


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       StableDiffusionDiffEditPipeline
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       frozenset(
     []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 SCREAMING_SNAKE_CASE__      =       frozenset([]							)




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Dict 							=       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					,  attention_head_dim=(2, 4)					,  use_linear_projection=_lowerCamelCase					,  )
       a						:int 							=       DDIMScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_one=_lowerCamelCase					,  )
       a						:Tuple 							=       DDIMInverseScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_zero=_lowerCamelCase					,  )
       torch.manual_seed(0    )
       a						:Dict 							=       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					,  sample_size=128					,  )
       torch.manual_seed(0    )
       a						:int 							=       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					,  hidden_act='''gelu'''					,  projection_dim=512					,  )
       a						:Optional[int] 							=       CLIPTextModel(_lowerCamelCase    )
       a						:str 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

       a						:Optional[Any] 							=       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''inverse_scheduler''': inverse_scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }

       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:Optional[Any] 							=       floats_tensor((1, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:Tuple 							=       floats_tensor((1, 2, 4, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:int 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:int 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Dict 							=       {
           '''prompt''': '''a dog and a newt''',
           '''mask_image''': mask,
           '''image_latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:str 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:str 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Union[str, Any] 							=       {
           '''image''': image,
           '''source_prompt''': '''a cat and a frog''',
           '''target_prompt''': '''a dog and a newt''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''num_maps_per_mask''': 2,
           '''mask_encode_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:List[str] 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:int 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:List[Any] 							=       {
           '''image''': image,
           '''prompt''': '''a cat and a frog''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''decode_latents''': True,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not hasattr(self.pipeline_class					,  '''_optional_components'''    ):
             return

       a						:Any 							=       self.get_dummy_components()
       a						:str 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       # set all optional components to None and update pipeline config accordingly
       for optional_component in pipe._optional_components:
             setattr(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

       a						:Union[str, Any] 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:List[Any] 							=       pipe(**_lowerCamelCase    )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase    )
             a						:int 							=       self.pipeline_class.from_pretrained(_lowerCamelCase    )
             pipe_loaded.to(_lowerCamelCase    )
             pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase    )

       for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(_lowerCamelCase					,  _lowerCamelCase    ) is None					,  F'''`{optional_component}` did not stay set to None after loading.'''					,  )

       a						:Dict 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe_loaded(**_lowerCamelCase    )[0]

       a						:List[str] 							=       np.abs(output - output_loaded    ).max()
       self.assertLess(_lowerCamelCase					,  1e-4    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:Optional[int] 							=       self.get_dummy_components()
       a						:List[str] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Any 							=       self.get_dummy_mask_inputs(_lowerCamelCase    )
       a						:str 							=       pipe.generate_mask(**_lowerCamelCase    )
       a						:List[str] 							=       mask[0, -3:, -3:]

       self.assertEqual(mask.shape					,  (1, 16, 16)    )
       a						:List[Any] 							=       np.array([0] * 9    )
       a						:str 							=       np.abs(mask_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )
       self.assertEqual(mask[0, -3, -4]					,  0    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:List[str] 							=       self.get_dummy_components()
       a						:List[Any] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Any 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Union[str, Any] 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:str 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:str 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       '''cpu'''

       a						:str 							=       self.get_dummy_components()

       a						:Union[str, Any] 							=       {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
       a						:Dict 							=       DPMSolverMultistepScheduler(**_lowerCamelCase    )
       a						:List[str] 							=       DPMSolverMultistepInverseScheduler(**_lowerCamelCase    )

       a						:int 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Any 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:int 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:Tuple 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )







@require_torch_gpu
@slow
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls    ):
       a						:Tuple 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

       a						:Union[str, Any] 							=       raw_image.convert('''RGB'''    ).resize((768, 768)    )

       a						:List[Any] 							=       raw_image




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       torch.manual_seed(0    )

       a						:int 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Optional[int] 							=       DDIMScheduler.from_config(pipe.scheduler.config    )
       a						:Optional[int] 							=       DDIMInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Optional[Any] 							=       '''a bowl of fruit'''
       a						:Any 							=       '''a bowl of pears'''

       a						:Any 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase    ).latents

       a						:List[str] 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  output_type='''numpy'''					,  ).images[0]

       a						:List[str] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       torch.manual_seed(0    )

       a						:List[Any] 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Any 							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
       a						:Union[str, Any] 							=       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Dict 							=       '''a bowl of fruit'''
       a						:Optional[Any] 							=       '''a bowl of pears'''

       a						:Tuple 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase					,  num_inference_steps=25					,  ).latents

       a						:str 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  num_inference_steps=25					,  output_type='''numpy'''					,  ).images[0]

       a						:List[Any] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1












'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    UpperCAmelCase	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				KandinskyVaaPipeline
 __lowercase   		=				[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __lowercase   		=				["""image_embeds""", """negative_image_embeds"""]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     torch.manual_seed(0							)

     A__				=			{
         "in_channels": 4,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image",
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     A__				=			UNetaDConditionModel(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }
 @property
 def      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :Dict							)->							Optional[Any]:
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			DDIMScheduler(
         num_train_timesteps=10_00						,       beta_schedule="linear"						,       beta_start=0.0_0_0_8_5						,       beta_end=0.0_1_2						,       clip_sample=lowercase_						,       set_alpha_to_one=lowercase_						,       steps_offset=1						,       prediction_type="epsilon"						,       thresholding=lowercase_						,       )

     A__				=			{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :int						,       lowercase_      :Union[str, Any]=0							)->							Dict:
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(seed + 1							)							).to(
         lowercase_							)
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__				=			{
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

     A__				=			self.pipeline_class(**lowercase_							)
     A__				=			pipe.to(lowercase_							)

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			pipe(**self.get_dummy_inputs(lowercase_							)							)
     A__				=			output.images

     A__				=			pipe(
         **self.get_dummy_inputs(lowercase_							)						,       return_dict=lowercase_						,       )[0]

     A__				=			image[0, -3:, -3:, -1]
     A__				=			image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     A__				=			np.array(
         [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

     assert (
         np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
     ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"							)

     A__				=			KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"						,       torch_dtype=torch.floataa							)
     pipe_prior.to(lowercase_							)

     A__				=			KandinskyVaaPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder"						,       torch_dtype=torch.floataa							)
     A__				=			pipeline.to(lowercase_							)
     pipeline.set_progress_bar_config(disable=lowercase_							)

     A__				=			"red cat, 4k photo"

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__,	A__				=			pipe_prior(
         lowercase_						,       generator=lowercase_						,       num_inference_steps=5						,       negative_prompt=""						,       ).to_tuple()

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__				=			pipeline(
         image_embeds=lowercase_						,       negative_image_embeds=lowercase_						,       generator=lowercase_						,       num_inference_steps=1_00						,       output_type="np"						,       )

     A__				=			output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowercase_						,       lowercase_							)



