


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_				:			Optional[Any]   =	'\\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_				:			Dict   =	'\\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_				:			List[Any]   =	'\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      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             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 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Dict=None					,							SCREAMING_SNAKE_CASE__    :	List[str]=None					,							SCREAMING_SNAKE_CASE__    :	Dict="auto"					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=-1					,							SCREAMING_SNAKE_CASE__    :	Any=0.9					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=5					,							SCREAMING_SNAKE_CASE__    :	List[Any]=5_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="gpt2-large"					,							SCREAMING_SNAKE_CASE__    :	Any=-1					,							SCREAMING_SNAKE_CASE__    :	str=1_0_2_4					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_5					,							SCREAMING_SNAKE_CASE__    :	Dict=5					,							SCREAMING_SNAKE_CASE__    :	int=True					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_5					,							)		->      Optional[int]:
             a_ :		List[str]							=  compute_mauve(
                 p_text=SCREAMING_SNAKE_CASE__					,							q_text=SCREAMING_SNAKE_CASE__					,							p_features=SCREAMING_SNAKE_CASE__					,							q_features=SCREAMING_SNAKE_CASE__					,							p_tokens=SCREAMING_SNAKE_CASE__					,							q_tokens=SCREAMING_SNAKE_CASE__					,							num_buckets=SCREAMING_SNAKE_CASE__					,							pca_max_data=SCREAMING_SNAKE_CASE__					,							kmeans_explained_var=SCREAMING_SNAKE_CASE__					,							kmeans_num_redo=SCREAMING_SNAKE_CASE__					,							kmeans_max_iter=SCREAMING_SNAKE_CASE__					,							featurize_model_name=SCREAMING_SNAKE_CASE__					,							device_id=SCREAMING_SNAKE_CASE__					,							max_text_length=SCREAMING_SNAKE_CASE__					,							divergence_curve_discretization_size=SCREAMING_SNAKE_CASE__					,							mauve_scaling_factor=SCREAMING_SNAKE_CASE__					,							verbose=SCREAMING_SNAKE_CASE__					,							seed=SCREAMING_SNAKE_CASE__					,							)
             return out






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase_				:			str   =	1.054571817e-34  # unit of ℏ : J * s

UpperCAmelCase_				:			Dict   =	3e8  # unit of c : m * s^-1







def 	SCREAMING_SNAKE_CASE_						(      __A			:      float  ,   __A			:      float  ,   __A			:      float    )     ->		dict[str, float]:

       """simple docstring"""

       if (force, area, distance).count(0    ) != 1:
              raise ValueError('One and only one argument must be 0'    )
       if force < 0:
              raise ValueError('Magnitude of force can not be negative'    )
       if distance < 0:
              raise ValueError('Distance can not be negative'    )
       if area < 0:
              raise ValueError('Area can not be negative'    )
       if force == 0:
              a_ :		Optional[Any]							=  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                  2_40 * (distance) ** 4
              )
              return {"force": force}
       elif area == 0:
              a_ :		List[str]							=  (2_40 * force * (distance) ** 4) / (
                  REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
              )
              return {"area": area}
       elif distance == 0:
              a_ :		Tuple							=  (
                  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
              ) ** (1 / 4)
              return {"distance": distance}
       raise ValueError('One and only one argument must be 0'    )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()


