"""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


__SCREAMING_SNAKE_CASE						:   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'

__SCREAMING_SNAKE_CASE						:   List[Any]         =			'\\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'

__SCREAMING_SNAKE_CASE						:   Tuple         =			'\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 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_="auto"				,    lowercase_=-1				,    lowercase_=0.9				,    lowercase_=5				,    lowercase_=500				,    lowercase_="gpt2-large"				,    lowercase_=-1				,    lowercase_=1_024				,    lowercase_=25				,    lowercase_=5				,    lowercase_=True				,    lowercase_=25				,    ):
													_snake_case						:					List[str]    =   compute_mauve(
													    p_text=lowercase_				,    q_text=lowercase_				,    p_features=lowercase_				,    q_features=lowercase_				,    p_tokens=lowercase_				,    q_tokens=lowercase_				,    num_buckets=lowercase_				,    pca_max_data=lowercase_				,    kmeans_explained_var=lowercase_				,    kmeans_num_redo=lowercase_				,    kmeans_max_iter=lowercase_				,    featurize_model_name=lowercase_				,    device_id=lowercase_				,    max_text_length=lowercase_				,    divergence_curve_discretization_size=lowercase_				,    mauve_scaling_factor=lowercase_				,    verbose=lowercase_				,    seed=lowercase_				,    )
													return out
def  snake_case						(__lowercase						)	->	list[int]:

						'''simple docstring'''



						if num <= 0:
												raise ValueError("Input must be a positive integer"						)

						_snake_case						:					Any    =   [True] * (num + 1)

						_snake_case						:					str    =   2
						while p * p <= num:
												if primes[p]:
																		for i in range(p * p ,      num + 1 ,      __lowercase						):
																								_snake_case						:					Optional[int]    =   False
												p += 1

						return [prime for prime in range(2 ,      num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__SCREAMING_SNAKE_CASE						:   Any         =			int(input('Enter a positive integer: ').strip())
				print(prime_sieve_eratosthenes(user_num))