


from sklearn.metrics import mean_squared_error

import datasets


UpperCAmelCase    =					"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

UpperCAmelCase    =					"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


UpperCAmelCase    =					"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION )
class lowerCAmelCase_		(				datasets.Metric ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       return datasets.MetricInfo(
           description=_DESCRIPTION	,   citation=_CITATION	,   inputs_description=_KWARGS_DESCRIPTION	,   features=datasets.Features(self._get_feature_types()      )	,   reference_urls=[
               '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
           ]	,   )



 def 		UpperCamelCase__  (			self      ):
       if self.config_name == "multilist":
             return {
                 "predictions": datasets.Sequence(datasets.Value('''float'''      )      ),
                 "references": datasets.Sequence(datasets.Value('''float'''      )      ),
             }
       else:
             return {
                 "predictions": datasets.Value('''float'''      ),
                 "references": datasets.Value('''float'''      ),
             }





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase="uniform_average"	,   _UpperCAmelCase=True      ):
       snake_case_ =   mean_squared_error(
           _UpperCAmelCase	,   _UpperCAmelCase	,   sample_weight=_UpperCAmelCase	,   multioutput=_UpperCAmelCase	,   squared=_UpperCAmelCase      )

       return {"mse": mse}



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase    =					{"""UserAgent""": UserAgent().random}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						dict:




      """simple docstring"""

      snake_case_ =   script.contents[0]
      snake_case_ =   json.loads(data[data.find('''{"config"'''      ) : -1]      )
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class lowerCAmelCase_		:

 '''simple docstring'''

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   requests.get(self.url	,   headers=_UpperCAmelCase      ).text
       snake_case_ =   BeautifulSoup(_UpperCAmelCase	,   '''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 		UpperCamelCase__  (			self      ):
       return self.user_data["username"]



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



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



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



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



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



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



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



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



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





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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = "github"      )->						None:




      """simple docstring"""

      import os

      if os.environ.get('''CI'''      ):
            return  # test failing on GitHub Actions
      snake_case_ =   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 > 150
      assert instagram_user.number_of_followers > 12_0000
      assert instagram_user.number_of_followings > 15
      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 = }''')