




'''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


a  :				Tuple									=  '\\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'

a  :				Optional[int]									=  '\\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'

a  :				Optional[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 						a				(   datasets.Metric     ):


				def   A_ (		self    :					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   A_ (		self    :					List[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					str=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					int=None       ,				lowercase_    :					str=None       ,				lowercase_    :					Any="auto"       ,				lowercase_    :					Union[str, Any]=-1       ,				lowercase_    :					Dict=0.9       ,				lowercase_    :					Optional[int]=5       ,				lowercase_    :					Tuple=500       ,				lowercase_    :					Union[str, Any]="gpt2-large"       ,				lowercase_    :					Optional[int]=-1       ,				lowercase_    :					Any=1024       ,				lowercase_    :					List[str]=25       ,				lowercase_    :					Any=5       ,				lowercase_    :					Union[str, Any]=True       ,				lowercase_    :					str=25       ,				):
									snake_case_									=				compute_mauve(
									    p_text=lowercase_       ,				q_text=lowercase_       ,				p_features=lowercase_       ,				q_features=lowercase_       ,				p_tokens=lowercase_       ,				q_tokens=lowercase_       ,				num_buckets=lowercase_       ,				pca_max_data=lowercase_       ,				kmeans_explained_var=lowercase_       ,				kmeans_num_redo=lowercase_       ,				kmeans_max_iter=lowercase_       ,				featurize_model_name=lowercase_       ,				device_id=lowercase_       ,				max_text_length=lowercase_       ,				divergence_curve_discretization_size=lowercase_       ,				mauve_scaling_factor=lowercase_       ,				verbose=lowercase_       ,				seed=lowercase_       ,				)
									return out







'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 						a				(   metaclass=_lowerCamelCase     ):
				snake_case_      =		["transformers", "torch", "note_seq"]


				def __init__(		self    :					Union[str, Any]       ,				*lowercase_    :					Optional[int]       ,				**lowercase_    :					int					):
									requires_backends(self       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					List[Any]       ,				*lowercase_    :					Optional[Any]       ,				**lowercase_    :					str					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					Tuple       ,				*lowercase_    :					Union[str, Any]       ,				**lowercase_    :					List[Any]					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)

