'''simple docstring'''





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			(KDPMaAncestralDiscreteScheduler,)
    A     :    Dict				=			10





    def    SCREAMING_SNAKE_CASE_     (					self     :						str							,							**UpperCAmelCase_     :						int):


         """simple docstring"""







         a					:							List[str]							= {
             'num_train_timesteps': 1_1_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**UpperCamelCase_)
         return config





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,							[0.00_02, 0.0_02, 0.02]):
              self.check_over_configs(beta_start=UpperCamelCase_							,							beta_end=UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         if torch_device == "mps":
              return
         a					:							Union[str, Any]							= self.scheduler_classes[0]
         a					:							Tuple							= self.get_scheduler_config()
         a					:							Any							= scheduler_class(**UpperCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps)

         a					:							Dict							= torch.manual_seed(0)

         a					:							Optional[Any]							= self.dummy_model()
         a					:							Tuple							= self.dummy_sample_deter * scheduler.init_noise_sigma
         a					:							Optional[Any]							= sample.to(UpperCamelCase_)

         for i, t in enumerate(scheduler.timesteps):
              a					:							Any							= scheduler.scale_model_input(UpperCamelCase_							,							UpperCamelCase_)

              a					:							Any							= model(UpperCamelCase_							,							UpperCamelCase_)

              a					:							List[str]							= scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							generator=UpperCamelCase_)
              a					:							int							= output.prev_sample

         a					:							Optional[Any]							= torch.sum(torch.abs(UpperCamelCase_))
         a					:							Dict							= torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7) < 1e-2
         assert abs(result_mean.item() - 18.03_31) < 5e-3





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=UpperCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         if torch_device == "mps":
              return
         a					:							Dict							= self.scheduler_classes[0]
         a					:							Optional[Any]							= self.get_scheduler_config(prediction_type='v_prediction')
         a					:							Optional[Any]							= scheduler_class(**UpperCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps)

         a					:							List[str]							= self.dummy_model()
         a					:							str							= self.dummy_sample_deter * scheduler.init_noise_sigma
         a					:							Optional[int]							= sample.to(UpperCamelCase_)

         a					:							Optional[Any]							= torch.manual_seed(0)

         for i, t in enumerate(scheduler.timesteps):
              a					:							Tuple							= scheduler.scale_model_input(UpperCamelCase_							,							UpperCamelCase_)

              a					:							List[Any]							= model(UpperCamelCase_							,							UpperCamelCase_)

              a					:							str							= scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							generator=UpperCamelCase_)
              a					:							Optional[Any]							= output.prev_sample

         a					:							List[Any]							= torch.sum(torch.abs(UpperCamelCase_))
         a					:							Any							= torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 3_2_8.9_9_7_0) < 1e-2
         assert abs(result_mean.item() - 0.42_84) < 1e-3







    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         if torch_device == "mps":
              return
         a					:							Tuple							= self.scheduler_classes[0]
         a					:							Any							= self.get_scheduler_config()
         a					:							Any							= scheduler_class(**UpperCamelCase_)

         scheduler.set_timesteps(self.num_inference_steps							,							device=UpperCamelCase_)
         a					:							Union[str, Any]							= torch.manual_seed(0)

         a					:							List[Any]							= self.dummy_model()
         a					:							List[str]							= self.dummy_sample_deter.to(UpperCamelCase_) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              a					:							Dict							= scheduler.scale_model_input(UpperCamelCase_							,							UpperCamelCase_)

              a					:							Union[str, Any]							= model(UpperCamelCase_							,							UpperCamelCase_)

              a					:							Optional[int]							= scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							generator=UpperCamelCase_)
              a					:							Any							= output.prev_sample

         a					:							Any							= torch.sum(torch.abs(UpperCamelCase_))
         a					:							int							= torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8) < 1e-1
         assert abs(result_mean.item() - 18.03_31) < 1e-3






'''simple docstring'''

import bsa
import requests







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


if __name__ == "__main__":
		import json

		_A					:     Optional[int]      =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)
		    )
		)
