"""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__						:						Optional[Any]		  =			'\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n'

lowerCAmelCase__						:						Any		  =			'\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

lowerCAmelCase__						:						List[Any]		  =			'\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    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\'].\n    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\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION       )
class        snake_case (				datasets.Metric       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			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 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Union[str, Any]=None					,lowerCamelCase__     :			Dict=None					,lowerCamelCase__     :			List[str]=None					,lowerCamelCase__     :			Optional[Any]=None					,lowerCamelCase__     :			Optional[Any]="auto"					,lowerCamelCase__     :			List[Any]=-1					,lowerCamelCase__     :			Optional[Any]=0.9					,lowerCamelCase__     :			List[str]=5					,lowerCamelCase__     :			Optional[int]=500					,lowerCamelCase__     :			List[Any]="gpt2-large"					,lowerCamelCase__     :			str=-1					,lowerCamelCase__     :			List[Any]=1_024					,lowerCamelCase__     :			Union[str, Any]=25					,lowerCamelCase__     :			int=5					,lowerCamelCase__     :			Optional[Any]=True					,lowerCamelCase__     :			Union[str, Any]=25					,):
									UpperCAmelCase__    =							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






"""simple docstring"""






import random
class        snake_case :




					"""simple docstring"""

					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[ord(lowerCamelCase__    ) for i in text]
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for i in plain:
													UpperCAmelCase__    =							random.randint(1					,300    )
													UpperCAmelCase__    =							(i + k) * k
													cipher.append(lowerCamelCase__    )
													key.append(lowerCamelCase__    )
									return cipher, key







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(lowerCamelCase__    )    ):
													UpperCAmelCase__    =							int((cipher[i] - (key[i]) ** 2) / key[i]    )
													plain.append(chr(lowerCamelCase__    )    )
									return "".join(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCAmelCase__  ,			lowerCAmelCase__						:						Dict		  =			Onepad().encrypt('Hello')
						print(c, k)
						print(Onepad().decrypt(c, k))



