



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_									=							"\\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_									=							"\\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_									=							"\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       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase="auto"       ,     _lowerCAmelCase=-1       ,     _lowerCAmelCase=0.9       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=500       ,     _lowerCAmelCase="gpt2-large"       ,     _lowerCAmelCase=-1       ,     _lowerCAmelCase=1_024       ,     _lowerCAmelCase=25       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=25       ,     ):
         _lowerCAmelCase 			=  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




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_									=							{"UserAgent": UserAgent().random}
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->dict:
      _lowerCAmelCase 			=  script.contents[0]
      _lowerCAmelCase 			=  json.loads(data[data.find('''{"config"'''				) : -1]				)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  F'''https://www.instagram.com/{username}/'''
         _lowerCAmelCase 			=  self.get_json()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  requests.get(self.url       ,     headers=_lowerCAmelCase			).text
         _lowerCAmelCase 			=  BeautifulSoup(_lowerCAmelCase       ,     '''html.parser'''			).find_all('''script'''			)
         try:
               return extract_user_profile(scripts[4]			)
         except (json.decoder.JSONDecodeError, KeyError):
               return extract_user_profile(scripts[3]			)





   def __repr__(	self			):
         return F'''{self.__class__.__name__}(\'{self.username}\')'''





   def __str__(	self			):
         return F'''{self.fullname} ({self.username}) is {self.biography}'''





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["username"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["full_name"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["biography"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["business_email"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["external_url"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_followed_by"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_follow"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["profile_pic_url_hd"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_verified"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_private"]






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str = "github"				)->None:
      import os

      if os.environ.get('''CI'''				):
            return  # test failing on GitHub Actions
      _lowerCAmelCase 			=  InstagramUser(_SCREAMING_SNAKE_CASE				)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data     ,				_SCREAMING_SNAKE_CASE				)
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 1_5_0
      assert instagram_user.number_of_followers > 1_2_0_0_0_0
      assert instagram_user.number_of_followings > 1_5
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.'''				)
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCAmelCase_									=							InstagramUser("github")
    print(instagram_user)
    print(F"""{instagram_user.number_of_posts = }""")
    print(F"""{instagram_user.number_of_followers = }""")
    print(F"""{instagram_user.number_of_followings = }""")
    print(F"""{instagram_user.email = }""")
    print(F"""{instagram_user.website = }""")
    print(F"""{instagram_user.profile_picture_url = }""")
    print(F"""{instagram_user.is_verified = }""")
    print(F"""{instagram_user.is_private = }""")