
"""simple docstring"""

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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					str=0.999 ,				lowerCAmelCase				:					Dict="cosine" ,				) ->      str:






    """simple docstring"""




    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowerCAmelCase				:					Optional[Any] ):
            return math.exp(t * -12.0 )

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

    UpperCAmelCase									=					[]
    for i in range(lowerCAmelCase ):
        UpperCAmelCase									=					i / num_diffusion_timesteps
        UpperCAmelCase									=					(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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Any							      =	[e.name for e in KarrasDiffusionSchedulers]
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	2




      @register_to_config
      def __init__(							self   ,       lowercase_ = 1_0_0_0   ,       lowercase_ = 0.0_0_0_8_5   ,       lowercase_ = 0.0_1_2   ,       lowercase_ = "linear"   ,       lowercase_ = None   ,       lowercase_ = "epsilon"   ,       lowercase_ = "linspace"   ,       lowercase_ = 0   ,       )						->					Tuple:
          if trained_betas is not None:
              UpperCAmelCase									=					torch.tensor(lowercase_   ,       dtype=torch.floataa							)
          elif beta_schedule == "linear":
              UpperCAmelCase									=					torch.linspace(lowercase_   ,       lowercase_   ,       lowercase_   ,       dtype=torch.floataa							)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase									=					(
                  torch.linspace(beta_start**0.5   ,       beta_end**0.5   ,       lowercase_   ,       dtype=torch.floataa							) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCAmelCase									=					betas_for_alpha_bar(lowercase_							)
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

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

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




      def     a_      (							self   ,       lowercase_   ,       lowercase_=None							)						->					Dict:
          if schedule_timesteps is None:
              UpperCAmelCase									=					self.timesteps

          UpperCAmelCase									=					(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:
              UpperCAmelCase									=					1 if len(lowercase_							) > 1 else 0
          else:
              UpperCAmelCase									=					timestep.cpu().item() if torch.is_tensor(lowercase_							) else timestep
              UpperCAmelCase									=					self._index_counter[timestep_int]

          return indices[pos].item()




      @property
      def     a_      (							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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       )						->					torch.FloatTensor:
          UpperCAmelCase									=					self.index_for_timestep(lowercase_							)

          if self.state_in_first_order:
              UpperCAmelCase									=					self.sigmas[step_index]
          else:
              UpperCAmelCase									=					self.sigmas_interpol[step_index]

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




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = None   ,       lowercase_ = None   ,       )						->					Any:
          UpperCAmelCase									=					num_inference_steps

          UpperCAmelCase									=					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":
              UpperCAmelCase									=					np.linspace(0   ,       num_train_timesteps - 1   ,       lowercase_   ,       dtype=lowercase_							)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
              UpperCAmelCase									=					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									=					(np.arange(0   ,       lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              UpperCAmelCase									=					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									=					(np.arange(lowercase_   ,       0   ,       -step_ratio							)).round().copy().astype(lowercase_							)
              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									=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
          UpperCAmelCase									=					torch.from_numpy(np.log(lowercase_							)							).to(lowercase_							)

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

          # interpolate sigmas
          UpperCAmelCase									=					sigmas.log().lerp(sigmas.roll(1							).log()   ,       0.5							).exp()

          UpperCAmelCase									=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
          UpperCAmelCase									=					torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

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

          # interpolate timesteps
          UpperCAmelCase									=					self.sigma_to_t(lowercase_							).to(lowercase_   ,       dtype=timesteps.dtype							)
          UpperCAmelCase									=					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   ,       dim=-1							).flatten()

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

          UpperCAmelCase									=					None

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




      def     a_      (							self   ,       lowercase_							)						->					int:
          # get log sigma
          UpperCAmelCase									=					sigma.log()

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

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

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

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

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




      @property
      def     a_      (							self							)						->					Dict:
          return self.sample is None




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = True   ,       )						->					Union[SchedulerOutput, Tuple]:
          UpperCAmelCase									=					self.index_for_timestep(lowercase_							)

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

          if self.state_in_first_order:
              UpperCAmelCase									=					self.sigmas[step_index]
              UpperCAmelCase									=					self.sigmas_interpol[step_index + 1]
              UpperCAmelCase									=					self.sigmas[step_index + 1]
          else:
              # 2nd order / KDPM2's method
              UpperCAmelCase									=					self.sigmas[step_index - 1]
              UpperCAmelCase									=					self.sigmas_interpol[step_index]
              UpperCAmelCase									=					self.sigmas[step_index]

          # 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
          UpperCAmelCase									=					0
          UpperCAmelCase									=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              UpperCAmelCase									=					sigma_hat if self.state_in_first_order else sigma_interpol
              UpperCAmelCase									=					sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase									=					sigma_hat if self.state_in_first_order else sigma_interpol
              UpperCAmelCase									=					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
              UpperCAmelCase									=					(sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              UpperCAmelCase									=					sigma_interpol - sigma_hat

              # store for 2nd order step
              UpperCAmelCase									=					sample
          else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              UpperCAmelCase									=					(sample - pred_original_sample) / sigma_interpol

              # 3. delta timestep
              UpperCAmelCase									=					sigma_next - sigma_hat

              UpperCAmelCase									=					self.sample
              UpperCAmelCase									=					None

          UpperCAmelCase									=					sample + derivative * dt

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=lowercase_							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCAmelCase									=					self.sigmas.to(device=original_samples.device   ,       dtype=original_samples.dtype							)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_							):
              # mps does not support float64
              UpperCAmelCase									=					self.timesteps.to(original_samples.device   ,       dtype=torch.floataa							)
              UpperCAmelCase									=					timesteps.to(original_samples.device   ,       dtype=torch.floataa							)
          else:
              UpperCAmelCase									=					self.timesteps.to(original_samples.device							)
              UpperCAmelCase									=					timesteps.to(original_samples.device							)

          UpperCAmelCase									=					[self.index_for_timestep(lowercase_   ,       lowercase_							) for t in timesteps]

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

          UpperCAmelCase									=					original_samples + noise * sigma
          return noisy_samples




      def __len__(							self							)						->					Union[str, Any]:
          return self.config.num_train_timesteps





"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	(DDPMScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					int:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
              self.check_over_configs(beta_start=lowercase_   ,       beta_end=lowercase_							)




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




      def     a_      (							self							)						->					Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




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




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




      def     a_      (							self							)						->					Union[str, Any]:
          for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1E-5




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

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

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(prediction_type='v_prediction'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

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

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

          for i, timestep in enumerate(lowercase_							):
              if i == len(lowercase_							) - 1:
                  UpperCAmelCase									=					-1
              else:
                  UpperCAmelCase									=					timesteps[i + 1]

              UpperCAmelCase									=					scheduler.previous_timestep(lowercase_							)
              UpperCAmelCase									=					prev_t.item()

              self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 5_1, 0]

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




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]
          UpperCAmelCase									=					len(lowercase_							)

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




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[scheduler.config.num_train_timesteps]

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



