




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       :       List[str]        =    '''\
@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       :       Optional[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       :       Dict        =    '''
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							):




      """simple docstring"""





      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         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				,      A				,      A				,      A=None				,      A=None				,      A=None				,      A=None				,      A="auto"				,      A=-1				,      A=0.9				,      A=5				,      A=5_0_0				,      A="gpt2-large"				,      A=-1				,      A=1_0_2_4				,      A=2_5				,      A=5				,      A=True				,      A=2_5				,      )		->				Optional[int]:
         snake_case      :				int				=       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 typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
