



"""simple docstring"""







import copy

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


lowercase__		:				Optional[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(SCREAMING_SNAKE_CASE_  )
class       UpperCamelCase__   (	SCREAMING_SNAKE_CASE_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """rag"""
      _SCREAMING_SNAKE_CASE  				=     True



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      str=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=" / "    ,			SCREAMING_SNAKE_CASE_				:      Tuple=" // "    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=5    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=8    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]="wiki_dpr"    ,			SCREAMING_SNAKE_CASE_				:      str="train"    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="compressed"    ,			SCREAMING_SNAKE_CASE_				:      Dict=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=False    ,			SCREAMING_SNAKE_CASE_				:      Tuple=0.0    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=True    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      int=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        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_   :				List[str]								= kwargs.pop('question_encoder'       )
        lowerCAmelCase_   :				Tuple								= question_encoder_config.pop('model_type'       )
        lowerCAmelCase_   :				Optional[Any]								= kwargs.pop('generator'       )
        lowerCAmelCase_   :				Tuple								= decoder_config.pop('model_type'       )

        from ..auto.configuration_auto import AutoConfig

        lowerCAmelCase_   :				int								= AutoConfig.for_model(_a    ,			**_a       )
        lowerCAmelCase_   :				Dict								= AutoConfig.for_model(_a    ,			**_a       )

        lowerCAmelCase_   :				List[str]								= reduce_loss
        lowerCAmelCase_   :				Dict								= label_smoothing
        lowerCAmelCase_   :				List[str]								= exclude_bos_score
        lowerCAmelCase_   :				Optional[Any]								= do_marginalize

        lowerCAmelCase_   :				Tuple								= title_sep
        lowerCAmelCase_   :				Optional[Any]								= doc_sep
        lowerCAmelCase_   :				Dict								= n_docs
        lowerCAmelCase_   :				str								= max_combined_length

        lowerCAmelCase_   :				Optional[Any]								= dataset
        lowerCAmelCase_   :				List[Any]								= dataset_split
        lowerCAmelCase_   :				Optional[Any]								= index_name

        lowerCAmelCase_   :				Tuple								= retrieval_vector_size
        lowerCAmelCase_   :				Optional[int]								= retrieval_batch_size
        lowerCAmelCase_   :				List[Any]								= passages_path
        lowerCAmelCase_   :				Union[str, Any]								= index_path
        lowerCAmelCase_   :				str								= use_dummy_dataset

        lowerCAmelCase_   :				Optional[Any]								= output_retrieved

        lowerCAmelCase_   :				List[Any]								= do_deduplication

        lowerCAmelCase_   :				Union[str, Any]								= use_cache

        if self.forced_eos_token_id is None:
          lowerCAmelCase_   :				List[str]								= getattr(self.generator    ,			'forced_eos_token_id'    ,			_a       )



      @classmethod
      def      SCREAMING_SNAKE_CASE__ (      cls				:      int    ,			SCREAMING_SNAKE_CASE_				:      PretrainedConfig    ,			SCREAMING_SNAKE_CASE_				:      PretrainedConfig    ,			**SCREAMING_SNAKE_CASE_				:      List[str]       ):
        return cls(question_encoder=question_encoder_config.to_dict()    ,			generator=generator_config.to_dict()    ,			**_a       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[Any]								= copy.deepcopy(self.__dict__       )
        lowerCAmelCase_   :				Union[str, Any]								= self.question_encoder.to_dict()
        lowerCAmelCase_   :				Any								= self.generator.to_dict()
        lowerCAmelCase_   :				Tuple								= self.__class__.model_type
        return output










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

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






