






'''simple docstring'''




import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				int=100      ,     _UpperCAmelCase  :				List[str]=" "   )   ->   List[Any]:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[int]											=   text.split(_UpperCAmelCase   )
       return [character.join(text[i : i + n]   ).strip() for i in range(0      ,     len(_UpperCAmelCase   )      ,     _UpperCAmelCase   )]

def      UpperCamelCase_							( _UpperCAmelCase  :				str   )   ->   Tuple:


       """simple docstring"""







       _UpperCAmelCase					:  Union[str, Any]											=   [], []
       for title, text in zip(documents["title"]      ,     documents["text"]   ):
              if text is not None:
                     for passage in split_text(_UpperCAmelCase   ):
                            titles.append(title if title is not None else ""   )
                            texts.append(_UpperCAmelCase   )
       return {"title": titles, "text": texts}

def      UpperCamelCase_							( _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Union[str, Any]      ,     _UpperCAmelCase  :				Any   )   ->   str:


       """simple docstring"""







       _UpperCAmelCase					:  int											=   load_dataset(
           "csv"      ,     data_files=[csv_path]      ,     split="train"      ,     delimiter="\t"      ,     column_names=["title", "text"]   )
       _UpperCAmelCase					:  Union[str, Any]											=   kb_dataset.map(
           _UpperCAmelCase      ,     batched=_UpperCAmelCase      ,     num_proc=1   )  # if you want you can load already splitted csv.
       _UpperCAmelCase					:  List[str]											=   [kb_dataset.shard(_UpperCAmelCase      ,     _UpperCAmelCase      ,     contiguous=_UpperCAmelCase   ) for i in range(_UpperCAmelCase   )]
       _UpperCAmelCase					:  Optional[Any]											=   kb_list[process_num]

       _UpperCAmelCase					:  Optional[int]											=   "data_" + str(_UpperCAmelCase   )
       _UpperCAmelCase					:  Optional[Any]											=   os.path.join(_UpperCAmelCase      ,     _UpperCAmelCase   )

       _UpperCAmelCase					:  Optional[Any]											=   DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"   )
       _UpperCAmelCase					:  int											=   ctx_encoder.to(device=_UpperCAmelCase   )

       def embed(
           _UpperCAmelCase  :				dict      ,     _UpperCAmelCase  :				DPRContextEncoder      ,     _UpperCAmelCase  :				DPRContextEncoderTokenizerFast      ,     _UpperCAmelCase  :				Tuple   ) -> dict:
              _UpperCAmelCase					:  Tuple											=   ctx_tokenizer(
                  documents["title"]      ,     documents["text"]      ,     truncation=_UpperCAmelCase      ,     padding="longest"      ,     return_tensors="pt"   )["input_ids"]
              _UpperCAmelCase					:  str											=   ctx_encoder(input_ids.to(device=_UpperCAmelCase   )      ,     return_dict=_UpperCAmelCase   ).pooler_output
              return {"embeddings": embeddings.detach().cpu().numpy()}

       _UpperCAmelCase					:  Tuple											=   Features(
           {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space

       _UpperCAmelCase					:  int											=   data_shrad.map(
           partial(_UpperCAmelCase      ,     ctx_encoder=_UpperCAmelCase      ,     ctx_tokenizer=_UpperCAmelCase      ,     device=_UpperCAmelCase   )      ,     batched=_UpperCAmelCase      ,     batch_size=16      ,     features=_UpperCAmelCase      ,     )
       dataset.save_to_disk(_UpperCAmelCase   )


def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				Optional[Any]   )   ->   Dict:


       """simple docstring"""







       _UpperCAmelCase					:  int											=   []

       for shard_address in glob(str(_UpperCAmelCase   ) + "/*/"   ):
              data_shard_list.append(load_from_disk(_UpperCAmelCase   )   )

       _UpperCAmelCase					:  Tuple											=   concatenate_datasets(_UpperCAmelCase   )
       faiss.omp_set_num_threads(96   )

       _UpperCAmelCase					:  List[Any]											=   faiss.IndexHNSWFlat(768      ,     128      ,     faiss.METRIC_INNER_PRODUCT   )
       concat.add_faiss_index("embeddings"      ,     custom_index=_UpperCAmelCase   )
       concat.get_index("embeddings"   ).save(
           _UpperCAmelCase   )  # since we load the index in to memory,we can directly update the index in the disk


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             return datasets.MetricInfo(
                 description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("int32"	)	),
                         "references": datasets.Sequence(datasets.Value("int32"	)	),
                     }
                     if self.config_name == "multilabel"
                     else {
                         "predictions": datasets.Value("int32"	),
                         "references": datasets.Value("int32"	),
                     }	)						, reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]						, )





      def 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

