


"""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_       							=    "\\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_       							=    "\\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_       							=    "\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):







  def  a_ (					self      :       int		)       ->						str:





      '''simple docstring'''







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





      '''simple docstring'''







      _A					  =			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



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)