





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      (CMStochasticIterativeScheduler,)
  __lowerCamelCase		=      10







  def   UpperCAmelCase					(      self  :List[str]   ,							**_lowercase  :str ):






     '''simple docstring'''





     lowercase__			       =  {
         "num_train_timesteps": 2_01,
         "sigma_min": 0.002,
         "sigma_max": 80.0,
     }

     config.update(**_lowercase )
     return config







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  10

     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  self.scheduler_classes[0](**_lowercase )

     scheduler.set_timesteps(_lowercase )

     lowercase__			       =  scheduler.timesteps[0]
     lowercase__			       =  scheduler.timesteps[1]

     lowercase__			       =  self.dummy_sample
     lowercase__			       =  0.1 * sample

     lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase ).prev_sample
     lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase ).prev_sample

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







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=_lowercase )







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     for clip_denoised in [True, False]:
        self.check_over_configs(clip_denoised=_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  1
     scheduler.set_timesteps(_lowercase )
     lowercase__			       =  scheduler.timesteps

     lowercase__			       =  torch.manual_seed(0 )

     lowercase__			       =  self.dummy_model()
     lowercase__			       =  self.dummy_sample_deter * scheduler.init_noise_sigma

     for i, t in enumerate(_lowercase ):
        # 1. scale model input
        lowercase__			       =  scheduler.scale_model_input(_lowercase   ,							_lowercase )

        # 2. predict noise residual
        lowercase__			       =  model(_lowercase   ,							_lowercase )

        # 3. predict previous sample x_t-1
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							generator=_lowercase ).prev_sample

        lowercase__			       =  pred_prev_sample

     lowercase__			       =  torch.sum(torch.abs(_lowercase ) )
     lowercase__			       =  torch.mean(torch.abs(_lowercase ) )

     assert abs(result_sum.item() - 192.7614 ) < 1e-2
     assert abs(result_mean.item() - 0.2510 ) < 1e-3







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  [1_06, 0]
     scheduler.set_timesteps(timesteps=_lowercase )
     lowercase__			       =  scheduler.timesteps

     lowercase__			       =  torch.manual_seed(0 )

     lowercase__			       =  self.dummy_model()
     lowercase__			       =  self.dummy_sample_deter * scheduler.init_noise_sigma

     for t in timesteps:
        # 1. scale model input
        lowercase__			       =  scheduler.scale_model_input(_lowercase   ,							_lowercase )

        # 2. predict noise residual
        lowercase__			       =  model(_lowercase   ,							_lowercase )

        # 3. predict previous sample x_t-1
        lowercase__			       =  scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							generator=_lowercase ).prev_sample

        lowercase__			       =  pred_prev_sample

     lowercase__			       =  torch.sum(torch.abs(_lowercase ) )
     lowercase__			       =  torch.mean(torch.abs(_lowercase ) )

     assert abs(result_sum.item() - 347.6357 ) < 1e-2
     assert abs(result_mean.item() - 0.4527 ) < 1e-3







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  [39, 30, 12, 15, 0]

     with self.assertRaises(_lowercase   ,							msg="`timesteps` must be in descending order." ):
        scheduler.set_timesteps(timesteps=_lowercase )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  [39, 30, 12, 1, 0]
     lowercase__			       =  len(_lowercase )

     with self.assertRaises(_lowercase   ,							msg="Can only pass one of `num_inference_steps` or `timesteps`." ):
        scheduler.set_timesteps(num_inference_steps=_lowercase   ,							timesteps=_lowercase )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.scheduler_classes[0]
     lowercase__			       =  self.get_scheduler_config()
     lowercase__			       =  scheduler_class(**_lowercase )

     lowercase__			       =  [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         _lowercase   ,							msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"   ,							):
        scheduler.set_timesteps(timesteps=_lowercase )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

_snake_case           =			namedtuple("""covid_data""", """cases deaths recovered""")


def _A       (   __magic_name__ = "https://www.worldometers.info/coronavirus/"	):
   lowercase__			       =  "//div[@class = \"maincounter-number\"]/span/text()"
   return covid_data(*html.fromstring(requests.get(__magic_name__	).content	).xpath(__magic_name__	)	)


_snake_case           =			"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""







print(fmt.format(*covid_stats()))






