



'''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    __lowerCamelCase							( UpperCamelCase_							,							UpperCamelCase_							,							unittest.TestCase   ):







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






	def  a_					(      self					):
						torch.manual_seed(0					)
						__SCREAMING_SNAKE_CASE				:    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=a__      ,     )
						__SCREAMING_SNAKE_CASE				:    str						    =							DDIMScheduler(
						    beta_start=0.00085      ,     beta_end=0.012      ,     beta_schedule="scaled_linear"      ,     clip_sample=a__      ,     set_alpha_to_one=a__      ,     )
						__SCREAMING_SNAKE_CASE				:    str						    =							DDIMInverseScheduler(
						    beta_start=0.00085      ,     beta_end=0.012      ,     beta_schedule="scaled_linear"      ,     clip_sample=a__      ,     set_alpha_to_zero=a__      ,     )
						torch.manual_seed(0					)
						__SCREAMING_SNAKE_CASE				:    int						    =							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					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							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      ,     )
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							CLIPTextModel(a__					)
						__SCREAMING_SNAKE_CASE				:    int						    =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

						__SCREAMING_SNAKE_CASE				:    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  a_					(      self      ,     a__      ,     a__=0					):
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							floats_tensor((1, 16, 16)      ,     rng=random.Random(a__					)					).to(a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							floats_tensor((1, 2, 4, 16, 16)      ,     rng=random.Random(a__					)					).to(a__					)
						if str(a__					).startswith("mps"					):
											__SCREAMING_SNAKE_CASE				:    Any						    =							torch.manual_seed(a__					)
						else:
											__SCREAMING_SNAKE_CASE				:    int						    =							torch.Generator(device=a__					).manual_seed(a__					)
						__SCREAMING_SNAKE_CASE				:    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  a_					(      self      ,     a__      ,     a__=0					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(a__					)					).to(a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							image.cpu().permute(0      ,     2      ,     3      ,     1					)[0]
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							Image.fromarray(np.uinta(a__					)					).convert("RGB"					)
						if str(a__					).startswith("mps"					):
											__SCREAMING_SNAKE_CASE				:    str						    =							torch.manual_seed(a__					)
						else:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							torch.Generator(device=a__					).manual_seed(a__					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							{
						    "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  a_					(      self      ,     a__      ,     a__=0					):
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(a__					)					).to(a__					)
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							image.cpu().permute(0      ,     2      ,     3      ,     1					)[0]
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							Image.fromarray(np.uinta(a__					)					).convert("RGB"					)
						if str(a__					).startswith("mps"					):
											__SCREAMING_SNAKE_CASE				:    Dict						    =							torch.manual_seed(a__					)
						else:
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							torch.Generator(device=a__					).manual_seed(a__					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							{
						    "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  a_					(      self					):

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

						__SCREAMING_SNAKE_CASE				:    int						    =							self.get_dummy_components()
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							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}					)

						__SCREAMING_SNAKE_CASE				:    str						    =							self.get_dummy_inputs(a__					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							pipe(**a__					)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(a__					)
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							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.'      ,     )

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self.get_dummy_inputs(a__					)
						__SCREAMING_SNAKE_CASE				:    int						    =							pipe_loaded(**a__					)[0]

						__SCREAMING_SNAKE_CASE				:    Dict						    =							np.abs(output - output_loaded					).max()
						self.assertLess(a__      ,     1e-4					)






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							"cpu"

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

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self.get_dummy_mask_inputs(a__					)
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							pipe.generate_mask(**a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							mask[0, -3:, -3:]

						self.assertEqual(mask.shape      ,     (1, 16, 16)					)
						__SCREAMING_SNAKE_CASE				:    str						    =							np.array([0] * 9					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							np.abs(mask_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(a__      ,     1e-3					)
						self.assertEqual(mask[0, -3, -4]      ,     0					)






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    int						    =							"cpu"

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

						__SCREAMING_SNAKE_CASE				:    List[str]						    =							self.get_dummy_inversion_inputs(a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							pipe.invert(**a__					).images
						__SCREAMING_SNAKE_CASE				:    Dict						    =							image[0, -1, -3:, -3:]

						self.assertEqual(image.shape      ,     (2, 32, 32, 3)					)
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]      ,     )
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(a__      ,     1e-3					)






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






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							"cpu"

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

						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							DPMSolverMultistepScheduler(**a__					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							DPMSolverMultistepInverseScheduler(**a__					)

						__SCREAMING_SNAKE_CASE				:    Any						    =							self.pipeline_class(**a__					)
						pipe.to(a__					)
						pipe.set_progress_bar_config(disable=a__					)

						__SCREAMING_SNAKE_CASE				:    Dict						    =							self.get_dummy_inversion_inputs(a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							pipe.invert(**a__					).images
						__SCREAMING_SNAKE_CASE				:    Any						    =							image[0, -1, -3:, -3:]

						self.assertEqual(image.shape      ,     (2, 32, 32, 3)					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]      ,     )
						__SCREAMING_SNAKE_CASE				:    int						    =							np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(a__      ,     1e-3					)






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







	'''simple docstring'''






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






	@classmethod
	def  a_					(      cls					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"					)

						__SCREAMING_SNAKE_CASE				:    Dict						    =							raw_image.convert("RGB"					).resize((768, 768)					)

						__SCREAMING_SNAKE_CASE				:    str						    =							raw_image






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							torch.manual_seed(0					)

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

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							"a bowl of fruit"
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							"a bowl of pears"

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							pipe.generate_mask(
						    image=self.raw_image      ,     source_prompt=a__      ,     target_prompt=a__      ,     generator=a__      ,     )

						__SCREAMING_SNAKE_CASE				:    str						    =							pipe.invert(
						    prompt=a__      ,     image=self.raw_image      ,     inpaint_strength=0.7      ,     generator=a__					).latents

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							pipe(
						    prompt=a__      ,     mask_image=a__      ,     image_latents=a__      ,     generator=a__      ,     negative_prompt=a__      ,     inpaint_strength=0.7      ,     output_type="numpy"      ,     ).images[0]

						__SCREAMING_SNAKE_CASE				:    List[str]						    =							(
						    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  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							torch.manual_seed(0					)

						__SCREAMING_SNAKE_CASE				:    str						    =							StableDiffusionDiffEditPipeline.from_pretrained(
						    "stabilityai/stable-diffusion-2-1"      ,     safety_checker=a__      ,     torch_dtype=torch.floataa					)
						__SCREAMING_SNAKE_CASE				:    int						    =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
						__SCREAMING_SNAKE_CASE				:    Any						    =							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=a__					)

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

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							pipe.generate_mask(
						    image=self.raw_image      ,     source_prompt=a__      ,     target_prompt=a__      ,     generator=a__      ,     )

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

						__SCREAMING_SNAKE_CASE				:    Any						    =							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]

						__SCREAMING_SNAKE_CASE				:    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




'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

     # show result image
     imshow("""negative of original image""", img)
     waitKey(0)
     destroyAllWindows()





