



import copy

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


a_						:     Union[str, Any]	     =		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(UpperCAmelCase_)
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'rag'
     _A       =			True







     def __init__(self				,			__a=None				,			__a=True				,			__a=None				,			__a=None				,			__a=None				,			__a=None				,			__a=None				,			__a=" / "				,			__a=" // "				,			__a=5				,			__a=3_00				,			__a=7_68				,			__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				,			):






           '''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								=		kwargs.pop("question_encoder"    )
           lowerCamelCase								=		question_encoder_config.pop("model_type"    )
           lowerCamelCase								=		kwargs.pop("generator"    )
           lowerCamelCase								=		decoder_config.pop("model_type"    )

           from ..auto.configuration_auto import AutoConfig

           lowerCamelCase								=		AutoConfig.for_model(__a				,			**__a    )
           lowerCamelCase								=		AutoConfig.for_model(__a				,			**__a    )

           lowerCamelCase								=		reduce_loss
           lowerCamelCase								=		label_smoothing
           lowerCamelCase								=		exclude_bos_score
           lowerCamelCase								=		do_marginalize

           lowerCamelCase								=		title_sep
           lowerCamelCase								=		doc_sep
           lowerCamelCase								=		n_docs
           lowerCamelCase								=		max_combined_length

           lowerCamelCase								=		dataset
           lowerCamelCase								=		dataset_split
           lowerCamelCase								=		index_name

           lowerCamelCase								=		retrieval_vector_size
           lowerCamelCase								=		retrieval_batch_size
           lowerCamelCase								=		passages_path
           lowerCamelCase								=		index_path
           lowerCamelCase								=		use_dummy_dataset

           lowerCamelCase								=		output_retrieved

           lowerCamelCase								=		do_deduplication

           lowerCamelCase								=		use_cache

           if self.forced_eos_token_id is None:
                 lowerCamelCase								=		getattr(self.generator				,			"forced_eos_token_id"				,			__a    )







     @classmethod
     def 						_a      (cls				,			__a				,			__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		copy.deepcopy(self.__dict__    )
           lowerCamelCase								=		self.question_encoder.to_dict()
           lowerCamelCase								=		self.generator.to_dict()
           lowerCamelCase								=		self.__class__.model_type
           return output




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    '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:
     a_						:     Any	     =		[
         '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

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)