




'''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_						:      int       =						'\\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_						:      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_						:      Union[str, 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        _a    (						datasets.Metric							):






     def     _lowercase     (			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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE="auto" ,_SCREAMING_SNAKE_CASE=-1 ,_SCREAMING_SNAKE_CASE=0.9 ,_SCREAMING_SNAKE_CASE=5 ,_SCREAMING_SNAKE_CASE=500 ,_SCREAMING_SNAKE_CASE="gpt2-large" ,_SCREAMING_SNAKE_CASE=-1 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=25 ,_SCREAMING_SNAKE_CASE=5 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=25 ,)							->      List[str]:
          _snake_case							=				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








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """simple docstring"""




        return x**3 + x**2

   print('''f(x) = x^3 + x^2''')
   print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
   UpperCamelCase_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


