


"""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


snake_case__		:     Optional[Any]    				=				'''\
@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}
}

'''

snake_case__		:     int    				=				'''\
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
'''

snake_case__		:     Optional[int]    				=				'''
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 		snake_case_(       datasets.Metric			):






     def      lowerCamelCase__     (	self  :					int				):
           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  :					int					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Optional[Any]=None					,				UpperCamelCase_  :					Dict=None					,				UpperCamelCase_  :					Union[str, Any]=None					,				UpperCamelCase_  :					Tuple=None					,				UpperCamelCase_  :					List[Any]="auto"					,				UpperCamelCase_  :					Dict=-1					,				UpperCamelCase_  :					Any=0.9					,				UpperCamelCase_  :					List[Any]=5					,				UpperCamelCase_  :					str=5_0_0					,				UpperCamelCase_  :					Union[str, Any]="gpt2-large"					,				UpperCamelCase_  :					List[str]=-1					,				UpperCamelCase_  :					Optional[Any]=1_0_2_4					,				UpperCamelCase_  :					Optional[Any]=2_5					,				UpperCamelCase_  :					List[str]=5					,				UpperCamelCase_  :					Optional[Any]=True					,				UpperCamelCase_  :					List[Any]=2_5					,				):
           lowerCAmelCase					:	Optional[Any]       =   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"""





from __future__ import annotations

from typing import Any



class 		snake_case_(       a__			):
     pass



class 		snake_case_:






     def __init__(	self  :					Any					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Any       =   data
           lowerCAmelCase					:	Node | None       =   None






     def __iter__(	self  :					int				):
           lowerCAmelCase					:	Any       =   self
           lowerCAmelCase					:	Union[str, Any]       =   []
           while node:
                 if node in visited:
                       raise ContainsLoopError
                 visited.append(UpperCamelCase_				)
                 yield node.data
                 lowerCAmelCase					:	Optional[int]       =   node.next_node






     @property
     def      lowerCamelCase__     (	self  :					str				):
           try:
                 list(self				)
                 return False
           except ContainsLoopError:
                 return True


if __name__ == "__main__":
 snake_case__		:     Dict    				=				Node(1)
 snake_case__		:     Any    				=				Node(2)
 snake_case__		:     int    				=				Node(3)
 snake_case__		:     Any    				=				Node(4)
 print(root_node.has_loop)  # False
 snake_case__		:     Tuple    				=				root_node.next_node
 print(root_node.has_loop)  # True

 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     int    				=				Node(6)
 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     Dict    				=				Node(6)
 print(root_node.has_loop)  # False

 snake_case__		:     Any    				=				Node(1)
 print(root_node.has_loop)  # False


