

"""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  :      str							      =		'''\\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[Any]							      =		'''\\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  :      Dict							      =		'''\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			:				Any		):
						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   ,lowercase_			:				str   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Any=None   ,lowercase_			:				Tuple=None   ,lowercase_			:				Optional[Any]=None   ,lowercase_			:				Union[str, Any]=None   ,lowercase_			:				str="auto"   ,lowercase_			:				Union[str, Any]=-1   ,lowercase_			:				List[str]=0.9   ,lowercase_			:				int=5   ,lowercase_			:				Tuple=5_0_0   ,lowercase_			:				Union[str, Any]="gpt2-large"   ,lowercase_			:				int=-1   ,lowercase_			:				Union[str, Any]=1_0_2_4   ,lowercase_			:				Union[str, Any]=2_5   ,lowercase_			:				str=5   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=2_5   ,):
						lowerCAmelCase__	:						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"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




