




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


_lowerCAmelCase						:   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}
}

"""

_lowerCAmelCase						:   List[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
"""

_lowerCAmelCase						:   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 			_UpperCamelCase      (				datasets.Metric		):
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			int:
       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   :str ,      lowerCamelCase   :Any ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Optional[int]=None ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Optional[int]=None ,      lowerCamelCase   :int="auto" ,      lowerCamelCase   :str=-1 ,      lowerCamelCase   :Optional[Any]=0.9 ,      lowerCamelCase   :Optional[Any]=5 ,      lowerCamelCase   :List[str]=500 ,      lowerCamelCase   :List[Any]="gpt2-large" ,      lowerCamelCase   :Tuple=-1 ,      lowerCamelCase   :List[Any]=1024 ,      lowerCamelCase   :Any=25 ,      lowerCamelCase   :Optional[int]=5 ,      lowerCamelCase   :Union[str, Any]=True ,      lowerCamelCase   :Union[str, Any]=25 ,      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						compute_mauve(
           p_text=lowerCamelCase ,      q_text=lowerCamelCase ,      p_features=lowerCamelCase ,      q_features=lowerCamelCase ,      p_tokens=lowerCamelCase ,      q_tokens=lowerCamelCase ,      num_buckets=lowerCamelCase ,      pca_max_data=lowerCamelCase ,      kmeans_explained_var=lowerCamelCase ,      kmeans_num_redo=lowerCamelCase ,      kmeans_max_iter=lowerCamelCase ,      featurize_model_name=lowerCamelCase ,      device_id=lowerCamelCase ,      max_text_length=lowerCamelCase ,      divergence_curve_discretization_size=lowerCamelCase ,      mauve_scaling_factor=lowerCamelCase ,      verbose=lowerCamelCase ,      seed=lowerCamelCase ,      )
       return out












from ..utils import DummyObject, requires_backends
class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :List[Any]      )     ->			Dict:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[int] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Dict ,      *lowerCamelCase   :int ,      **lowerCamelCase   :str      )     ->			str:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[int] ,      *lowerCamelCase   :Tuple ,      **lowerCamelCase   :List[Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[str] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Any      )     ->			Union[str, Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :str ,      **lowerCamelCase   :List[Any]      )     ->			Tuple:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Dict      )     ->			int:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Union[str, Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Dict      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :str      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[str] ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			Union[str, Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Optional[Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Tuple ,      *lowerCamelCase   :str ,      **lowerCamelCase   :List[str]      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Any ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Any      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Optional[Any]      )     ->			Tuple:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Any ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :List[str]      )     ->			List[str]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :Dict      )     ->			Union[str, Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :str      )     ->			str:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :str ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Union[str, Any]      )     ->			Optional[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :List[str]      )     ->			int:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Union[str, Any]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Dict ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :int      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Tuple ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			Dict:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Any ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :List[str]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Optional[Any]      )     ->			List[str]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :List[Any]      )     ->			Any:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :str      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :List[Any] ,      *lowerCamelCase   :str ,      **lowerCamelCase   :int      )     ->			List[Any]:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[str] ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Any      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Tuple      )     ->			int:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :Any ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :List[Any] ,      *lowerCamelCase   :int ,      **lowerCamelCase   :Union[str, Any]      )     ->			List[Any]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :str ,      *lowerCamelCase   :int ,      **lowerCamelCase   :List[str]      )     ->			List[str]:
       requires_backends(cls ,      ["flax"]      )



class 			_UpperCamelCase      (				metaclass=lowerCAmelCase		):
      UpperCAmelCase_        =		["""flax"""]
      def __init__(			self   :int ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Optional[int]      )     ->			str:
       requires_backends(self ,      ["flax"]      )
      @classmethod
      def 	UpperCAmelCase_						(			cls   :Union[str, Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :Union[str, Any]      )     ->			Optional[int]:
       requires_backends(cls ,      ["flax"]      )







      @classmethod
      def 	UpperCAmelCase_						(			cls   :int ,      *lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Any      )     ->			Optional[Any]:
       requires_backends(cls ,      ["flax"]      )





