

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_							:					Union[str, 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_							:					Union[str, Any]		 =   '''\
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_							:					int		 =   '''
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      lowerCamelCase_      (	datasets.Metric	):




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








from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





