


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(DPMSolverSinglestepScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 2_5),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{
					    """num_train_timesteps""": 1000,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					    """solver_order""": 2,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					    """sample_max_value""": 1.0,
					    """algorithm_type""": """dpmsolver++""",
					    """solver_type""": """midpoint""",
					    """lambda_min_clipped""": -float("""inf""" ),
					    """variance_type""": None,
					}

					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Optional[int]:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[: scheduler.config.solver_order]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							new_scheduler.set_timesteps(a__ )
							# copy over dummy past residuals
							A             =					dummy_past_residuals[: new_scheduler.config.solver_order]

						A       ,      A             =					sample, sample
						for t in range(a__					, time_step + scheduler.config.solver_order + 1 ):
							A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
							A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					List[str]:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Dict:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.10]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(a__ )

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self					, a__=None					, **a__ )						->					List[Any]:
					if scheduler is None:
						A             =					self.scheduler_classes[0]
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )

					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					return sample

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
					A             =					50
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					# make sure that the first t is uneven
					for i, t in enumerate(scheduler.timesteps[3:] ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.25_74 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [25, 50, 100, 999, 1000]:
						self.check_over_configs(num_train_timesteps=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					# make sure that iterating over schedulers with same config names gives same results
					# for defaults
					A             =					DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
					A             =					self.full_loop(scheduler=a__ )
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.27_91 ) < 1e-3

					A             =					DEISMultistepScheduler.from_config(scheduler.config )
					A             =					DPMSolverMultistepScheduler.from_config(scheduler.config )
					A             =					UniPCMultistepScheduler.from_config(scheduler.config )
					A             =					DPMSolverSinglestepScheduler.from_config(scheduler.config )

					A             =					self.full_loop(scheduler=a__ )
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.27_91 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					int:
					self.check_over_configs(thresholding=a__ )
					for order in [1, 2, 3]:
						for solver_type in ["midpoint", "heun"]:
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    thresholding=a__					, prediction_type=a__					, sample_max_value=a__					, algorithm_type="""dpmsolver++"""					, solver_order=a__					, solver_type=a__					, )

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=a__ )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					for algorithm_type in ["dpmsolver", "dpmsolver++"]:
						for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
								for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    solver_order=a__					, solver_type=a__					, prediction_type=a__					, algorithm_type=a__					, )
									A             =					self.full_loop(
									    solver_order=a__					, solver_type=a__					, prediction_type=a__					, algorithm_type=a__					, )
									assert not torch.isnan(a__ ).any(), "Samples have nan numbers"

				def   _UpperCAmelCase       (    self )						->					Dict:
					self.check_over_configs(lower_order_final=a__ )
					self.check_over_configs(lower_order_final=a__ )

				def   _UpperCAmelCase       (    self )						->					Dict:
					self.check_over_configs(lambda_min_clipped=-float("""inf""" ) )
					self.check_over_configs(lambda_min_clipped=-5.1 )

				def   _UpperCAmelCase       (    self )						->					Dict:
					self.check_over_configs(variance_type=a__ )
					self.check_over_configs(variance_type="""learned_range""" )

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

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.27_91 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop(use_karras_sigmas=a__ )
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.22_48 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					self.full_loop(prediction_type="""v_prediction""" )
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.14_53 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					Tuple:
					A             =					self.full_loop(prediction_type="""v_prediction"""					, use_karras_sigmas=a__ )
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 0.06_49 ) < 1e-3

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(thresholding=a__					, dynamic_thresholding_ratio=0 )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter.half()
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					assert sample.dtype == torch.floataa







import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase					( UpperCamelCase__:  str = "https://www.worldometers.info/coronavirus"    )			-> dict:





	"""simple docstring"""







	A             =					BeautifulSoup(requests.get(UpperCamelCase__    ).text				,       """html.parser"""    )
	A             =					soup.findAll("""h1"""    )
	A             =					soup.findAll("""div"""				,       {"""class""": """maincounter-number"""}    )
	keys += soup.findAll("""span"""				,       {"""class""": """panel-title"""}    )
	values += soup.findAll("""div"""				,       {"""class""": """number-table-main"""}    )
	return {key.text.strip(): value.text.strip() for key, value in zip(UpperCamelCase__				,       UpperCamelCase__    )}


if __name__ == "__main__":
				print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
				for key, value in world_covidaa_stats().items():
								print(f'''{key}\n{value}\n''')



