






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__UpperCAmelCase      				=					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					a_(						lowercase__					):



     """simple docstring"""



     __snake_case						:							torch.FloatTensor
     __snake_case						:							Optional[torch.FloatTensor]       			=None





def 		A_			(		lowercase_					,       lowercase_=0.9_9_9					,       lowercase_="cosine"					,       )							->Any:







 """simple docstring"""

 if alpha_transform_type == "cosine":

  def alpha_bar_fn(lowercase_	):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(lowercase_	):
   return math.exp(t * -1_2.0	)

 else:
  raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

 SCREAMING_SNAKE_CASE							  =						[]
 for i in range(_UpperCamelCase	):
  SCREAMING_SNAKE_CASE							  =						i / num_diffusion_timesteps
  SCREAMING_SNAKE_CASE							  =						(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_UpperCamelCase	) / alpha_bar_fn(_UpperCamelCase	)					,       _UpperCamelCase	)	)
 return torch.tensor(_UpperCamelCase					,       dtype=torch.floataa	)
class 					a_(						lowercase__	,    lowercase__					):



     """simple docstring"""



     __snake_case						:							str       			=[e.name for e in KarrasDiffusionSchedulers]
     __snake_case						:							Tuple       			=1





     @register_to_config
     def __init__(						self			:					List[Any]       ,       lowerCAmelCase__			:					int = 1_0_0_0       ,       lowerCAmelCase__			:					float = 0.00_01       ,       lowerCAmelCase__			:					float = 0.02       ,       lowerCAmelCase__			:					str = "linear"       ,       lowerCAmelCase__			:					Optional[Union[np.ndarray, List[float]]] = None       ,       lowerCAmelCase__			:					str = "epsilon"       ,       lowerCAmelCase__			:					str = "linspace"       ,       lowerCAmelCase__			:					int = 0       ,       )    ->					Optional[Any]:

      """simple docstring"""






      if trained_betas is not None:
       SCREAMING_SNAKE_CASE							  =						torch.tensor(lowerCAmelCase__       ,       dtype=torch.floataa)
      elif beta_schedule == "linear":
       SCREAMING_SNAKE_CASE							  =						torch.linspace(lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__       ,       dtype=torch.floataa)
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       SCREAMING_SNAKE_CASE							  =						(
           torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       lowerCAmelCase__       ,       dtype=torch.floataa) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       SCREAMING_SNAKE_CASE							  =						betas_for_alpha_bar(lowerCAmelCase__)
      else:
       raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

      SCREAMING_SNAKE_CASE							  =						1.0 - self.betas
      SCREAMING_SNAKE_CASE							  =						torch.cumprod(self.alphas       ,       dim=0)

      SCREAMING_SNAKE_CASE							  =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
      SCREAMING_SNAKE_CASE							  =						np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
      SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowerCAmelCase__)

      # setable values
      SCREAMING_SNAKE_CASE							  =						None
      SCREAMING_SNAKE_CASE							  =						np.linspace(0       ,       num_train_timesteps - 1       ,       lowerCAmelCase__       ,       dtype=lowerCAmelCase__)[::-1].copy()
      SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						False





     @property
     def   __UpperCamelCase		(						self			:					str)    ->					Dict:

      """simple docstring"""






      if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       lowerCAmelCase__			:					Union[float, torch.FloatTensor])    ->					Optional[int]:

      """simple docstring"""






      if isinstance(lowerCAmelCase__       ,       torch.Tensor):
       SCREAMING_SNAKE_CASE							  =						timestep.to(self.timesteps.device)
      SCREAMING_SNAKE_CASE							  =						(self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE							  =						self.sigmas[step_index]
      SCREAMING_SNAKE_CASE							  =						sample / ((sigma**2 + 1) ** 0.5)
      SCREAMING_SNAKE_CASE							  =						True
      return sample





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					Union[str, torch.device] = None)    ->					Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
       SCREAMING_SNAKE_CASE							  =						np.linspace(0       ,       self.config.num_train_timesteps - 1       ,       lowerCAmelCase__       ,       dtype=lowerCAmelCase__)[
           ::-1
       ].copy()
      elif self.config.timestep_spacing == "leading":
       SCREAMING_SNAKE_CASE							  =						self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE							  =						(np.arange(0       ,       lowerCAmelCase__) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__)
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       SCREAMING_SNAKE_CASE							  =						self.config.num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE							  =						(np.arange(self.config.num_train_timesteps       ,       0       ,       -step_ratio)).round().copy().astype(lowerCAmelCase__)
       timesteps -= 1
      else:
       raise ValueError(
           f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

      SCREAMING_SNAKE_CASE							  =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
      SCREAMING_SNAKE_CASE							  =						np.interp(lowerCAmelCase__       ,       np.arange(0       ,       len(lowerCAmelCase__))       ,       lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						np.concatenate([sigmas, [0.0]]).astype(np.floataa)
      SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__)
      if str(lowerCAmelCase__).startswith('mps'):
       # mps does not support float64
       SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowerCAmelCase__).to(lowerCAmelCase__       ,       dtype=torch.floataa)
      else:
       SCREAMING_SNAKE_CASE							  =						torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       lowerCAmelCase__			:					Union[float, torch.FloatTensor]       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       lowerCAmelCase__			:					Optional[torch.Generator] = None       ,       lowerCAmelCase__			:					bool = True       ,       )    ->					List[Any]:

      """simple docstring"""






      if (
          isinstance(lowerCAmelCase__       ,       lowerCAmelCase__)
          or isinstance(lowerCAmelCase__       ,       torch.IntTensor)
          or isinstance(lowerCAmelCase__       ,       torch.LongTensor)
      ):
       raise ValueError(
           (
               'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
               ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
               ' one of the `scheduler.timesteps` as a timestep.'
           )       ,       )

      if not self.is_scale_input_called:
       logger.warning(
           'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
           'See `StableDiffusionPipeline` for a usage example.')

      if isinstance(lowerCAmelCase__       ,       torch.Tensor):
       SCREAMING_SNAKE_CASE							  =						timestep.to(self.timesteps.device)

      SCREAMING_SNAKE_CASE							  =						(self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE							  =						self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
       SCREAMING_SNAKE_CASE							  =						sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       SCREAMING_SNAKE_CASE							  =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
       raise NotImplementedError('prediction_type not implemented yet: sample')
      else:
       raise ValueError(
           f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

      SCREAMING_SNAKE_CASE							  =						self.sigmas[step_index]
      SCREAMING_SNAKE_CASE							  =						self.sigmas[step_index + 1]
      SCREAMING_SNAKE_CASE							  =						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      SCREAMING_SNAKE_CASE							  =						(sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      SCREAMING_SNAKE_CASE							  =						(sample - pred_original_sample) / sigma

      SCREAMING_SNAKE_CASE							  =						sigma_down - sigma

      SCREAMING_SNAKE_CASE							  =						sample + derivative * dt

      SCREAMING_SNAKE_CASE							  =						model_output.device
      SCREAMING_SNAKE_CASE							  =						randn_tensor(model_output.shape       ,       dtype=model_output.dtype       ,       device=lowerCAmelCase__       ,       generator=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						prev_sample + noise * sigma_up

      if not return_dict:
       return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=lowerCAmelCase__       ,       pred_original_sample=lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       lowerCAmelCase__			:					torch.FloatTensor       ,       )    ->					str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype)
      if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__):
       # mps does not support float64
       SCREAMING_SNAKE_CASE							  =						self.timesteps.to(original_samples.device       ,       dtype=torch.floataa)
       SCREAMING_SNAKE_CASE							  =						timesteps.to(original_samples.device       ,       dtype=torch.floataa)
      else:
       SCREAMING_SNAKE_CASE							  =						self.timesteps.to(original_samples.device)
       SCREAMING_SNAKE_CASE							  =						timesteps.to(original_samples.device)

      SCREAMING_SNAKE_CASE							  =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      SCREAMING_SNAKE_CASE							  =						sigmas[step_indices].flatten()
      while len(sigma.shape) < len(original_samples.shape):
       SCREAMING_SNAKE_CASE							  =						sigma.unsqueeze(-1)

      SCREAMING_SNAKE_CASE							  =						original_samples + noise * sigma
      return noisy_samples





     def __len__(						self			:					Optional[int])    ->					Dict:

      """simple docstring"""
      return self.config.num_train_timesteps












from datetime import date

import requests

__UpperCAmelCase      				=							"https://api.carbonintensity.org.uk/intensity"





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						requests.get(lowercase_	).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def 		A_			(		lowercase_					,       lowercase_	)							->list:







 """simple docstring"""

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


if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
            print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'{fetch_last_half_hour() = }')




