
'''simple docstring'''







import copy

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


_lowercase			:  List[Any]   							=	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(a__					)
class 			lowerCAmelCase__       (  a__					):
					lowerCAmelCase_					       =					'rag'
					lowerCAmelCase_					       =					True






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=" / "    ,							__SCREAMING_SNAKE_CASE=" // "    ,							__SCREAMING_SNAKE_CASE=5    ,							__SCREAMING_SNAKE_CASE=3_00    ,							__SCREAMING_SNAKE_CASE=7_68    ,							__SCREAMING_SNAKE_CASE=8    ,							__SCREAMING_SNAKE_CASE="wiki_dpr"    ,							__SCREAMING_SNAKE_CASE="train"    ,							__SCREAMING_SNAKE_CASE="compressed"    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""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"
											lowercase_     :					Tuple													=	kwargs.pop('''question_encoder'''		)
											lowercase_     :					Optional[Any]													=	question_encoder_config.pop('''model_type'''		)
											lowercase_     :					Dict													=	kwargs.pop('''generator'''		)
											lowercase_     :					List[str]													=	decoder_config.pop('''model_type'''		)

											from ..auto.configuration_auto import AutoConfig

											lowercase_     :					Dict													=	AutoConfig.for_model(_lowerCamelCase    ,							**_lowerCamelCase		)
											lowercase_     :					Tuple													=	AutoConfig.for_model(_lowerCamelCase    ,							**_lowerCamelCase		)

											lowercase_     :					Union[str, Any]													=	reduce_loss
											lowercase_     :					int													=	label_smoothing
											lowercase_     :					Optional[Any]													=	exclude_bos_score
											lowercase_     :					Dict													=	do_marginalize

											lowercase_     :					str													=	title_sep
											lowercase_     :					str													=	doc_sep
											lowercase_     :					str													=	n_docs
											lowercase_     :					List[Any]													=	max_combined_length

											lowercase_     :					str													=	dataset
											lowercase_     :					int													=	dataset_split
											lowercase_     :					Optional[int]													=	index_name

											lowercase_     :					Any													=	retrieval_vector_size
											lowercase_     :					Union[str, Any]													=	retrieval_batch_size
											lowercase_     :					Tuple													=	passages_path
											lowercase_     :					str													=	index_path
											lowercase_     :					Tuple													=	use_dummy_dataset

											lowercase_     :					int													=	output_retrieved

											lowercase_     :					Any													=	do_deduplication

											lowercase_     :					Tuple													=	use_cache

											if self.forced_eos_token_id is None:
																	lowercase_     :					Dict													=	getattr(self.generator    ,							'''forced_eos_token_id'''    ,							_lowerCamelCase		)






					@classmethod
					def      _snake_case   (       cls    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(question_encoder=question_encoder_config.to_dict()    ,							generator=generator_config.to_dict()    ,							**_lowerCamelCase		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	copy.deepcopy(self.__dict__		)
											lowercase_     :					Optional[Any]													=	self.question_encoder.to_dict()
											lowercase_     :					Any													=	self.generator.to_dict()
											lowercase_     :					Dict													=	self.__class__.model_type
											return output





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase			:  Any   							=	logging.get_logger(__name__)






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											warnings.warn(
											    '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use DonutImageProcessor instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
											super().__init__(*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)



