


'''simple docstring'''
import copy

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


UpperCAmelCase_ :     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(snake_case_				)
class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Optional[int]    							=						'''rag'''
 __lowercase				:		str    							=						True


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

  """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"
  __A  :		int       =			kwargs.pop('question_encoder'						)
  __A  :		Optional[int]       =			question_encoder_config.pop('model_type'						)
  __A  :		int       =			kwargs.pop('generator'						)
  __A  :		str       =			decoder_config.pop('model_type'						)

  from ..auto.configuration_auto import AutoConfig

  __A  :		List[Any]       =			AutoConfig.for_model(__lowercase      ,      **__lowercase						)
  __A  :		List[str]       =			AutoConfig.for_model(__lowercase      ,      **__lowercase						)

  __A  :		int       =			reduce_loss
  __A  :		List[str]       =			label_smoothing
  __A  :		Tuple       =			exclude_bos_score
  __A  :		Optional[int]       =			do_marginalize

  __A  :		List[str]       =			title_sep
  __A  :		Dict       =			doc_sep
  __A  :		Dict       =			n_docs
  __A  :		Union[str, Any]       =			max_combined_length

  __A  :		List[Any]       =			dataset
  __A  :		Dict       =			dataset_split
  __A  :		Tuple       =			index_name

  __A  :		List[str]       =			retrieval_vector_size
  __A  :		List[Any]       =			retrieval_batch_size
  __A  :		str       =			passages_path
  __A  :		str       =			index_path
  __A  :		List[Any]       =			use_dummy_dataset

  __A  :		int       =			output_retrieved

  __A  :		Any       =			do_deduplication

  __A  :		Union[str, Any]       =			use_cache

  if self.forced_eos_token_id is None:
   __A  :		Any       =			getattr(self.generator      ,      'forced_eos_token_id'      ,      __lowercase						)


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

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			copy.deepcopy(self.__dict__						)
  __A  :		List[Any]       =			self.question_encoder.to_dict()
  __A  :		List[Any]       =			self.generator.to_dict()
  __A  :		int       =			self.__class__.model_type
  return output










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[int]								=      {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						'''vit_msn'''


 def __init__(      self      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-06      ,      __lowercase=224      ,      __lowercase=16      ,      __lowercase=3      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)

  __A  :		Tuple       =			hidden_size
  __A  :		Optional[int]       =			num_hidden_layers
  __A  :		str       =			num_attention_heads
  __A  :		Dict       =			intermediate_size
  __A  :		Optional[Any]       =			hidden_act
  __A  :		Union[str, Any]       =			hidden_dropout_prob
  __A  :		Optional[Any]       =			attention_probs_dropout_prob
  __A  :		List[str]       =			initializer_range
  __A  :		Dict       =			layer_norm_eps
  __A  :		List[Any]       =			image_size
  __A  :		Optional[int]       =			patch_size
  __A  :		List[Any]       =			num_channels
  __A  :		Optional[Any]       =			qkv_bias






