




"""simple docstring"""






import copy

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


UpperCamelCase__						=			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(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''rag'''
						snake_case__             = True





						def __init__(							self      :	str     ,a__      :	Optional[int]=None     ,a__      :	List[Any]=True     ,a__      :	str=None     ,a__      :	List[str]=None     ,a__      :	Tuple=None     ,a__      :	str=None     ,a__      :	int=None     ,a__      :	List[Any]=" / "     ,a__      :	Dict=" // "     ,a__      :	List[str]=5     ,a__      :	int=300     ,a__      :	Optional[int]=768     ,a__      :	Optional[int]=8     ,a__      :	Any="wiki_dpr"     ,a__      :	int="train"     ,a__      :	List[str]="compressed"     ,a__      :	Union[str, Any]=None     ,a__      :	int=None     ,a__      :	List[Any]=False     ,a__      :	Tuple=False     ,a__      :	List[Any]=0.0     ,a__      :	str=True     ,a__      :	str=False     ,a__      :	Tuple=False     ,a__      :	Optional[Any]=False     ,a__      :	Dict=True     ,a__      :	Optional[int]=None     ,**a__      :	Tuple     ,)						->       int:




											"""simple docstring"""







											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"
											_lowerCAmelCase:Union[str, Any]									=  kwargs.pop('''question_encoder''')
											_lowerCAmelCase:Optional[int]									=  question_encoder_config.pop('''model_type''')
											_lowerCAmelCase:Union[str, Any]									=  kwargs.pop('''generator''')
											_lowerCAmelCase:List[str]									=  decoder_config.pop('''model_type''')

											from ..auto.configuration_auto import AutoConfig

											_lowerCAmelCase:str									=  AutoConfig.for_model(a__     ,**a__)
											_lowerCAmelCase:Tuple									=  AutoConfig.for_model(a__     ,**a__)

											_lowerCAmelCase:int									=  reduce_loss
											_lowerCAmelCase:int									=  label_smoothing
											_lowerCAmelCase:str									=  exclude_bos_score
											_lowerCAmelCase:Optional[int]									=  do_marginalize

											_lowerCAmelCase:Tuple									=  title_sep
											_lowerCAmelCase:Dict									=  doc_sep
											_lowerCAmelCase:Any									=  n_docs
											_lowerCAmelCase:int									=  max_combined_length

											_lowerCAmelCase:str									=  dataset
											_lowerCAmelCase:Tuple									=  dataset_split
											_lowerCAmelCase:Optional[Any]									=  index_name

											_lowerCAmelCase:str									=  retrieval_vector_size
											_lowerCAmelCase:Optional[Any]									=  retrieval_batch_size
											_lowerCAmelCase:Dict									=  passages_path
											_lowerCAmelCase:Dict									=  index_path
											_lowerCAmelCase:str									=  use_dummy_dataset

											_lowerCAmelCase:Tuple									=  output_retrieved

											_lowerCAmelCase:Optional[int]									=  do_deduplication

											_lowerCAmelCase:Any									=  use_cache

											if self.forced_eos_token_id is None:
																_lowerCAmelCase:Any									=  getattr(self.generator     ,'''forced_eos_token_id'''     ,a__)





						@classmethod
						def       __UpperCamelCase     (							cls      :	int     ,a__      :	PretrainedConfig     ,a__      :	PretrainedConfig     ,**a__      :	List[Any])						->       PretrainedConfig:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	Optional[int])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  copy.deepcopy(self.__dict__)
											_lowerCAmelCase:str									=  self.question_encoder.to_dict()
											_lowerCAmelCase:str									=  self.generator.to_dict()
											_lowerCAmelCase:str									=  self.__class__.model_type
											return output






"""simple docstring"""






from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:List[Any]									=  ArgumentParser('''Transformers CLI tool'''      ,       usage='''transformers-cli <command> [<args>]'''    )
					_lowerCAmelCase:Union[str, Any]									=  parser.add_subparsers(help='''transformers-cli command helpers'''    )

					# Register commands
					ConvertCommand.register_subcommand(snake_case    )
					DownloadCommand.register_subcommand(snake_case    )
					EnvironmentCommand.register_subcommand(snake_case    )
					RunCommand.register_subcommand(snake_case    )
					ServeCommand.register_subcommand(snake_case    )
					UserCommands.register_subcommand(snake_case    )
					AddNewModelCommand.register_subcommand(snake_case    )
					AddNewModelLikeCommand.register_subcommand(snake_case    )
					LfsCommands.register_subcommand(snake_case    )
					PTtoTFCommand.register_subcommand(snake_case    )

					# Let's go
					_lowerCAmelCase:Union[str, Any]									=  parser.parse_args()

					if not hasattr(snake_case      ,       '''func'''    ):
										parser.print_help()
										exit(1    )

					# Run
					_lowerCAmelCase:Any									=  args.func(snake_case    )
					service.run()


if __name__ == "__main__":
				main()






