






"""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


_lowerCAmelCase         =							"\\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"

_lowerCAmelCase         =							"\\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"

_lowerCAmelCase         =							"\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      SCREAMING_SNAKE_CASE  (				datasets.Metric						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_="auto"     ,     lowerCAmelCase_=-1     ,     lowerCAmelCase_=0.9     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=500     ,     lowerCAmelCase_="gpt2-large"     ,     lowerCAmelCase_=-1     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=25     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=25     ,     ):
		A_		:						List[str]	    =   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"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

