





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


snake_case								=							"""\
@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}
}

"""

snake_case								=							"""\
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
"""

snake_case								=							"""
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 					A_					(   datasets.Metric						):







       """simple docstring"""





       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       List[str]:
         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						:						Optional[Any]    ,__A						:						List[str]    ,__A						:						List[str]=None    ,__A						:						List[Any]=None    ,__A						:						Dict=None    ,__A						:						str=None    ,__A						:						int="auto"    ,__A						:						List[str]=-1    ,__A						:						Union[str, Any]=0.9    ,__A						:						Dict=5    ,__A						:						Optional[Any]=500    ,__A						:						int="gpt2-large"    ,__A						:						List[Any]=-1    ,__A						:						List[Any]=1024    ,__A						:						Tuple=25    ,__A						:						Optional[Any]=5    ,__A						:						int=True    ,__A						:						int=25    ,)    ->       List[str]:
         _lowercase						    =			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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




