




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  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					StableDiffusionModelEditingPipeline
 lowercase_       					=					TEXT_TO_IMAGE_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_       					=					TEXT_TO_IMAGE_IMAGE_PARAMS



 def    lowerCAmelCase_						(							self						:	List[Any]		):
       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=1_000       ,       )
       SCREAMING_SNAKE_CASE_					=    CLIPTextModel(_lowerCAmelCase		)
       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    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Dict=0		):
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       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    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).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    lowerCAmelCase_						(							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(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    'french fries'
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase       ,       negative_prompt=_lowerCAmelCase		)
       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    lowerCAmelCase_						(							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_					=    EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085       ,       beta_end=0.012       ,       beta_schedule='scaled_linear'		)
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).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    lowerCAmelCase_						(							self						:	List[str]		):
       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(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       # the pipeline does not expect pndm so test if it raises error.
       with self.assertRaises(_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images



 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().test_inference_batch_single_identical(expected_max_diff=5E-3		)




 def    lowerCAmelCase_						(							self						:	Union[str, Any]		):
       super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)





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


 '''simple docstring'''







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



 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	str=0		):
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {
           'prompt': 'A field of roses',
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    'CompVis/stable-diffusion-v1-4'
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline.from_pretrained(_lowerCAmelCase       ,       safety_checker=_lowerCAmelCase		)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    self.get_inputs()
       SCREAMING_SNAKE_CASE_					=    pipe(**_lowerCAmelCase		).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(**_lowerCAmelCase		).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    lowerCAmelCase_						(							self						:	Dict		):
       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(_lowerCAmelCase       ,       subfolder='scheduler'		)
       SCREAMING_SNAKE_CASE_					=    StableDiffusionModelEditingPipeline.from_pretrained(
           _lowerCAmelCase       ,       scheduler=_lowerCAmelCase       ,       safety_checker=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing(1		)
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE_					=    self.get_inputs()
       SCREAMING_SNAKE_CASE_					=    pipe(**_lowerCAmelCase		)

       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





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    UNetaDModel(
           block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,       up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 3, 32, 32)		)
       SCREAMING_SNAKE_CASE_					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    (image > 0).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





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


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'		)

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    repaint(
           _lowerCAmelCase       ,       _lowerCAmelCase       ,       num_inference_steps=250       ,       eta=0.0       ,       jump_length=10       ,       jump_n_sample=10       ,       generator=_lowerCAmelCase       ,       output_type='np'       ,       )
       SCREAMING_SNAKE_CASE_					=    output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image		).mean() < 1E-2