


'''simple docstring'''
import copy

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


__SCREAMING_SNAKE_CASE					:				int									=		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 					__lowerCAmelCase   (   snake_case__						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[Any]   						='rag'
						_UpperCAmelCase		:       Dict   						=True






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      Optional[Any]=None ,  lowerCAmelCase							:      List[Any]=True ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      Optional[Any]=None ,  lowerCAmelCase							:      List[Any]=None ,  lowerCAmelCase							:      Union[str, Any]=None ,  lowerCAmelCase							:      str=None ,  lowerCAmelCase							:      Dict=" / " ,  lowerCAmelCase							:      int=" // " ,  lowerCAmelCase							:      Any=5 ,  lowerCAmelCase							:      List[Any]=3_00 ,  lowerCAmelCase							:      Tuple=7_68 ,  lowerCAmelCase							:      Optional[int]=8 ,  lowerCAmelCase							:      str="wiki_dpr" ,  lowerCAmelCase							:      Union[str, Any]="train" ,  lowerCAmelCase							:      Union[str, Any]="compressed" ,  lowerCAmelCase							:      List[str]=None ,  lowerCAmelCase							:      Tuple=None ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      List[Any]=False ,  lowerCAmelCase							:      str=0.0 ,  lowerCAmelCase							:      List[Any]=True ,  lowerCAmelCase							:      List[Any]=False ,  lowerCAmelCase							:      Optional[Any]=False ,  lowerCAmelCase							:      Union[str, Any]=False ,  lowerCAmelCase							:      Tuple=True ,  lowerCAmelCase							:      Optional[Any]=None ,  **lowerCAmelCase							:      Optional[Any] ,  ):
													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							:      Optional[int] ,  lowerCAmelCase							:      PretrainedConfig ,  lowerCAmelCase							:      PretrainedConfig ,  **lowerCAmelCase							:      Tuple	):
													return cls(question_encoder=question_encoder_config.to_dict() ,  generator=generator_config.to_dict() ,  **lowerCAmelCase	)






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












import os
import sys

_lowercase		:  List[str]			       =os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowercase		:  Optional[int]			       =[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]


@add_start_docstrings(AutoConfig.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoConfig.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoTokenizer.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoTokenizer.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModel.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModel.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForCausalLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForMaskedLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)
