





"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	(CMStochasticIterativeScheduler,)
 _lowerCamelCase								=	10

 def    UpperCamelCase__(       self   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[Any]							=	{
         '''num_train_timesteps''': 201,
         '''sigma_min''': 0.0_0_2,
         '''sigma_max''': 8_0.0,
     }

     config.update(**__lowerCamelCase    )
     return config

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	10

     __A       :		List[str]							=	self.get_scheduler_config()
     __A       :		Tuple							=	self.scheduler_classes[0](**__lowerCamelCase    )

     scheduler.set_timesteps(__lowerCamelCase    )

     __A       :		Optional[int]							=	scheduler.timesteps[0]
     __A       :		str							=	scheduler.timesteps[1]

     __A       :		List[str]							=	self.dummy_sample
     __A       :		Union[str, Any]							=	0.1 * sample

     __A       :		Optional[int]							=	scheduler.step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ).prev_sample
     __A       :		Optional[int]							=	scheduler.step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    ).prev_sample

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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	self.scheduler_classes[0]
     __A       :		Optional[int]							=	self.get_scheduler_config()
     __A       :		List[Any]							=	scheduler_class(**__lowerCamelCase    )

     __A       :		int							=	1
     scheduler.set_timesteps(__lowerCamelCase    )
     __A       :		List[Any]							=	scheduler.timesteps

     __A       :		Tuple							=	torch.manual_seed(0    )

     __A       :		int							=	self.dummy_model()
     __A       :		List[Any]							=	self.dummy_sample_deter * scheduler.init_noise_sigma

     for i, t in enumerate(__lowerCamelCase    ):
         # 1. scale model input
         __A       :		Optional[int]							=	scheduler.scale_model_input(__lowerCamelCase   ,		__lowerCamelCase    )

         # 2. predict noise residual
         __A       :		Tuple							=	model(__lowerCamelCase   ,		__lowerCamelCase    )

         # 3. predict previous sample x_t-1
         __A       :		int							=	scheduler.step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		generator=__lowerCamelCase    ).prev_sample

         __A       :		Any							=	pred_prev_sample

     __A       :		Optional[Any]							=	torch.sum(torch.abs(__lowerCamelCase    )    )
     __A       :		List[Any]							=	torch.mean(torch.abs(__lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_9_2.7_6_1_4    ) < 1e-2
     assert abs(result_mean.item() - 0.2_5_1_0    ) < 1e-3

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	self.scheduler_classes[0]
     __A       :		List[Any]							=	self.get_scheduler_config()
     __A       :		Tuple							=	scheduler_class(**__lowerCamelCase    )

     __A       :		List[Any]							=	[106, 0]
     scheduler.set_timesteps(timesteps=__lowerCamelCase    )
     __A       :		Tuple							=	scheduler.timesteps

     __A       :		List[Any]							=	torch.manual_seed(0    )

     __A       :		List[str]							=	self.dummy_model()
     __A       :		str							=	self.dummy_sample_deter * scheduler.init_noise_sigma

     for t in timesteps:
         # 1. scale model input
         __A       :		List[str]							=	scheduler.scale_model_input(__lowerCamelCase   ,		__lowerCamelCase    )

         # 2. predict noise residual
         __A       :		Dict							=	model(__lowerCamelCase   ,		__lowerCamelCase    )

         # 3. predict previous sample x_t-1
         __A       :		Union[str, Any]							=	scheduler.step(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		generator=__lowerCamelCase    ).prev_sample

         __A       :		Any							=	pred_prev_sample

     __A       :		str							=	torch.sum(torch.abs(__lowerCamelCase    )    )
     __A       :		Tuple							=	torch.mean(torch.abs(__lowerCamelCase    )    )

     assert abs(result_sum.item() - 3_4_7.6_3_5_7    ) < 1e-2
     assert abs(result_mean.item() - 0.4_5_2_7    ) < 1e-3

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	self.scheduler_classes[0]
     __A       :		Dict							=	self.get_scheduler_config()
     __A       :		Any							=	scheduler_class(**__lowerCamelCase    )

     __A       :		Optional[int]							=	[39, 30, 12, 15, 0]

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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	self.scheduler_classes[0]
     __A       :		Optional[int]							=	self.get_scheduler_config()
     __A       :		int							=	scheduler_class(**__lowerCamelCase    )

     __A       :		List[str]							=	[39, 30, 12, 1, 0]
     __A       :		str							=	len(__lowerCamelCase    )

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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	self.scheduler_classes[0]
     __A       :		Optional[int]							=	self.get_scheduler_config()
     __A       :		Union[str, Any]							=	scheduler_class(**__lowerCamelCase    )

     __A       :		List[str]							=	[scheduler.config.num_train_timesteps]

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











"""simple docstring"""



import requests

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







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	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>""")




