


'''simple docstring'''






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					1

    @register_to_config
    def __init__(   self    :  Any    ,						lowerCamelCase    :  Optional[int]=20_00    ,						lowerCamelCase    :  Dict=0.1    ,						lowerCamelCase    :  Optional[int]=20    ,						lowerCamelCase    :  Any=1E-3		)   ->  List[str]:
      lowerCAmelCase_			:      List[str]			=   None
      lowerCAmelCase_			:      int			=   None
      lowerCAmelCase_			:      Union[str, Any]			=   None

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  Union[str, torch.device] = None		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Tuple			=   torch.linspace(1    ,						self.config.sampling_eps    ,						lowerCamelCase    ,						device=lowerCamelCase		)

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Any=None		)   ->  Tuple:
      if self.timesteps is None:
        raise ValueError(
            """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""		)

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      lowerCAmelCase_			:      int			=   (
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      lowerCAmelCase_			:      List[Any]			=   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
      lowerCAmelCase_			:      List[Any]			=   std.flatten()
      while len(std.shape		) < len(score.shape		):
        lowerCAmelCase_			:      Optional[int]			=   std.unsqueeze(-1		)
      lowerCAmelCase_			:      List[Any]			=   -score / std

      # compute
      lowerCAmelCase_			:      Optional[int]			=   -1.0 / len(self.timesteps		)

      lowerCAmelCase_			:      Optional[Any]			=   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      lowerCAmelCase_			:      Union[str, Any]			=   beta_t.flatten()
      while len(beta_t.shape		) < len(x.shape		):
        lowerCAmelCase_			:      Any			=   beta_t.unsqueeze(-1		)
      lowerCAmelCase_			:      int			=   -0.5 * beta_t * x

      lowerCAmelCase_			:      List[Any]			=   torch.sqrt(lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   drift - diffusion**2 * score
      lowerCAmelCase_			:      Any			=   x + drift * dt

      # add noise
      lowerCAmelCase_			:      Optional[Any]			=   randn_tensor(x.shape    ,						layout=x.layout    ,						generator=lowerCamelCase    ,						device=x.device    ,						dtype=x.dtype		)
      lowerCAmelCase_			:      Union[str, Any]			=   x_mean + diffusion * math.sqrt(-dt		) * noise

      return x, x_mean





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







'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      List[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          new_scheduler.set_timesteps(lowerCamelCase		)
          # copy over dummy past residuals
          lowerCAmelCase_			:      str			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      int			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      Optional[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(lowerCamelCase		)

          # copy over dummy past residual (must be after setting timesteps)
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

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

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      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"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[int]:
      for t, num_inference_steps in zip([1, 5, 10]    ,						[10, 50, 1_00]		):
        self.check_over_forward(num_inference_steps=lowerCamelCase    ,						time_step=lowerCamelCase		)





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10



