




"""simple docstring"""






import copy

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


UpperCamelCase__						=			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(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''rag'''
						snake_case__             = True





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




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

											from ..auto.configuration_auto import AutoConfig

											_lowerCAmelCase:str									=  AutoConfig.for_model(a__     ,**a__)
											_lowerCAmelCase:Tuple									=  AutoConfig.for_model(a__     ,**a__)

											_lowerCAmelCase:int									=  reduce_loss
											_lowerCAmelCase:int									=  label_smoothing
											_lowerCAmelCase:str									=  exclude_bos_score
											_lowerCAmelCase:Optional[int]									=  do_marginalize

											_lowerCAmelCase:Tuple									=  title_sep
											_lowerCAmelCase:Dict									=  doc_sep
											_lowerCAmelCase:Any									=  n_docs
											_lowerCAmelCase:int									=  max_combined_length

											_lowerCAmelCase:str									=  dataset
											_lowerCAmelCase:Tuple									=  dataset_split
											_lowerCAmelCase:Optional[Any]									=  index_name

											_lowerCAmelCase:str									=  retrieval_vector_size
											_lowerCAmelCase:Optional[Any]									=  retrieval_batch_size
											_lowerCAmelCase:Dict									=  passages_path
											_lowerCAmelCase:Dict									=  index_path
											_lowerCAmelCase:str									=  use_dummy_dataset

											_lowerCAmelCase:Tuple									=  output_retrieved

											_lowerCAmelCase:Optional[int]									=  do_deduplication

											_lowerCAmelCase:Any									=  use_cache

											if self.forced_eos_token_id is None:
																_lowerCAmelCase:Any									=  getattr(self.generator     ,'''forced_eos_token_id'''     ,a__)





						@classmethod
						def       __UpperCamelCase     (							cls      :	int     ,a__      :	PretrainedConfig     ,a__      :	PretrainedConfig     ,**a__      :	List[Any])						->       PretrainedConfig:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	Optional[int])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  copy.deepcopy(self.__dict__)
											_lowerCAmelCase:str									=  self.question_encoder.to_dict()
											_lowerCAmelCase:str									=  self.generator.to_dict()
											_lowerCAmelCase:str									=  self.__class__.model_type
											return output












"""simple docstring"""






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 							UpperCAmelCase		(		snake_case			:					Any    ):
					return 1.0 / (1.0 + np.exp(-_outputs    ))





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Optional[Any]									=  np.max(_outputs      ,       axis=-1      ,       keepdims=snake_case    )
					_lowerCAmelCase:List[Any]									=  np.exp(_outputs - maxes    )
					return shifted_exp / shifted_exp.sum(axis=-1      ,       keepdims=snake_case    )




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''sigmoid'''
						snake_case__             = '''softmax'''
						snake_case__             = '''none'''




@add_end_docstrings(
    UpperCamelCase_					,	r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''					,	)
class   a__			(       UpperCamelCase_  ):
						snake_case__             = False
						snake_case__             = ClassificationFunction.NONE





						def __init__(							self      :	str     ,**a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											super().__init__(**a__)

											self.check_model_type(
											    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
											    if self.framework == '''tf'''
											    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Union[str, Any]=None     ,a__      :	Optional[Any]=None     ,a__      :	Dict=""     ,**a__      :	Dict)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  tokenizer_kwargs

											_lowerCAmelCase:Union[str, Any]									=  {}
											if hasattr(self.model.config     ,'''return_all_scores''') and return_all_scores is None:
																_lowerCAmelCase:str									=  self.model.config.return_all_scores

											if isinstance(a__     ,a__) or top_k is None:
																_lowerCAmelCase:Union[str, Any]									=  top_k
																_lowerCAmelCase:Any									=  False
											elif return_all_scores is not None:
																warnings.warn(
																    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
																    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''     ,a__     ,)
																if return_all_scores:
																					_lowerCAmelCase:Tuple									=  None
																else:
																					_lowerCAmelCase:Union[str, Any]									=  1

											if isinstance(a__     ,a__):
																_lowerCAmelCase:Union[str, Any]									=  ClassificationFunction[function_to_apply.upper()]

											if function_to_apply is not None:
																_lowerCAmelCase:Union[str, Any]									=  function_to_apply
											return preprocess_params, {}, postprocess_params





						def __call__(							self      :	List[str]     ,*a__      :	Union[str, Any]     ,**a__      :	List[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  super().__call__(*a__     ,**a__)
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											_lowerCAmelCase:str									=  '''top_k''' not in kwargs
											if isinstance(args[0]     ,a__) and _legacy:
																# This pipeline is odd, and return a list when single item is run
																return [result]
											else:
																return result





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Optional[Any]     ,**a__      :	Optional[int])						->       Dict[str, GenericTensor]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.framework
											if isinstance(a__     ,a__):
																return self.tokenizer(**a__     ,return_tensors=a__     ,**a__)
											elif isinstance(a__     ,a__) and len(a__) == 1 and isinstance(inputs[0]     ,a__) and len(inputs[0]) == 2:
																# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
																return self.tokenizer(
																    text=inputs[0][0]     ,text_pair=inputs[0][1]     ,return_tensors=a__     ,**a__)
											elif isinstance(a__     ,a__):
																# This is likely an invalid usage of the pipeline attempting to pass text pairs.
																raise ValueError(
																    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
																    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''')
											return self.tokenizer(a__     ,return_tensors=a__     ,**a__)





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[str])						->       Optional[Any]:




											"""simple docstring"""







											return self.model(**a__)







						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	Optional[Any]     ,a__      :	Any=None     ,a__      :	Optional[Any]=1     ,a__      :	Any=True)						->       Union[str, Any]:




											"""simple docstring"""







											if function_to_apply is None:
																if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																					_lowerCAmelCase:Union[str, Any]									=  ClassificationFunction.SIGMOID
																elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																					_lowerCAmelCase:Any									=  ClassificationFunction.SOFTMAX
																elif hasattr(self.model.config     ,'''function_to_apply''') and function_to_apply is None:
																					_lowerCAmelCase:List[Any]									=  self.model.config.function_to_apply
																else:
																					_lowerCAmelCase:List[Any]									=  ClassificationFunction.NONE

											_lowerCAmelCase:List[Any]									=  model_outputs['''logits'''][0]
											_lowerCAmelCase:Optional[Any]									=  outputs.numpy()

											if function_to_apply == ClassificationFunction.SIGMOID:
																_lowerCAmelCase:Dict									=  sigmoid(a__)
											elif function_to_apply == ClassificationFunction.SOFTMAX:
																_lowerCAmelCase:Dict									=  softmax(a__)
											elif function_to_apply == ClassificationFunction.NONE:
																_lowerCAmelCase:Union[str, Any]									=  outputs
											else:
																raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}')

											if top_k == 1 and _legacy:
																return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

											_lowerCAmelCase:Union[str, Any]									=  [
											    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(a__)
											]
											if not _legacy:
																dict_scores.sort(key=lambda a__: x["score"]     ,reverse=a__)
																if top_k is not None:
																					_lowerCAmelCase:Dict									=  dict_scores[:top_k]
											return dict_scores






