






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 : Union[str, 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}
}

'''

lowerCamelCase : Optional[Any]						   =       '''\
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
'''

lowerCamelCase : List[str]						   =       '''
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       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[Any]:


												"""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       lowerCAmelCase				(  self				:	str		,     __a				:	Any		,     __a				:	int		,     __a				:	Optional[int]=None		,     __a				:	Optional[Any]=None		,     __a				:	int=None		,     __a				:	List[Any]=None		,     __a				:	Tuple="auto"		,     __a				:	Union[str, Any]=-1		,     __a				:	Union[str, Any]=0.9		,     __a				:	List[str]=5		,     __a				:	Tuple=500		,     __a				:	Optional[int]="gpt2-large"		,     __a				:	Any=-1		,     __a				:	int=1024		,     __a				:	List[str]=25		,     __a				:	int=5		,     __a				:	List[str]=True		,     __a				:	str=25		,     )			->  Tuple:


												"""simple docstring"""

												__lowercase      :							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







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)