





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			):



      """simple docstring"""






      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        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 ,  )				->      Tuple:






        '''simple docstring'''






        __snake_case      : Optional[Any]						=				compute_mauve(
            p_text=__lowerCamelCase ,  q_text=__lowerCamelCase ,  p_features=__lowerCamelCase ,  q_features=__lowerCamelCase ,  p_tokens=__lowerCamelCase ,  q_tokens=__lowerCamelCase ,  num_buckets=__lowerCamelCase ,  pca_max_data=__lowerCamelCase ,  kmeans_explained_var=__lowerCamelCase ,  kmeans_num_redo=__lowerCamelCase ,  kmeans_max_iter=__lowerCamelCase ,  featurize_model_name=__lowerCamelCase ,  device_id=__lowerCamelCase ,  max_text_length=__lowerCamelCase ,  divergence_curve_discretization_size=__lowerCamelCase ,  mauve_scaling_factor=__lowerCamelCase ,  verbose=__lowerCamelCase ,  seed=__lowerCamelCase ,  )
        return out





def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      while a != 0:
            lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  b % a, a
      return b


def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      if gcd(_A    ,					_A						) != 1:
            lowerCamelCase__		:				List[str]    =  F"mod inverse of {a!r} and {m!r} does not exist"
            raise ValueError(_A						)
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  1, 0, a
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  0, 1, m
      while va != 0:
            lowerCamelCase__		:				Tuple    =  ua // va
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
      return ua % m


