
"""simple docstring"""
import copy

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


__magic_name__									=					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(__A    )
class  _lowerCAmelCase    (    __A    ):
							lowercase_  :  Optional[int]      =				'''rag'''
							lowercase_  :  Optional[Any]      =				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_			,    )				->				Union[str, Any]:
								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"
								_UpperCAmelCase			=    kwargs.pop("question_encoder" )
								_UpperCAmelCase			=    question_encoder_config.pop("model_type" )
								_UpperCAmelCase			=    kwargs.pop("generator" )
								_UpperCAmelCase			=    decoder_config.pop("model_type" )

								from ..auto.configuration_auto import AutoConfig

								_UpperCAmelCase			=    AutoConfig.for_model(__A			,    **__A )
								_UpperCAmelCase			=    AutoConfig.for_model(__A			,    **__A )

								_UpperCAmelCase			=    reduce_loss
								_UpperCAmelCase			=    label_smoothing
								_UpperCAmelCase			=    exclude_bos_score
								_UpperCAmelCase			=    do_marginalize

								_UpperCAmelCase			=    title_sep
								_UpperCAmelCase			=    doc_sep
								_UpperCAmelCase			=    n_docs
								_UpperCAmelCase			=    max_combined_length

								_UpperCAmelCase			=    dataset
								_UpperCAmelCase			=    dataset_split
								_UpperCAmelCase			=    index_name

								_UpperCAmelCase			=    retrieval_vector_size
								_UpperCAmelCase			=    retrieval_batch_size
								_UpperCAmelCase			=    passages_path
								_UpperCAmelCase			=    index_path
								_UpperCAmelCase			=    use_dummy_dataset

								_UpperCAmelCase			=    output_retrieved

								_UpperCAmelCase			=    do_deduplication

								_UpperCAmelCase			=    use_cache

								if self.forced_eos_token_id is None:
									_UpperCAmelCase			=    getattr(self.generator			,    "forced_eos_token_id"			,    __A )



							@classmethod
							def    _a (   cls			,    a_			,    a_			,    **a_ )				->				Union[str, Any]:
								return cls(question_encoder=question_encoder_config.to_dict()			,    generator=generator_config.to_dict()			,    **__A )



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    copy.deepcopy(self.__dict__ )
								_UpperCAmelCase			=    self.question_encoder.to_dict()
								_UpperCAmelCase			=    self.generator.to_dict()
								_UpperCAmelCase			=    self.__class__.model_type
								return output








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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout



