


'''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


_UpperCAmelCase				: Any							    =				"""\
@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}
}

"""

_UpperCAmelCase				: List[str]							    =				"""\
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
"""

_UpperCAmelCase				: Tuple							    =				"""
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       a__				( datasets.Metric      ):







				"""simple docstring"""



				def 					lowercase  (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 					lowercase  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase="auto"       ,			__lowercase=-1       ,			__lowercase=0.9       ,			__lowercase=5       ,			__lowercase=5_00       ,			__lowercase="gpt2-large"       ,			__lowercase=-1       ,			__lowercase=10_24       ,			__lowercase=25       ,			__lowercase=5       ,			__lowercase=True       ,			__lowercase=25       ,			):
										__lowerCAmelCase							       =				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





'''simple docstring'''

from __future__ import annotations

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				sorted(numsa + numsa)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				divmod(len(lowerCamelCase),				2)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCAmelCase				: Union[str, Any]							    =				[float(x) for x in input("""Enter the elements of first array: """).split()]
	_UpperCAmelCase				: int							    =				[float(x) for x in input("""Enter the elements of second array: """).split()]
	print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")

