






"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				(IPNDMScheduler,)
	_A       :				Any    =				(("""num_inference_steps""", 50),)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   {"""num_train_timesteps""": 1000}
		config.update(**lowerCAmelCase_							)
		return config





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0     ,     **lowerCAmelCase_							):
		A_		:						List[Any]	    =   dict(self.forward_default_kwargs							)
		A_		:						List[str]	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)
		A_		:						int	    =   self.dummy_sample
		A_		:						str	    =   0.1 * sample
		A_		:						Union[str, Any]	    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

		for scheduler_class in self.scheduler_classes:
			A_		:						Optional[Any]	    =   self.get_scheduler_config(**lowerCAmelCase_							)
			A_		:						str	    =   scheduler_class(**lowerCAmelCase_							)
			scheduler.set_timesteps(lowerCAmelCase_							)
			# copy over dummy past residuals
			A_		:						str	    =   dummy_past_residuals[:]

			if time_step is None:
				A_		:						Tuple	    =   scheduler.timesteps[len(scheduler.timesteps							) // 2]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_							)
				A_		:						List[Any]	    =   scheduler_class.from_pretrained(lowerCAmelCase_							)
				new_scheduler.set_timesteps(lowerCAmelCase_							)
				# copy over dummy past residuals
				A_		:						Union[str, Any]	    =   dummy_past_residuals[:]

			A_		:						Union[str, Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						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"

			A_		:						List[Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						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    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0     ,     **lowerCAmelCase_							):
		A_		:						Any	    =   dict(self.forward_default_kwargs							)
		A_		:						Optional[int]	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)
		A_		:						List[Any]	    =   self.dummy_sample
		A_		:						int	    =   0.1 * sample
		A_		:						Dict	    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

		for scheduler_class in self.scheduler_classes:
			A_		:						str	    =   self.get_scheduler_config()
			A_		:						str	    =   scheduler_class(**lowerCAmelCase_							)
			scheduler.set_timesteps(lowerCAmelCase_							)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:						Any	    =   dummy_past_residuals[:]

			if time_step is None:
				A_		:						Tuple	    =   scheduler.timesteps[len(scheduler.timesteps							) // 2]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_							)
				A_		:						Dict	    =   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)
				A_		:						List[str]	    =   dummy_past_residuals[:]

			A_		:						Any	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						Tuple	    =   new_scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

			A_		:						List[Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						List[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    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Any	    =   self.scheduler_classes[0]
		A_		:						List[str]	    =   self.get_scheduler_config(**lowerCAmelCase_							)
		A_		:						List[str]	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						str	    =   10
		A_		:						Union[str, Any]	    =   self.dummy_model()
		A_		:						Optional[Any]	    =   self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_							)

		for i, t in enumerate(scheduler.timesteps							):
			A_		:						Tuple	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)
			A_		:						Union[str, Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							).prev_sample

		for i, t in enumerate(scheduler.timesteps							):
			A_		:						str	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)
			A_		:						Any	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							).prev_sample

		return sample





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   dict(self.forward_default_kwargs							)

		A_		:						List[Any]	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)

		for scheduler_class in self.scheduler_classes:
			A_		:						int	    =   self.get_scheduler_config()
			A_		:						Union[str, Any]	    =   scheduler_class(**lowerCAmelCase_							)

			A_		:						str	    =   self.dummy_sample
			A_		:						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"""							):
				A_		:						int	    =   num_inference_steps

			# copy over dummy past residuals (must be done after set_timesteps)
			A_		:						Optional[int]	    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
			A_		:						Union[str, Any]	    =   dummy_past_residuals[:]

			A_		:						int	    =   scheduler.timesteps[5]
			A_		:						int	    =   scheduler.timesteps[6]

			A_		:						Dict	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						str	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

			self.assertEqual(output_a.shape     ,     sample.shape							)
			self.assertEqual(output_a.shape     ,     output_a.shape							)

			A_		:						Optional[Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						Optional[Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

			self.assertEqual(output_a.shape     ,     sample.shape							)
			self.assertEqual(output_a.shape     ,     output_a.shape							)





	def    lowerCamelCase(self							):
		for timesteps in [100, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_     ,     time_step=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for t, num_inference_steps in zip([1, 5, 10]     ,     [10, 50, 100]							):
			self.check_over_forward(num_inference_steps=lowerCAmelCase_     ,     time_step=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   self.full_loop()
		A_		:						Any	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_mean.item() - 2540529							) < 10









"""simple docstring"""
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							print("Googling.....")
							_lowerCAmelCase         =							"https://www.google.com/search?q=" + " ".join(sys.argv[1:])
							_lowerCAmelCase         =							requests.get(url, headers={"UserAgent": UserAgent().random})
							# res.raise_for_status()
							with open("project1a.html", "wb") as out_file:  # only for knowing the class
														for data in res.iter_content(10_000):
																					out_file.write(data)
							_lowerCAmelCase         =							BeautifulSoup(res.text, "html.parser")
							_lowerCAmelCase         =							list(soup.select(".eZt8xd"))[:5]

							print(len(links))
							for link in links:
														if link.text == "Maps":
																					webbrowser.open(link.get("href"))
														else:
																					webbrowser.open(F'https://google.com{link.get("href")}')

