

import copy

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


snake_case_			  = 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						:       Dict														=     """rag"""
    __lowerCamelCase						:       List[str]														=     True





    def __init__(      self						,   a=None						,   a=True						,   a=None						,   a=None						,   a=None						,   a=None						,   a=None						,   a=" / "						,   a=" // "						,   a=5						,   a=300						,   a=768						,   a=8						,   a="wiki_dpr"						,   a="train"						,   a="compressed"						,   a=None						,   a=None						,   a=False						,   a=False						,   a=0.0						,   a=True						,   a=False						,   a=False						,   a=False						,   a=True						,   a=None						,   **a						,   ):
         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"
         lowercase__    :   Any      =       kwargs.pop('question_encoder')
         lowercase__    :   int      =       question_encoder_config.pop('model_type')
         lowercase__    :   Optional[int]      =       kwargs.pop('generator')
         lowercase__    :   List[Any]      =       decoder_config.pop('model_type')

         from ..auto.configuration_auto import AutoConfig

         lowercase__    :   List[Any]      =       AutoConfig.for_model(a						,   **a)
         lowercase__    :   str      =       AutoConfig.for_model(a						,   **a)

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

         lowercase__    :   int      =       title_sep
         lowercase__    :   str      =       doc_sep
         lowercase__    :   int      =       n_docs
         lowercase__    :   Optional[int]      =       max_combined_length

         lowercase__    :   int      =       dataset
         lowercase__    :   str      =       dataset_split
         lowercase__    :   Any      =       index_name

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

         lowercase__    :   Tuple      =       output_retrieved

         lowercase__    :   Dict      =       do_deduplication

         lowercase__    :   Optional[Any]      =       use_cache

         if self.forced_eos_token_id is None:
              lowercase__    :   Union[str, Any]      =       getattr(self.generator						,   'forced_eos_token_id'						,   a)





    @classmethod
    def       snake_case_				(      cls						,   a						,   a						,   **a):
         return cls(question_encoder=question_encoder_config.to_dict()						,   generator=generator_config.to_dict()						,   **a)





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       copy.deepcopy(self.__dict__)
         lowercase__    :   List[Any]      =       self.question_encoder.to_dict()
         lowercase__    :   List[str]      =       self.generator.to_dict()
         lowercase__    :   List[str]      =       self.__class__.model_type
         return output




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

