

'''simple docstring'''



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__snake_case					:      List[str] 		=     {"UserAgent": UserAgent().random}
def 		__lowerCamelCase			(  __snake_case	:						Dict			)			->				dict:






						"""simple docstring"""





						A__	:  Union[str, Any]           =script.contents[0]
						A__	:  Optional[int]           =json.loads(data[data.find("""{\"config\""""			) : -1]			)
						return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]



class lowerCamelCase					:



			'''simple docstring'''






			def __init__( self			:		List[str]						,     lowerCAmelCase_			:		Dict					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Any           =f"https://www.instagram.com/{username}/"
									A__	:  Tuple           =self.get_json()


			def lowercase__		( self			:		Optional[int]					)     ->						Dict:





									'''simple docstring'''
									A__	:  Tuple           =requests.get(self.url						,     headers=a_					).text
									A__	:  Tuple           =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					)     ->						Optional[Any]:





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


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





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


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





									'''simple docstring'''
									return self.user_data["username"]


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





									'''simple docstring'''
									return self.user_data["full_name"]


			@property
			def lowercase__		( self			:		List[Any]					)     ->						Optional[int]:





									'''simple docstring'''
									return self.user_data["biography"]


			@property
			def lowercase__		( self			:		int					)     ->						str:





									'''simple docstring'''
									return self.user_data["business_email"]


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





									'''simple docstring'''
									return self.user_data["external_url"]


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





									'''simple docstring'''
									return self.user_data["edge_followed_by"]["count"]


			@property
			def lowercase__		( self			:		Optional[Any]					)     ->						List[Any]:





									'''simple docstring'''
									return self.user_data["edge_follow"]["count"]


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





									'''simple docstring'''
									return self.user_data["edge_owner_to_timeline_media"]["count"]


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





									'''simple docstring'''
									return self.user_data["profile_pic_url_hd"]


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





									'''simple docstring'''
									return self.user_data["is_verified"]



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





									'''simple docstring'''
									return self.user_data["is_private"]


def 		__lowerCamelCase			(  __snake_case	:						Optional[Any] = "github"			)			->				None:






						"""simple docstring"""





						import os

						if os.environ.get("""CI"""			):
												return  # test failing on GitHub Actions
						A__	:  Optional[Any]           =InstagramUser(snake_case__			)
						assert instagram_user.user_data
						assert isinstance(instagram_user.user_data,						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 > 120_000
						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()
		__snake_case					:      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 = }""")






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase	:   Any    =   False


class 	__snake_case   ( unittest.TestCase ):
		pass



@nightly
@require_torch_gpu
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a_						)
										lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained(a_    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  generator.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  'cyberpunk 2077'
						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt=a_    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'    ,).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

						lowerCAmelCase__		  =  'A painting of a squirrel eating a burger '
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.text_to_image(
						    prompt=a_    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

						lowerCAmelCase__		  =  pipe.image_variation(a_    ,generator=a_    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1
