code: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  class       a_  (       SCREAMING_SNAKE_CASE__ ):
  
      '''simple docstring'''
      UpperCamelCase									=				(DDIMScheduler,)
      UpperCamelCase									=				(('''eta''', 0.0), ('''num_inference_steps''', 50))
  
  
      def     snake_case_(	self				,	**A )	->     str:
          _SCREAMING_SNAKE_CASE             =    {
              """num_train_timesteps""": 1000,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }
  
          config.update(**A )
          return config
  
  
      def     snake_case_(	self				,	**A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE             =    self.get_scheduler_config(**A )
          _SCREAMING_SNAKE_CASE             =    scheduler_class(**A )
  
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    10, 0.0
  
          _SCREAMING_SNAKE_CASE             =    self.dummy_model()
          _SCREAMING_SNAKE_CASE             =    self.dummy_sample_deter
  
          scheduler.set_timesteps(A )
  
          for t in scheduler.timesteps:
              _SCREAMING_SNAKE_CASE             =    model(A				,	A )
              _SCREAMING_SNAKE_CASE             =    scheduler.step(A				,	A				,	A				,	A ).prev_sample
  
          return sample
  
  
      def     snake_case_(	self )	->     Any:
  
  
  
          for timesteps in [100, 500, 1000]:
              self.check_over_configs(num_train_timesteps=A )
  
  
      def     snake_case_(	self )	->     int:
  
  
  
          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=A )
  
          _SCREAMING_SNAKE_CASE             =    self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE             =    self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE             =    scheduler_class(**A )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps				,	torch.LongTensor([801, 601, 401, 201, 1] ) )
  
  
      def     snake_case_(	self )	->     str:
  
  
  
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,	[0.002, 0.02, 0.2, 2] ):
              self.check_over_configs(beta_start=A				,	beta_end=A )
  
  
      def     snake_case_(	self )	->     str:
  
  
  
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=A )
  
  
      def     snake_case_(	self )	->     List[Any]:
  
  
  
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=A )
  
  
      def     snake_case_(	self )	->     str:
  
  
  
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=A )
  
  
      def     snake_case_(	self )	->     Tuple:
  
  
  
          for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=A )
  
  
      def     snake_case_(	self )	->     List[Any]:
  
  
  
          for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=A )
  
  
      def     snake_case_(	self )	->     Optional[int]:
          self.check_over_configs(thresholding=A )
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=A				,	prediction_type=A				,	sample_max_value=A				,	)
  
  
      def     snake_case_(	self )	->     List[Any]:
  
  
  
          for t in [1, 10, 49]:
              self.check_over_forward(time_step=A )
  
  
      def     snake_case_(	self )	->     Tuple:
  
  
  
          for t, num_inference_steps in zip([1, 10, 50]				,	[10, 50, 500] ):
              self.check_over_forward(time_step=A				,	num_inference_steps=A )
  
  
      def     snake_case_(	self )	->     int:
  
  
  
          for t, eta in zip([1, 10, 49]				,	[0.0, 0.5, 1.0] ):
              self.check_over_forward(time_step=A				,	eta=A )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE             =    self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE             =    scheduler_class(**A )
  
          assert torch.sum(torch.abs(scheduler._get_variance(0				,	0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420				,	400 ) - 0.1_4771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980				,	960 ) - 0.3_2460 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0				,	0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				,	486 ) - 0.0_0979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				,	998 ) - 0.02 ) ) < 1e-5
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.full_loop()
  
          _SCREAMING_SNAKE_CASE             =    torch.sum(torch.abs(A ) )
          _SCREAMING_SNAKE_CASE             =    torch.mean(torch.abs(A ) )
  
          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.22_3967 ) < 1e-3
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    self.full_loop(prediction_type="""v_prediction""" )
  
          _SCREAMING_SNAKE_CASE             =    torch.sum(torch.abs(A ) )
          _SCREAMING_SNAKE_CASE             =    torch.mean(torch.abs(A ) )
  
          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.full_loop(set_alpha_to_one=A				,	beta_start=0.01 )
          _SCREAMING_SNAKE_CASE             =    torch.sum(torch.abs(A ) )
          _SCREAMING_SNAKE_CASE             =    torch.mean(torch.abs(A ) )
  
          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3
  
  
  
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    self.full_loop(set_alpha_to_one=A				,	beta_start=0.01 )
          _SCREAMING_SNAKE_CASE             =    torch.sum(torch.abs(A ) )
          _SCREAMING_SNAKE_CASE             =    torch.mean(torch.abs(A ) )
  
          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  @flax.struct.dataclass
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				42
  
      # setable values
      UpperCamelCase									=				42
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
      @classmethod
      def     snake_case_(	cls				,	A				,	A				,	A				,	A				,	)	->     List[Any]:
          return cls(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
  
  
  @dataclass
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
      UpperCamelCase									=				42
  
  
      @property
      def     snake_case_(	self )	->     List[Any]:
          return True
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = True				,	A = 0				,	A = "epsilon"				,	A = jnp.floataa				,	)	->     int:
          _SCREAMING_SNAKE_CASE             =    dtype
  
  
      def     snake_case_(	self				,	A = None )	->     DDIMSchedulerState:
          if common is None:
              _SCREAMING_SNAKE_CASE             =    CommonSchedulerState.create(self )
  
          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _SCREAMING_SNAKE_CASE             =    (
              jnp.array(1.0				,	dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )
  
          # standard deviation of the initial noise distribution
          _SCREAMING_SNAKE_CASE             =    jnp.array(1.0				,	dtype=self.dtype )
  
          _SCREAMING_SNAKE_CASE             =    jnp.arange(0				,	self.config.num_train_timesteps ).round()[::-1]
  
          return DDIMSchedulerState.create(
              common=A				,	final_alpha_cumprod=A				,	init_noise_sigma=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A = None )	->     jnp.ndarray:
          return sample
  
  
      def     snake_case_(	self				,	A				,	A				,	A = () )	->     DDIMSchedulerState:
          _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _SCREAMING_SNAKE_CASE             =    (jnp.arange(0				,	A ) * step_ratio).round()[::-1] + self.config.steps_offset
  
          return state.replace(
              num_inference_steps=A				,	timesteps=A				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     int:
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(
              prev_timestep >= 0				,	state.common.alphas_cumprod[prev_timestep]				,	state.final_alpha_cumprod )
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t_prev
  
          _SCREAMING_SNAKE_CASE             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
          return variance
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A = 0.0				,	A = True				,	)	->     Union[FlaxDDIMSchedulerOutput, Tuple]:
          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""" )
  
          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding
  
          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"
  
          # 1. get previous step value (=t-1)
          _SCREAMING_SNAKE_CASE             =    timestep - self.config.num_train_timesteps // state.num_inference_steps
  
          _SCREAMING_SNAKE_CASE             =    state.common.alphas_cumprod
          _SCREAMING_SNAKE_CASE             =    state.final_alpha_cumprod
  
          # 2. compute alphas, betas
          _SCREAMING_SNAKE_CASE             =    alphas_cumprod[timestep]
          _SCREAMING_SNAKE_CASE             =    jnp.where(prev_timestep >= 0				,	alphas_cumprod[prev_timestep]				,	A )
  
          _SCREAMING_SNAKE_CASE             =    1 - alpha_prod_t
  
          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              _SCREAMING_SNAKE_CASE             =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              _SCREAMING_SNAKE_CASE             =    model_output
          elif self.config.prediction_type == "sample":
              _SCREAMING_SNAKE_CASE             =    model_output
              _SCREAMING_SNAKE_CASE             =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              _SCREAMING_SNAKE_CASE             =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                  """ `v_prediction`""" )
  
          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _SCREAMING_SNAKE_CASE             =    self._get_variance(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    eta * variance ** 0.5
  
          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _SCREAMING_SNAKE_CASE             =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
          if not return_dict:
              return (prev_sample, state)
  
          return FlaxDDIMSchedulerOutput(prev_sample=A				,	state=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return add_noise_common(state.common				,	A				,	A				,	A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A				,	)	->     jnp.ndarray:
          return get_velocity_common(state.common				,	A				,	A				,	A )
  
  
  
  
  
      def __len__(	self )	->     Any:
          return self.config.num_train_timesteps
  
  
  
  