
"""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     :    Optional[int]       =			'\\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'

UpperCamelCase     :    Optional[int]       =			'\\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'

UpperCamelCase     :    Optional[int]       =			'\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 __lowerCAmelCase		(				datasets.Metric      ):





     def 						UpperCAmelCase (     self				):

      '''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     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="auto"     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0.9     ,					__UpperCAmelCase=5     ,					__UpperCAmelCase=500     ,					__UpperCAmelCase="gpt2-large"     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=1024     ,					__UpperCAmelCase=25     ,					__UpperCAmelCase=5     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=25     ,					):

      '''simple docstring'''




      __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





"""simple docstring"""

import heapq as hq
import math
from collections.abc import Iterator

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	str(id_				)
      __UpperCamelCase      =	None
      __UpperCamelCase      =	None
      __UpperCamelCase      =	[]
      __UpperCamelCase      =	{}  # {vertex:distance}





     def __lt__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return self.key < other.key





     def __repr__(     self				):

      '''simple docstring'''




      return self.id





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      self.neighbors.append(__UpperCAmelCase				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	weight






def 							A						(    snake_case				:List[Any]    , snake_case				:Dict    , snake_case				:Any    , snake_case				:str		)				->     List[str]:
 # add the neighbors:
 graph[a - 1].add_neighbor(graph[b - 1]		)
 graph[b - 1].add_neighbor(graph[a - 1]		)
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]    , snake_case		)
 graph[b - 1].add_edge(graph[a - 1]    , snake_case		)






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     list:
 __UpperCamelCase      =	[]
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0
 __UpperCamelCase      =	graph[:]
 while q:
  __UpperCamelCase      =	min(snake_case		)
  q.remove(snake_case		)
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
 for i in range(1    , len(snake_case		)		):
  a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
 return a






def 							A						(    snake_case				:list    , snake_case				:Vertex		)				->     Iterator[tuple]:
 for u in graph:
  __UpperCamelCase      =	math.inf
  __UpperCamelCase      =	None
 __UpperCamelCase      =	0

 __UpperCamelCase      =	list(snake_case		)
 hq.heapify(snake_case		)

 while h:
  __UpperCamelCase      =	hq.heappop(snake_case		)
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    __UpperCamelCase      =	u
    __UpperCamelCase      =	u.edges[v.id]
    hq.heapify(snake_case		)

 for i in range(1    , len(snake_case		)		):
  yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)






def 							A						(    )				->     None:
 pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



