code: 
  
  
  
  
  
  '''simple docstring'''
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  @skip_mps
  class       a_  (       _SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					_SCREAMING_SNAKE_CASE   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				StableDiffusionModelEditingPipeline
      UpperCamelCase									=				TEXT_TO_IMAGE_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
      def     snake_case_(	self )	->     int:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    UNetaDConditionModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=4				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler()
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    AutoencoderKL(
              block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    CLIPTextConfig(
              bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	intermediate_size=37				,	layer_norm_eps=1e-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1000				,	)
          _SCREAMING_SNAKE_CASE             =    CLIPTextModel(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
  
  
      def     snake_case_(	self				,	A				,	A=0 )	->     Any:
          _SCREAMING_SNAKE_CASE             =    torch.manual_seed(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A field of roses""",
              """generator""": generator,
              # Setting height and width to None to prevent OOMs on CPU.
              """height""": None,
              """width""": None,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(UpperCamelCase__ )
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**UpperCamelCase__ ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(UpperCamelCase__ )
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    """french fries"""
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**UpperCamelCase__				,	negative_prompt=UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    output.images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    EulerAncestralDiscreteScheduler(
              beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule="""scaled_linear""" )
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(UpperCamelCase__ )
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**UpperCamelCase__ ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    PNDMScheduler()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(UpperCamelCase__ )
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(UpperCamelCase__ )
          # the pipeline does not expect pndm so test if it raises error.
          with self.assertRaises(UpperCamelCase__ ):
              _SCREAMING_SNAKE_CASE             =    sd_pipe(**UpperCamelCase__ ).images
  
  
      def     snake_case_(	self )	->     str:
          super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
  
  
  
  
  
      def     snake_case_(	self )	->     List[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=5e-3 )
  
  
  
  
  @slow
  @require_torch_gpu
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
      def     snake_case_(	self				,	A=0 )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    torch.manual_seed(UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A field of roses""",
              """generator""": generator,
              """num_inference_steps""": 3,
              """guidance_scale""": 7.5,
              """output_type""": """numpy""",
          }
          return inputs
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    """CompVis/stable-diffusion-v1-4"""
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline.from_pretrained(UpperCamelCase__				,	safety_checker=UpperCamelCase__ )
          pipe.to(UpperCamelCase__ )
          pipe.set_progress_bar_config(disable=UpperCamelCase__ )
          pipe.enable_attention_slicing()
  
          _SCREAMING_SNAKE_CASE             =    self.get_inputs()
          _SCREAMING_SNAKE_CASE             =    pipe(**UpperCamelCase__ ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 512, 512, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array(
              [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658] )
  
          assert np.abs(expected_slice - image_slice ).max() < 1e-2
  
          # make sure image changes after editing
          pipe.edit_model("""A pack of roses"""				,	"""A pack of blue roses""" )
  
          _SCREAMING_SNAKE_CASE             =    pipe(**UpperCamelCase__ ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 512, 512, 3)
  
          assert np.abs(expected_slice - image_slice ).max() > 1e-1
  
  
  
  
  
      def     snake_case_(	self )	->     List[Any]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          _SCREAMING_SNAKE_CASE             =    """CompVis/stable-diffusion-v1-4"""
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler.from_pretrained(UpperCamelCase__				,	subfolder="""scheduler""" )
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline.from_pretrained(
              UpperCamelCase__				,	scheduler=UpperCamelCase__				,	safety_checker=UpperCamelCase__ )
          _SCREAMING_SNAKE_CASE             =    pipe.to(UpperCamelCase__ )
          pipe.set_progress_bar_config(disable=UpperCamelCase__ )
          pipe.enable_attention_slicing(1 )
          pipe.enable_sequential_cpu_offload()
  
          _SCREAMING_SNAKE_CASE             =    self.get_inputs()
          _SCREAMING_SNAKE_CASE             =    pipe(**UpperCamelCase__ )
  
          _SCREAMING_SNAKE_CASE             =    torch.cuda.max_memory_allocated()
          # make sure that less than 4.4 GB is allocated
          assert mem_bytes < 4.4 * 10**9
  
  
  
  
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
  
  
  
  