






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       :       Dict        =				'''\
@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       :       Optional[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
'''

lowerCamelCase       :       List[str]        =				'''
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	):

					'''simple docstring'''

					def 					lowerCAmelCase (     self :		str							)	->     Union[str, Any]:


												"""simple docstring"""

												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 :		Tuple						,   __a :		Any						,   __a :		Dict						,   __a :		int=None						,   __a :		List[str]=None						,   __a :		str=None						,   __a :		List[str]=None						,   __a :		Optional[Any]="auto"						,   __a :		str=-1						,   __a :		Optional[Any]=0.9						,   __a :		Optional[int]=5						,   __a :		Union[str, Any]=500						,   __a :		Dict="gpt2-large"						,   __a :		Optional[Any]=-1						,   __a :		Union[str, Any]=1024						,   __a :		Tuple=25						,   __a :		Any=5						,   __a :		Tuple=True						,   __a :		List[Any]=25						,   )	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Union[str, 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 itertools import permutations
def 					snake_case_						(   lowerCAmelCase_				:  tuple					):
							if num[3] % 2 != 0:
														return False

							if (num[2] + num[3] + num[4]) % 3 != 0:
														return False

							if num[5] % 5 != 0:
														return False

							__lowercase					:							Dict			=   [7, 11, 13, 17]
							for i, test in enumerate(lowerCAmelCase_					):
														if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10					):
							return sum(
							    int("""""".join(map(lowerCAmelCase_				,				lowerCAmelCase_					)					)					)
							    for num in permutations(range(lowerCAmelCase_					)					)
							    if is_substring_divisible(lowerCAmelCase_					)					)


if __name__ == "__main__":
	print(f'''{solution() = }''')