


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]							     =   """\
@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__							:						int							     =   """\
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__							:						Any							     =   """
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 		__magic_name__						(datasets.Metric				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       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:List[str]     ,		_a:Any     ,		_a:Tuple     ,		_a:str=None     ,		_a:str=None     ,		_a:List[Any]=None     ,		_a:Dict=None     ,		_a:List[Any]="auto"     ,		_a:Optional[int]=-1     ,		_a:int=0.9     ,		_a:str=5     ,		_a:List[str]=5_00     ,		_a:Tuple="gpt2-large"     ,		_a:Union[str, Any]=-1     ,		_a:Optional[int]=10_24     ,		_a:Optional[Any]=25     ,		_a:Optional[Any]=5     ,		_a:Optional[Any]=True     ,		_a:List[str]=25     ,		):
       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





import numpy as np







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return 1 / (1 + np.exp(-vector				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return vector * sigmoid(__lowerCAmelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

