ,   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 ,   )							->Union[str, Any]:



         '''simple docstring'''
         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"
         __a				     =    kwargs.pop('question_encoder'      )
         __a				     =    question_encoder_config.pop('model_type'      )
         __a				     =    kwargs.pop('generator'      )
         __a				     =    decoder_config.pop('model_type'      )

         from ..auto.configuration_auto import AutoConfig

         __a				     =    AutoConfig.for_model(lowerCamelCase ,   **lowerCamelCase      )
         __a				     =    AutoConfig.for_model(lowerCamelCase ,   **lowerCamelCase      )

         __a				     =    reduce_loss
         __a				     =    label_smoothing
         __a				     =    exclude_bos_score
         __a				     =    do_marginalize

         __a				     =    title_sep
         __a				     =    doc_sep
         __a				     =    n_docs
         __a				     =    max_combined_length

         __a				     =    dataset
         __a				     =    dataset_split
         __a				     =    index_name

         __a				     =    retrieval_vector_size
         __a				     =    retrieval_batch_size
         __a				     =    passages_path
         __a				     =    index_path
         __a				     =    use_dummy_dataset

         __a				     =    output_retrieved

         __a				     =    do_deduplication

         __a				     =    use_cache

         if self.forced_eos_token_id is None:
            __a				     =    getattr(self.generator ,   'forced_eos_token_id' ,   lowerCamelCase      )




      @classmethod
      def  __UpperCamelCase							(  cls ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      )							->PretrainedConfig:



         '''simple docstring'''
         return cls(question_encoder=question_encoder_config.to_dict() ,   generator=generator_config.to_dict() ,   **lowerCamelCase      )






      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    copy.deepcopy(self.__dict__      )
         __a				     =    self.question_encoder.to_dict()
         __a				     =    self.generator.to_dict()
         __a				     =    self.__class__.model_type
         return output







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Any      			=      {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vivit"




      def __init__(  self ,   lowerCamelCase=224 ,   lowerCamelCase=32 ,   lowerCamelCase=[2, 16, 16] ,   lowerCamelCase=3 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu_fast" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps

         __a				     =    image_size
         __a				     =    num_frames
         __a				     =    tubelet_size
         __a				     =    num_channels
         __a				     =    qkv_bias

         super().__init__(**lowerCamelCase      )