





from cva import destroyAllWindows, imread, imshow, waitKey



def 		A_				(     _lowerCAmelCase     )						-> int:
 # getting number of pixels in the image
 UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(_lowerCAmelCase     ):
  for j in range(_lowerCAmelCase     ):
   UpperCamelCase	:					Tuple       	=   [255, 255, 255] - img[i][j]

 return img


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

    # convert to its negative
    __lowerCamelCase       :     Optional[int]   =     convert_to_negative(img)

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







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = StableDiffusionInstructPixaPixPipeline
  a_        = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
  a_        = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  a_        = IMAGE_TO_IMAGE_IMAGE_PARAMS
  a_        = IMAGE_TO_IMAGE_IMAGE_PARAMS






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  sample_size=3_2					,  in_channels=8					,  out_channels=4					,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,  cross_attention_dim=3_2					,  )
     __lowerCAmelCase								=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
     torch.manual_seed(0     )
     __lowerCAmelCase								=	AutoencoderKL(
         block_out_channels=[3_2, 6_4]					,  in_channels=3					,  out_channels=3					,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,  latent_channels=4					,  )
     torch.manual_seed(0     )
     __lowerCAmelCase								=	CLIPTextConfig(
         bos_token_id=0					,  eos_token_id=2					,  hidden_size=3_2					,  intermediate_size=3_7					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1_0_0_0					,  )
     __lowerCAmelCase								=	CLIPTextModel(lowerCAmelCase_     )
     __lowerCAmelCase								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     __lowerCAmelCase								=	{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Tuple=0     )							->						Dict:
     __lowerCAmelCase								=	floats_tensor((1, 3, 3_2, 3_2)					,  rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )
     __lowerCAmelCase								=	image.cpu().permute(0					,  2					,  3					,  1     )[0]
     __lowerCAmelCase								=	Image.fromarray(np.uinta(lowerCAmelCase_     )     ).convert('RGB'     )
     if str(lowerCAmelCase_     ).startswith('mps'     ):
        __lowerCAmelCase								=	torch.manual_seed(lowerCAmelCase_     )
     else:
        __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )
     __lowerCAmelCase								=	{
         'prompt': 'A painting of a squirrel eating a burger',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
         'image_guidance_scale': 1,
         'output_type': 'numpy',
     }
     return inputs






  def 				lowercase		(				self :		Tuple     )							->						List[Any]:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]
     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		List[str]     )							->						Dict:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	'french fries'
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_					,  negative_prompt=lowerCAmelCase_     )
     __lowerCAmelCase								=	output.images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	[inputs['prompt']] * 2

     __lowerCAmelCase								=	np.array(inputs['image']     ).astype(np.floataa     ) / 2_55.0
     __lowerCAmelCase								=	torch.from_numpy(lowerCAmelCase_     ).unsqueeze(0     ).to(lowerCAmelCase_     )
     __lowerCAmelCase								=	image / 2 + 0.5
     __lowerCAmelCase								=	image.permute(0					,  3					,  1					,  2     )
     __lowerCAmelCase								=	image.repeat(2					,  1					,  1					,  1     )

     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[-1, -3:, -3:, -1]

     assert image.shape == (2, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Dict     )							->						Optional[Any]:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85					,  beta_end=0.0_12					,  beta_schedule='scaled_linear'     )
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]

     __lowerCAmelCase								=	[round(lowerCAmelCase_					,  4     ) for x in image_slice.flatten().tolist()]
     print(','.join([str(lowerCAmelCase_     ) for x in slice]     )     )

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	VaeImageProcessor(do_resize=lowerCAmelCase_					,  do_normalize=lowerCAmelCase_     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )     )[0]

     __lowerCAmelCase								=	components['vae']
     __lowerCAmelCase								=	self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )

     for image_param in self.image_latents_params:
        if image_param in inputs.keys():
           __lowerCAmelCase								=	vae.encode(inputs[image_param]     ).latent_dist.mode()

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )[0]

     __lowerCAmelCase								=	np.abs(out - out_latents_inputs     ).max()
     self.assertLess(lowerCAmelCase_					,  1e-4					,  'passing latents as image input generate different result from passing image'     )





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

  """simple docstring"""


  def 				lowercase		(				self :		int     )							->						Optional[int]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		List[Any]=0     )							->						Any:
     __lowerCAmelCase								=	torch.manual_seed(lowerCAmelCase_     )
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'     )
     __lowerCAmelCase								=	{
         'prompt': 'turn him into a cyborg',
         'image': image,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 7.5,
         'image_guidance_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs






  def 				lowercase		(				self :		List[Any]     )							->						str:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Tuple     )							->						List[str]:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[Any]     )							->						Dict:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	DDIMScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[int]     )							->						int:
     __lowerCAmelCase								=	0

     def callback_fn(lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		torch.FloatTensor     ) -> None:
        __lowerCAmelCase								=	True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
        elif step == 2:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2

     __lowerCAmelCase								=	False

     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_					,  torch_dtype=torch.floataa     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     pipe(**lowerCAmelCase_					,  callback=lowerCAmelCase_					,  callback_steps=1     )
     assert callback_fn.has_been_called
     assert number_of_steps == 3






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_					,  torch_dtype=torch.floataa     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )

     __lowerCAmelCase								=	torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 1_0**9






  def 				lowercase		(				self :		List[Any]     )							->						Any:
     __lowerCAmelCase								=	self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     __lowerCAmelCase								=	inputs['image'].resize((5_0_4, 5_0_4)     )

     __lowerCAmelCase								=	'timbrooks/instruct-pix2pix'
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowerCAmelCase_					,  safety_checker=lowerCAmelCase_					,  )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )
     __lowerCAmelCase								=	output.images[0]

     __lowerCAmelCase								=	image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert image.shape == (5_0_4, 5_0_4, 3)
     __lowerCAmelCase								=	np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3

