

'''simple docstring'''

import bsa
import requests

def      __magic_name__							(			A				)      ->		dict[str, str]:
	return {
	    "name": soup.ha.a.text,
	    "genre": soup.find('span'       , class_='genre'				).text.strip(),
	    "rating": soup.strong.text,
	    "page_link": F'''https://www.imdb.com{soup.a.get("href"				)}''',
	}

def      __magic_name__							(			A = 5				)      ->		tuple:
	snake_case							=					int(float(A				)				)
	if num_movies < 1:
		return ()
	snake_case							=					(
	    'https://www.imdb.com/search/title?title_type='
	    F'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	snake_case							=					bsa.BeautifulSoup(requests.get(A				).content       , 'html.parser'				)
	return tuple(
	    get_movie_data_from_soup(A				)
	    for movie in source.find_all('div'       , class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
						import json

						lowerCAmelCase_     =      int(input("How many movies would you like to see? "))
						print(
						    ", ".join(
						        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
						    )
						)












import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					(LMSDiscreteScheduler,)
   lowerCAmelCase__  	=					10




   def _lowerCAmelCase (self      :List[str]       ,  **_UpperCamelCase      :Optional[int]					)->   List[str]:
         __A			  =							{
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase					)
         return config




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Dict					)->   Optional[int]:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
               self.check_over_configs(beta_start=_UpperCamelCase       ,  beta_end=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_UpperCamelCase					)




   def _lowerCAmelCase (self      :List[str]					)->   str:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase					)




   def _lowerCAmelCase (self      :str					)->   List[str]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

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

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config(prediction_type='''v_prediction'''					)
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

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

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3




   def _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A			  =							sample.to(_UpperCamelCase					)

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3






   def _lowerCAmelCase (self      :List[str]					)->   int:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase       ,  use_karras_sigmas=_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter.to(_UpperCamelCase					) * scheduler.init_noise_sigma
         __A			  =							sample.to(_UpperCamelCase					)

         for t in scheduler.timesteps:
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
         assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3





