

'''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__						:       Optional[Any]										=						"""\
@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     a			(				datasets.Metric       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      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       __magic_name__							(							self					:      Tuple			,			snake_case_					:      str			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]=None			,			snake_case_					:      Any=None			,			snake_case_					:      List[str]=None			,			snake_case_					:      Dict=None			,			snake_case_					:      Optional[Any]="auto"			,			snake_case_					:      str=-1			,			snake_case_					:      Dict=0.9			,			snake_case_					:      Optional[Any]=5			,			snake_case_					:      Union[str, Any]=5_0_0			,			snake_case_					:      str="gpt2-large"			,			snake_case_					:      List[Any]=-1			,			snake_case_					:      Optional[Any]=1_0_2_4			,			snake_case_					:      Optional[int]=2_5			,			snake_case_					:      Optional[Any]=5			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      List[str]=2_5			,			):



								'''simple docstring'''



								snake_case__						:       int       						=  compute_mauve(
								    p_text=snake_case_			,			q_text=snake_case_			,			p_features=snake_case_			,			q_features=snake_case_			,			p_tokens=snake_case_			,			q_tokens=snake_case_			,			num_buckets=snake_case_			,			pca_max_data=snake_case_			,			kmeans_explained_var=snake_case_			,			kmeans_num_redo=snake_case_			,			kmeans_max_iter=snake_case_			,			featurize_model_name=snake_case_			,			device_id=snake_case_			,			max_text_length=snake_case_			,			divergence_curve_discretization_size=snake_case_			,			mauve_scaling_factor=snake_case_			,			verbose=snake_case_			,			seed=snake_case_			,			)
								return out






'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		if p < 2:
				raise ValueError('''p should not be less than 2!'''     )
		elif p == 2:
				return True

		snake_case__						:       Any       						=  4
		snake_case__						:       int       						=  (1 << p) - 1
		for _ in range(p - 2     ):
				snake_case__						:       Tuple       						=  ((s * s) - 2) % m
		return s == 0


if __name__ == "__main__":
					print(lucas_lehmer_test(7))
					print(lucas_lehmer_test(11))



