




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_		:						Optional[int]             =    """\
@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_		:						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
"""

a_		:						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 		_snake_case  (  datasets.Metric					):






			def    SCREAMING_SNAKE_CASE__    (       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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=None		,					a=None		,					a=None		,					a=None		,					a="auto"		,					a=-1		,					a=0.9		,					a=5		,					a=500		,					a="gpt2-large"		,					a=-1		,					a=1024		,					a=25		,					a=5		,					a=True		,					a=25		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= 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 __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a_		:						str             =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_		:						list[int]             =    [ord(letter) for letter in string.ascii_lowercase]
a_		:						set[int]             =    {ord(char) for char in VALID_CHARS}

a_		:						list[str]             =    ["the", "be", "to", "of", "and", "in", "that", "have"]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ""
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase)							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase)

			return decoded






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for key in product(_UpperCAmelCase							,       repeat=3):
						SCREAMING_SNAKE_CASE						= try_key(_UpperCAmelCase							,       _UpperCAmelCase)
						if encoded is not None:
									possibles.append(_UpperCAmelCase)
			return possibles






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return [possible for possible in possibles if common_word in possible.lower()]






def 				lowerCamelCase__				(_UpperCAmelCase = "p059_cipher.txt"):
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase).parent.joinpath(_UpperCAmelCase).read_text(encoding='utf-8')

			SCREAMING_SNAKE_CASE						= [int(_UpperCAmelCase) for number in data.strip().split(',')]

			SCREAMING_SNAKE_CASE						= filter_valid_chars(_UpperCAmelCase)
			for common_word in COMMON_WORDS:
						SCREAMING_SNAKE_CASE						= filter_common_word(_UpperCAmelCase							,       _UpperCAmelCase)
						if len(_UpperCAmelCase) == 1:
									break

			SCREAMING_SNAKE_CASE						= possibles[0]
			return sum(ord(_UpperCAmelCase) for char in decoded_text)


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




