
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]							,A      :				int							,A      :				List[Any]				):
					super().__init__()
					self.register_modules(unet=A							,scheduler=A				)







	@torch.no_grad()
	def __call__( self      :				Optional[int]							,A      :				int = 1							,A      :				Optional[torch.Generator] = None							,A      :				int = 50							,A      :				Optional[str] = "pil"							,A      :				bool = True							,**A      :				List[str]							,):
					__A         =				torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,generator=A							,)
					__A         =				image.to(self.device				)

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

					for t in self.progress_bar(self.scheduler.timesteps				):
									# 1. predict noise model_output
									__A         =				self.unet(A							,A				).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
									__A         =				self.scheduler.step(A							,A							,A				).prev_sample

					__A         =				(image / 2 + 0.5).clamp(0							,1				)
					__A         =				image.cpu().permute(0							,2							,3							,1				).numpy()
					if output_type == "pil":
									__A         =				self.numpy_to_pil(A				)

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

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


from datetime import date

import requests

SCREAMING_SNAKE_CASE							:str											=       'https://api.carbonintensity.org.uk/intensity'


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				requests.get(a_		).json()["data"][0]
				return last_half_hour["intensity"]["actual"]


def 		UpperCAmelCase    (			a_	,     a_		)	->			list:







				"""simple docstring"""



				return requests.get(F'''{BASE_URL}/{start}/{end}'''		).json()["data"]


if __name__ == "__main__":
					for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
										print('from {from} to {to}: {intensity[actual]}'.format(**entry))
					print(f'''{fetch_last_half_hour() = }''')
