




"""simple docstring"""


import copy

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


A_						=				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(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


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




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

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output




"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      :    Optional[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Dict    							= {
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   '''audio-spectrogram-transformer'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10_24   ,       SCREAMING_SNAKE_CASE__=1_28   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       str           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       int           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       Tuple           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       Any           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       int           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       int           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Dict           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   frequency_stride
											SCREAMING_SNAKE_CASE__						:       Any           =   time_stride
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   max_length
											SCREAMING_SNAKE_CASE__						:       Any           =   num_mel_bins




