




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      List[str]						       =    (DDPMParallelScheduler,)




			def    lowerCAmelCase__				(					self				,							**a__							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "variance_type": "fixed_small",
										    "clip_sample": True,
										}

										config.update(**a__							)
										return config




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										for timesteps in [1, 5, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




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

										'''simple docstring'''




										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=a__				,							beta_end=a__							)




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=a__							)




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

										'''simple docstring'''




										for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=a__							)




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

										'''simple docstring'''




										for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=a__							)




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

										'''simple docstring'''




										self.check_over_configs(thresholding=a__							)
										for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=a__				,							prediction_type=a__				,							sample_max_value=a__				,							)




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										for t in [0, 500, 999]:
																	self.check_over_forward(time_step=a__							)




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1e-5




			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      len(a__							)

										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter
										snake_case_						=      self.dummy_sample_deter + 0.1
										snake_case_						=      self.dummy_sample_deter - 0.1

										snake_case_						=      samplea.shape[0]
										snake_case_						=      torch.stack([samplea, samplea, samplea]				,							dim=0							)
										snake_case_						=      torch.arange(a__							)[0:3, None].repeat(1				,							a__							)

										snake_case_						=      model(samples.flatten(0				,							1							)				,							timesteps.flatten(0				,							1							)							)
										snake_case_						=      scheduler.batch_step_no_noise(a__				,							timesteps.flatten(0				,							1							)				,							samples.flatten(0				,							1							)							)

										snake_case_						=      torch.sum(torch.abs(a__							)							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1e-2
										assert abs(result_mean.item() - 0.5_0_0_5							) < 1e-3




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

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      len(a__							)

										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter
										snake_case_						=      torch.manual_seed(0							)

										for t in reversed(range(a__							)							):
																	# 1. predict noise residual
																	snake_case_						=      model(a__				,							a__							)

																	# 2. predict previous mean of sample x_t-1
																	snake_case_						=      scheduler.step(a__				,							a__				,							a__				,							generator=a__							).prev_sample

																	snake_case_						=      pred_prev_sample

										snake_case_						=      torch.sum(torch.abs(a__							)							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1e-2
										assert abs(result_mean.item() - 0.3_3_7_2							) < 1e-3




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

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config(prediction_type="v_prediction"							)
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      len(a__							)

										snake_case_						=      self.dummy_model()
										snake_case_						=      self.dummy_sample_deter
										snake_case_						=      torch.manual_seed(0							)

										for t in reversed(range(a__							)							):
																	# 1. predict noise residual
																	snake_case_						=      model(a__				,							a__							)

																	# 2. predict previous mean of sample x_t-1
																	snake_case_						=      scheduler.step(a__				,							a__				,							a__				,							generator=a__							).prev_sample

																	snake_case_						=      pred_prev_sample

										snake_case_						=      torch.sum(torch.abs(a__							)							)
										snake_case_						=      torch.mean(torch.abs(a__							)							)

										assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1e-2
										assert abs(result_mean.item() - 0.2_6_3_1							) < 1e-3




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      [100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=a__							)

										snake_case_						=      scheduler.timesteps

										for i, timestep in enumerate(a__							):
																	if i == len(a__							) - 1:
																								snake_case_						=      -1
																	else:
																								snake_case_						=      timesteps[i + 1]

																	snake_case_						=      scheduler.previous_timestep(a__							)
																	snake_case_						=      prev_t.item()

																	self.assertEqual(a__				,							a__							)




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

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      [100, 87, 50, 51, 0]

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




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      [100, 87, 50, 1, 0]
										snake_case_						=      len(a__							)

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




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      self.scheduler_classes[0]
										snake_case_						=      self.get_scheduler_config()
										snake_case_						=      scheduler_class(**a__							)

										snake_case_						=      [scheduler.config.num_train_timesteps]

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






import requests

_A   =     "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	requests.get(_NEWS_API + bbc_news_api_key				).json()
			# each article in the list is a dict
			for i, article in enumerate(bbc_news_page["articles"]    ,							1				):
						print(F"""{i}.) {article['title']}"""				)


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



