



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				int       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				List[Any]	):



											'''simple docstring'''


											super().__init__()
											self.register_modules(unet=snake_case       ,	scheduler=snake_case	)







						@torch.no_grad()
						def __call__(  self    :				int       ,	snake_case    :				int = 1       ,	snake_case    :				Optional[torch.Generator] = None       ,	snake_case    :				int = 50       ,	snake_case    :				Optional[str] = "pil"       ,	snake_case    :				bool = True       ,	**snake_case    :				int       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,	generator=snake_case       ,	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image.to(self.device	)

											# set step values
											self.scheduler.set_timesteps(snake_case	)

											for t in self.progress_bar(self.scheduler.timesteps	):
																# 1. predict noise model_output
																SCREAMING_SNAKE_CASE							:     str						=				self.unet(snake_case       ,	snake_case	).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																SCREAMING_SNAKE_CASE							:     Tuple						=				self.scheduler.step(snake_case       ,	snake_case       ,	snake_case	).prev_sample

											SCREAMING_SNAKE_CASE							:     int						=				(image / 2 + 0.5).clamp(0       ,	1	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image.cpu().permute(0       ,	2       ,	3       ,	1	).numpy()
											if output_type == "pil":
																SCREAMING_SNAKE_CASE							:     str						=				self.numpy_to_pil(snake_case	)

											if not return_dict:
																return (image,), "This is a local test"

											return ImagePipelineOutput(images=snake_case	), "This is a local test"




import requests

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

def        __a			(   __lowerCAmelCase   )     ->     None:
					# fetching a list of articles in json format
					SCREAMING_SNAKE_CASE							:     List[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>""")