




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	:						Tuple			=     "\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    __lowerCAmelCase       (							datasets.Metric):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[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 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					Any       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					Optional[int]=None       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]=None       ,					_lowerCAmelCase:					Optional[Any]=None       ,					_lowerCAmelCase:					Union[str, Any]="auto"       ,					_lowerCAmelCase:					List[Any]=-1       ,					_lowerCAmelCase:					str=0.9       ,					_lowerCAmelCase:					Optional[int]=5       ,					_lowerCAmelCase:					int=5_00       ,					_lowerCAmelCase:					List[str]="gpt2-large"       ,					_lowerCAmelCase:					Any=-1       ,					_lowerCAmelCase:					List[Any]=10_24       ,					_lowerCAmelCase:					int=25       ,					_lowerCAmelCase:					int=5       ,					_lowerCAmelCase:					Optional[Any]=True       ,					_lowerCAmelCase:					int=25       ,					):
         lowercase    :Dict      	=						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









def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Dict      	=						set()

  # Replace all the whitespace in our sentence
  lowercase    :Optional[int]      	=						input_str.replace(" ",			""   )
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()   )
  return len(lowerCamelCase   ) == 26



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  lowercase    :Optional[Any]      	=						[False] * 26
  for char in input_str:
    if char.islower():
      lowercase    :Union[str, Any]      	=						True
    elif char.isupper():
      lowercase    :int      	=						True
  return all(lowerCamelCase   )



def       UpperCAmelCase__  (     lowerCamelCase = "The quick brown fox jumps over the lazy dog",			):
  return len({char for char in input_str.lower() if char.isalpha()}   ) == 26



def       UpperCAmelCase__  (     ):
  from timeit import timeit

  lowercase    :Optional[Any]      	=						"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_faster()",			setup=lowerCamelCase   )   )
  print(timeit("is_pangram_fastest()",			setup=lowerCamelCase   )   )
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



