

'''simple docstring'''






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


snake_case_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							42
    _A			    =							None






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	int=0.999  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]="cosine"  ,  )							->   Optional[int]:
      """simple docstring"""

      if alpha_transform_type == "cosine":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_     :	Tuple    ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_     :	Tuple    ):
                  return math.exp(t * -12.0    )

      else:
            raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"    )

      SCREAMING_SNAKE_CASE_      :			List[str]     =       []
      for i in range(SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_    )  ,  SCREAMING_SNAKE_CASE_    )    )
      return torch.tensor(SCREAMING_SNAKE_CASE_  ,  dtype=torch.floataa    )





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,_UpperCAmelCase      ):
    _A			    =							[e.name for e in KarrasDiffusionSchedulers]
    _A			    =							1




    @register_to_config
    def __init__(					self				,     lowercase__ = 1000				,     lowercase__ = 0.0001				,     lowercase__ = 0.02				,     lowercase__ = "linear"				,     lowercase__ = None				,     lowercase__ = "epsilon"				,     lowercase__ = "linspace"				,     lowercase__ = 0				,     ):






          """simple docstring"""






          if trained_betas is not None:
                SCREAMING_SNAKE_CASE_      :			Any     =       torch.tensor(lowercase__				,     dtype=torch.floataa			)
          elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.linspace(lowercase__				,     lowercase__				,     lowercase__				,     dtype=torch.floataa			)
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       (
                    torch.linspace(beta_start**0.5				,     beta_end**0.5				,     lowercase__				,     dtype=torch.floataa			) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       betas_for_alpha_bar(lowercase__			)
          else:
                raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

          SCREAMING_SNAKE_CASE_      :			Any     =       1.0 - self.betas
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.cumprod(self.alphas				,     dim=0			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.from_numpy(lowercase__			)

          # setable values
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       None
          SCREAMING_SNAKE_CASE_      :			str     =       np.linspace(0				,     num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[::-1].copy()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.from_numpy(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			int     =       False




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Any     =       timestep.to(self.timesteps.device			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample / ((sigma**2 + 1) ** 0.5)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       True
          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                SCREAMING_SNAKE_CASE_      :			Dict     =       np.linspace(0				,     self.config.num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE_      :			Dict     =       self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       (np.arange(0				,     lowercase__			) * step_ratio).round()[::-1].copy().astype(lowercase__			)
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_      :			Any     =       (np.arange(self.config.num_train_timesteps				,     0				,     -step_ratio			)).round().copy().astype(lowercase__			)
                timesteps -= 1
          else:
                raise ValueError(
                    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

          SCREAMING_SNAKE_CASE_      :			int     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       np.interp(lowercase__				,     np.arange(0				,     len(lowercase__			)			)				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)
          if str(lowercase__			).startswith("mps"			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.from_numpy(lowercase__			).to(lowercase__				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          if (
              isinstance(lowercase__				,     lowercase__			)
              or isinstance(lowercase__				,     torch.IntTensor			)
              or isinstance(lowercase__				,     torch.LongTensor			)
          ):
                raise ValueError(
                    (
                        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                        " one of the `scheduler.timesteps` as a timestep."
                    )				,     )

          if not self.is_scale_input_called:
                logger.warning(
                    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                    "See `StableDiffusionPipeline` for a usage example."			)

          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       timestep.to(self.timesteps.device			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                SCREAMING_SNAKE_CASE_      :			Any     =       sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                SCREAMING_SNAKE_CASE_      :			Any     =       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
                raise NotImplementedError("prediction_type not implemented yet: sample"			)
          else:
                raise ValueError(
                    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Any     =       self.sigmas[step_index + 1]
          SCREAMING_SNAKE_CASE_      :			Any     =       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sample - pred_original_sample) / sigma

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sigma_down - sigma

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample + derivative * dt

          SCREAMING_SNAKE_CASE_      :			Dict     =       model_output.device
          SCREAMING_SNAKE_CASE_      :			int     =       randn_tensor(model_output.shape				,     dtype=model_output.dtype				,     device=lowercase__				,     generator=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowercase__				,     pred_original_sample=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas.to(device=original_samples.device				,     dtype=original_samples.dtype			)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.timesteps.to(original_samples.device				,     dtype=torch.floataa			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       timesteps.to(original_samples.device				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.timesteps.to(original_samples.device			)
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       timesteps.to(original_samples.device			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       sigmas[step_indices].flatten()
          while len(sigma.shape			) < len(original_samples.shape			):
                SCREAMING_SNAKE_CASE_      :			int     =       sigma.unsqueeze(-1			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       original_samples + noise * sigma
          return noisy_samples







    def __len__(					self			):






          """simple docstring"""






          return self.config.num_train_timesteps








"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    Any							)	->      Any:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				@property
				def 				snake_case	( self		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    1
										_UpperCamelCase			:					Union[str, Any]     =    3
										_UpperCamelCase			:					Tuple     =    (3_2, 3_2)

										_UpperCamelCase			:					Any     =    floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0							)							).to(lowerCAmelCase__							)
										return image






				@property
				def 				snake_case	( self		:    Tuple							)	->      Union[str, Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Dict     =    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=3_2, )
										return model






				@property
				def 				snake_case	( self		:    Dict							)	->      List[Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					List[str]     =    AutoencoderKL(
										    block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
										return model






				@property
				def 				snake_case	( self		:    List[str]							)	->      Tuple:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Any     =    CLIPTextConfig(
										    bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-0_5, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
										return CLIPTextModel(lowerCAmelCase__							)






				@property
				def 				snake_case	( self		:    Dict							)	->      str:




										'''simple docstring'''



										def extract(*lowerCAmelCase__		:    int, **lowerCAmelCase__		:    int							):




																class  _a						:






																				def __init__( self		:    str							)	->      Optional[Any]:




																										'''simple docstring'''



																										_UpperCamelCase			:					Optional[int]     =    torch.ones([0]							)






																				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    List[str]							)	->      Tuple:




																										'''simple docstring'''



																										self.pixel_values.to(lowerCAmelCase__							)
																										return self

																return Out()

										return extract






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_cond_unet
										_UpperCamelCase			:					int     =    DDIMScheduler(
										    beta_start=0.00_085, beta_end=0.012, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase__, set_alpha_to_one=lowerCAmelCase__, )

										_UpperCamelCase			:					Union[str, Any]     =    self.dummy_vae
										_UpperCamelCase			:					Any     =    self.dummy_text_encoder
										_UpperCamelCase			:					Any     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Tuple     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					str     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''

										_UpperCamelCase			:					Any     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)
										_UpperCamelCase			:					Optional[Any]     =    output.images

										_UpperCamelCase			:					str     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					List[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Tuple     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Tuple     =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[int]     =    self.dummy_cond_unet
										_UpperCamelCase			:					List[str]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					int     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					List[str]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Tuple     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					str     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)

										_UpperCamelCase			:					Dict     =    output.images

										_UpperCamelCase			:					List[Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					int     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Union[str, Any]     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Optional[Any]     =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe''', safety_checker=lowerCAmelCase__							)
										assert isinstance(lowerCAmelCase__, lowerCAmelCase__							)
										assert isinstance(pipe.scheduler, lowerCAmelCase__							)
										assert pipe.safety_checker is None

										_UpperCamelCase			:					Optional[Any]     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained(lowerCAmelCase__							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_UpperCamelCase			:					int     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None






				@unittest.skipIf(torch_device != '''cuda''', '''This test requires a GPU'''							)
				def 				snake_case	( self		:    Any							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    self.dummy_cond_unet
										_UpperCamelCase			:					Union[str, Any]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					List[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# put models in fp16
										_UpperCamelCase			:					int     =    unet.half()
										_UpperCamelCase			:					Any     =    vae.half()
										_UpperCamelCase			:					List[Any]     =    bert.half()

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Optional[int]     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					Optional[int]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					List[str]     =    sd_pipe([prompt], num_inference_steps=2, output_type='''np'''							).images

										assert image.shape == (1, 6_4, 6_4, 3)




@nightly
@require_torch_gpu
class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    str							)	->      List[Any]:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 				snake_case	( self		:    int							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					List[Any]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					str     =    '''a photo of a cat'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Optional[int]     =    3
										_UpperCamelCase			:					int     =    7

										# no sega enabled
										_UpperCamelCase			:					Tuple     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Tuple     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[str]     =    [
										    0.34_673_113,
										    0.38_492_733,
										    0.37_597_352,
										    0.34_086_335,
										    0.35_650_748,
										    0.35_579_205,
										    0.3_384_763,
										    0.34_340_236,
										    0.3_573_271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Dict     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[int]     =    [
										    0.41_887_826,
										    0.37_728_766,
										    0.30_138_272,
										    0.41_416_335,
										    0.41_664_985,
										    0.36_283_392,
										    0.36_191_246,
										    0.43_364_465,
										    0.43_001_732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					List[Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					int     =    '''an image of a crowded boulevard, realistic, 4k'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Dict     =    9
										_UpperCamelCase			:					Optional[Any]     =    7

										# no sega enabled
										_UpperCamelCase			:					Optional[int]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					List[str]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.43_497_998,
										    0.91_814_065,
										    0.7_540_739,
										    0.55_580_205,
										    0.8_467_265,
										    0.5_389_691,
										    0.62_574_506,
										    0.58_897_763,
										    0.50_926_757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					Optional[int]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.3_089_719,
										    0.30_500_144,
										    0.29_016_042,
										    0.30_630_964,
										    0.325_687,
										    0.29_419_225,
										    0.2_908_091,
										    0.28_723_598,
										    0.27_696_294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					Union[str, Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[int]     =    '''a castle next to a river'''
										_UpperCamelCase			:					Optional[int]     =    {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    4_8
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Optional[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					str     =    [
										    0.75_163_555,
										    0.76_037_145,
										    0.61_785,
										    0.9_189_673,
										    0.8_627_701,
										    0.85_189_694,
										    0.8_512_813,
										    0.87_012_076,
										    0.8_312_857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					List[str]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.73_553_365,
										    0.7_537_271,
										    0.74_341_905,
										    0.66_480_356,
										    0.6_472_925,
										    0.63_039_416,
										    0.64_812_905,
										    0.6_749_717,
										    0.6_517_102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    '''cuda'''
										_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', torch_dtype=torch.floataa							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					List[Any]     =    '''a photo of a cat'''
										_UpperCamelCase			:					str     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    3
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Any     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Union[str, Any]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.34_887_695,
										    0.3_876_953,
										    0.375,
										    0.34_423_828,
										    0.3_581_543,
										    0.35_717_773,
										    0.3_383_789,
										    0.34_570_312,
										    0.359_375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Tuple     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[Any]     =    [
										    0.42_285_156,
										    0.36_914_062,
										    0.29_077_148,
										    0.42_041_016,
										    0.41_918_945,
										    0.35_498_047,
										    0.3_618_164,
										    0.4_423_828,
										    0.43_115_234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2



