

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


_UpperCAmelCase						:  Tuple									=			"""\
@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}
}

"""

_UpperCAmelCase						:  Dict									=			"""\
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
"""

_UpperCAmelCase						:  List[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 					lowercase							(			datasets.Metric     ):




				def        a				(   self			):
											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 ,  snake_case ,  snake_case ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  snake_case="auto" ,  snake_case=-1 ,  snake_case=0.9 ,  snake_case=5 ,  snake_case=500 ,  snake_case="gpt2-large" ,  snake_case=-1 ,  snake_case=1024 ,  snake_case=25 ,  snake_case=5 ,  snake_case=True ,  snake_case=25 ,  ):
											snake_case_     							=       compute_mauve(
											    p_text=snake_case ,  q_text=snake_case ,  p_features=snake_case ,  q_features=snake_case ,  p_tokens=snake_case ,  q_tokens=snake_case ,  num_buckets=snake_case ,  pca_max_data=snake_case ,  kmeans_explained_var=snake_case ,  kmeans_num_redo=snake_case ,  kmeans_max_iter=snake_case ,  featurize_model_name=snake_case ,  device_id=snake_case ,  max_text_length=snake_case ,  divergence_curve_discretization_size=snake_case ,  mauve_scaling_factor=snake_case ,  verbose=snake_case ,  seed=snake_case ,  )
											return out







import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {' ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}' ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  int									=			argparse.ArgumentParser()
					parser.add_argument("""--version""", help="""Release version.""")
					_UpperCAmelCase						:  str									=			parser.parse_args()
					update_custom_js(args.version)




