


'''simple docstring'''







import copy

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


lowerCAmelCase    :     Tuple					=			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(__snake_case	)
class 	UpperCamelCase__					( __snake_case	):



     """simple docstring"""






     __magic_name__    			=    "rag"
     __magic_name__    			=    True







     def __init__( self      ,	snake_case__=None      ,	snake_case__=True      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=" / "      ,	snake_case__=" // "      ,	snake_case__=5      ,	snake_case__=300      ,	snake_case__=768      ,	snake_case__=8      ,	snake_case__="wiki_dpr"      ,	snake_case__="train"      ,	snake_case__="compressed"      ,	snake_case__=None      ,	snake_case__=None      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=0.0      ,	snake_case__=True      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=False      ,	snake_case__=True      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''simple docstring'''

            super().__init__(
                bos_token_id=_lowercase      ,	pad_token_id=_lowercase      ,	eos_token_id=_lowercase      ,	decoder_start_token_id=_lowercase      ,	forced_eos_token_id=_lowercase      ,	is_encoder_decoder=_lowercase      ,	prefix=_lowercase      ,	vocab_size=_lowercase      ,	**_lowercase      ,	)
            assert (
                "question_encoder" in kwargs and "generator" in kwargs
            ), "Config has to be initialized with question_encoder and generator config"
            _lowerCAmelCase    :							Optional[int]   			=       kwargs.pop('question_encoder'   )
            _lowerCAmelCase    :							List[str]   			=       question_encoder_config.pop('model_type'   )
            _lowerCAmelCase    :							List[Any]   			=       kwargs.pop('generator'   )
            _lowerCAmelCase    :							List[Any]   			=       decoder_config.pop('model_type'   )

            from ..auto.configuration_auto import AutoConfig

            _lowerCAmelCase    :							Optional[Any]   			=       AutoConfig.for_model(_lowercase      ,	**_lowercase   )
            _lowerCAmelCase    :							Optional[Any]   			=       AutoConfig.for_model(_lowercase      ,	**_lowercase   )

            _lowerCAmelCase    :							Dict   			=       reduce_loss
            _lowerCAmelCase    :							Tuple   			=       label_smoothing
            _lowerCAmelCase    :							List[Any]   			=       exclude_bos_score
            _lowerCAmelCase    :							Optional[int]   			=       do_marginalize

            _lowerCAmelCase    :							int   			=       title_sep
            _lowerCAmelCase    :							Any   			=       doc_sep
            _lowerCAmelCase    :							str   			=       n_docs
            _lowerCAmelCase    :							int   			=       max_combined_length

            _lowerCAmelCase    :							List[str]   			=       dataset
            _lowerCAmelCase    :							Optional[int]   			=       dataset_split
            _lowerCAmelCase    :							Optional[Any]   			=       index_name

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

            _lowerCAmelCase    :							List[str]   			=       output_retrieved

            _lowerCAmelCase    :							List[Any]   			=       do_deduplication

            _lowerCAmelCase    :							int   			=       use_cache

            if self.forced_eos_token_id is None:
                   _lowerCAmelCase    :							str   			=       getattr(self.generator      ,	'forced_eos_token_id'      ,	_lowercase   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase    :     int					=						{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TrOCRForCausalLM""",
           """TrOCRPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       lowerCAmelCase    :     Any					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

