


'''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__					=   '\\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__					=   '\\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__					=   '\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		):




			def 							lowerCAmelCase__			(						self     :   int       )      ->Optional[Any]:



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



					'''simple docstring'''







					_UpperCAmelCase						:				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'''


import math






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return math.sqrt(__lowerCAmelCase      ) * math.sqrt(__lowerCAmelCase      ) == num






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					0
		_UpperCAmelCase						:				Tuple	    =					n
		while left <= right:
				_UpperCAmelCase						:				int	    =					(left + right) // 2
				if mid**2 == n:
						return True
				elif mid**2 > n:
						_UpperCAmelCase						:				str	    =					mid - 1
				else:
						_UpperCAmelCase						:				List[str]	    =					mid + 1
		return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



