

import numpy as np







def 	lowerCamelCase__			(   __A						:Tuple  ,__A						:List[Any]  ,__A						:Optional[Any]		):


       """simple docstring"""




       __snake_case					  =       n_draws * class_counts / class_counts.sum()
       # floored means we don't overshoot n_samples, but probably undershoot
       __snake_case					  =       np.floor(_UpperCAmelCase		)
       # we add samples according to how much "left over" probability
       # they had, until we arrive at n_samples
       __snake_case					  =       int(n_draws - floored.sum()		)
       if need_to_add > 0:
              __snake_case					  =       continuous - floored
              __snake_case					  =       np.sort(np.unique(_UpperCAmelCase		)		)[::-1]
              # add according to remainder, but break ties
              # randomly to avoid biases
              for value in values:
                     (__snake_case  )					  =       np.where(remainder == value		)
                     # if we need_to_add less than what's in inds
                     # we draw randomly from them.
                     # if we need to add more, we add them all and
                     # go to the next value
                     __snake_case					  =       min(len(_UpperCAmelCase		)  ,_UpperCAmelCase		)
                     __snake_case					  =       rng.choice(_UpperCAmelCase  ,size=_UpperCAmelCase  ,replace=_UpperCAmelCase		)
                     floored[inds] += 1
                     need_to_add -= add_now
                     if need_to_add == 0:
                            break
       return floored.astype(np.intaa		)







def 	lowerCamelCase__			(   __A						:Dict  ,__A						:Dict  ,__A						:Optional[int]  ,__A						:List[str]  ,__A						:str=1_0		):


       """simple docstring"""




       __snake_case					  =       np.unique(_UpperCAmelCase  ,return_inverse=_UpperCAmelCase		)
       __snake_case					  =       classes.shape[0]
       __snake_case					  =       np.bincount(_UpperCAmelCase		)
       if np.min(_UpperCAmelCase		) < 2:
              raise ValueError("""Minimum class count error"""		)
       if n_train < n_classes:
              raise ValueError(
                  """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)		)
       if n_test < n_classes:
              raise ValueError(
                  """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)		)
       __snake_case					  =       np.split(np.argsort(_UpperCAmelCase  ,kind="""mergesort"""		)  ,np.cumsum(_UpperCAmelCase		)[:-1]		)
       for _ in range(_UpperCAmelCase		):
              __snake_case					  =       approximate_mode(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase		)
              __snake_case					  =       class_counts - n_i
              __snake_case					  =       approximate_mode(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase		)

              __snake_case					  =       []
              __snake_case					  =       []

              for i in range(_UpperCAmelCase		):
                     __snake_case					  =       rng.permutation(class_counts[i]		)
                     __snake_case					  =       class_indices[i].take(_UpperCAmelCase  ,mode="""clip"""		)
                     train.extend(perm_indices_class_i[: n_i[i]]		)
                     test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]		)
              __snake_case					  =       rng.permutation(_UpperCAmelCase		)
              __snake_case					  =       rng.permutation(_UpperCAmelCase		)

              yield train, test




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       BeautifulSoup(requests.get(__A  ,headers=__A		).text  ,"""html.parser"""		)
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:title"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:image"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""p"""  ,id="""description"""		).get_text()
       __snake_case  ,						__snake_case					  =       os.path.splitext(os.path.basename(__A		)		)
       __snake_case					  =       title.strip().replace(""" """  ,"""_"""		)
       __snake_case					  =       F'{image_title}{image_extension}'
       save_image(__A  ,__A		)
       return (title, description, image_title)


if __name__ == "__main__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

