






import copy

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


SCREAMING_SNAKE_CASE__				     =			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(__lowerCamelCase		)
class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							'''rag'''
		__SCREAMING_SNAKE_CASE  :       Dict							=							True

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

					from ..auto.configuration_auto import AutoConfig

					__a						:  List[Any]									=     AutoConfig.for_model(A_    ,       **A_    )
					__a						:  Union[str, Any]									=     AutoConfig.for_model(A_    ,       **A_    )

					__a						:  Tuple									=     reduce_loss
					__a						:  Tuple									=     label_smoothing
					__a						:  int									=     exclude_bos_score
					__a						:  Optional[Any]									=     do_marginalize

					__a						:  List[str]									=     title_sep
					__a						:  Any									=     doc_sep
					__a						:  List[str]									=     n_docs
					__a						:  Optional[int]									=     max_combined_length

					__a						:  Any									=     dataset
					__a						:  Tuple									=     dataset_split
					__a						:  Optional[Any]									=     index_name

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

					__a						:  Union[str, Any]									=     output_retrieved

					__a						:  Dict									=     do_deduplication

					__a						:  List[Any]									=     use_cache

					if self.forced_eos_token_id is None:
								__a						:  Union[str, Any]									=     getattr(self.generator    ,       'forced_eos_token_id'    ,       A_    )

		@classmethod
		def    __lowerCAmelCase  (					cls		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       **SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



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



		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     copy.deepcopy(self.__dict__    )
					__a						:  Optional[int]									=     self.question_encoder.to_dict()
					__a						:  Dict									=     self.generator.to_dict()
					__a						:  Dict									=     self.__class__.model_type
					return output












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			(    A     ):




   '''simple docstring'''

   return 1.0 / (1.0 + np.exp(-_outputs     ))






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=np.max(_outputs				,							axis=-1				,							keepdims=A     )
   UpperCAmelCase__						=np.exp(_outputs - maxes     )
   return shifted_exp / shifted_exp.sum(axis=-1				,							keepdims=A     )

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'sigmoid'
 __UpperCamelCase       =      'softmax'
 __UpperCamelCase       =      'none'

@add_end_docstrings(
    a,		R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    ',		)
class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      False
 __UpperCamelCase       =      ClassificationFunction.NONE

 def __init__(  self,		**A_		)	->       Optional[int]:
    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,		A_=None,		A_=None,		A_="",		**A_		)	->       Optional[int]:
    # Using "" as default argument because we're going to use `top_k=None` in user code to declare
    # "No top_k"
    UpperCAmelCase__						=tokenizer_kwargs

    UpperCAmelCase__						={}
    if hasattr(self.model.config,		"return_all_scores"		) and return_all_scores is None:
       UpperCAmelCase__						=self.model.config.return_all_scores

    if isinstance(A_,		A_		) or top_k is None:
       UpperCAmelCase__						=top_k
       UpperCAmelCase__						=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:
          UpperCAmelCase__						=None
       else:
          UpperCAmelCase__						=1

    if isinstance(A_,		A_		):
       UpperCAmelCase__						=ClassificationFunction[function_to_apply.upper()]

    if function_to_apply is not None:
       UpperCAmelCase__						=function_to_apply
    return preprocess_params, {}, postprocess_params

 def __call__(  self,		*A_,		**A_		)	->       str:
    UpperCAmelCase__						=super().__call__(*A_,		**A_		)
    # TODO try and retrieve it in a nicer way from _sanitize_parameters.
    UpperCAmelCase__						="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,		A_,		**A_		)	->       Dict[str, GenericTensor]:
    UpperCAmelCase__						=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,		A_		)	->       Dict:
    return self.model(**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_=None,		A_=1,		A_=True		)	->       Any:
    # `_legacy` is used to determine if we're running the naked pipeline and in backward
    # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
    # the more natural result containing the list.
    # Default value before `set_parameters`
    if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
          UpperCAmelCase__						=ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
          UpperCAmelCase__						=ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config,		"function_to_apply"		) and function_to_apply is None:
          UpperCAmelCase__						=self.model.config.function_to_apply
       else:
          UpperCAmelCase__						=ClassificationFunction.NONE

    UpperCAmelCase__						=model_outputs["logits"][0]
    UpperCAmelCase__						=outputs.numpy()

    if function_to_apply == ClassificationFunction.SIGMOID:
       UpperCAmelCase__						=sigmoid(A_		)
    elif function_to_apply == ClassificationFunction.SOFTMAX:
       UpperCAmelCase__						=softmax(A_		)
    elif function_to_apply == ClassificationFunction.NONE:
       UpperCAmelCase__						=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()}

    UpperCAmelCase__						=[
        {"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:
          UpperCAmelCase__						=dict_scores[:top_k]
    return dict_scores



