






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


_a      	=  '''\
@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}
}

'''

_a      	=  '''\
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
'''

_a      	=  '''
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 __lowerCamelCase (      datasets.Metric):





	"""simple docstring"""






	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=UpperCAmelCase       ,    q_text=UpperCAmelCase       ,    p_features=UpperCAmelCase       ,    q_features=UpperCAmelCase       ,    p_tokens=UpperCAmelCase       ,    q_tokens=UpperCAmelCase       ,    num_buckets=UpperCAmelCase       ,    pca_max_data=UpperCAmelCase       ,    kmeans_explained_var=UpperCAmelCase       ,    kmeans_num_redo=UpperCAmelCase       ,    kmeans_max_iter=UpperCAmelCase       ,    featurize_model_name=UpperCAmelCase       ,    device_id=UpperCAmelCase       ,    max_text_length=UpperCAmelCase       ,    divergence_curve_discretization_size=UpperCAmelCase       ,    mauve_scaling_factor=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    seed=UpperCAmelCase       ,    )
							return out

"""simple docstring"""


UpperCAmelCase__     						=  [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Return True if there is node that has not iterated.
  _UpperCAmelCase         =					[False] * len(lowercase						)
  _UpperCAmelCase         =					[s]
  _UpperCAmelCase         =					True

  while queue:
    _UpperCAmelCase         =					queue.pop(0						)
    for ind in range(len(graph[u]						)						):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase						)
        _UpperCAmelCase         =					True
        _UpperCAmelCase         =					u

  return visited[t]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[-1] * (len(lowercase						))
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					[i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase      ,lowercase      ,lowercase      ,lowercase						):
    _UpperCAmelCase         =					float("""Inf"""						)
    _UpperCAmelCase         =					sink

    while s != source:
      # Find the minimum value in select path
      _UpperCAmelCase         =					min(lowercase      ,graph[parent[s]][s]						)
      _UpperCAmelCase         =					parent[s]

    max_flow += path_flow
    _UpperCAmelCase         =					sink

    while v != source:
      _UpperCAmelCase         =					parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _UpperCAmelCase         =					parent[v]

  for i in range(len(lowercase						)						):
    for j in range(len(graph[0]						)						):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)						)

  return res


if __name__ == "__main__":
    print(mincut(test_graph, source=0, sink=5))
