import numpy as np

import datasets


lowerCAmelCase			:							Optional[int]     ="\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowerCAmelCase			:							int     ="\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowerCAmelCase			:							Tuple     ="\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,  _KWARGS_DESCRIPTION		)
class 	__snake_case	( datasets.Metric		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Optional[Any]:


          """simple docstring"""


          return datasets.MetricInfo(
              description=_DESCRIPTION    ,  citation=_CITATION    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
                  {
                      """X""": datasets.Sequence(datasets.Value("""float"""    ,  id="""sequence""")    ,  id="""X"""),
                  })    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						np.array(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						np.array(_UpperCamelCase)

          # Assert that arrays are 2D
          if len(X.shape) != 2:
              raise ValueError("""Expected `X` to be a 2D vector""")
          if len(reference_distribution.shape) != 2:
              raise ValueError("""Expected `reference_distribution` to be a 2D vector""")
          if reference_distribution.shape[0] < 2:
              raise ValueError(
                  """Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension""")

          # Get mahalanobis distance for each prediction
          _lowerCamelCase     :		int 						=						X - np.mean(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						np.cov(reference_distribution.T)
          try:
              _lowerCamelCase     :		Tuple 						=						np.linalg.inv(_UpperCamelCase)
          except np.linalg.LinAlgError:
              _lowerCamelCase     :		Tuple 						=						np.linalg.pinv(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						np.dot(_UpperCamelCase    ,  _UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						np.dot(_UpperCamelCase    ,  X_minus_mu.T).diagonal()

          return {"mahalanobis": mahal_dist}





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        "repo_name",
        "path",
        "copies",
        "size",
        "content",
        "license",
        "hash",
        "line_mean",
        "line_max",
        "alpha_frac",
        "autogenerated",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




