

"""simple docstring"""
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


_A						=     logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			lowerCamelCase  (					lowerCAmelCase__      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     42
  SCREAMING_SNAKE_CASE							=     None




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=0.999 ,  lowerCAmelCase="cosine" ,  )  ->   Union[str, Any]:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(lowerCAmelCase						):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(lowerCAmelCase						):
                  return math.exp(t * -12.0						)

      else:
            raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)

      UpperCAmelCase__ :       Any						=						[]
      for i in range(lowerCAmelCase						):
            UpperCAmelCase__ :       Any						=						i / num_diffusion_timesteps
            UpperCAmelCase__ :       Optional[Any]						=						(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(lowerCAmelCase						) / alpha_bar_fn(lowerCAmelCase						) ,  lowerCAmelCase						)						)
      return torch.tensor(lowerCAmelCase ,  dtype=torch.floataa						)





class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE							=     1



  @register_to_config
  def __init__(self       ,					_lowerCamelCase = 1000       ,					_lowerCamelCase = 0.0_001       ,					_lowerCamelCase = 0.02       ,					_lowerCamelCase = "linear"       ,					_lowerCamelCase = None       ,					_lowerCamelCase = "epsilon"       ,					_lowerCamelCase = "linspace"       ,					_lowerCamelCase = 0       ,					):


        """simple docstring"""

        if trained_betas is not None:
              UpperCAmelCase__ :       Union[str, Any]						=						torch.tensor(_lowerCamelCase       ,					dtype=torch.floataa  )
        elif beta_schedule == "linear":
              UpperCAmelCase__ :       Dict						=						torch.linspace(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase__ :       Optional[int]						=						(
                  torch.linspace(beta_start**0.5       ,					beta_end**0.5       ,					_lowerCamelCase       ,					dtype=torch.floataa  ) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCAmelCase__ :       Any						=						betas_for_alpha_bar(_lowerCamelCase  )
        else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""  )

        UpperCAmelCase__ :       List[str]						=						1.0 - self.betas
        UpperCAmelCase__ :       Any						=						torch.cumprod(self.alphas       ,					dim=0  )

        UpperCAmelCase__ :       Tuple						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        UpperCAmelCase__ :       Optional[int]						=						np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
        UpperCAmelCase__ :       Tuple						=						torch.from_numpy(_lowerCamelCase  )

        # setable values
        UpperCAmelCase__ :       int						=						None
        UpperCAmelCase__ :       Any						=						np.linspace(0       ,					num_train_timesteps - 1       ,					_lowerCamelCase       ,					dtype=_lowerCamelCase  )[::-1].copy()
        UpperCAmelCase__ :       int						=						torch.from_numpy(_lowerCamelCase  )
        UpperCAmelCase__ :       Optional[Any]						=						False



  @property
  def    _a			(self  ):


        """simple docstring"""

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

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



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


        """simple docstring"""

        if isinstance(_lowerCamelCase       ,					torch.Tensor  ):
              UpperCAmelCase__ :       Optional[int]						=						timestep.to(self.timesteps.device  )
        UpperCAmelCase__ :       List[str]						=						(self.timesteps == timestep).nonzero().item()
        UpperCAmelCase__ :       int						=						self.sigmas[step_index]
        UpperCAmelCase__ :       List[Any]						=						sample / ((sigma**2 + 1) ** 0.5)
        UpperCAmelCase__ :       int						=						True
        return sample



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):


        """simple docstring"""

        UpperCAmelCase__ :       Dict						=						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":
              UpperCAmelCase__ :       Union[str, Any]						=						np.linspace(0       ,					self.config.num_train_timesteps - 1       ,					_lowerCamelCase       ,					dtype=_lowerCamelCase  )[
                  ::-1
              ].copy()
        elif self.config.timestep_spacing == "leading":
              UpperCAmelCase__ :       Any						=						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
              UpperCAmelCase__ :       Tuple						=						(np.arange(0       ,					_lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase  )
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              UpperCAmelCase__ :       int						=						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
              UpperCAmelCase__ :       int						=						(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'."""  )

        UpperCAmelCase__ :       Any						=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        UpperCAmelCase__ :       Dict						=						np.interp(_lowerCamelCase       ,					np.arange(0       ,					len(_lowerCamelCase  )  )       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       Union[str, Any]						=						np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
        UpperCAmelCase__ :       Tuple						=						torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
        if str(_lowerCamelCase  ).startswith("""mps"""  ):
              # mps does not support float64
              UpperCAmelCase__ :       List[Any]						=						torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase       ,					dtype=torch.floataa  )
        else:
              UpperCAmelCase__ :       List[Any]						=						torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = True       ,					):


        """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  ):
              UpperCAmelCase__ :       Tuple						=						timestep.to(self.timesteps.device  )

        UpperCAmelCase__ :       Dict						=						(self.timesteps == timestep).nonzero().item()
        UpperCAmelCase__ :       Optional[Any]						=						self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              UpperCAmelCase__ :       Tuple						=						sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              UpperCAmelCase__ :       Tuple						=						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`"""  )

        UpperCAmelCase__ :       Dict						=						self.sigmas[step_index]
        UpperCAmelCase__ :       Optional[int]						=						self.sigmas[step_index + 1]
        UpperCAmelCase__ :       List[Any]						=						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        UpperCAmelCase__ :       List[Any]						=						(sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        UpperCAmelCase__ :       Union[str, Any]						=						(sample - pred_original_sample) / sigma

        UpperCAmelCase__ :       Optional[Any]						=						sigma_down - sigma

        UpperCAmelCase__ :       Tuple						=						sample + derivative * dt

        UpperCAmelCase__ :       Dict						=						model_output.device
        UpperCAmelCase__ :       Optional[Any]						=						randn_tensor(model_output.shape       ,					dtype=model_output.dtype       ,					device=_lowerCamelCase       ,					generator=_lowerCamelCase  )

        UpperCAmelCase__ :       Optional[Any]						=						prev_sample + noise * sigma_up

        if not return_dict:
              return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=_lowerCamelCase       ,					pred_original_sample=_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						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
              UpperCAmelCase__ :       Any						=						self.timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
              UpperCAmelCase__ :       str						=						timesteps.to(original_samples.device       ,					dtype=torch.floataa  )
        else:
              UpperCAmelCase__ :       List[str]						=						self.timesteps.to(original_samples.device  )
              UpperCAmelCase__ :       Dict						=						timesteps.to(original_samples.device  )

        UpperCAmelCase__ :       Optional[int]						=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        UpperCAmelCase__ :       Optional[int]						=						sigmas[step_indices].flatten()
        while len(sigma.shape  ) < len(original_samples.shape  ):
              UpperCAmelCase__ :       List[Any]						=						sigma.unsqueeze(-1  )

        UpperCAmelCase__ :       Union[str, Any]						=						original_samples + noise * sigma
        return noisy_samples




  def __len__(self  ):


        """simple docstring"""

        return self.config.num_train_timesteps




"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
  SCREAMING_SNAKE_CASE							=     ()



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						4
        UpperCAmelCase__ :       Tuple						=						3
        UpperCAmelCase__ :       Optional[Any]						=						8
        UpperCAmelCase__ :       Dict						=						8

        UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )

        return sample



  @property
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						4
        UpperCAmelCase__ :       Optional[Any]						=						3
        UpperCAmelCase__ :       List[str]						=						8
        UpperCAmelCase__ :       Dict						=						8

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

        return sample



  def    _a			(self  ):


        """simple docstring"""

        def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
              return sample * t / (t + 1)

        return model



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						{
            """num_train_timesteps""": 2000,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1348,
            """sampling_eps""": 1e-5,
        }

        config.update(**_lowerCamelCase  )
        return config



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__ :       int						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

              UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
              UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

              UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

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

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

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



  def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
        kwargs.update(_lowerCamelCase  )

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

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCamelCase  )
                    UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )

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

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

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

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



  def    _a			(self  ):


        """simple docstring"""

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



  def    _a			(self  ):


        """simple docstring"""

        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
              self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )



  def    _a			(self  ):


        """simple docstring"""

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



  def    _a			(self  ):


        """simple docstring"""

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

        UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
        UpperCAmelCase__ :       int						=						self.get_scheduler_config()
        UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )

        UpperCAmelCase__ :       List[str]						=						3

        UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
        UpperCAmelCase__ :       int						=						self.dummy_sample_deter

        scheduler.set_sigmas(_lowerCamelCase  )
        scheduler.set_timesteps(_lowerCamelCase  )
        UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )

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

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

              with torch.no_grad():
                    UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )

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

        UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
        UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )

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




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )

        UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )

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

              UpperCAmelCase__ :       str						=						self.dummy_sample
              UpperCAmelCase__ :       Tuple						=						0.1 * sample

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

              UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
              UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample

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

