





"""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  :			int        =       '\\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  :			List[str]        =       '\\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  :			List[str]        =       '\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        lowercase      (    datasets.Metric):







				def 					a_  (							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 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Any=None      ,     _lowerCamelCase   :					Optional[int]=None      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					List[str]="auto"      ,     _lowerCamelCase   :					Tuple=-1      ,     _lowerCamelCase   :					Union[str, Any]=0.9      ,     _lowerCamelCase   :					Tuple=5      ,     _lowerCamelCase   :					Any=5_00      ,     _lowerCamelCase   :					Dict="gpt2-large"      ,     _lowerCamelCase   :					Union[str, Any]=-1      ,     _lowerCamelCase   :					Optional[Any]=10_24      ,     _lowerCamelCase   :					List[Any]=25      ,     _lowerCamelCase   :					str=5      ,     _lowerCamelCase   :					Any=True      ,     _lowerCamelCase   :					Optional[int]=25      ,     ):



					"""simple docstring"""

					A_  :				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











"""simple docstring"""

from __future__ import annotations

from collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





