




"""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


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 					lowercase(     datasets.Metric ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		str		):




   '''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:		Tuple,      a_:		Any,      a_:		Dict,      a_:		int=None,      a_:		List[str]=None,      a_:		str=None,      a_:		List[str]=None,      a_:		Optional[Any]="auto",      a_:		str=-1,      a_:		Optional[Any]=0.9,      a_:		Optional[int]=5,      a_:		Union[str, Any]=500,      a_:		Dict="gpt2-large",      a_:		Optional[Any]=-1,      a_:		Union[str, Any]=1_024,      a_:		Tuple=25,      a_:		Any=5,      a_:		Tuple=True,      a_:		List[Any]=25,      ):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  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










lowerCamelCase       :       Tuple        =				{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase       :       int        =				['''a''', '''b''', '''c''', '''d''', '''e''']
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Dict			=   start
							# add current to visited
							visited.append(lowerCAmelCase_					)
							__lowercase					:							Dict			=   edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					__lowercase					:							List[Any]			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # if all neighbors visited add current to sort
							sort.append(lowerCAmelCase_					)
							# if all vertices haven't been visited select a new one to visit
							if len(lowerCAmelCase_					) != len(lowerCAmelCase_					):
														for vertice in vertices:
																					if vertice not in visited:
																												__lowercase					:							Tuple			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # return sort
							return sort


if __name__ == "__main__":
	lowerCamelCase       :       Any        =				topological_sort('''a''', [], [])
	print(sort)