



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 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



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



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    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		,)
						lowerCAmelCase__					:    int												=					DDIMScheduler(
						    beta_start=0.0_0085		,beta_end=0.012		,beta_schedule='''scaled_linear'''		,clip_sample=__lowerCamelCase		,set_alpha_to_one=__lowerCamelCase		,)
						lowerCAmelCase__					:    List[Any]												=					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			)
						lowerCAmelCase__					:    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=1_28		,)
						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					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=10_00		,hidden_act='''gelu'''		,projection_dim=5_12		,)
						lowerCAmelCase__					:    Dict												=					CLIPTextModel(__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

						lowerCAmelCase__					:    Tuple												=					{
						    '''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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					floats_tensor((1, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 2, 4, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    List[Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Union[str, Any]												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					image.cpu().permute(0		,2		,3		,1			)[0]
						lowerCAmelCase__					:    Tuple												=					Image.fromarray(np.uinta(__lowerCamelCase			)			).convert('''RGB'''			)
						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Optional[int]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    str												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					{
						    '''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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					image.cpu().permute(0		,2		,3		,1			)[0]
						lowerCAmelCase__					:    Union[str, Any]												=					Image.fromarray(np.uinta(__lowerCamelCase			)			).convert('''RGB'''			)
						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    List[Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Dict												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					{
						    '''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 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






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

						lowerCAmelCase__					:    List[Any]												=					self.get_dummy_components()
						lowerCAmelCase__					:    Optional[Any]												=					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}			)

						lowerCAmelCase__					:    Tuple												=					self.get_dummy_inputs(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					pipe(**__lowerCamelCase			)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowerCamelCase			)
										lowerCAmelCase__					:    Dict												=					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."""		,)

						lowerCAmelCase__					:    Any												=					self.get_dummy_inputs(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					pipe_loaded(**__lowerCamelCase			)[0]

						lowerCAmelCase__					:    str												=					np.abs(output - output_loaded			).max()
						self.assertLess(__lowerCamelCase		,1e-4			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					'''cpu'''

						lowerCAmelCase__					:    Union[str, Any]												=					self.get_dummy_components()
						lowerCAmelCase__					:    List[Any]												=					self.pipeline_class(**__lowerCamelCase			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					self.get_dummy_mask_inputs(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					pipe.generate_mask(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					mask[0, -3:, -3:]

						self.assertEqual(mask.shape		,(1, 16, 16)			)
						lowerCAmelCase__					:    Optional[int]												=					np.array([0] * 9			)
						lowerCAmelCase__					:    Union[str, Any]												=					np.abs(mask_slice.flatten() - expected_slice			).max()
						self.assertLessEqual(__lowerCamelCase		,1e-3			)
						self.assertEqual(mask[0, -3, -4]		,0			)



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					'''cpu'''

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

						lowerCAmelCase__					:    Tuple												=					self.get_dummy_inversion_inputs(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.invert(**__lowerCamelCase			).images
						lowerCAmelCase__					:    Optional[int]												=					image[0, -1, -3:, -3:]

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



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					'''cpu'''

						lowerCAmelCase__					:    str												=					self.get_dummy_components()

						lowerCAmelCase__					:    Optional[int]												=					{'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
						lowerCAmelCase__					:    Optional[Any]												=					DPMSolverMultistepScheduler(**__lowerCamelCase			)
						lowerCAmelCase__					:    List[str]												=					DPMSolverMultistepInverseScheduler(**__lowerCamelCase			)

						lowerCAmelCase__					:    Any												=					self.pipeline_class(**__lowerCamelCase			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

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

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



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




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






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



		@classmethod
		def 		lowerCAmelCase__				(cls			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''			)

						lowerCAmelCase__					:    Tuple												=					raw_image.convert('''RGB'''			).resize((7_68, 7_68)			)

						lowerCAmelCase__					:    Any												=					raw_image



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					torch.manual_seed(0			)

						lowerCAmelCase__					:    Union[str, Any]												=					StableDiffusionDiffEditPipeline.from_pretrained(
						    '''stabilityai/stable-diffusion-2-1'''		,safety_checker=__lowerCamelCase		,torch_dtype=torch.floataa			)
						lowerCAmelCase__					:    Tuple												=					DDIMScheduler.from_config(pipe.scheduler.config			)
						lowerCAmelCase__					:    Dict												=					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					'''a bowl of fruit'''
						lowerCAmelCase__					:    Dict												=					'''a bowl of pears'''

						lowerCAmelCase__					:    Optional[int]												=					pipe.generate_mask(
						    image=self.raw_image		,source_prompt=__lowerCamelCase		,target_prompt=__lowerCamelCase		,generator=__lowerCamelCase		,)

						lowerCAmelCase__					:    Optional[Any]												=					pipe.invert(
						    prompt=__lowerCamelCase		,image=self.raw_image		,inpaint_strength=0.7		,generator=__lowerCamelCase			).latents

						lowerCAmelCase__					:    Union[str, Any]												=					pipe(
						    prompt=__lowerCamelCase		,mask_image=__lowerCamelCase		,image_latents=__lowerCamelCase		,generator=__lowerCamelCase		,negative_prompt=__lowerCamelCase		,inpaint_strength=0.7		,output_type='''numpy'''		,).images[0]

						lowerCAmelCase__					:    Optional[int]												=					(
						    np.array(
						        load_image(
						            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						            '''/diffedit/pears.png'''			).resize((7_68, 7_68)			)			)
						    / 2_55
						)
						assert np.abs((expected_image - image).max()			) < 5e-1



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					torch.manual_seed(0			)

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

						lowerCAmelCase__					:    int												=					'''a bowl of fruit'''
						lowerCAmelCase__					:    Dict												=					'''a bowl of pears'''

						lowerCAmelCase__					:    Any												=					pipe.generate_mask(
						    image=self.raw_image		,source_prompt=__lowerCamelCase		,target_prompt=__lowerCamelCase		,generator=__lowerCamelCase		,)

						lowerCAmelCase__					:    Optional[int]												=					pipe.invert(
						    prompt=__lowerCamelCase		,image=self.raw_image		,inpaint_strength=0.7		,generator=__lowerCamelCase		,num_inference_steps=25		,).latents

						lowerCAmelCase__					:    List[Any]												=					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]

						lowerCAmelCase__					:    int												=					(
						    np.array(
						        load_image(
						            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						            '''/diffedit/pears.png'''			).resize((7_68, 7_68)			)			)
						    / 2_55
						)
						assert np.abs((expected_image - image).max()			) < 5e-1






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		int):





				'''simple docstring'''

				while b:
								lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					b, a % b
				return a






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		int):





				'''simple docstring'''

				return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase_	,a % b)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3	,5)}""")
				print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5	,3)}""")
				print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1	,3)}""")
				print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3	,6)}""")
				print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6	,3)}""")

				print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3	,5)}""")
				print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5	,3)}""")
				print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1	,3)}""")
				print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3	,6)}""")
				print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6	,3)}""")


if __name__ == "__main__":
							main()

