





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 	a				:
 snake_case__    	=    42

 # setable values
 snake_case__    	=    42
 snake_case__    	=    42
 snake_case__    	=    42
 snake_case__    	=    None

 # running values
 snake_case__    	=    None



 @classmethod
 def 					UpperCamelCase__   (			cls							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  return cls(common=_snake_case							,    init_noise_sigma=_snake_case							,    timesteps=_snake_case							,    sigmas=_snake_case			)







@dataclass
class 	a				(					a__   ):
 snake_case__    	=    42







class 	a				(					a__					,      a__   ):
 snake_case__    	=    [e.name for e in FlaxKarrasDiffusionSchedulers]

 snake_case__    	=    42



 @property
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return True



 @register_to_config
 def __init__(			self							,    _snake_case = 10_00							,    _snake_case = 0.0_001							,    _snake_case = 0.02							,    _snake_case = "linear"							,    _snake_case = None							,    _snake_case = "epsilon"							,    _snake_case = jnp.floataa							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      dtype



 def 					UpperCamelCase__   (			self							,    _snake_case = None			):







  """simple docstring"""






  if common is None:
   lowerCAmelCase  							=      CommonSchedulerState.create(self			)

  lowerCAmelCase  							=      jnp.arange(0							,    self.config.num_train_timesteps			).round()[::-1]
  lowerCAmelCase  							=      ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

  # standard deviation of the initial noise distribution
  lowerCAmelCase  							=      sigmas.max()

  return LMSDiscreteSchedulerState.create(
      common=_snake_case							,    init_noise_sigma=_snake_case							,    timesteps=_snake_case							,    sigmas=_snake_case							,    )



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  ((lowerCAmelCase)							,)  							=      jnp.where(state.timesteps == timestep							,    size=1			)
  lowerCAmelCase  							=      step_index[0]

  lowerCAmelCase  							=      state.sigmas[step_index]
  lowerCAmelCase  							=      sample / ((sigma**2 + 1) ** 0.5)
  return sample



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  def lms_derivative(_snake_case			):
   lowerCAmelCase  							=      1.0
   for k in range(_snake_case			):
    if current_order == k:
     continue
    prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
   return prod

  lowerCAmelCase  							=      integrate.quad(_snake_case							,    state.sigmas[t]							,    state.sigmas[t + 1]							,    epsrel=1E-4			)[0]

  return integrated_coeff



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case = ()			):







  """simple docstring"""






  lowerCAmelCase  							=      jnp.linspace(self.config.num_train_timesteps - 1							,    0							,    _snake_case							,    dtype=self.dtype			)

  lowerCAmelCase  							=      jnp.floor(_snake_case			).astype(jnp.intaa			)
  lowerCAmelCase  							=      jnp.ceil(_snake_case			).astype(jnp.intaa			)

  lowerCAmelCase  							=      jnp.mod(_snake_case							,    1.0			)

  lowerCAmelCase  							=      ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
  lowerCAmelCase  							=      (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
  lowerCAmelCase  							=      jnp.concatenate([sigmas, jnp.array([0.0]							,    dtype=self.dtype			)]			)

  lowerCAmelCase  							=      timesteps.astype(jnp.intaa			)

  # initial running values
  lowerCAmelCase  							=      jnp.zeros((0,) + shape							,    dtype=self.dtype			)

  return state.replace(
      timesteps=_snake_case							,    sigmas=_snake_case							,    num_inference_steps=_snake_case							,    derivatives=_snake_case							,    )



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case = 4							,    _snake_case = True							,    ):







  """simple docstring"""






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

  lowerCAmelCase  							=      state.sigmas[timestep]

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  if self.config.prediction_type == "epsilon":
   lowerCAmelCase  							=      sample - sigma * model_output
  elif self.config.prediction_type == "v_prediction":
   # * c_out + input * c_skip
   lowerCAmelCase  							=      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
  lowerCAmelCase  							=      (sample - pred_original_sample) / sigma
  lowerCAmelCase  							=      state.replace(derivatives=jnp.append(state.derivatives							,    _snake_case			)			)
  if len(state.derivatives			) > order:
   lowerCAmelCase  							=      state.replace(derivatives=jnp.delete(state.derivatives							,    0			)			)

  # 3. Compute linear multistep coefficients
  lowerCAmelCase  							=      min(timestep + 1							,    _snake_case			)
  lowerCAmelCase  							=      [self.get_lms_coefficient(_snake_case							,    _snake_case							,    _snake_case							,    _snake_case			) for curr_order in range(_snake_case			)]

  # 4. Compute previous sample based on the derivatives path
  lowerCAmelCase  							=      sample + sum(
      coeff * derivative for coeff, derivative in zip(_snake_case							,    reversed(state.derivatives			)			)			)

  if not return_dict:
   return (prev_sample, state)

  return FlaxLMSSchedulerOutput(prev_sample=_snake_case							,    state=_snake_case			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      state.sigmas[timesteps].flatten()
  lowerCAmelCase  							=      broadcast_to_shape_from_left(_snake_case							,    noise.shape			)

  lowerCAmelCase  							=      original_samples + noise * sigma

  return noisy_samples



 def __len__(			self			):







  """simple docstring"""






  return self.config.num_train_timesteps












"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	a				(					a__   ):
 snake_case__    	=    (HeunDiscreteScheduler,)
 snake_case__    	=    1_0



 def 					UpperCamelCase__   (			self							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'num_train_timesteps': 11_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**_snake_case			)
  return config



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,    [0.0_002, 0.002, 0.02]			):
   self.check_over_configs(beta_start=_snake_case							,    beta_end=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config(prediction_type='v_prediction'			)
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
   assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if str(_snake_case			).startswith('cpu'			):
   # The following sum varies between 148 and 156 on mps. Why?
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  elif str(_snake_case			).startswith('mps'			):
   # Larger tolerance on mps
   assert abs(result_mean.item() - 0.0_002			) < 1E-2
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case							,    use_karras_sigmas=_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  assert abs(result_sum.item() - 0.00_015			) < 1E-2
  assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2





