
import copy

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


__A				=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(snake_case_						)
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'rag'
	lowerCAmelCase__         = True



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

					from ..auto.configuration_auto import AutoConfig

					lowerCamelCase_						=			AutoConfig.for_model(lowercase						,  **lowercase )
					lowerCamelCase_						=			AutoConfig.for_model(lowercase						,  **lowercase )

					lowerCamelCase_						=			reduce_loss
					lowerCamelCase_						=			label_smoothing
					lowerCamelCase_						=			exclude_bos_score
					lowerCamelCase_						=			do_marginalize

					lowerCamelCase_						=			title_sep
					lowerCamelCase_						=			doc_sep
					lowerCamelCase_						=			n_docs
					lowerCamelCase_						=			max_combined_length

					lowerCamelCase_						=			dataset
					lowerCamelCase_						=			dataset_split
					lowerCamelCase_						=			index_name

					lowerCamelCase_						=			retrieval_vector_size
					lowerCamelCase_						=			retrieval_batch_size
					lowerCamelCase_						=			passages_path
					lowerCamelCase_						=			index_path
					lowerCamelCase_						=			use_dummy_dataset

					lowerCamelCase_						=			output_retrieved

					lowerCamelCase_						=			do_deduplication

					lowerCamelCase_						=			use_cache

					if self.forced_eos_token_id is None:
									lowerCamelCase_						=			getattr(self.generator						,  "forced_eos_token_id"						,  lowercase )



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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			copy.deepcopy(self.__dict__ )
					lowerCamelCase_						=			self.question_encoder.to_dict()
					lowerCamelCase_						=			self.generator.to_dict()
					lowerCamelCase_						=			self.__class__.model_type
					return output









from math import loga



def A_	(		A__				)  ->		int:
       if a < 0:
              raise ValueError('Input value must be a positive integer'				)
       elif isinstance(A__		,      A__				):
              raise TypeError('Input value must be a \'int\' type'				)
       return 0 if (a == 0) else int(loga(a & -a				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


