





from __future__ import annotations

from collections.abc import MutableSequence




class 			__UpperCamelCase    :





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        if len(_UpperCAmelCase					) != degree + 1:
           raise ValueError(
               '''The number of coefficients should be equal to the degree + 1.'''					)

        _UpperCAmelCase           =   list(_UpperCAmelCase					)
        _UpperCAmelCase           =   degree





     def __add__(     self				,							_UpperCamelCase					):

        if self.degree > polynomial_a.degree:
           _UpperCAmelCase           =   self.coefficients[:]
           for i in range(polynomial_a.degree + 1					):
              coefficients[i] += polynomial_a.coefficients[i]
           return Polynomial(self.degree				,							_UpperCAmelCase					)
        else:
           _UpperCAmelCase           =   polynomial_a.coefficients[:]
           for i in range(self.degree + 1					):
              coefficients[i] += self.coefficients[i]
           return Polynomial(polynomial_a.degree				,							_UpperCAmelCase					)





     def __sub__(     self				,							_UpperCamelCase					):
        return self + polynomial_a * Polynomial(0				,							[-1]					)





     def __neg__(     self					):
        return Polynomial(self.degree				,							[-c for c in self.coefficients]					)





     def __mul__(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   [0] * (self.degree + polynomial_a.degree + 1)
        for i in range(self.degree + 1					):
           for j in range(polynomial_a.degree + 1					):
              coefficients[i + j] += (
                  self.coefficients[i] * polynomial_a.coefficients[j]
              )

        return Polynomial(self.degree + polynomial_a.degree				,							_UpperCAmelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   0
        for i in range(self.degree + 1					):
           result += self.coefficients[i] * (substitution**i)
        return result





     def __str__(     self					):
        _UpperCAmelCase           =   ''
        for i in range(self.degree				,							-1				,							-1					):
           if self.coefficients[i] == 0:
              continue
           elif self.coefficients[i] > 0:
              if polynomial:
                 polynomial += " + "
           else:
              polynomial += " - "

           if i == 0:
              polynomial += str(abs(self.coefficients[i]					)					)
           elif i == 1:
              polynomial += str(abs(self.coefficients[i]					)					) + "x"
           else:
              polynomial += str(abs(self.coefficients[i]					)					) + "x^" + str(_UpperCAmelCase					)

        return polynomial





     def __repr__(     self					):
        return self.__str__()





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   [0] * self.degree
        for i in range(self.degree					):
           _UpperCAmelCase           =   self.coefficients[i + 1] * (i + 1)
        return Polynomial(self.degree - 1				,							_UpperCAmelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase = 0					):
        _UpperCAmelCase           =   [0] * (self.degree + 2)
        _UpperCAmelCase           =   constant
        for i in range(self.degree + 1					):
           _UpperCAmelCase           =   self.coefficients[i] / (i + 1)
        return Polynomial(self.degree + 1				,							_UpperCAmelCase					)





     def __eq__(     self				,							_UpperCamelCase					):
        if not isinstance(_UpperCAmelCase				,							_UpperCAmelCase					):
           return False

        if self.degree != polynomial_a.degree:
           return False

        for i in range(self.degree + 1					):
           if self.coefficients[i] != polynomial_a.coefficients[i]:
              return False

        return True





     def __ne__(     self				,							_UpperCamelCase					):
        return not self.__eq__(_UpperCAmelCase					)




'''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


__lowerCAmelCase		     =  '''\
@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}
}

'''

__lowerCAmelCase		     =  '''\
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
'''

__lowerCAmelCase		     =  '''
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       __magic_name__		(       datasets.Metric  ):







							def 				__lowercase     (				self   :			Optional[Any]						):
									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   :			Tuple      ,_UpperCAmelCase   :			Optional[Any]      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			Any=None      ,_UpperCAmelCase   :			Tuple=None      ,_UpperCAmelCase   :			List[str]=None      ,_UpperCAmelCase   :			Optional[Any]=None      ,_UpperCAmelCase   :			Any="auto"      ,_UpperCAmelCase   :			Tuple=-1      ,_UpperCAmelCase   :			Union[str, Any]=0.9      ,_UpperCAmelCase   :			List[Any]=5      ,_UpperCAmelCase   :			Optional[int]=500      ,_UpperCAmelCase   :			int="gpt2-large"      ,_UpperCAmelCase   :			int=-1      ,_UpperCAmelCase   :			Tuple=1024      ,_UpperCAmelCase   :			Any=25      ,_UpperCAmelCase   :			int=5      ,_UpperCAmelCase   :			Optional[int]=True      ,_UpperCAmelCase   :			Optional[int]=25      ,):
									_a	: 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




