





import copy

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


lowerCamelCase_       =		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(A  )
class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''rag'''
       SCREAMING_SNAKE_CASE_       :				int     						=  True




       def __init__(						self		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=" / "		,SCREAMING_SNAKE_CASE__=" // "		,SCREAMING_SNAKE_CASE__=5		,SCREAMING_SNAKE_CASE__=3_00		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__="wiki_dpr"		,SCREAMING_SNAKE_CASE__="train"		,SCREAMING_SNAKE_CASE__="compressed"		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)      ->				List[Any]:





             """simple docstring"""






             super().__init__(
                 bos_token_id=SCREAMING_SNAKE_CASE__		,pad_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,forced_eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,prefix=SCREAMING_SNAKE_CASE__		,vocab_size=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)
             assert (
                 "question_encoder" in kwargs and "generator" in kwargs
             ), "Config has to be initialized with question_encoder and generator config"
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kwargs.pop('''question_encoder'''							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  question_encoder_config.pop('''model_type'''							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  kwargs.pop('''generator'''							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  decoder_config.pop('''model_type'''							)

             from ..auto.configuration_auto import AutoConfig

             __SCREAMING_SNAKE_CASE       :Dict		    =  AutoConfig.for_model(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  AutoConfig.for_model(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  reduce_loss
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  label_smoothing
             __SCREAMING_SNAKE_CASE       :str		    =  exclude_bos_score
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  do_marginalize

             __SCREAMING_SNAKE_CASE       :Tuple		    =  title_sep
             __SCREAMING_SNAKE_CASE       :Any		    =  doc_sep
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  n_docs
             __SCREAMING_SNAKE_CASE       :Any		    =  max_combined_length

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dataset
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  dataset_split
             __SCREAMING_SNAKE_CASE       :List[str]		    =  index_name

             __SCREAMING_SNAKE_CASE       :Tuple		    =  retrieval_vector_size
             __SCREAMING_SNAKE_CASE       :Any		    =  retrieval_batch_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  passages_path
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  index_path
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  use_dummy_dataset

             __SCREAMING_SNAKE_CASE       :Dict		    =  output_retrieved

             __SCREAMING_SNAKE_CASE       :List[str]		    =  do_deduplication

             __SCREAMING_SNAKE_CASE       :Tuple		    =  use_cache

             if self.forced_eos_token_id is None:
                   __SCREAMING_SNAKE_CASE       :int		    =  getattr(self.generator		,'''forced_eos_token_id'''		,SCREAMING_SNAKE_CASE__							)




       @classmethod
       def    _UpperCamelCase					(						cls		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				PretrainedConfig:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






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






"""simple docstring"""
from typing import Callable, Optional, Union

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


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''xlm-prophetnet'''
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  {
           '''num_attention_heads''': '''num_encoder_attention_heads''',
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = "gelu"		,SCREAMING_SNAKE_CASE__ = 3_05_22		,SCREAMING_SNAKE_CASE__ = 10_24		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 40_96		,SCREAMING_SNAKE_CASE__ = 12		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 0.1		,SCREAMING_SNAKE_CASE__ = 5_12		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1_28		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2		,**SCREAMING_SNAKE_CASE__		,)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Tuple		    =  hidden_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  num_encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[str]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Dict		    =  init_std  # Normal(0, this parameter)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  activation_function

             # parameters for xlmprophetnet
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ngram
             __SCREAMING_SNAKE_CASE       :int		    =  num_buckets
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  relative_max_distance
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  disable_ngram_loss
             __SCREAMING_SNAKE_CASE       :Dict		    =  eps

             # 3 Types of Dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dropout

             __SCREAMING_SNAKE_CASE       :int		    =  use_cache

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,add_cross_attention=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.num_encoder_layers + self.num_decoder_layers




       @num_hidden_layers.setter
       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''							)