





"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class  __UpperCamelCase       (		__lowercase		):
       lowerCamelCase     :      Optional[Any]         =(DDPMScheduler,)






       def 					__a     (     self   ,      **lowerCAmelCase__   )			->				Optional[int]:
        a   :	Tuple			=       {
            "num_train_timesteps": 1000,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**lowerCAmelCase__   )
        return config






       def 					__a     (     self   )			->				List[str]:
        for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Optional[int]:
        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]   ,      [0.002, 0.02, 0.2, 2]   ):
         self.check_over_configs(beta_start=lowerCAmelCase__   ,      beta_end=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Tuple:
        for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowerCAmelCase__   )






       def 					__a     (     self   )			->				List[str]:
        for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=lowerCAmelCase__   )






       def 					__a     (     self   )			->				str:
        for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Tuple:
        self.check_over_configs(thresholding=lowerCAmelCase__   )
        for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(
              thresholding=lowerCAmelCase__   ,      prediction_type=lowerCAmelCase__   ,      sample_max_value=lowerCAmelCase__   ,      )






       def 					__a     (     self   )			->				int:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Union[str, Any]:
        for t in [0, 500, 999]:
         self.check_over_forward(time_step=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Dict:
        a   :	Union[str, Any]			=       self.scheduler_classes[0]
        a   :	Any			=       self.get_scheduler_config()
        a   :	str			=       scheduler_class(**lowerCAmelCase__   )

        assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.00_979   )   ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5






       def 					__a     (     self   )			->				List[Any]:
        a   :	Union[str, Any]			=       self.scheduler_classes[0]
        a   :	Union[str, Any]			=       self.get_scheduler_config()
        a   :	str			=       scheduler_class(**lowerCAmelCase__   )

        a   :	Optional[int]			=       len(lowerCAmelCase__   )

        a   :	Optional[Any]			=       self.dummy_model()
        a   :	List[str]			=       self.dummy_sample_deter
        a   :	Any			=       torch.manual_seed(0   )

        for t in reversed(range(lowerCAmelCase__   )   ):
         # 1. predict noise residual
         a   :	Tuple			=       model(lowerCAmelCase__   ,      lowerCAmelCase__   )

         # 2. predict previous mean of sample x_t-1
         a   :	int			=       scheduler.step(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      generator=lowerCAmelCase__   ).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         a   :	int			=       pred_prev_sample

        a   :	Optional[Any]			=       torch.sum(torch.abs(lowerCAmelCase__   )   )
        a   :	int			=       torch.mean(torch.abs(lowerCAmelCase__   )   )

        assert abs(result_sum.item() - 258.9_606   ) < 1E-2
        assert abs(result_mean.item() - 0.3_372   ) < 1E-3






       def 					__a     (     self   )			->				Any:
        a   :	Tuple			=       self.scheduler_classes[0]
        a   :	str			=       self.get_scheduler_config(prediction_type="v_prediction"   )
        a   :	Optional[int]			=       scheduler_class(**lowerCAmelCase__   )

        a   :	List[str]			=       len(lowerCAmelCase__   )

        a   :	Dict			=       self.dummy_model()
        a   :	Union[str, Any]			=       self.dummy_sample_deter
        a   :	List[Any]			=       torch.manual_seed(0   )

        for t in reversed(range(lowerCAmelCase__   )   ):
         # 1. predict noise residual
         a   :	Tuple			=       model(lowerCAmelCase__   ,      lowerCAmelCase__   )

         # 2. predict previous mean of sample x_t-1
         a   :	List[Any]			=       scheduler.step(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      generator=lowerCAmelCase__   ).prev_sample

         # if t > 0:
         #     noise = self.dummy_sample_deter
         #     variance = scheduler.get_variance(t) ** (0.5) * noise
         #
         # sample = pred_prev_sample + variance
         a   :	int			=       pred_prev_sample

        a   :	List[str]			=       torch.sum(torch.abs(lowerCAmelCase__   )   )
        a   :	Optional[int]			=       torch.mean(torch.abs(lowerCAmelCase__   )   )

        assert abs(result_sum.item() - 202.0_296   ) < 1E-2
        assert abs(result_mean.item() - 0.2_631   ) < 1E-3






       def 					__a     (     self   )			->				Any:
        a   :	List[Any]			=       self.scheduler_classes[0]
        a   :	int			=       self.get_scheduler_config()
        a   :	Union[str, Any]			=       scheduler_class(**lowerCAmelCase__   )

        a   :	Any			=       [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=lowerCAmelCase__   )

        a   :	Dict			=       scheduler.timesteps

        for i, timestep in enumerate(lowerCAmelCase__   ):
         if i == len(lowerCAmelCase__   ) - 1:
          a   :	Tuple			=       -1
         else:
          a   :	Any			=       timesteps[i + 1]

         a   :	Optional[Any]			=       scheduler.previous_timestep(lowerCAmelCase__   )
         a   :	Union[str, Any]			=       prev_t.item()

         self.assertEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )






       def 					__a     (     self   )			->				Any:
        a   :	Union[str, Any]			=       self.scheduler_classes[0]
        a   :	Any			=       self.get_scheduler_config()
        a   :	Optional[Any]			=       scheduler_class(**lowerCAmelCase__   )

        a   :	Dict			=       [100, 87, 50, 51, 0]

        with self.assertRaises(lowerCAmelCase__   ,      msg="`custom_timesteps` must be in descending order."   ):
         scheduler.set_timesteps(timesteps=lowerCAmelCase__   )






       def 					__a     (     self   )			->				int:
        a   :	Tuple			=       self.scheduler_classes[0]
        a   :	int			=       self.get_scheduler_config()
        a   :	List[Any]			=       scheduler_class(**lowerCAmelCase__   )

        a   :	Union[str, Any]			=       [100, 87, 50, 1, 0]
        a   :	Optional[Any]			=       len(lowerCAmelCase__   )

        with self.assertRaises(lowerCAmelCase__   ,      msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."   ):
         scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__   ,      timesteps=lowerCAmelCase__   )






       def 					__a     (     self   )			->				Optional[Any]:
        a   :	str			=       self.scheduler_classes[0]
        a   :	Optional[Any]			=       self.get_scheduler_config()
        a   :	List[str]			=       scheduler_class(**lowerCAmelCase__   )

        a   :	Optional[Any]			=       [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            lowerCAmelCase__   ,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"   ,      ):
         scheduler.set_timesteps(timesteps=lowerCAmelCase__   )









"""simple docstring"""




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

import numpy as np
import torch

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


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


