





import copy

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


UpperCamelCase__      =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(__lowercase    )
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'rag'
       __snake_case 			=    True




       def __init__(      self    ,						__lowerCamelCase=None    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=" / "    ,						__lowerCamelCase=" // "    ,						__lowerCamelCase=5    ,						__lowerCamelCase=3_0_0    ,						__lowerCamelCase=7_6_8    ,						__lowerCamelCase=8    ,						__lowerCamelCase="wiki_dpr"    ,						__lowerCamelCase="train"    ,						__lowerCamelCase="compressed"    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						**__lowerCamelCase    ,						)			->     Union[str, 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"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							kwargs.pop("question_encoder"							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							question_encoder_config.pop("model_type"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							kwargs.pop("generator"							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							decoder_config.pop("model_type"							)

            from ..auto.configuration_auto import AutoConfig

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							AutoConfig.for_model(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							AutoConfig.for_model(__lowerCamelCase    ,						**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							reduce_loss
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							label_smoothing
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							exclude_bos_score
            _SCREAMING_SNAKE_CASE       :	Dict						    =							do_marginalize

            _SCREAMING_SNAKE_CASE       :	Any						    =							title_sep
            _SCREAMING_SNAKE_CASE       :	int						    =							doc_sep
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							n_docs
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							max_combined_length

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							dataset
            _SCREAMING_SNAKE_CASE       :	int						    =							dataset_split
            _SCREAMING_SNAKE_CASE       :	Any						    =							index_name

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							retrieval_vector_size
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							retrieval_batch_size
            _SCREAMING_SNAKE_CASE       :	int						    =							passages_path
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							index_path
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							use_dummy_dataset

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							output_retrieved

            _SCREAMING_SNAKE_CASE       :	str						    =							do_deduplication

            _SCREAMING_SNAKE_CASE       :	Dict						    =							use_cache

            if self.forced_eos_token_id is None:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							getattr(self.generator    ,						"forced_eos_token_id"    ,						__lowerCamelCase							)




       @classmethod
       def     UpperCamelCase_			(      cls    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							)			->     PretrainedConfig:
            return cls(question_encoder=question_encoder_config.to_dict()    ,						generator=generator_config.to_dict()    ,						**__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							copy.deepcopy(self.__dict__							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.question_encoder.to_dict()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.generator.to_dict()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.__class__.model_type
            return output






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)