
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


lowercase__		:str			  =   "\\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"

lowercase__		:Tuple			  =   "\\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"

lowercase__		: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		):




							def 			A__				(    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 			A__				(    self       ,A__       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__="auto"       ,A__=-1       ,A__=0.9       ,A__=5       ,A__=5_0_0       ,A__="gpt2-large"       ,A__=-1       ,A__=1_0_2_4       ,A__=2_5       ,A__=5       ,A__=True       ,A__=2_5       ,):
												lowercase						     =						compute_mauve(
												    p_text=A__       ,q_text=A__       ,p_features=A__       ,q_features=A__       ,p_tokens=A__       ,q_tokens=A__       ,num_buckets=A__       ,pca_max_data=A__       ,kmeans_explained_var=A__       ,kmeans_num_redo=A__       ,kmeans_max_iter=A__       ,featurize_model_name=A__       ,device_id=A__       ,max_text_length=A__       ,divergence_curve_discretization_size=A__       ,mauve_scaling_factor=A__       ,verbose=A__       ,seed=A__       ,)
												return out






lowercase__		:Any			  =   8.3_144_598



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if temperature < 0:
										raise Exception('''Temperature cannot be less than 0 K'''		)
					if molar_mass <= 0:
										raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol'''		)
					else:
										return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							lowercase__		:Optional[Any]			  =   300
							lowercase__		:List[Any]			  =   28
							lowercase__		:Dict			  =   rms_speed_of_molecule(temperature, molar_mass)
							print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')




