






"""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


UpperCamelCase_	:				int										= """\
@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_	:				Any										= """\
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_	:				Optional[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 _UpperCamelCase						(    self				:	int						):





            """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 _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	List[Any]							,   a				:	Optional[int]							,   a				:	Optional[int]=None							,   a				:	Any=None							,   a				:	Optional[Any]=None							,   a				:	Optional[int]=None							,   a				:	List[Any]="auto"							,   a				:	Tuple=-1							,   a				:	Union[str, Any]=0.9							,   a				:	Union[str, Any]=5							,   a				:	int=5_0_0							,   a				:	List[str]="gpt2-large"							,   a				:	List[Any]=-1							,   a				:	int=1_0_2_4							,   a				:	List[str]=2_5							,   a				:	Union[str, Any]=5							,   a				:	List[Any]=True							,   a				:	Dict=2_5							,   ):





            """simple docstring"""







            __snake_case      :    str  			=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










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_	:				Union[str, Any]										= {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


