


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[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'

lowerCAmelCase__			:      Optional[int]								='\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  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""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 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__="auto"			,						lowerCAmelCase__=-1			,						lowerCAmelCase__=0.9			,						lowerCAmelCase__=5			,						lowerCAmelCase__=5_0_0			,						lowerCAmelCase__="gpt2-large"			,						lowerCAmelCase__=-1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=2_5			,						lowerCAmelCase__=5			,						lowerCAmelCase__=True			,						lowerCAmelCase__=2_5			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					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 math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


if __name__ == "__main__":
 print(f"""{solution() = }""")