




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	:		Dict							       =   """\
@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	:		Union[str, Any]							       =   """\
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	:		Optional[Any]							       =   """
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 				UpperCamelCase_    ( datasets.Metric     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Tuple:





           '''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        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Optional[int]=None					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Optional[Any]="auto"					, UpperCAmelCase__      :	Tuple=-1					, UpperCAmelCase__      :	Any=0.9					, UpperCAmelCase__      :	Optional[Any]=5					, UpperCAmelCase__      :	Any=500					, UpperCAmelCase__      :	str="gpt2-large"					, UpperCAmelCase__      :	Dict=-1					, UpperCAmelCase__      :	int=1_024					, UpperCAmelCase__      :	Any=25					, UpperCAmelCase__      :	Optional[int]=5					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[Any]=25					, )						->Any:





           '''simple docstring'''

           A__       =    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"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


