






"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				return numa ^ numa < 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()











"""simple docstring"""






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_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					StableDiffusionDiffEditPipeline
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							int   			=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
	SCREAMING_SNAKE_CASE_:							str   			=					frozenset(
	    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	SCREAMING_SNAKE_CASE_:							int   			=					frozenset([]       )


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       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=__a  ,					)
					A__							 =       DDIMScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_one=__a  ,					)
					A__							 =       DDIMInverseScheduler(
					    beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'  ,					clip_sample=__a  ,					set_alpha_to_zero=__a  ,					)
					torch.manual_seed(0						)
					A__							 =       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__							 =       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__							 =       CLIPTextModel(__a						)
					A__							 =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       floats_tensor((1, 2, 4, 16, 16)  ,					rng=random.Random(__a						)						).to(__a						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(__a						)						).to(__a						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__a						)						).convert('RGB'						)
					if str(__a						).startswith('mps'						):
									A__							 =       torch.manual_seed(__a						)
					else:
									A__							 =       torch.Generator(device=__a						).manual_seed(__a						)
					A__							 =       {
					    '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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe(**__a						)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(__a						)
									A__							 =       self.pipeline_class.from_pretrained(__a						)
									pipe_loaded.to(__a						)
									pipe_loaded.set_progress_bar_config(disable=__a						)

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

					A__							 =       self.get_dummy_inputs(__a						)
					A__							 =       pipe_loaded(**__a						)[0]

					A__							 =       np.abs(output - output_loaded						).max()
					self.assertLess(__a  ,					1E-4						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_mask_inputs(__a						)
					A__							 =       pipe.generate_mask(**__a						)
					A__							 =       mask[0, -3:, -3:]

					self.assertEqual(mask.shape  ,					(1, 16, 16)						)
					A__							 =       np.array([0] * 9						)
					A__							 =       np.abs(mask_slice.flatten() - expected_slice						).max()
					self.assertLessEqual(__a  ,					1E-3						)
					self.assertEqual(mask[0, -3, -4]  ,					0						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inversion_inputs(__a						)
					A__							 =       pipe.invert(**__a						).images
					A__							 =       image[0, -1, -3:, -3:]

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=5E-3						)






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()

					A__							 =       {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
					A__							 =       DPMSolverMultistepScheduler(**__a						)
					A__							 =       DPMSolverMultistepInverseScheduler(**__a						)

					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       self.get_dummy_inversion_inputs(__a						)
					A__							 =       pipe.invert(**__a						).images
					A__							 =       image[0, -1, -3:, -3:]

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





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



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	@classmethod
	def       _UpperCAmelCase						(      cls						):


					"""simple docstring"""

					A__							 =       load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'						)

					A__							 =       raw_image.convert('RGB'						).resize((768, 768)						)

					A__							 =       raw_image


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

					A__							 =       StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'  ,					safety_checker=__a  ,					torch_dtype=torch.floataa						)
					A__							 =       DDIMScheduler.from_config(pipe.scheduler.config						)
					A__							 =       DDIMInverseScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

					A__							 =       pipe.invert(
					    prompt=__a  ,					image=self.raw_image  ,					inpaint_strength=0.7  ,					generator=__a						).latents

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       torch.manual_seed(0						)

					A__							 =       StableDiffusionDiffEditPipeline.from_pretrained(
					    'stabilityai/stable-diffusion-2-1'  ,					safety_checker=__a  ,					torch_dtype=torch.floataa						)
					A__							 =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
					A__							 =       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=__a						)

					A__							 =       'a bowl of fruit'
					A__							 =       'a bowl of pears'

					A__							 =       pipe.generate_mask(
					    image=self.raw_image  ,					source_prompt=__a  ,					target_prompt=__a  ,					generator=__a  ,					)

					A__							 =       pipe.invert(
					    prompt=__a  ,					image=self.raw_image  ,					inpaint_strength=0.7  ,					generator=__a  ,					num_inference_steps=25  ,					).latents

					A__							 =       pipe(
					    prompt=__a  ,					mask_image=__a  ,					image_latents=__a  ,					generator=__a  ,					negative_prompt=__a  ,					inpaint_strength=0.7  ,					num_inference_steps=25  ,					output_type='numpy'  ,					).images[0]

					A__							 =       (
					    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


