

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						Optional[int]=0.999	,				_SCREAMING_SNAKE_CASE   :						List[Any]="cosine"	,				):





    """simple docstring"""


    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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





class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					[e.name for e in KarrasDiffusionSchedulers]
  __lowerCAmelCase       :		Union[str, Any]  	=					2

  @register_to_config
  def __init__(   self  ,						_A = 1000  ,						_A = 0.0_0_0_8_5  ,						_A = 0.0_1_2  ,						_A = "linear"  ,						_A = None  ,						_A = "epsilon"  ,						_A = "linspace"  ,						_A = 0  ,						):
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE_													=  torch.tensor(_A  ,						dtype=torch.floataa)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE_													=  torch.linspace(_A  ,						_A  ,						_A  ,						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  ,						_A  ,						dtype=torch.floataa) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE_													=  betas_for_alpha_bar(_A)
      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)

      #  set all values
      self.set_timesteps(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A=None):
      if schedule_timesteps is None:
          SCREAMING_SNAKE_CASE_													=  self.timesteps

      SCREAMING_SNAKE_CASE_													=  (schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter) == 0:
          SCREAMING_SNAKE_CASE_													=  1 if len(_A) > 1 else 0
      else:
          SCREAMING_SNAKE_CASE_													=  timestep.cpu().item() if torch.is_tensor(_A) else timestep
          SCREAMING_SNAKE_CASE_													=  self._index_counter[timestep_int]

      return indices[pos].item()

  @property
  def   lowerCAmelCase__       (   self):
      # 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  ,						_A  ,						_A  ,						):
      SCREAMING_SNAKE_CASE_													=  self.index_for_timestep(_A)

      if self.state_in_first_order:
          SCREAMING_SNAKE_CASE_													=  self.sigmas[step_index]
      else:
          SCREAMING_SNAKE_CASE_													=  self.sigmas_interpol[step_index - 1]

      SCREAMING_SNAKE_CASE_													=  sample / ((sigma**2 + 1) ** 0.5)
      return sample

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  num_inference_steps

      SCREAMING_SNAKE_CASE_													=  num_train_timesteps or self.config.num_train_timesteps

      # "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  ,						num_train_timesteps - 1  ,						_A  ,						dtype=_A)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE_													=  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  ,						_A) * step_ratio).round()[::-1].copy().astype(_A)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE_													=  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(_A  ,						0  ,						-step_ratio)).round().copy().astype(_A)
          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_													=  torch.from_numpy(np.log(_A)).to(_A)

      SCREAMING_SNAKE_CASE_													=  np.interp(_A  ,						np.arange(0  ,						len(_A))  ,						_A)
      SCREAMING_SNAKE_CASE_													=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
      SCREAMING_SNAKE_CASE_													=  torch.from_numpy(_A).to(device=_A)

      # compute up and down sigmas
      SCREAMING_SNAKE_CASE_													=  sigmas.roll(-1)
      SCREAMING_SNAKE_CASE_													=  0.0
      SCREAMING_SNAKE_CASE_													=  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      SCREAMING_SNAKE_CASE_													=  (sigmas_next**2 - sigmas_up**2) ** 0.5
      SCREAMING_SNAKE_CASE_													=  0.0

      # compute interpolated sigmas
      SCREAMING_SNAKE_CASE_													=  sigmas.log().lerp(sigmas_down.log()  ,						0.5).exp()
      SCREAMING_SNAKE_CASE_													=  0.0

      # set sigmas
      SCREAMING_SNAKE_CASE_													=  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
      SCREAMING_SNAKE_CASE_													=  torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
      SCREAMING_SNAKE_CASE_													=  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
      SCREAMING_SNAKE_CASE_													=  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

      if str(_A).startswith('mps'):
          # mps does not support float64
          SCREAMING_SNAKE_CASE_													=  torch.from_numpy(_A).to(_A  ,						dtype=torch.floataa)
      else:
          SCREAMING_SNAKE_CASE_													=  torch.from_numpy(_A).to(_A)

      SCREAMING_SNAKE_CASE_													=  self.sigma_to_t(_A).to(_A  ,						dtype=timesteps.dtype)
      SCREAMING_SNAKE_CASE_													=  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,						dim=-1).flatten()

      SCREAMING_SNAKE_CASE_													=  torch.cat([timesteps[:1], interleaved_timesteps])

      SCREAMING_SNAKE_CASE_													=  None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      SCREAMING_SNAKE_CASE_													=  defaultdict(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      # get log sigma
      SCREAMING_SNAKE_CASE_													=  sigma.log()

      # get distribution
      SCREAMING_SNAKE_CASE_													=  log_sigma - self.log_sigmas[:, None]

      # get sigmas range
      SCREAMING_SNAKE_CASE_													=  dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
      SCREAMING_SNAKE_CASE_													=  low_idx + 1

      SCREAMING_SNAKE_CASE_													=  self.log_sigmas[low_idx]
      SCREAMING_SNAKE_CASE_													=  self.log_sigmas[high_idx]

      # interpolate sigmas
      SCREAMING_SNAKE_CASE_													=  (low - log_sigma) / (low - high)
      SCREAMING_SNAKE_CASE_													=  w.clamp(0  ,						1)

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

  @property
  def   lowerCAmelCase__       (   self):
      return self.sample is None

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A  ,						_A = None  ,						_A = True  ,						):
      SCREAMING_SNAKE_CASE_													=  self.index_for_timestep(_A)

      # advance index counter by 1
      SCREAMING_SNAKE_CASE_													=  timestep.cpu().item() if torch.is_tensor(_A) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
          SCREAMING_SNAKE_CASE_													=  self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_interpol[step_index]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_up[step_index]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_down[step_index - 1]
      else:
          # 2nd order / KPDM2's method
          SCREAMING_SNAKE_CASE_													=  self.sigmas[step_index - 1]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_interpol[step_index - 1]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_up[step_index - 1]
          SCREAMING_SNAKE_CASE_													=  self.sigmas_down[step_index - 1]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      SCREAMING_SNAKE_CASE_													=  0
      SCREAMING_SNAKE_CASE_													=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      SCREAMING_SNAKE_CASE_													=  model_output.device
      SCREAMING_SNAKE_CASE_													=  randn_tensor(model_output.shape  ,						dtype=model_output.dtype  ,						device=_A  ,						generator=_A)

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          SCREAMING_SNAKE_CASE_													=  sigma_hat if self.state_in_first_order else sigma_interpol
          SCREAMING_SNAKE_CASE_													=  sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
          SCREAMING_SNAKE_CASE_													=  sigma_hat if self.state_in_first_order else sigma_interpol
          SCREAMING_SNAKE_CASE_													=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
              sample / (sigma_input**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`""")

      if self.state_in_first_order:
          # 2. Convert to an ODE derivative for 1st order
          SCREAMING_SNAKE_CASE_													=  (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          SCREAMING_SNAKE_CASE_													=  sigma_interpol - sigma_hat

          # store for 2nd order step
          SCREAMING_SNAKE_CASE_													=  sample
          SCREAMING_SNAKE_CASE_													=  dt
          SCREAMING_SNAKE_CASE_													=  sample + derivative * dt
      else:
          # DPM-Solver-2
          # 2. Convert to an ODE derivative for 2nd order
          SCREAMING_SNAKE_CASE_													=  (sample - pred_original_sample) / sigma_interpol
          # 3. delta timestep
          SCREAMING_SNAKE_CASE_													=  sigma_down - sigma_hat

          SCREAMING_SNAKE_CASE_													=  self.sample
          SCREAMING_SNAKE_CASE_													=  None

          SCREAMING_SNAKE_CASE_													=  sample + derivative * dt
          SCREAMING_SNAKE_CASE_													=  prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=_A)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A  ,						):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      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(_A):
          # 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_													=  [self.index_for_timestep(_A  ,						_A) 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):
      return self.config.num_train_timesteps





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

  def   lowerCAmelCase__       (   self):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
          self.check_over_configs(prediction_type=_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2


