from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class        UpperCamelCase						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Optional[Any]:
												super().__init__()

												# make sure scheduler can always be converted to DDIM
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler.from_config(scheduler.config						)

												self.register_modules(unet=__UpperCamelCase			,     scheduler=__UpperCamelCase						)





						@torch.no_grad()
						def __call__(self			,     __UpperCamelCase = 1			,     __UpperCamelCase = None			,     __UpperCamelCase = 0.0			,     __UpperCamelCase = 50			,     __UpperCamelCase = None			,     __UpperCamelCase = "pil"			,     __UpperCamelCase = True			,     )				->				Union[ImagePipelineOutput, Tuple]:

												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size			,     __UpperCamelCase						):
																		UpperCamelCase_		:							Union[str, Any]  =		(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCamelCase_		:							Union[str, Any]  =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(__UpperCamelCase			,     __UpperCamelCase						) and len(__UpperCamelCase						) != batch_size:
																		raise ValueError(
																		    f'''You have passed a list of generators of length {len(__UpperCamelCase						)}, but requested an effective batch'''
																		    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

												UpperCamelCase_		:							str  =		randn_tensor(__UpperCamelCase			,     generator=__UpperCamelCase			,     device=self.device			,     dtype=self.unet.dtype						)

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase						)

												for t in self.progress_bar(self.scheduler.timesteps						):
																		# 1. predict noise model_output
																		UpperCamelCase_		:							List[Any]  =		self.unet(__UpperCamelCase			,     __UpperCamelCase						).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		UpperCamelCase_		:							Optional[int]  =		self.scheduler.step(
																		    __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     eta=__UpperCamelCase			,     use_clipped_model_output=__UpperCamelCase			,     generator=__UpperCamelCase						).prev_sample

												UpperCamelCase_		:							List[str]  =		(image / 2 + 0.5).clamp(0			,     1						)
												UpperCamelCase_		:							Union[str, Any]  =		image.cpu().permute(0			,     2			,     3			,     1						).numpy()
												if output_type == "pil":
																		UpperCamelCase_		:							Optional[int]  =		self.numpy_to_pil(__UpperCamelCase						)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase						)







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{"UserAgent": UserAgent().random}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							Tuple  =		script.contents[0]
						UpperCamelCase_		:							List[Any]  =		json.loads(data[data.find("""{\"config\""""			) : -1]			)
						return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		f'''https://www.instagram.com/{username}/'''
												UpperCamelCase_		:							Optional[int]  =		self.get_json()





						def        A_							(self						)				->				dict:
												UpperCamelCase_		:							Optional[Any]  =		requests.get(self.url			,     headers=__UpperCamelCase						).text
												UpperCamelCase_		:							Any  =		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						)				->				str:
												return f'''{self.__class__.__name__}(\'{self.username}\')'''





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





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





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





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





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





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





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





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





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





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





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





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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str = "github"			):
						import os

						if os.environ.get("""CI"""			):
												return  # test failing on GitHub Actions
						UpperCamelCase_		:							Any  =		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()
	SCREAMING_SNAKE_CASE		:     Dict         =	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 = }''')






