


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class     UpperCamelCase(  __UpperCAmelCase   ):
					snake_case_		:				Union[str, Any]										=	(CMStochasticIterativeScheduler,)
					snake_case_		:				str										=	10


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		{
											    "num_train_timesteps": 2_0_1,
											    "sigma_min": 0.002,
											    "sigma_max": 80.0,
											}

											config.update(**UpperCAmelCase_						)
											return config


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		1_0

											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		self.scheduler_classes[0](**UpperCAmelCase_						)

											scheduler.set_timesteps(UpperCAmelCase_						)

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

											__snake_case				=		self.dummy_sample
											__snake_case				=		0.1 * sample

											__snake_case				=		scheduler.step(UpperCAmelCase_     ,    UpperCAmelCase_     ,    UpperCAmelCase_						).prev_sample
											__snake_case				=		scheduler.step(UpperCAmelCase_     ,    UpperCAmelCase_     ,    UpperCAmelCase_						).prev_sample

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	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	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**UpperCAmelCase_						)

											__snake_case				=		1
											scheduler.set_timesteps(UpperCAmelCase_						)
											__snake_case				=		scheduler.timesteps

											__snake_case				=		torch.manual_seed(0						)

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

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

																	# 2. predict noise residual
																	__snake_case				=		model(UpperCAmelCase_     ,    UpperCAmelCase_						)

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

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(UpperCAmelCase_						)						)
											__snake_case				=		torch.mean(torch.abs(UpperCAmelCase_						)						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**UpperCAmelCase_						)

											__snake_case				=		[1_0_6, 0]
											scheduler.set_timesteps(timesteps=UpperCAmelCase_						)
											__snake_case				=		scheduler.timesteps

											__snake_case				=		torch.manual_seed(0						)

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

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

																	# 2. predict noise residual
																	__snake_case				=		model(UpperCAmelCase_     ,    UpperCAmelCase_						)

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

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(UpperCAmelCase_						)						)
											__snake_case				=		torch.mean(torch.abs(UpperCAmelCase_						)						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Any:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**UpperCAmelCase_						)

											__snake_case				=		[3_9, 3_0, 1_2, 1_5, 0]

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


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




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**UpperCAmelCase_						)

											__snake_case				=		[3_9, 3_0, 1_2, 1, 0]
											__snake_case				=		len(UpperCAmelCase_						)

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**UpperCAmelCase_						)

											__snake_case				=		[scheduler.config.num_train_timesteps]

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



from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase       (    _lowerCAmelCase = "" )						->	dict[str, float]:


						'''simple docstring'''






						__snake_case				=		url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase ).text      ,	"html.parser" )
						__snake_case				=		soup.find_all("td"      ,	attrs="titleColumn" )
						__snake_case				=		soup.find_all("td"      ,	class_="ratingColumn imdbRating" )
						return {
						    title.a.text: float(rating.strong.text )
						    for title, rating in zip(_lowerCAmelCase      ,	_lowerCAmelCase )
						}





def 	_lowerCAmelCase       (    _lowerCAmelCase = "IMDb_Top_250_Movies.csv" )						->	None:


						'''simple docstring'''






						__snake_case				=		get_imdb_top_aaa_movies()
						with open(_lowerCAmelCase      ,	"w"      ,	newline="" ) as out_file:
												__snake_case				=		csv.writer(_lowerCAmelCase )
												writer.writerow(["Movie title", "IMDb rating"] )
												for title, rating in movies.items():
																		writer.writerow([title, rating] )


if __name__ == "__main__":
	write_movies()





