
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     (KDPMaDiscreteScheduler,)
					SCREAMING_SNAKE_CASE           =     1_0

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**__UpperCamelCase )
												return config

					def  lowercase__   (						self       :  Optional[Any] ):
												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase )

					def  lowercase__   (						self       :  Dict ):
												for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,       [0.0002, 0.002, 0.02] ):
																			self.check_over_configs(beta_start=__UpperCamelCase ,       beta_end=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config(prediction_type="""v_prediction""" )
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter * scheduler.init_noise_sigma
												lowerCamelCase_					=       sample.to(__UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																			assert abs(result_mean.item() - 0.0002 ) < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter * scheduler.init_noise_sigma
												lowerCamelCase_					=       sample.to(__UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3



					def  lowercase__   (						self       :  Optional[int] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps ,       device=__UpperCamelCase )

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter.to(__UpperCamelCase ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if str(__UpperCamelCase ).startswith("""cpu""" ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



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





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       requests.get(self.url ,       headers=__UpperCamelCase ).text
												lowerCamelCase_					=       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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

					@property
					def  lowercase__   (						self       :  Dict ):
												return self.user_data["username"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["full_name"]

					@property
					def  lowercase__   (						self       :  Any ):
												return self.user_data["biography"]

					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["business_email"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["edge_followed_by"]["count"]

					@property
					def  lowercase__   (						self       :  str ):
												return self.user_data["edge_follow"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["edge_owner_to_timeline_media"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["is_private"]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							import os

							if os.environ.get("""CI"""							):
														return  # test failing on GitHub Actions
							lowerCamelCase_					=       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_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()
		lowercase   =	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 = }""")





