'''simple docstring'''


import math
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 SchedulerMixin, SchedulerOutput





class       _a    (		__a ,				__a      ):


  """simple docstring"""

  A_    							=						1




  @register_to_config
  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       int = 1_000   ,				lowercase_      :       Optional[Union[np.ndarray, List[float]]] = None      ):







    '''simple docstring'''


    self.set_timesteps(lowercase_      )

    # standard deviation of the initial noise distribution
    lowercase_     =       1.0

    # For now we only support F-PNDM, i.e. the runge-kutta method
    # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
    # mainly at formula (9), (12), (13) and the Algorithm 2.
    lowercase_     =       4

    # running values
    lowercase_     =       []




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       int   ,				lowercase_      :       Union[str, torch.device] = None      ):







    '''simple docstring'''


    lowercase_     =       num_inference_steps
    lowercase_     =       torch.linspace(1   ,				0   ,				num_inference_steps + 1      )[:-1]
    lowercase_     =       torch.cat([steps, torch.tensor([0.0]      )]      )

    if self.config.trained_betas is not None:
      lowercase_     =       torch.tensor(self.config.trained_betas   ,				dtype=torch.floataa      )
    else:
      lowercase_     =       torch.sin(steps * math.pi / 2      ) ** 2

    lowercase_     =       (1.0 - self.betas**2) ** 0.5

    lowercase_     =       (torch.atana(self.betas   ,				self.alphas      ) / math.pi * 2)[:-1]
    lowercase_     =       timesteps.to(lowercase_      )

    lowercase_     =       []




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       torch.FloatTensor   ,				lowercase_      :       int   ,				lowercase_      :       torch.FloatTensor   ,				lowercase_      :       bool = True   ,				):







    '''simple docstring'''


    if self.num_inference_steps is None:
      raise ValueError(
          """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""      )

    lowercase_     =       (self.timesteps == timestep).nonzero().item()
    lowercase_     =       timestep_index + 1

    lowercase_     =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
    self.ets.append(lowercase_      )

    if len(self.ets      ) == 1:
      lowercase_     =       self.ets[-1]
    elif len(self.ets      ) == 2:
      lowercase_     =       (3 * self.ets[-1] - self.ets[-2]) / 2
    elif len(self.ets      ) == 3:
      lowercase_     =       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
    else:
      lowercase_     =       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

    lowercase_     =       self._get_prev_sample(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_      )

    if not return_dict:
      return (prev_sample,)

    return SchedulerOutput(prev_sample=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       torch.FloatTensor   ,				*lowercase_      :       Dict   ,				**lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    return sample




  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Tuple   ,				lowercase_      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     =       self.alphas[timestep_index]
    lowercase_     =       self.betas[timestep_index]

    lowercase_     =       self.alphas[prev_timestep_index]
    lowercase_     =       self.betas[prev_timestep_index]

    lowercase_     =       (sample - sigma * ets) / max(lowercase_   ,				1e-8      )
    lowercase_     =       next_alpha * pred + ets * next_sigma

    return prev_sample





  def __len__(		self      :       List[Any]      ):







    '''simple docstring'''


    return self.config.num_train_timesteps


'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """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 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    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 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    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 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for t in [0, 500, 999]:
      self.check_over_forward(time_step=lowercase_      )




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       self.dummy_sample_deter + 0.1
    lowercase_     =       self.dummy_sample_deter - 0.1

    lowercase_     =       samplea.shape[0]
    lowercase_     =       torch.stack([samplea, samplea, samplea]   ,				dim=0      )
    lowercase_     =       torch.arange(lowercase_      )[0:3, None].repeat(1   ,				lowercase_      )

    lowercase_     =       model(samples.flatten(0   ,				1      )   ,				timesteps.flatten(0   ,				1      )      )
    lowercase_     =       scheduler.batch_step_no_noise(lowercase_   ,				timesteps.flatten(0   ,				1      )   ,				samples.flatten(0   ,				1      )      )

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

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       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 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

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

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

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

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       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 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

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

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

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 51, 0]

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




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]
    lowercase_     =       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 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [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_      )

