





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 				=     '\\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'

_lowerCAmelCase 				=     '\\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'

_lowerCAmelCase 				=     '\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    _UpperCAmelCase							(		datasets.Metric     ):
      def 		_lowerCamelCase      (		self       ):
         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      ,       a__      ,       a__      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__=None      ,       a__="auto"      ,       a__=-1      ,       a__=0.9      ,       a__=5      ,       a__=500      ,       a__="gpt2-large"      ,       a__=-1      ,       a__=1024      ,       a__=25      ,       a__=5      ,       a__=True      ,       a__=25      ,       ):
         A_     :				Dict			      =	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 re



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Any			      =	re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$"""     )
   if match := re.search(_lowerCAmelCase  ,_lowerCAmelCase     ):
      return match.string == phone
   return False


if __name__ == "__main__":
      print(indian_phone_validator("""+918827897895"""))






