



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__  :  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}
}

'''

A__  :  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
'''

A__  :  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 				__snake_case						(     datasets.Metric						):




							def    UpperCAmelCase__			(       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    UpperCAmelCase__			(       self     :       Any				,				A_     :       str				,				A_     :       int				,				A_     :       Optional[Any]=None				,				A_     :       Optional[int]=None				,				A_     :       Tuple=None				,				A_     :       Any=None				,				A_     :       Optional[Any]="auto"				,				A_     :       Optional[int]=-1				,				A_     :       Dict=0.9				,				A_     :       Dict=5				,				A_     :       int=5_0_0				,				A_     :       str="gpt2-large"				,				A_     :       Dict=-1				,				A_     :       Dict=1_0_2_4				,				A_     :       Dict=2_5				,				A_     :       List[Any]=5				,				A_     :       Tuple=True				,				A_     :       Tuple=2_5				,				):
									lowerCAmelCase_			:     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










import string


def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		for key in range(len(string.ascii_uppercase ) ):
				lowerCAmelCase_			:     List[Any]									= ''''''
				for symbol in message:
						if symbol in string.ascii_uppercase:
								lowerCAmelCase_			:     Optional[int]									= string.ascii_uppercase.find(__UpperCamelCase )
								lowerCAmelCase_			:     Optional[int]									= num - key
								if num < 0:
										lowerCAmelCase_			:     int									= num + len(string.ascii_uppercase )
								lowerCAmelCase_			:     int									= translated + string.ascii_uppercase[num]
						else:
								lowerCAmelCase_			:     Optional[int]									= translated + symbol
				print(f"""Decryption using Key #{key}: {translated}""" )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Dict									= input('''Encrypted message: ''' )
		lowerCAmelCase_			:     List[str]									= message.upper()
		decrypt(__UpperCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





