

'''simple docstring'''
import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Tuple=()							,			_UpperCamelCase						:					List[Any]=None							,			_UpperCamelCase						:					List[Any]="no"							,			_UpperCamelCase						:					Optional[int]="29500"    )				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =False
     _SCREAMING_SNAKE_CASE							   =False
     if any(key.startswith('KAGGLE'    ) for key in os.environ.keys()    ):
          _SCREAMING_SNAKE_CASE							   =True
     elif "IPython" in sys.modules:
          _SCREAMING_SNAKE_CASE							   ='google.colab' in str(sys.modules['IPython'].get_ipython()    )

     try:
          _SCREAMING_SNAKE_CASE							   =PrecisionType(mixed_precision.lower()    )
     except ValueError:
          raise ValueError(
              f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."    )

     if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'							,			_UpperCamelCase    ) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state    ) > 0:
               raise ValueError(
                   'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
                   'your training function. Restart your notebook and make sure no cells initializes an '
                   '`Accelerator`.'    )
          if num_processes is None:
               _SCREAMING_SNAKE_CASE							   =8

          _SCREAMING_SNAKE_CASE							   =PrepareForLaunch(_UpperCamelCase							,			distributed_type='TPU'    )
          print(f"Launching a training on {num_processes} TPU cores."    )
          xmp.spawn(_UpperCamelCase							,			args=_UpperCamelCase							,			nprocs=_UpperCamelCase							,			start_method='fork'    )
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print('Launching training on one GPU.'    )
          else:
               print('Launching training on one CPU.'    )
          function(*_UpperCamelCase    )
     else:
          if num_processes is None:
               raise ValueError(
                   'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'    )

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state    ) > 0:
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
                        'inside your training function. Restart your notebook and make sure no cells initializes an '
                        '`Accelerator`.'    )

               if torch.cuda.is_initialized():
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
                        'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
                        'function.'    )

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=_UpperCamelCase							,			master_addr='127.0.01'							,			master_port=_UpperCamelCase							,			mixed_precision=_UpperCamelCase    ):
                    _SCREAMING_SNAKE_CASE							   =PrepareForLaunch(_UpperCamelCase							,			distributed_type='MULTI_GPU'    )
                    print(f"Launching training on {num_processes} GPUs."    )
                    try:
                         start_processes(_UpperCamelCase							,			args=_UpperCamelCase							,			nprocs=_UpperCamelCase							,			start_method='fork'    )
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                                  'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                                  'Please review your imports and test them when running the `notebook_launcher()` to identify '
                                  'which one is problematic.'    ) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    _SCREAMING_SNAKE_CASE							   ='1'
                    print('Launching training on MPS.'    )
               elif torch.cuda.is_available():
                    print('Launching training on one GPU.'    )
               else:
                    print('Launching training on CPU.'    )
               function(*_UpperCamelCase    )







def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					Union[str, Any]=()							,			_UpperCamelCase						:					Union[str, Any]=2    )				->      List[Any]:
     """simple docstring"""




     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=_UpperCamelCase							,			master_addr='127.0.01'							,			master_port='29500'							,			accelerate_mixed_precision='no'							,			accelerate_debug_rdv_file=tmp_file.name							,			accelerate_use_cpu='yes'							,			):
               _SCREAMING_SNAKE_CASE							   =PrepareForLaunch(_UpperCamelCase							,			debug=_UpperCamelCase    )
               start_processes(_UpperCamelCase							,			args=_UpperCamelCase							,			nprocs=_UpperCamelCase							,			start_method='fork'    )



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

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCamelCase			:				List[str] 			=					{"UserAgent": UserAgent().random}


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      dict:
     """simple docstring"""




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







class 							A__   :





  def __init__(		self				:     int							, _a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =f"https://www.instagram.com/{username}/"
       _SCREAMING_SNAKE_CASE							   =self.get_json()





  def 						A  (		self				:     Optional[int]			)     ->   dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =requests.get(self.url							, headers=_a			).text
       _SCREAMING_SNAKE_CASE							   =BeautifulSoup(_a							, '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				:     str			)     ->   str:





       '''simple docstring'''






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





  def __str__(		self				:     Optional[int]			)     ->   str:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     List[Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["username"]





  @property
  def 						A  (		self				:     str			)     ->   str:





       '''simple docstring'''






       return self.user_data["full_name"]





  @property
  def 						A  (		self				:     Any			)     ->   str:





       '''simple docstring'''






       return self.user_data["biography"]





  @property
  def 						A  (		self				:     Optional[Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["business_email"]





  @property
  def 						A  (		self				:     Tuple			)     ->   str:





       '''simple docstring'''






       return self.user_data["external_url"]





  @property
  def 						A  (		self				:     Optional[int]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     List[str]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["profile_pic_url_hd"]





  @property
  def 						A  (		self				:     Dict			)     ->   bool:





       '''simple docstring'''






       return self.user_data["is_verified"]





  @property
  def 						A  (		self				:     Tuple			)     ->   bool:





       '''simple docstring'''






       return self.user_data["is_private"]


def 	_lowerCAmelCase (						_UpperCamelCase						:					str = "github"    )				->      None:
     """simple docstring"""




     import os

     if os.environ.get('CI'    ):
          return  # test failing on GitHub Actions
     _SCREAMING_SNAKE_CASE							   =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 > 1_50
     assert instagram_user.number_of_followers > 12_00_00
     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()
     lowerCamelCase			:				Optional[int] 			=					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 = }''')
