




"""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							      =     '''\
@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							      =     '''\
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							      =     '''
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 							lowercase_			(	datasets.Metric				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					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      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[int]=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			str=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[int]="auto"       ,				_UpperCAmelCase			:			Optional[int]=-1       ,				_UpperCAmelCase			:			Optional[int]=0.9       ,				_UpperCAmelCase			:			int=5       ,				_UpperCAmelCase			:			int=500       ,				_UpperCAmelCase			:			int="gpt2-large"       ,				_UpperCAmelCase			:			Any=-1       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			Tuple=25       ,				_UpperCAmelCase			:			str=5       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			Any=25       ,				):
       _A     =  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"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

