




'''simple docstring'''






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			(DDIMParallelScheduler,)
     A_											=			(("eta", 0.0), ("num_inference_steps", 5_0))





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							**__lowerCamelCase     :					Tuple			):
           snake_case__       :   Union[str, Any]	      =    {
               'num_train_timesteps': 1000,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
               'clip_sample': True,
           }

           config.update(**__lowerCamelCase			)
           return config





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							**__lowerCamelCase     :					int			):
           snake_case__       :   Any	      =    self.scheduler_classes[0]
           snake_case__       :   int	      =    self.get_scheduler_config(**__lowerCamelCase			)
           snake_case__       :   Dict	      =    scheduler_class(**__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    10, 0.0

           snake_case__       :   List[str]	      =    self.dummy_model()
           snake_case__       :   Tuple	      =    self.dummy_sample_deter

           scheduler.set_timesteps(__lowerCamelCase			)

           for t in scheduler.timesteps:
                 snake_case__       :   List[str]	      =    model(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Dict	      =    scheduler.step(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			).prev_sample

           return sample





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowerCamelCase			)

           snake_case__       :   List[Any]	      =    self.scheduler_classes[0]
           snake_case__       :   int	      =    self.get_scheduler_config(steps_offset=1			)
           snake_case__       :   List[str]	      =    scheduler_class(**__lowerCamelCase			)
           scheduler.set_timesteps(5			)
           assert torch.equal(scheduler.timesteps     ,							torch.LongTensor([801, 601, 401, 201, 1]			)			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]     ,							[0.0_0_2, 0.0_2, 0.2, 2]			):
                 self.check_over_configs(beta_start=__lowerCamelCase     ,							beta_end=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           self.check_over_configs(thresholding=__lowerCamelCase			)
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__lowerCamelCase     ,							prediction_type=__lowerCamelCase     ,							sample_max_value=__lowerCamelCase     ,							)





     def 			_lowerCAmelCase      (     self     :					Any			):
           for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           for t, num_inference_steps in zip([1, 10, 50]     ,							[10, 50, 500]			):
                 self.check_over_forward(time_step=__lowerCamelCase     ,							num_inference_steps=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           for t, eta in zip([1, 10, 49]     ,							[0.0, 0.5, 1.0]			):
                 self.check_over_forward(time_step=__lowerCamelCase     ,							eta=__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[Any]	      =    self.scheduler_classes[0]
           snake_case__       :   Tuple	      =    self.get_scheduler_config()
           snake_case__       :   List[Any]	      =    scheduler_class(**__lowerCamelCase			)

           assert torch.sum(torch.abs(scheduler._get_variance(0     ,							0			) - 0.0			)			) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(420     ,							400			) - 0.1_4_7_7_1			)			) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(980     ,							960			) - 0.3_2_4_6_0			)			) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(0     ,							0			) - 0.0			)			) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487     ,							486			) - 0.0_0_9_7_9			)			) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999     ,							998			) - 0.0_2			)			) < 1E-5





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Optional[int]	      =    self.scheduler_classes[0]
           snake_case__       :   List[Any]	      =    self.get_scheduler_config()
           snake_case__       :   Any	      =    scheduler_class(**__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Union[str, Any]	      =    10, 0.0
           scheduler.set_timesteps(__lowerCamelCase			)

           snake_case__       :   Any	      =    self.dummy_model()
           snake_case__       :   str	      =    self.dummy_sample_deter
           snake_case__       :   Union[str, Any]	      =    self.dummy_sample_deter + 0.1
           snake_case__       :   str	      =    self.dummy_sample_deter - 0.1

           snake_case__       :   Optional[int]	      =    samplea.shape[0]
           snake_case__       :   Dict	      =    torch.stack([samplea, samplea, samplea]     ,							dim=0			)
           snake_case__       :   Optional[Any]	      =    torch.arange(__lowerCamelCase			)[0:3, None].repeat(1     ,							__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    model(samples.flatten(0     ,							1			)     ,							timesteps.flatten(0     ,							1			)			)
           snake_case__       :   int	      =    scheduler.batch_step_no_noise(__lowerCamelCase     ,							timesteps.flatten(0     ,							1			)     ,							samples.flatten(0     ,							1			)     ,							__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    torch.sum(torch.abs(__lowerCamelCase			)			)
           snake_case__       :   Optional[Any]	      =    torch.mean(torch.abs(__lowerCamelCase			)			)

           assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1E-2
           assert abs(result_mean.item() - 0.4_9_8_2			) < 1E-3





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Optional[int]	      =    self.full_loop()

           snake_case__       :   Any	      =    torch.sum(torch.abs(__lowerCamelCase			)			)
           snake_case__       :   Dict	      =    torch.mean(torch.abs(__lowerCamelCase			)			)

           assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1E-2
           assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1E-3





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   str	      =    self.full_loop(prediction_type='v_prediction'			)

           snake_case__       :   List[Any]	      =    torch.sum(torch.abs(__lowerCamelCase			)			)
           snake_case__       :   Optional[Any]	      =    torch.mean(torch.abs(__lowerCamelCase			)			)

           assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1E-2
           assert abs(result_mean.item() - 0.0_6_8_4			) < 1E-3





     def 			_lowerCAmelCase      (     self     :					int			):
           # We specify different beta, so that the first alpha is 0.99
           snake_case__       :   Optional[Any]	      =    self.full_loop(set_alpha_to_one=__lowerCamelCase     ,							beta_start=0.0_1			)
           snake_case__       :   Optional[Any]	      =    torch.sum(torch.abs(__lowerCamelCase			)			)
           snake_case__       :   Dict	      =    torch.mean(torch.abs(__lowerCamelCase			)			)

           assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1E-2
           assert abs(result_mean.item() - 0.1_9_5_1			) < 1E-3





     def 			_lowerCAmelCase      (     self     :					int			):
           # We specify different beta, so that the first alpha is 0.99
           snake_case__       :   Optional[Any]	      =    self.full_loop(set_alpha_to_one=__lowerCamelCase     ,							beta_start=0.0_1			)
           snake_case__       :   Tuple	      =    torch.sum(torch.abs(__lowerCamelCase			)			)
           snake_case__       :   Dict	      =    torch.mean(torch.abs(__lowerCamelCase			)			)

           assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1E-2
           assert abs(result_mean.item() - 0.1_9_4_1			) < 1E-3






'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
