





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    (HeunDiscreteScheduler,)
							lowerCamelCase_							=    10


							def    _snake_case     (							self			:List[Any]							, **__A			:Any						)      ->      int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					{
									    """num_train_timesteps""": 1100,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**__A						)
									return config


							def    _snake_case     (							self			:Optional[int]						)      ->      Tuple:



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[Any]						)      ->      int:



									"""simple docstring"""



									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							, [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
											self.check_over_configs(beta_start=__A							, beta_end=__A						)


							def    _snake_case     (							self			:Any						)      ->      str:



									"""simple docstring"""



									for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=__A						)


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



									"""simple docstring"""



									for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=__A							, clip_sample=__A						)


							def    _snake_case     (							self			:Any						)      ->      int:



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[str]						)      ->      Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								=					scheduler_class(**__A						)

									scheduler.set_timesteps(self.num_inference_steps						)

									SCREAMING_SNAKE_CASE__								=					self.dummy_model()
									SCREAMING_SNAKE_CASE__								=					self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__								=					sample.to(__A						)

									for i, t in enumerate(scheduler.timesteps						):
											SCREAMING_SNAKE_CASE__								=					scheduler.scale_model_input(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					model(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					scheduler.step(__A							, __A							, __A						)
											SCREAMING_SNAKE_CASE__								=					output.prev_sample

									SCREAMING_SNAKE_CASE__								=					torch.sum(torch.abs(__A						)						)
									SCREAMING_SNAKE_CASE__								=					torch.mean(torch.abs(__A						)						)

									if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3
									else:
											# CUDA
											assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=					self.get_scheduler_config(prediction_type="""v_prediction"""						)
									SCREAMING_SNAKE_CASE__								=					scheduler_class(**__A						)

									scheduler.set_timesteps(self.num_inference_steps						)

									SCREAMING_SNAKE_CASE__								=					self.dummy_model()
									SCREAMING_SNAKE_CASE__								=					self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__								=					sample.to(__A						)

									for i, t in enumerate(scheduler.timesteps						):
											SCREAMING_SNAKE_CASE__								=					scheduler.scale_model_input(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					model(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					scheduler.step(__A							, __A							, __A						)
											SCREAMING_SNAKE_CASE__								=					output.prev_sample

									SCREAMING_SNAKE_CASE__								=					torch.sum(torch.abs(__A						)						)
									SCREAMING_SNAKE_CASE__								=					torch.mean(torch.abs(__A						)						)

									if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6_934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1_112E-10						) < 1E-3
									else:
											# CUDA
											assert abs(result_sum.item() - 4.693_428_650_170_972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3


							def    _snake_case     (							self			:List[Any]						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								=					scheduler_class(**__A						)

									scheduler.set_timesteps(self.num_inference_steps							, device=__A						)

									SCREAMING_SNAKE_CASE__								=					self.dummy_model()
									SCREAMING_SNAKE_CASE__								=					self.dummy_sample_deter.to(__A						) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE__								=					scheduler.scale_model_input(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					model(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					scheduler.step(__A							, __A							, __A						)
											SCREAMING_SNAKE_CASE__								=					output.prev_sample

									SCREAMING_SNAKE_CASE__								=					torch.sum(torch.abs(__A						)						)
									SCREAMING_SNAKE_CASE__								=					torch.mean(torch.abs(__A						)						)

									if str(__A						).startswith("""cpu"""						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3
									elif str(__A						).startswith("""mps"""						):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-2
									else:
											# CUDA
											assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
											assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3


							def    _snake_case     (							self			:str						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=					self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								=					scheduler_class(**__A							, use_karras_sigmas=__A						)

									scheduler.set_timesteps(self.num_inference_steps							, device=__A						)

									SCREAMING_SNAKE_CASE__								=					self.dummy_model()
									SCREAMING_SNAKE_CASE__								=					self.dummy_sample_deter.to(__A						) * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__								=					sample.to(__A						)

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE__								=					scheduler.scale_model_input(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					model(__A							, __A						)

											SCREAMING_SNAKE_CASE__								=					scheduler.step(__A							, __A							, __A						)
											SCREAMING_SNAKE_CASE__								=					output.prev_sample

									SCREAMING_SNAKE_CASE__								=					torch.sum(torch.abs(__A						)						)
									SCREAMING_SNAKE_CASE__								=					torch.mean(torch.abs(__A						)						)

									assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1E-2
									assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07						) < 1E-2






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() = }''')