code: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  lowercase_			       =				logging.get_logger(__name__)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					bool = True					)		->int:
      _SCREAMING_SNAKE_CASE             =    {}
      for name in model.attn_processors.keys():
          _SCREAMING_SNAKE_CASE             =    name.endswith("""attn2.processor"""					) and not (
              name.startswith("""transformer_in"""					) or """temp_attentions""" in name.split("""."""					)
          )
          _SCREAMING_SNAKE_CASE             =    model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith("""mid_block"""					):
              _SCREAMING_SNAKE_CASE             =    model.config.block_out_channels[-1]
          elif name.startswith("""up_blocks"""					):
              _SCREAMING_SNAKE_CASE             =    int(name[len("""up_blocks."""					)]					)
              _SCREAMING_SNAKE_CASE             =    list(reversed(model.config.block_out_channels					)					)[block_id]
          elif name.startswith("""down_blocks"""					):
              _SCREAMING_SNAKE_CASE             =    int(name[len("""down_blocks."""					)]					)
              _SCREAMING_SNAKE_CASE             =    model.config.block_out_channels[block_id]
          elif name.startswith("""transformer_in"""					):
              # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
              _SCREAMING_SNAKE_CASE             =    8 * model.config.attention_head_dim
  
          _SCREAMING_SNAKE_CASE             =    LoRAAttnProcessor(hidden_size=__lowerCamelCase      , cross_attention_dim=__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    lora_attn_procs[name].to(model.device					)
  
          if mock_weights:
              # add 1 to weights to mock trained weights
              with torch.no_grad():
                  lora_attn_procs[name].to_q_lora.up.weight += 1
                  lora_attn_procs[name].to_k_lora.up.weight += 1
                  lora_attn_procs[name].to_v_lora.up.weight += 1
                  lora_attn_procs[name].to_out_lora.up.weight += 1
  
      return lora_attn_procs
  
  
  
  
  
  
  @skip_mps
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				UNetaDConditionModel
      UpperCamelCase									=				'''sample'''
  
  
      @property
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    4
          _SCREAMING_SNAKE_CASE             =    4
          _SCREAMING_SNAKE_CASE             =    4
          _SCREAMING_SNAKE_CASE             =    (32, 32)
  
          _SCREAMING_SNAKE_CASE             =    floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(A )
          _SCREAMING_SNAKE_CASE             =    torch.tensor([10] ).to(A )
          _SCREAMING_SNAKE_CASE             =    floats_tensor((batch_size, 4, 32) ).to(A )
  
          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
      @property
      def     snake_case_(	self )	->     Optional[Any]:
          return (4, 4, 32, 32)
  
  
      @property
      def     snake_case_(	self )	->     int:
          return (4, 4, 32, 32)
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    {
              """block_out_channels""": (32, 64),
              """down_block_types""": (
                  """CrossAttnDownBlock3D""",
                  """DownBlock3D""",
              ),
              """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
              """cross_attention_dim""": 32,
              """attention_head_dim""": 8,
              """out_channels""": 4,
              """in_channels""": 4,
              """layers_per_block""": 1,
              """sample_size""": 32,
          }
          _SCREAMING_SNAKE_CASE             =    self.dummy_input
          return init_dict, inputs_dict
  
  
      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
  
          model.enable_xformers_memory_efficient_attention()
  
          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    32
  
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
          model.eval()
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
  
              if isinstance(A				,	A ):
                  _SCREAMING_SNAKE_CASE             =    output.sample
  
          self.assertIsNotNone(A )
          _SCREAMING_SNAKE_CASE             =    inputs_dict["""sample"""].shape
          self.assertEqual(output.shape				,	A				,	"""Input and output shapes do not match""" )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
          model.eval()
  
          with torch.no_grad():
              # Warmup pass when using mps (see #372)
              if torch_device == "mps" and isinstance(A				,	A ):
                  model(**self.dummy_input )
  
              _SCREAMING_SNAKE_CASE             =    model(**A )
              if isinstance(A				,	A ):
                  _SCREAMING_SNAKE_CASE             =    first.sample
  
              _SCREAMING_SNAKE_CASE             =    model(**A )
              if isinstance(A				,	A ):
                  _SCREAMING_SNAKE_CASE             =    second.sample
  
          _SCREAMING_SNAKE_CASE             =    first.cpu().numpy()
          _SCREAMING_SNAKE_CASE             =    second.cpu().numpy()
          _SCREAMING_SNAKE_CASE             =    out_a[~np.isnan(A )]
          _SCREAMING_SNAKE_CASE             =    out_a[~np.isnan(A )]
          _SCREAMING_SNAKE_CASE             =    np.amax(np.abs(out_a - out_a ) )
          self.assertLessEqual(A				,	1e-5 )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
          model.eval()
  
          model.set_attention_slice("""auto""" )
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
          assert output is not None
  
          model.set_attention_slice("""max""" )
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
          assert output is not None
  
          model.set_attention_slice(2 )
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
          assert output is not None
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A )
  
          # make sure we can set a list of attention processors
          model.set_attn_processor(A )
          model.to(A )
  
          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.0} ).sample
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
  
          assert (samplea - samplea).abs().max() < 3e-3
          assert (samplea - samplea).abs().max() < 3e-3
  
          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3e-3
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A )
          model.set_attn_processor(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(A )
              self.assertTrue(os.path.isfile(os.path.join(A				,	"""pytorch_lora_weights.bin""" ) ) )
              torch.manual_seed(0 )
              _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
              new_model.to(A )
              new_model.load_attn_procs(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    new_model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
  
          assert (sample - new_sample).abs().max() < 1e-3
  
          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1e-4
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A )
          model.set_attn_processor(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(A				,	safe_serialization=A )
              self.assertTrue(os.path.isfile(os.path.join(A				,	"""pytorch_lora_weights.safetensors""" ) ) )
              torch.manual_seed(0 )
              _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
              new_model.to(A )
              new_model.load_attn_procs(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    new_model(**A				,	cross_attention_kwargs={"""scale""": 0.5} ).sample
  
          assert (sample - new_sample).abs().max() < 3e-3
  
          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1e-4
  
  
      def     snake_case_(	self )	->     List[str]:
          # enable deterministic behavior for gradient checkpointing
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A				,	mock_weights=A )
          model.set_attn_processor(A )
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(A )
              self.assertTrue(os.path.isfile(os.path.join(A				,	"""pytorch_lora_weights.bin""" ) ) )
              torch.manual_seed(0 )
              _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
              new_model.to(A )
              new_model.load_attn_procs(A				,	weight_name="""pytorch_lora_weights.bin""" )
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A				,	mock_weights=A )
          model.set_attn_processor(A )
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(A )
              self.assertTrue(os.path.isfile(os.path.join(A				,	"""pytorch_lora_weights.bin""" ) ) )
              torch.manual_seed(0 )
              _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
              new_model.to(A )
              with self.assertRaises(A ) as e:
                  new_model.load_attn_procs(A				,	use_safetensors=A )
              self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""				,	str(e.exception ) )
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    8
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A )
          model.set_attn_processor(A )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A				,	cross_attention_kwargs={"""scale""": 0.0} ).sample
  
          model.set_attn_processor(AttnProcessor() )
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          assert (sample - new_sample).abs().max() < 1e-4
          assert (sample - old_sample).abs().max() < 3e-3
  
  
      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
      def     snake_case_(	self )	->     Optional[Any]:
          # enable deterministic behavior for gradient checkpointing
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
  
          _SCREAMING_SNAKE_CASE             =    4
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
          _SCREAMING_SNAKE_CASE             =    create_lora_layers(A )
          model.set_attn_processor(A )
  
          # default
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
              model.enable_xformers_memory_efficient_attention()
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
              model.disable_xformers_memory_efficient_attention()
              _SCREAMING_SNAKE_CASE             =    model(**A ).sample
  
          assert (sample - on_sample).abs().max() < 1e-4
          assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    32
  
          _SCREAMING_SNAKE_CASE             =    self.model_class(**A )
          model.to(A )
          model.eval()
  
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )[0]
  
          model.enable_forward_chunking()
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )[0]
  
          self.assertEqual(output.shape				,	output_a.shape				,	"""Shape doesn't match""" )
          assert np.abs(output.cpu() - output_a.cpu() ).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
  
  
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
  
  
  
  