




"""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    :		str			   =       "\\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"

lowerCamelCase    :		Tuple			   =       "\\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"

lowerCamelCase    :		Optional[int]			   =       "\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   A(						datasets.Metric       ):



 '''simple docstring'''


 def     a__			(    self :  int  )       ->     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     a__			(    self :  Any						,							A_ :  Optional[Any]						,							A_ :  List[str]						,							A_ :  Optional[Any]=None						,							A_ :  Dict=None						,							A_ :  Optional[Any]=None						,							A_ :  Dict=None						,							A_ :  Dict="auto"						,							A_ :  Dict=-1						,							A_ :  Tuple=0.9						,							A_ :  Any=5						,							A_ :  Dict=500						,							A_ :  Union[str, Any]="gpt2-large"						,							A_ :  List[Any]=-1						,							A_ :  int=1024						,							A_ :  Optional[Any]=25						,							A_ :  Optional[Any]=5						,							A_ :  Any=True						,							A_ :  List[Any]=25						,							)       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   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









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()



