



'''simple docstring'''





import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowerCAmelCase										=     '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

'''

_lowerCAmelCase										=     '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''

_lowerCAmelCase										=     '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: "c" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
'''






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




  def 							UpperCAmelCase_     (   self				)   ->		int:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,citation=_CITATION  ,homepage="""https://github.com/krishnap25/mauve"""  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""  ,id="""sequence"""				),
                  """references""": datasets.Value("""string"""  ,id="""sequence"""				),
              }				)  ,codebase_urls=["""https://github.com/krishnap25/mauve"""]  ,reference_urls=[
              """https://arxiv.org/abs/2102.01454""",
              """https://github.com/krishnap25/mauve""",
          ]  ,)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="auto"  ,__UpperCAmelCase=-1  ,__UpperCAmelCase=0.9  ,__UpperCAmelCase=5  ,__UpperCAmelCase=500  ,__UpperCAmelCase="gpt2-large"  ,__UpperCAmelCase=-1  ,__UpperCAmelCase=1024  ,__UpperCAmelCase=25  ,__UpperCAmelCase=5  ,__UpperCAmelCase=True  ,__UpperCAmelCase=25  ,)   ->		List[Any]:
      lowerCAmelCase__			:  Optional[int]											= compute_mauve(
          p_text=__UpperCAmelCase  ,q_text=__UpperCAmelCase  ,p_features=__UpperCAmelCase  ,q_features=__UpperCAmelCase  ,p_tokens=__UpperCAmelCase  ,q_tokens=__UpperCAmelCase  ,num_buckets=__UpperCAmelCase  ,pca_max_data=__UpperCAmelCase  ,kmeans_explained_var=__UpperCAmelCase  ,kmeans_num_redo=__UpperCAmelCase  ,kmeans_max_iter=__UpperCAmelCase  ,featurize_model_name=__UpperCAmelCase  ,device_id=__UpperCAmelCase  ,max_text_length=__UpperCAmelCase  ,divergence_curve_discretization_size=__UpperCAmelCase  ,mauve_scaling_factor=__UpperCAmelCase  ,verbose=__UpperCAmelCase  ,seed=__UpperCAmelCase  ,)
      return out







'''simple docstring'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase = 1000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= -1
    lowerCAmelCase__			:  Optional[Any]											= 0
    for a in range(1							,   n // 3      ):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        lowerCAmelCase__			:  Optional[Any]											= (n * n - 2 * a * n) // (2 * n - 2 * a)
        lowerCAmelCase__			:  Tuple											= n - a - b
        if c * c == (a * a + b * b):
            lowerCAmelCase__			:  int											= a * b * c
            if candidate >= product:
                lowerCAmelCase__			:  Any											= candidate
    return product


if __name__ == "__main__":
 print(F"""{solution() = }""")


