
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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





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




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase					)							->			str:
					lowerCamelCase_         =     f'https://www.instagram.com/{username}/'
					lowerCamelCase_         =     self.get_json()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			dict:
					lowerCamelCase_         =     requests.get(self.url		,    headers=lowercase					).text
					lowerCamelCase_         =     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					)							->			str:
					return f'{self.__class__.__name__}(\'{self.username}\')'



	def __str__(       self					)							->			str:
					return f'{self.fullname} ({self.username}) is {self.biography}'



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["username"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["full_name"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["biography"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["business_email"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["external_url"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.user_data["edge_followed_by"]["count"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.user_data["edge_follow"]["count"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.user_data["edge_owner_to_timeline_media"]["count"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return self.user_data["profile_pic_url_hd"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			bool:
					return self.user_data["is_verified"]



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			bool:
					return self.user_data["is_private"]





def      lowerCamelCase_					(							lowerCamelCase__ = "github"       ):
				import os

				if os.environ.get("CI"       ):
								return  # test failing on GitHub Actions
				lowerCamelCase_         =     InstagramUser(lowerCamelCase__       )
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data      ,					lowerCamelCase__       )
				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()
			__A							=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 = }""")









import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """path-to-your-trained-model"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    pipe.to(device)

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
  SCREAMING_SNAKE_CASE__							:      Dict         =    pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

