

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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      torch.FloatTensor
      lowerCamelCase_	:      Optional[torch.FloatTensor]          =					None



def    UpperCamelCase(					lowercase_			,					lowercase_=0.9_99			,					lowercase_="cosine"			,					)	->				Dict:





  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase_							):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

  elif alpha_transform_type == "exp":

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

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

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







class  __lowerCamelCase				(	__snake_case  ,							__snake_case	):
      lowerCamelCase_	:      Union[str, Any]          =					[e.name for e in KarrasDiffusionSchedulers]
      lowerCamelCase_	:      List[Any]          =					1







      @register_to_config
      def __init__(     self							,  lowerCamelCase = 1000							,  lowerCamelCase = 0.0001							,  lowerCamelCase = 0.02							,  lowerCamelCase = "linear"							,  lowerCamelCase = None							,  lowerCamelCase = "epsilon"							,  lowerCamelCase = "linear"							,  lowerCamelCase = False							,  lowerCamelCase = "linspace"							,  lowerCamelCase = 0							,  )						->       Optional[int]:
        if trained_betas is not None:
          snake_case_       						=      torch.tensor(lowerCamelCase							,  dtype=torch.floataa   )
        elif beta_schedule == "linear":
          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.
          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
          snake_case_       						=      betas_for_alpha_bar(lowerCamelCase   )
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

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

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

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







      @property
      def 	lowerCAmelCase_    (     self   )						->       Dict:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       torch.FloatTensor:
        if isinstance(lowerCamelCase							,  torch.Tensor   ):
          snake_case_       						=      timestep.to(self.timesteps.device   )
        snake_case_       						=      (self.timesteps == timestep).nonzero().item()
        snake_case_       						=      self.sigmas[step_index]

        snake_case_       						=      sample / ((sigma**2 + 1) ** 0.5)

        snake_case_       						=      True
        return sample







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = None   )						->       Any:
        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":
          snake_case_       						=      np.linspace(0							,  self.config.num_train_timesteps - 1							,  lowerCamelCase							,  dtype=lowerCamelCase   )[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          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
          snake_case_       						=      (np.arange(0							,  lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase   )
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          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
          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\'.'''   )

        snake_case_       						=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
        snake_case_       						=      np.log(lowerCamelCase   )

        if self.config.interpolation_type == "linear":
          snake_case_       						=      np.interp(lowerCamelCase							,  np.arange(0							,  len(lowerCamelCase   )   )							,  lowerCamelCase   )
        elif self.config.interpolation_type == "log_linear":
          snake_case_       						=      torch.linspace(np.log(sigmas[-1]   )							,  np.log(sigmas[0]   )							,  num_inference_steps + 1   ).exp()
        else:
          raise ValueError(
              f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
              """ 'linear' or 'log_linear'"""   )

        if self.use_karras_sigmas:
          snake_case_       						=      self._convert_to_karras(in_sigmas=lowerCamelCase							,  num_inference_steps=self.num_inference_steps   )
          snake_case_       						=      np.array([self._sigma_to_t(lowerCamelCase							,  lowerCamelCase   ) for sigma in sigmas]   )

        snake_case_       						=      np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
        snake_case_       						=      torch.from_numpy(lowerCamelCase   ).to(device=lowerCamelCase   )
        if str(lowerCamelCase   ).startswith("""mps"""   ):
          # mps does not support float64
          snake_case_       						=      torch.from_numpy(lowerCamelCase   ).to(lowerCamelCase							,  dtype=torch.floataa   )
        else:
          snake_case_       						=      torch.from_numpy(lowerCamelCase   ).to(device=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        # get log sigma
        snake_case_       						=      np.log(lowerCamelCase   )

        # get distribution
        snake_case_       						=      log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        snake_case_       						=      np.cumsum((dists >= 0)							,  axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
        snake_case_       						=      low_idx + 1

        snake_case_       						=      log_sigmas[low_idx]
        snake_case_       						=      log_sigmas[high_idx]

        # interpolate sigmas
        snake_case_       						=      (low - log_sigma) / (low - high)
        snake_case_       						=      np.clip(lowerCamelCase							,  0							,  1   )

        # transform interpolation to time range
        snake_case_       						=      (1 - w) * low_idx + w * high_idx
        snake_case_       						=      t.reshape(sigma.shape   )
        return t







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       torch.FloatTensor:
        snake_case_       						=      in_sigmas[-1].item()
        snake_case_       						=      in_sigmas[0].item()

        snake_case_       						=      7.0  # 7.0 is the value used in the paper
        snake_case_       						=      np.linspace(0							,  1							,  lowerCamelCase   )
        snake_case_       						=      sigma_min ** (1 / rho)
        snake_case_       						=      sigma_max ** (1 / rho)
        snake_case_       						=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase = 0.0							,  lowerCamelCase = 0.0							,  lowerCamelCase = float("""inf"""   )							,  lowerCamelCase = 1.0							,  lowerCamelCase = None							,  lowerCamelCase = True							,  )						->       Union[EulerDiscreteSchedulerOutput, Tuple]:

        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   ):
          snake_case_       						=      timestep.to(self.timesteps.device   )

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

        snake_case_       						=      min(s_churn / (len(self.sigmas   ) - 1)							,  2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

        snake_case_       						=      randn_tensor(
            model_output.shape							,  dtype=model_output.dtype							,  device=model_output.device							,  generator=lowerCamelCase   )

        snake_case_       						=      noise * s_noise
        snake_case_       						=      sigma * (gamma + 1)

        if gamma > 0:
          snake_case_       						=      sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
        # backwards compatibility
        if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
          snake_case_       						=      model_output
        elif self.config.prediction_type == "epsilon":
          snake_case_       						=      sample - sigma_hat * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_       						=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

        # 2. Convert to an ODE derivative
        snake_case_       						=      (sample - pred_original_sample) / sigma_hat

        snake_case_       						=      self.sigmas[step_index + 1] - sigma_hat

        snake_case_       						=      sample + derivative * dt

        if not return_dict:
          return (prev_sample,)

        return EulerDiscreteSchedulerOutput(prev_sample=lowerCamelCase							,  pred_original_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  )						->       torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        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
          snake_case_       						=      self.timesteps.to(original_samples.device							,  dtype=torch.floataa   )
          snake_case_       						=      timesteps.to(original_samples.device							,  dtype=torch.floataa   )
        else:
          snake_case_       						=      self.timesteps.to(original_samples.device   )
          snake_case_       						=      timesteps.to(original_samples.device   )

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

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

        snake_case_       						=      original_samples + noise * sigma
        return noisy_samples







      def __len__(     self   )						->       int:
        return self.config.num_train_timesteps


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3