


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class     UpperCamelCase(  SCREAMING_SNAKE_CASE_   ):
					snake_case_		:				int										=	(UniPCMultistepScheduler,)
					snake_case_		:				Optional[int]										=	(("""num_inference_steps""", 25),)


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




											'''simple docstring'''

											__snake_case				=		{
											    '''num_train_timesteps''': 1_0_0_0,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''solver_order''': 2,
											    '''solver_type''': '''bh2''',
											}

											config.update(**UpperCamelCase__						)
											return config


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]=0     ,    **SCREAMING_SNAKE_CASE	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		dict(self.forward_default_kwargs						)
											__snake_case				=		kwargs.pop("num_inference_steps"     ,    UpperCamelCase__						)
											__snake_case				=		self.dummy_sample
											__snake_case				=		0.1 * sample
											__snake_case				=		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	__snake_case				=		self.get_scheduler_config(**UpperCamelCase__						)
																	__snake_case				=		scheduler_class(**UpperCamelCase__						)
																	scheduler.set_timesteps(UpperCamelCase__						)
																	# copy over dummy past residuals
																	__snake_case				=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(UpperCamelCase__						)
																							__snake_case				=		scheduler_class.from_pretrained(UpperCamelCase__						)
																							new_scheduler.set_timesteps(UpperCamelCase__						)
																							# copy over dummy past residuals
																							__snake_case				=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	__snake_case				=		sample, sample
																	for t in range(UpperCamelCase__     ,    time_step + scheduler.config.solver_order + 1						):
																							__snake_case				=		scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    **UpperCamelCase__						).prev_sample
																							__snake_case				=		new_scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    **UpperCamelCase__						).prev_sample

																							assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[str]=0     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		dict(self.forward_default_kwargs						)
											__snake_case				=		kwargs.pop("num_inference_steps"     ,    UpperCamelCase__						)
											__snake_case				=		self.dummy_sample
											__snake_case				=		0.1 * sample
											__snake_case				=		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																	__snake_case				=		self.get_scheduler_config()
																	__snake_case				=		scheduler_class(**UpperCamelCase__						)
																	scheduler.set_timesteps(UpperCamelCase__						)

																	# copy over dummy past residuals (must be after setting timesteps)
																	__snake_case				=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(UpperCamelCase__						)
																							__snake_case				=		scheduler_class.from_pretrained(UpperCamelCase__						)
																							# copy over dummy past residuals
																							new_scheduler.set_timesteps(UpperCamelCase__						)

																							# copy over dummy past residual (must be after setting timesteps)
																							__snake_case				=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	__snake_case				=		scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    **UpperCamelCase__						).prev_sample
																	__snake_case				=		new_scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    **UpperCamelCase__						).prev_sample

																	assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											if scheduler is None:
																	__snake_case				=		self.scheduler_classes[0]
																	__snake_case				=		self.get_scheduler_config(**UpperCamelCase__						)
																	__snake_case				=		scheduler_class(**UpperCamelCase__						)

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

											__snake_case				=		1_0
											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter
											scheduler.set_timesteps(UpperCamelCase__						)

											for i, t in enumerate(scheduler.timesteps						):
																	__snake_case				=		model(UpperCamelCase__     ,    UpperCamelCase__						)
																	__snake_case				=		scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__						).prev_sample

											return sample


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




											'''simple docstring'''

											__snake_case				=		dict(self.forward_default_kwargs						)

											__snake_case				=		kwargs.pop("num_inference_steps"     ,    UpperCamelCase__						)

											for scheduler_class in self.scheduler_classes:
																	__snake_case				=		self.get_scheduler_config()
																	__snake_case				=		scheduler_class(**UpperCamelCase__						)

																	__snake_case				=		self.dummy_sample
																	__snake_case				=		0.1 * sample

																	if num_inference_steps is not None and hasattr(UpperCamelCase__     ,    "set_timesteps"						):
																							scheduler.set_timesteps(UpperCamelCase__						)
																	elif num_inference_steps is not None and not hasattr(UpperCamelCase__     ,    "set_timesteps"						):
																							__snake_case				=		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	__snake_case				=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	__snake_case				=		dummy_past_residuals[: scheduler.config.solver_order]

																	__snake_case				=		scheduler.timesteps[5]
																	__snake_case				=		scheduler.timesteps[6]

																	__snake_case				=		scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__     ,    **UpperCamelCase__						).prev_sample
																	__snake_case				=		scheduler.step(UpperCamelCase__     ,    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	:		int						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		UniPCMultistepScheduler(**self.get_scheduler_config()						)
											__snake_case				=		self.full_loop(scheduler=UpperCamelCase__						)
											__snake_case				=		torch.mean(torch.abs(UpperCamelCase__						)						)

											assert abs(result_mean.item() - 0.2464						) < 1e-3

											__snake_case				=		DPMSolverSinglestepScheduler.from_config(scheduler.config						)
											__snake_case				=		DEISMultistepScheduler.from_config(scheduler.config						)
											__snake_case				=		DPMSolverMultistepScheduler.from_config(scheduler.config						)
											__snake_case				=		UniPCMultistepScheduler.from_config(scheduler.config						)

											__snake_case				=		self.full_loop(scheduler=UpperCamelCase__						)
											__snake_case				=		torch.mean(torch.abs(UpperCamelCase__						)						)

											assert abs(result_mean.item() - 0.2464						) < 1e-3


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=UpperCamelCase__						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




											'''simple docstring'''

											self.check_over_configs(thresholding=UpperCamelCase__						)
											for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																							for threshold in [0.5, 1.0, 2.0]:
																													for prediction_type in ["epsilon", "sample"]:
																																			self.check_over_configs(
																																			    thresholding=UpperCamelCase__     ,    prediction_type=UpperCamelCase__     ,    sample_max_value=UpperCamelCase__     ,    solver_order=UpperCamelCase__     ,    solver_type=UpperCamelCase__     ,    )


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




											'''simple docstring'''

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


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




											'''simple docstring'''

											for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																							for prediction_type in ["epsilon", "sample"]:
																													self.check_over_configs(
																													    solver_order=UpperCamelCase__     ,    solver_type=UpperCamelCase__     ,    prediction_type=UpperCamelCase__     ,    )
																													__snake_case				=		self.full_loop(
																													    solver_order=UpperCamelCase__     ,    solver_type=UpperCamelCase__     ,    prediction_type=UpperCamelCase__     ,    )
																													assert not torch.isnan(UpperCamelCase__						).any(), "Samples have nan numbers"


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Optional[Any]:




											'''simple docstring'''

											self.check_over_configs(lower_order_final=UpperCamelCase__						)
											self.check_over_configs(lower_order_final=UpperCamelCase__						)


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




											'''simple docstring'''

											for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=UpperCamelCase__     ,    time_step=0						)


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




											'''simple docstring'''

											__snake_case				=		self.full_loop()
											__snake_case				=		torch.mean(torch.abs(UpperCamelCase__						)						)

											assert abs(result_mean.item() - 0.2464						) < 1e-3


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.full_loop(prediction_type="v_prediction"						)
											__snake_case				=		torch.mean(torch.abs(UpperCamelCase__						)						)

											assert abs(result_mean.item() - 0.1014						) < 1e-3


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




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(thresholding=UpperCamelCase__     ,    dynamic_thresholding_ratio=0						)
											__snake_case				=		scheduler_class(**UpperCamelCase__						)

											__snake_case				=		1_0
											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter.half()
											scheduler.set_timesteps(UpperCamelCase__						)

											for i, t in enumerate(scheduler.timesteps						):
																	__snake_case				=		model(UpperCamelCase__     ,    UpperCamelCase__						)
																	__snake_case				=		scheduler.step(UpperCamelCase__     ,    UpperCamelCase__     ,    UpperCamelCase__						).prev_sample

											assert sample.dtype == torch.floataa






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




											'''simple docstring'''

											for scheduler_class in self.scheduler_classes:
																	__snake_case				=		self.get_scheduler_config(**UpperCamelCase__						)
																	__snake_case				=		scheduler_class(**UpperCamelCase__						)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
																	assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps



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()





