




'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = 42
					lowercase__    = 42






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase ):
							super().__init__()

							_snake_case	       =       PNDMScheduler.from_config(scheduler.config )

							self.register_modules(unet=__UpperCamelCase,  scheduler=__UpperCamelCase )






					@torch.no_grad()
					def __call__(    self,  __UpperCamelCase = 1,  __UpperCamelCase = 50,  __UpperCamelCase = None,  __UpperCamelCase = "pil",  __UpperCamelCase = True,  **__UpperCamelCase,  ):
							_snake_case	       =       randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),  generator=__UpperCamelCase,  device=self.device,  )

							self.scheduler.set_timesteps(__UpperCamelCase )
							for t in self.progress_bar(self.scheduler.timesteps ):
									_snake_case	       =       self.unet(__UpperCamelCase,  __UpperCamelCase ).sample

									_snake_case	       =       self.scheduler.step(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ).prev_sample

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

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase )












from datetime import date

import requests

_lowerCamelCase       =	'https://api.carbonintensity.org.uk/intensity'






def  SCREAMING_SNAKE_CASE__							(				):
		SCREAMING_SNAKE_CASE__								=					requests.get(UpperCamelCase__			).json()["""data"""][0]
		return last_half_hour["intensity"]["actual"]






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      UpperCamelCase__:							Optional[int]			):
		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() = }''')