






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class 							__snake_case(       unittest.TestCase   ):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 lowercase			:							int		   =		(ScoreSdeVeScheduler,)
 lowercase			:							str		   =		()



 @property
 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			4
    lowercase__    :						Tuple       =			3
    lowercase__    :						Tuple       =			8
    lowercase__    :						Any       =			8

    lowercase__    :						Tuple       =			torch.rand((batch_size, num_channels, height, width)						)

    return sample



 @property
 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    lowercase__    :						Dict       =			4
    lowercase__    :						List[Any]       =			3
    lowercase__    :						Dict       =			8
    lowercase__    :						List[str]       =			8

    lowercase__    :						List[Any]       =			batch_size * num_channels * height * width
    lowercase__    :						int       =			torch.arange(UpperCAmelCase__						)
    lowercase__    :						List[Any]       =			sample.reshape(UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    lowercase__    :						int       =			sample / num_elems
    lowercase__    :						List[str]       =			sample.permute(3			,		0			,		1			,		2						)

    return sample



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    def model(UpperCAmelCase__ : Optional[Any]			,		UpperCAmelCase__ : Optional[int]			,		*UpperCAmelCase__ : str						):
       return sample * t / (t + 1)

    return model



 def     UpperCAmelCase     (						self : List[Any]			,		**UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						str       =			{
        """num_train_timesteps""": 2_000,
        """snr""": 0.15,
        """sigma_min""": 0.01,
        """sigma_max""": 1_348,
        """sampling_eps""": 1e-5,
    }

    config.update(**UpperCAmelCase__						)
    return config



 def     UpperCAmelCase     (						self : Dict			,		UpperCAmelCase__ : str=0			,		**UpperCAmelCase__ : Dict						):

    """simple docstring"""






    lowercase__    :						int       =			dict(self.forward_default_kwargs						)

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						List[Any]       =			self.dummy_sample
       lowercase__    :						List[Any]       =			0.1 * sample

       lowercase__    :						Optional[int]       =			self.get_scheduler_config(**UpperCAmelCase__						)
       lowercase__    :						Any       =			scheduler_class(**UpperCAmelCase__						)

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(UpperCAmelCase__						)
          lowercase__    :						str       =			scheduler_class.from_pretrained(UpperCAmelCase__						)

       lowercase__    :						Any       =			scheduler.step_pred(
           UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample
       lowercase__    :						Dict       =			new_scheduler.step_pred(
           UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample

       assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       lowercase__    :						List[Any]       =			scheduler.step_correct(UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample
       lowercase__    :						List[str]       =			new_scheduler.step_correct(
           UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample

       assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler correction are not identical"



 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : str=0			,		**UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						Any       =			dict(self.forward_default_kwargs						)
    kwargs.update(UpperCAmelCase__						)

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						List[str]       =			self.dummy_sample
       lowercase__    :						List[str]       =			0.1 * sample

       lowercase__    :						Union[str, Any]       =			self.get_scheduler_config()
       lowercase__    :						List[Any]       =			scheduler_class(**UpperCAmelCase__						)

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(UpperCAmelCase__						)
          lowercase__    :						Tuple       =			scheduler_class.from_pretrained(UpperCAmelCase__						)

       lowercase__    :						Dict       =			scheduler.step_pred(
           UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample
       lowercase__    :						List[str]       =			new_scheduler.step_pred(
           UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample

       assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

       lowercase__    :						Optional[Any]       =			scheduler.step_correct(UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample
       lowercase__    :						Optional[int]       =			new_scheduler.step_correct(
           UpperCAmelCase__			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample

       assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler correction are not identical"



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    for timesteps in [10, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]			,		[1, 100, 1_000]						):
       self.check_over_configs(sigma_min=UpperCAmelCase__			,		sigma_max=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    for t in [0.1, 0.5, 0.75]:
       self.check_over_forward(time_step=UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			dict(self.forward_default_kwargs						)

    lowercase__    :						int       =			self.scheduler_classes[0]
    lowercase__    :						int       =			self.get_scheduler_config()
    lowercase__    :						Union[str, Any]       =			scheduler_class(**UpperCAmelCase__						)

    lowercase__    :						List[str]       =			3

    lowercase__    :						List[str]       =			self.dummy_model()
    lowercase__    :						int       =			self.dummy_sample_deter

    scheduler.set_sigmas(UpperCAmelCase__						)
    scheduler.set_timesteps(UpperCAmelCase__						)
    lowercase__    :						Optional[Any]       =			torch.manual_seed(0						)

    for i, t in enumerate(scheduler.timesteps						):
       lowercase__    :						str       =			scheduler.sigmas[i]

       for _ in range(scheduler.config.correct_steps						):
          with torch.no_grad():
             lowercase__    :						Optional[int]       =			model(UpperCAmelCase__			,		UpperCAmelCase__						)
          lowercase__    :						Dict       =			scheduler.step_correct(UpperCAmelCase__			,		UpperCAmelCase__			,		generator=UpperCAmelCase__			,		**UpperCAmelCase__						).prev_sample

       with torch.no_grad():
          lowercase__    :						List[str]       =			model(UpperCAmelCase__			,		UpperCAmelCase__						)

       lowercase__    :						Any       =			scheduler.step_pred(UpperCAmelCase__			,		UpperCAmelCase__			,		UpperCAmelCase__			,		generator=UpperCAmelCase__			,		**UpperCAmelCase__						)
       lowercase__  ,       lowercase__    :						List[str]       =			output.prev_sample, output.prev_sample_mean

    lowercase__    :						Union[str, Any]       =			torch.sum(torch.abs(UpperCAmelCase__						)						)
    lowercase__    :						str       =			torch.mean(torch.abs(UpperCAmelCase__						)						)

    assert np.isclose(result_sum.item()			,		14_372_758_528.0						)
    assert np.isclose(result_mean.item()			,		18_714_530.0						)







 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			dict(self.forward_default_kwargs						)

    lowercase__    :						int       =			kwargs.pop("""num_inference_steps"""			,		UpperCAmelCase__						)

    for scheduler_class in self.scheduler_classes:
       lowercase__    :						List[str]       =			self.get_scheduler_config()
       lowercase__    :						Dict       =			scheduler_class(**UpperCAmelCase__						)

       lowercase__    :						List[str]       =			self.dummy_sample
       lowercase__    :						int       =			0.1 * sample

       if num_inference_steps is not None and hasattr(UpperCAmelCase__			,		"""set_timesteps"""						):
          scheduler.set_timesteps(UpperCAmelCase__						)
       elif num_inference_steps is not None and not hasattr(UpperCAmelCase__			,		"""set_timesteps"""						):
          lowercase__    :						Any       =			num_inference_steps

       lowercase__    :						List[Any]       =			scheduler.step_pred(UpperCAmelCase__			,		0			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample
       lowercase__    :						Optional[int]       =			scheduler.step_pred(UpperCAmelCase__			,		1			,		UpperCAmelCase__			,		generator=torch.manual_seed(0						)			,		**UpperCAmelCase__						).prev_sample

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










from datetime import date

import requests

a 			=							'https://api.carbonintensity.org.uk/intensity'
def    a__    (	):
   lowercase__    :						Union[str, Any]       =			requests.get(A       ).json()["""data"""][0]
   return last_half_hour["intensity"]["actual"]
def    a__    (	A		,					A       ):
   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() = }')


