
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		(				__UpperCamelCase ,     __UpperCamelCase ,     unittest.TestCase					):
    _a		:						List[str]=    StableDiffusionDiffEditPipeline
    _a		:						Dict=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
    _a		:						Union[str, Any]=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
    _a		:						Optional[Any]=    frozenset(
        []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    _a		:						Tuple=    frozenset([]					)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           torch.manual_seed(0				)
           lowercase							:					List[Any]	     =				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=snake_case		,)
           lowercase							:					Tuple	     =				DDIMScheduler(
               beta_start=0.00_085		,beta_end=0.012		,beta_schedule="""scaled_linear"""		,clip_sample=snake_case		,set_alpha_to_one=snake_case		,)
           lowercase							:					List[Any]	     =				DDIMInverseScheduler(
               beta_start=0.00_085		,beta_end=0.012		,beta_schedule="""scaled_linear"""		,clip_sample=snake_case		,set_alpha_to_zero=snake_case		,)
           torch.manual_seed(0				)
           lowercase							:					List[str]	     =				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				)
           lowercase							:					Tuple	     =				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		,)
           lowercase							:					int	     =				CLIPTextModel(snake_case				)
           lowercase							:					Optional[Any]	     =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

           lowercase							:					Dict	     =				{
               """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		,snake_case		,snake_case=0				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				floats_tensor((1, 16, 16)		,rng=random.Random(snake_case				)				).to(snake_case				)
           lowercase							:					Union[str, Any]	     =				floats_tensor((1, 2, 4, 16, 16)		,rng=random.Random(snake_case				)				).to(snake_case				)
           if str(snake_case				).startswith("""mps"""				):
                  lowercase							:					List[Any]	     =				torch.manual_seed(snake_case				)
           else:
                  lowercase							:					int	     =				torch.Generator(device=snake_case				).manual_seed(snake_case				)
           lowercase							:					Optional[Any]	     =				{
               """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		,snake_case		,snake_case=0				):



           '''simple docstring'''







           lowercase							:					Any	     =				floats_tensor((1, 3, 32, 32)		,rng=random.Random(snake_case				)				).to(snake_case				)
           lowercase							:					List[str]	     =				image.cpu().permute(0		,2		,3		,1				)[0]
           lowercase							:					str	     =				Image.fromarray(np.uinta(snake_case				)				).convert("""RGB"""				)
           if str(snake_case				).startswith("""mps"""				):
                  lowercase							:					int	     =				torch.manual_seed(snake_case				)
           else:
                  lowercase							:					Any	     =				torch.Generator(device=snake_case				).manual_seed(snake_case				)
           lowercase							:					List[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		,snake_case		,snake_case=0				):



           '''simple docstring'''







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



           '''simple docstring'''







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

           lowercase							:					List[str]	     =				self.get_dummy_components()
           lowercase							:					Optional[Any]	     =				self.pipeline_class(**snake_case				)
           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)

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

           lowercase							:					str	     =				self.get_dummy_inputs(snake_case				)
           lowercase							:					Any	     =				pipe(**snake_case				)[0]

           with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(snake_case				)
                  lowercase							:					List[str]	     =				self.pipeline_class.from_pretrained(snake_case				)
                  pipe_loaded.to(snake_case				)
                  pipe_loaded.set_progress_bar_config(disable=snake_case				)

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

           lowercase							:					Any	     =				self.get_dummy_inputs(snake_case				)
           lowercase							:					Tuple	     =				pipe_loaded(**snake_case				)[0]

           lowercase							:					Any	     =				np.abs(output - output_loaded				).max()
           self.assertLess(snake_case		,1e-4				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				"""cpu"""

           lowercase							:					Optional[Any]	     =				self.get_dummy_components()
           lowercase							:					Tuple	     =				self.pipeline_class(**snake_case				)
           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					Tuple	     =				self.get_dummy_mask_inputs(snake_case				)
           lowercase							:					List[str]	     =				pipe.generate_mask(**snake_case				)
           lowercase							:					Dict	     =				mask[0, -3:, -3:]

           self.assertEqual(mask.shape		,(1, 16, 16)				)
           lowercase							:					Any	     =				np.array([0] * 9				)
           lowercase							:					Dict	     =				np.abs(mask_slice.flatten() - expected_slice				).max()
           self.assertLessEqual(snake_case		,1e-3				)
           self.assertEqual(mask[0, -3, -4]		,0				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				"""cpu"""

           lowercase							:					Optional[int]	     =				self.get_dummy_components()
           lowercase							:					int	     =				self.pipeline_class(**snake_case				)
           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					Dict	     =				self.get_dummy_inversion_inputs(snake_case				)
           lowercase							:					Tuple	     =				pipe.invert(**snake_case				).images
           lowercase							:					Any	     =				image[0, -1, -3:, -3:]

           self.assertEqual(image.shape		,(2, 32, 32, 3)				)
           lowercase							:					List[Any]	     =				np.array(
               [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]		,)
           lowercase							:					List[str]	     =				np.abs(image_slice.flatten() - expected_slice				).max()
           self.assertLessEqual(snake_case		,1e-3				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					str	     =				"""cpu"""

           lowercase							:					int	     =				self.get_dummy_components()

           lowercase							:					Optional[Any]	     =				{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
           lowercase							:					Dict	     =				DPMSolverMultistepScheduler(**snake_case				)
           lowercase							:					Any	     =				DPMSolverMultistepInverseScheduler(**snake_case				)

           lowercase							:					List[Any]	     =				self.pipeline_class(**snake_case				)
           pipe.to(snake_case				)
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					Tuple	     =				self.get_dummy_inversion_inputs(snake_case				)
           lowercase							:					int	     =				pipe.invert(**snake_case				).images
           lowercase							:					str	     =				image[0, -1, -3:, -3:]

           self.assertEqual(image.shape		,(2, 32, 32, 3)				)
           lowercase							:					List[str]	     =				np.array(
               [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]		,)
           lowercase							:					List[Any]	     =				np.abs(image_slice.flatten() - expected_slice				).max()
           self.assertLessEqual(snake_case		,1e-3				)






@require_torch_gpu
@slow
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""				)

           lowercase							:					Any	     =				raw_image.convert("""RGB"""				).resize((768, 768)				)

           lowercase							:					Union[str, Any]	     =				raw_image
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				torch.manual_seed(0				)

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

           lowercase							:					Tuple	     =				"""a bowl of fruit"""
           lowercase							:					Any	     =				"""a bowl of pears"""

           lowercase							:					Dict	     =				pipe.generate_mask(
               image=self.raw_image		,source_prompt=snake_case		,target_prompt=snake_case		,generator=snake_case		,)

           lowercase							:					int	     =				pipe.invert(
               prompt=snake_case		,image=self.raw_image		,inpaint_strength=0.7		,generator=snake_case				).latents

           lowercase							:					str	     =				pipe(
               prompt=snake_case		,mask_image=snake_case		,image_latents=snake_case		,generator=snake_case		,negative_prompt=snake_case		,inpaint_strength=0.7		,output_type="""numpy"""		,).images[0]

           lowercase							:					Optional[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
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				torch.manual_seed(0				)

           lowercase							:					List[Any]	     =				StableDiffusionDiffEditPipeline.from_pretrained(
               """stabilityai/stable-diffusion-2-1"""		,safety_checker=snake_case		,torch_dtype=torch.floataa				)
           lowercase							:					Dict	     =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
           lowercase							:					int	     =				DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config				)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=snake_case				)

           lowercase							:					int	     =				"""a bowl of fruit"""
           lowercase							:					Dict	     =				"""a bowl of pears"""

           lowercase							:					Dict	     =				pipe.generate_mask(
               image=self.raw_image		,source_prompt=snake_case		,target_prompt=snake_case		,generator=snake_case		,)

           lowercase							:					List[Any]	     =				pipe.invert(
               prompt=snake_case		,image=self.raw_image		,inpaint_strength=0.7		,generator=snake_case		,num_inference_steps=25		,).latents

           lowercase							:					Optional[Any]	     =				pipe(
               prompt=snake_case		,mask_image=snake_case		,image_latents=snake_case		,generator=snake_case		,negative_prompt=snake_case		,inpaint_strength=0.7		,num_inference_steps=25		,output_type="""numpy"""		,).images[0]

           lowercase							:					Optional[int]	     =				(
               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

print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
