






"""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


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

_UpperCamelCase					      =  "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_UpperCamelCase					      =  "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    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'].\n    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\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"







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






				def     _UpperCamelCase							(			self ):
										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     ,A     ,A     ,A=None     ,A=None     ,A=None     ,A=None     ,A="auto"     ,A=-1     ,A=0.9     ,A=5     ,A=500     ,A="gpt2-large"     ,A=-1     ,A=1_024     ,A=25     ,A=5     ,A=True     ,A=25     ,):
										UpperCAmelCase          =	compute_mauve(
										    p_text=A__     ,q_text=A__     ,p_features=A__     ,q_features=A__     ,p_tokens=A__     ,q_tokens=A__     ,num_buckets=A__     ,pca_max_data=A__     ,kmeans_explained_var=A__     ,kmeans_num_redo=A__     ,kmeans_max_iter=A__     ,featurize_model_name=A__     ,device_id=A__     ,max_text_length=A__     ,divergence_curve_discretization_size=A__     ,mauve_scaling_factor=A__     ,verbose=A__     ,seed=A__     ,)
										return out











"""simple docstring"""







def _a			(		_snake_case = 6008_5147_5143							):







      """simple docstring"""






      try:
            UpperCAmelCase          =	int(_snake_case							)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""							)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""							)
      UpperCAmelCase          =	1
      UpperCAmelCase          =	2
      while i * i <= n:
            while n % i == 0:
                  UpperCAmelCase          =	i
                  n //= i
            i += 1
      if n > 1:
            UpperCAmelCase          =	n
      return int(_snake_case							)


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



