

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class _UpperCamelCase							(							UpperCAmelCase__      ):
			'''simple docstring'''







			lowerCamelCase__							=(DEISMultistepScheduler,)
			lowerCamelCase__							=(("num_inference_steps", 25),)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					**a							:       List[Any]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				}

				config.update(**lowerCamelCase__		)
				return config
			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       Optional[int]=0					,					**a							:       Any		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				dict(self.forward_default_kwargs		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				kwargs.pop("num_inference_steps"					,					lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					str    =				self.dummy_sample
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0.1 * sample
				SCREAMING_SNAKE_CASE      :					List[Any]    =				[residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_scheduler_config(**lowerCamelCase__		)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				scheduler_class(**lowerCamelCase__		)
					scheduler.set_timesteps(lowerCamelCase__		)
					# copy over dummy past residuals
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(lowerCamelCase__		)
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				scheduler_class.from_pretrained(lowerCamelCase__		)
						new_scheduler.set_timesteps(lowerCamelCase__		)
						# copy over dummy past residuals
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				dummy_past_residuals[: new_scheduler.config.solver_order]

					SCREAMING_SNAKE_CASE      :					List[Any]    =				sample, sample
					for t in range(lowerCamelCase__					,					time_step + scheduler.config.solver_order + 1		):
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample
						SCREAMING_SNAKE_CASE      :					Any    =				new_scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample

						assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
			def 							__UpperCamelCase       (	self							:       str		)							->				List[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       str=0					,					**a							:       Tuple		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				dict(self.forward_default_kwargs		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				kwargs.pop("num_inference_steps"					,					lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					Any    =				self.dummy_sample
				SCREAMING_SNAKE_CASE      :					str    =				0.1 * sample
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.get_scheduler_config()
					SCREAMING_SNAKE_CASE      :					int    =				scheduler_class(**lowerCamelCase__		)
					scheduler.set_timesteps(lowerCamelCase__		)

					# copy over dummy past residuals (must be after setting timesteps)
					SCREAMING_SNAKE_CASE      :					Any    =				dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(lowerCamelCase__		)
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				scheduler_class.from_pretrained(lowerCamelCase__		)
						# copy over dummy past residuals
						new_scheduler.set_timesteps(lowerCamelCase__		)

						# copy over dummy past residual (must be after setting timesteps)
						SCREAMING_SNAKE_CASE      :					Any    =				dummy_past_residuals[: new_scheduler.config.solver_order]

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample
					SCREAMING_SNAKE_CASE      :					Any    =				new_scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample

					assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Dict=None					,					**a							:       List[str]		)							->				Any:

				"""simple docstring"""

				if scheduler is None:
					SCREAMING_SNAKE_CASE      :					Tuple    =				self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.get_scheduler_config(**lowerCamelCase__		)
					SCREAMING_SNAKE_CASE      :					Tuple    =				scheduler_class(**lowerCamelCase__		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					Any    =				self.get_scheduler_config(**lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				scheduler_class(**lowerCamelCase__		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				10
				SCREAMING_SNAKE_CASE      :					int    =				self.dummy_model()
				SCREAMING_SNAKE_CASE      :					Dict    =				self.dummy_sample_deter
				scheduler.set_timesteps(lowerCamelCase__		)

				for i, t in enumerate(scheduler.timesteps		):
					SCREAMING_SNAKE_CASE      :					Dict    =				model(lowerCamelCase__					,					lowerCamelCase__		)
					SCREAMING_SNAKE_CASE      :					int    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__		).prev_sample

				return sample
			def 							__UpperCamelCase       (	self							:       str		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				dict(self.forward_default_kwargs		)

				SCREAMING_SNAKE_CASE      :					int    =				kwargs.pop("num_inference_steps"					,					lowerCamelCase__		)

				for scheduler_class in self.scheduler_classes:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_scheduler_config()
					SCREAMING_SNAKE_CASE      :					Any    =				scheduler_class(**lowerCamelCase__		)

					SCREAMING_SNAKE_CASE      :					str    =				self.dummy_sample
					SCREAMING_SNAKE_CASE      :					str    =				0.1 * sample

					if num_inference_steps is not None and hasattr(lowerCamelCase__					,					"set_timesteps"		):
						scheduler.set_timesteps(lowerCamelCase__		)
					elif num_inference_steps is not None and not hasattr(lowerCamelCase__					,					"set_timesteps"		):
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				[residual + 0.2, residual + 0.15, residual + 0.10]
					SCREAMING_SNAKE_CASE      :					str    =				dummy_past_residuals[: scheduler.config.solver_order]

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				scheduler.timesteps[5]
					SCREAMING_SNAKE_CASE      :					str    =				scheduler.timesteps[6]

					SCREAMING_SNAKE_CASE      :					str    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample
					SCREAMING_SNAKE_CASE      :					str    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__					,					**lowerCamelCase__		).prev_sample

					self.assertEqual(output_a.shape					,					sample.shape		)
					self.assertEqual(output_a.shape					,					output_a.shape		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				DEISMultistepScheduler(**self.get_scheduler_config()		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.full_loop(scheduler=lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.mean(torch.abs(lowerCamelCase__		)		)

				assert abs(result_mean.item() - 0.2_3916		) < 1e-3

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				DPMSolverSinglestepScheduler.from_config(scheduler.config		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				DPMSolverMultistepScheduler.from_config(scheduler.config		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				UniPCMultistepScheduler.from_config(scheduler.config		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				DEISMultistepScheduler.from_config(scheduler.config		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.full_loop(scheduler=lowerCamelCase__		)
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.mean(torch.abs(lowerCamelCase__		)		)

				assert abs(result_mean.item() - 0.2_3916		) < 1e-3
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				int:

				"""simple docstring"""

				for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase__		)
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				self.check_over_configs(thresholding=lowerCamelCase__		)
				for order in [1, 2, 3]:
					for solver_type in ["logrho"]:
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    thresholding=lowerCamelCase__					,					prediction_type=lowerCamelCase__					,					sample_max_value=lowerCamelCase__					,					algorithm_type="deis"					,					solver_order=lowerCamelCase__					,					solver_type=lowerCamelCase__					,					)
			def 							__UpperCamelCase       (	self							:       Any		)							->				Union[str, Any]:

				"""simple docstring"""

				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase__		)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[Any]:

				"""simple docstring"""

				for algorithm_type in ["deis"]:
					for solver_type in ["logrho"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=lowerCamelCase__					,					solver_type=lowerCamelCase__					,					prediction_type=lowerCamelCase__					,					algorithm_type=lowerCamelCase__					,					)
								SCREAMING_SNAKE_CASE      :					List[str]    =				self.full_loop(
								    solver_order=lowerCamelCase__					,					solver_type=lowerCamelCase__					,					prediction_type=lowerCamelCase__					,					algorithm_type=lowerCamelCase__					,					)
								assert not torch.isnan(lowerCamelCase__		).any(), "Samples have nan numbers"
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				self.check_over_configs(lower_order_final=lowerCamelCase__		)
				self.check_over_configs(lower_order_final=lowerCamelCase__		)
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Tuple:

				"""simple docstring"""

				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
					self.check_over_forward(num_inference_steps=lowerCamelCase__					,					time_step=0		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.full_loop()
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.mean(torch.abs(lowerCamelCase__		)		)

				assert abs(result_mean.item() - 0.2_3916		) < 1e-3
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.full_loop(prediction_type="v_prediction"		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.mean(torch.abs(lowerCamelCase__		)		)

				assert abs(result_mean.item() - 0.091		) < 1e-3






			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_scheduler_config(thresholding=lowerCamelCase__					,					dynamic_thresholding_ratio=0		)
				SCREAMING_SNAKE_CASE      :					int    =				scheduler_class(**lowerCamelCase__		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				10
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.dummy_model()
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowerCamelCase__		)

				for i, t in enumerate(scheduler.timesteps		):
					SCREAMING_SNAKE_CASE      :					List[Any]    =				model(lowerCamelCase__					,					lowerCamelCase__		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				scheduler.step(lowerCamelCase__					,					lowerCamelCase__					,					lowerCamelCase__		).prev_sample

				assert sample.dtype == torch.floataa



'''simple docstring'''


from __future__ import annotations

lowerCamelCase__					=   'Muhammad Umer Farooq'
lowerCamelCase__					=   'MIT'
lowerCamelCase__					=   '1.0.0'
lowerCamelCase__					=   'Muhammad Umer Farooq'
lowerCamelCase__					=   'contact@muhammadumerfarooq.me'
lowerCamelCase__					=   'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Any		,     lowerCamelCase__     :   str       )      ->None:



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase						:				list[str]	    =					[]
					_UpperCAmelCase						:				List[str]	    =					domain






			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   list[tuple[str, str | None]]       )      ->None:



					'''simple docstring'''







					if tag == "a":
							# Check the list of defined attributes.
							for name, value in attrs:
									# If href is defined, and not empty nor # print it.
									if name == "href" and value != "#" and value != "":
											# If not already in urls.
											if value not in self.urls:
													_UpperCAmelCase						:				Dict	    =					parse.urljoin(self.domain		,     lowerCamelCase__       )
													self.urls.append(lowerCamelCase__       )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return ".".join(get_sub_domain_name(__lowerCAmelCase      ).split("."      )[-2:]      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return parse.urlparse(__lowerCAmelCase      ).netloc






def        __lowerCAmelCase       (__lowerCAmelCase = "https://github.com"      ):
		_UpperCAmelCase						:				List[str]	    =					get_domain_name(__lowerCAmelCase      )

		# Initialize the parser
		_UpperCAmelCase						:				int	    =					Parser(__lowerCAmelCase      )

		try:
				# Open URL
				_UpperCAmelCase						:				Any	    =					requests.get(__lowerCAmelCase      )

				# pass the raw HTML to the parser to get links
				parser.feed(r.text      )

				# Get links and loop through
				_UpperCAmelCase						:				Any	    =					set()
				for link in parser.urls:
						# open URL.
						# read = requests.get(link)
						try:
								_UpperCAmelCase						:				List[str]	    =					requests.get(__lowerCAmelCase      )
								# Get the valid email.
								_UpperCAmelCase						:				Optional[int]	    =					re.findall("[a-zA-Z0-9]+@" + domain					,       read.text      )
								# If not in list then append it.
								for email in emails:
										valid_emails.add(__lowerCAmelCase      )
						except ValueError:
								pass
		except ValueError:
				raise SystemExit(1      )

		# Finally return a sorted list of email addresses with no duplicates.
		return sorted(__lowerCAmelCase      )


if __name__ == "__main__":
						lowerCamelCase__					=   emails_from_url('https://github.com')
						print(F'''{len(emails)} emails found:''')
						print('\n'.join(sorted(emails)))



