





"""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


A 			=  '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

'''

A 			=  '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''

A 			=  '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    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\'].
    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
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: "c" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,      _KWARGS_DESCRIPTION							)
class 				__lowercase			(			datasets.Metric							):







					'''simple docstring'''




					def  _lowerCamelCase		(						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  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase="auto"	,			_UpperCAmelCase=-1	,			_UpperCAmelCase=0.9	,			_UpperCAmelCase=5	,			_UpperCAmelCase=500	,			_UpperCAmelCase="gpt2-large"	,			_UpperCAmelCase=-1	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=25	,			_UpperCAmelCase=5	,			_UpperCAmelCase=True	,			_UpperCAmelCase=25	,			):
									__a :						Optional[Any]			       =  compute_mauve(
									    p_text=_UpperCAmelCase	,			q_text=_UpperCAmelCase	,			p_features=_UpperCAmelCase	,			q_features=_UpperCAmelCase	,			p_tokens=_UpperCAmelCase	,			q_tokens=_UpperCAmelCase	,			num_buckets=_UpperCAmelCase	,			pca_max_data=_UpperCAmelCase	,			kmeans_explained_var=_UpperCAmelCase	,			kmeans_num_redo=_UpperCAmelCase	,			kmeans_max_iter=_UpperCAmelCase	,			featurize_model_name=_UpperCAmelCase	,			device_id=_UpperCAmelCase	,			max_text_length=_UpperCAmelCase	,			divergence_curve_discretization_size=_UpperCAmelCase	,			mauve_scaling_factor=_UpperCAmelCase	,			verbose=_UpperCAmelCase	,			seed=_UpperCAmelCase	,			)
									return out






"""simple docstring"""






def     __A      (			a_						:int   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float(moles / volume) * nfactor)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (volume)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((pressure * volume) / (0.0_8_2_1 * moles)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()