






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


 def _lowercase	(     self		:						List[Any]							)					->       Union[str, Any]:
   torch.manual_seed(0							)
   _a  :       str             =	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=lowercase_							,						)
   _a  :       str             =	DDIMScheduler(
       beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule="""scaled_linear"""							,						clip_sample=lowercase_							,						set_alpha_to_one=lowercase_							,						)
   _a  :       str             =	DDIMInverseScheduler(
       beta_start=0.0_0_0_8_5							,						beta_end=0.0_1_2							,						beta_schedule="""scaled_linear"""							,						clip_sample=lowercase_							,						set_alpha_to_zero=lowercase_							,						)
   torch.manual_seed(0							)
   _a  :       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							)
   _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(lowercase_							)
   _a  :       Union[str, Any]             =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

   _a  :       List[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 _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						int=0							)					->       List[Any]:
   _a  :       Optional[Any]             =	floats_tensor((1, 16, 16)							,						rng=random.Random(lowercase_							)							).to(lowercase_							)
   _a  :       int             =	floats_tensor((1, 2, 4, 16, 16)							,						rng=random.Random(lowercase_							)							).to(lowercase_							)
   if str(lowercase_							).startswith("""mps"""							):
     _a  :       List[Any]             =	torch.manual_seed(lowercase_							)
   else:
     _a  :       Optional[int]             =	torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
   _a  :       Tuple             =	{
       "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 _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						Union[str, Any]							,						UpperCAmelCase__		:						List[Any]=0							)					->       List[str]:
   _a  :       Any             =	floats_tensor((1, 3, 32, 32)							,						rng=random.Random(lowercase_							)							).to(lowercase_							)
   _a  :       List[str]             =	image.cpu().permute(0							,						2							,						3							,						1							)[0]
   _a  :       Optional[int]             =	Image.fromarray(np.uinta(lowercase_							)							).convert("""RGB"""							)
   if str(lowercase_							).startswith("""mps"""							):
     _a  :       int             =	torch.manual_seed(lowercase_							)
   else:
     _a  :       Optional[Any]             =	torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
   _a  :       Tuple             =	{
       "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 _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Union[str, Any]=0							)					->       Dict:
   _a  :       int             =	floats_tensor((1, 3, 32, 32)							,						rng=random.Random(lowercase_							)							).to(lowercase_							)
   _a  :       int             =	image.cpu().permute(0							,						2							,						3							,						1							)[0]
   _a  :       Dict             =	Image.fromarray(np.uinta(lowercase_							)							).convert("""RGB"""							)
   if str(lowercase_							).startswith("""mps"""							):
     _a  :       Union[str, Any]             =	torch.manual_seed(lowercase_							)
   else:
     _a  :       str             =	torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
   _a  :       Union[str, 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 _lowercase	(     self		:						List[Any]							)					->       Tuple:
   if not hasattr(self.pipeline_class							,						"""_optional_components"""							):
     return

   _a  :       str             =	self.get_dummy_components()
   _a  :       List[str]             =	self.pipeline_class(**lowercase_							)
   pipe.to(lowercase_							)
   pipe.set_progress_bar_config(disable=lowercase_							)

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

   _a  :       List[Any]             =	self.get_dummy_inputs(lowercase_							)
   _a  :       Dict             =	pipe(**lowercase_							)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(lowercase_							)
     _a  :       Optional[int]             =	self.pipeline_class.from_pretrained(lowercase_							)
     pipe_loaded.to(lowercase_							)
     pipe_loaded.set_progress_bar_config(disable=lowercase_							)

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

   _a  :       Union[str, Any]             =	self.get_dummy_inputs(lowercase_							)
   _a  :       Optional[Any]             =	pipe_loaded(**lowercase_							)[0]

   _a  :       Any             =	np.abs(output - output_loaded							).max()
   self.assertLess(lowercase_							,						1E-4							)


 def _lowercase	(     self		:						Tuple							)					->       str:
   _a  :       Dict             =	"cpu"

   _a  :       List[Any]             =	self.get_dummy_components()
   _a  :       Any             =	self.pipeline_class(**lowercase_							)
   pipe.to(lowercase_							)
   pipe.set_progress_bar_config(disable=lowercase_							)

   _a  :       Dict             =	self.get_dummy_mask_inputs(lowercase_							)
   _a  :       str             =	pipe.generate_mask(**lowercase_							)
   _a  :       Optional[Any]             =	mask[0, -3:, -3:]

   self.assertEqual(mask.shape							,						(1, 16, 16)							)
   _a  :       Union[str, Any]             =	np.array([0] * 9							)
   _a  :       Dict             =	np.abs(mask_slice.flatten() - expected_slice							).max()
   self.assertLessEqual(lowercase_							,						1E-3							)
   self.assertEqual(mask[0, -3, -4]							,						0							)


 def _lowercase	(     self		:						Any							)					->       Optional[Any]:
   _a  :       List[Any]             =	"cpu"

   _a  :       str             =	self.get_dummy_components()
   _a  :       Optional[int]             =	self.pipeline_class(**lowercase_							)
   pipe.to(lowercase_							)
   pipe.set_progress_bar_config(disable=lowercase_							)

   _a  :       Dict             =	self.get_dummy_inversion_inputs(lowercase_							)
   _a  :       Optional[Any]             =	pipe.invert(**lowercase_							).images
   _a  :       Union[str, Any]             =	image[0, -1, -3:, -3:]

   self.assertEqual(image.shape							,						(2, 32, 32, 3)							)
   _a  :       Optional[int]             =	np.array(
       [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
   _a  :       Optional[Any]             =	np.abs(image_slice.flatten() - expected_slice							).max()
   self.assertLessEqual(lowercase_							,						1E-3							)


 def _lowercase	(     self		:						List[Any]							)					->       Optional[Any]:
   super().test_inference_batch_single_identical(expected_max_diff=5E-3							)







 def _lowercase	(     self		:						int							)					->       int:
   _a  :       int             =	"cpu"

   _a  :       Optional[int]             =	self.get_dummy_components()

   _a  :       Optional[Any]             =	{"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
   _a  :       Any             =	DPMSolverMultistepScheduler(**lowercase_							)
   _a  :       Dict             =	DPMSolverMultistepInverseScheduler(**lowercase_							)

   _a  :       Optional[int]             =	self.pipeline_class(**lowercase_							)
   pipe.to(lowercase_							)
   pipe.set_progress_bar_config(disable=lowercase_							)

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

   self.assertEqual(image.shape							,						(2, 32, 32, 3)							)
   _a  :       Optional[Any]             =	np.array(
       [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]							,						)
   _a  :       Optional[Any]             =	np.abs(image_slice.flatten() - expected_slice							).max()
   self.assertLessEqual(lowercase_							,						1E-3							)







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


 def _lowercase	(     self		:						List[Any]							)					->       str:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 @classmethod
 def _lowercase	(     cls		:						Union[str, Any]							)					->       Any:
   _a  :       str             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""							)

   _a  :       int             =	raw_image.convert("""RGB"""							).resize((768, 768)							)

   _a  :       List[Any]             =	raw_image


 def _lowercase	(     self		:						int							)					->       Tuple:
   _a  :       Tuple             =	torch.manual_seed(0							)

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

   _a  :       Optional[Any]             =	"a bowl of fruit"
   _a  :       Any             =	"a bowl of pears"

   _a  :       List[str]             =	pipe.generate_mask(
       image=self.raw_image							,						source_prompt=lowercase_							,						target_prompt=lowercase_							,						generator=lowercase_							,						)

   _a  :       List[Any]             =	pipe.invert(
       prompt=lowercase_							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=lowercase_							).latents

   _a  :       Tuple             =	pipe(
       prompt=lowercase_							,						mask_image=lowercase_							,						image_latents=lowercase_							,						generator=lowercase_							,						negative_prompt=lowercase_							,						inpaint_strength=0.7							,						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







 def _lowercase	(     self		:						List[Any]							)					->       Dict:
   _a  :       Tuple             =	torch.manual_seed(0							)

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

   _a  :       List[str]             =	"a bowl of fruit"
   _a  :       Optional[Any]             =	"a bowl of pears"

   _a  :       List[Any]             =	pipe.generate_mask(
       image=self.raw_image							,						source_prompt=lowercase_							,						target_prompt=lowercase_							,						generator=lowercase_							,						)

   _a  :       Tuple             =	pipe.invert(
       prompt=lowercase_							,						image=self.raw_image							,						inpaint_strength=0.7							,						generator=lowercase_							,						num_inference_steps=25							,						).latents

   _a  :       List[str]             =	pipe(
       prompt=lowercase_							,						mask_image=lowercase_							,						image_latents=lowercase_							,						generator=lowercase_							,						negative_prompt=lowercase_							,						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"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


