


'''simple docstring'''
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_			:  List[str]										=   {'UserAgent': UserAgent().random}


def       _lowercase							(					UpperCamelCase__							:       Tuple      ):
 __A	:		int	  =      script.contents[0]
 __A	:		int	  =      json.loads(data[data.find('{"config"'      ) : -1]      )
 return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      F"""https://www.instagram.com/{username}/"""
  __A	:		List[str]	  =      self.get_json()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      requests.get(self.url   ,   headers=__lowercase    ).text
  __A	:		Optional[Any]	  =      BeautifulSoup(__lowercase   ,   '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    ):

  """simple docstring"""







  return F"""{self.__class__.__name__}('{self.username}')"""


 def __str__(   self    ):

  """simple docstring"""







  return F"""{self.fullname} ({self.username}) is {self.biography}"""


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["username"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["full_name"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["biography"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["business_email"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["external_url"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["edge_followed_by"]["count"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["edge_follow"]["count"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["edge_owner_to_timeline_media"]["count"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["profile_pic_url_hd"]


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["is_verified"]







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.user_data["is_private"]


def       _lowercase							(					UpperCamelCase__							:       str = "github"      ):
 import os

 if os.environ.get('CI'      ):
  return  # test failing on GitHub Actions
 __A	:		Optional[Any]	  =      InstagramUser(UpperCamelCase__      )
 assert instagram_user.user_data
 assert isinstance(instagram_user.user_data,					UpperCamelCase__      )
 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 > 120000
 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_			:  Optional[Any]										=   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 = }''')










'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






