





from cva import destroyAllWindows, imread, imshow, waitKey






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		str:






				"""simple docstring"""


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

				# converting each pixel's color to its negative
				for i in range(_UpperCamelCase					):
								for j in range(_UpperCamelCase					):
												snake_case_		:       List[str]           =    [255, 255, 255] - img[i][j]

				return img


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

	# convert to its negative
	lowerCAmelCase_        =							convert_to_negative(img)

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


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, 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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							StableDiffusionXLImgaImgPipeline
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   __lowerCAmelCase				=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase				=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   def      __magic_name__     (     self							):
          torch.manual_seed(0							)
          lowercase       :							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")      ,       attention_head_dim=(2, 4)      ,       use_linear_projection=_a      ,       addition_embed_type="text_time"      ,       addition_time_embed_dim=8      ,       transformer_layers_per_block=(1, 2)      ,       projection_class_embeddings_input_dim=80      ,       cross_attention_dim=64      ,       )
          lowercase       :							Optional[Any]									=				EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       steps_offset=1      ,       beta_schedule="scaled_linear"      ,       timestep_spacing="leading"      ,       )
          torch.manual_seed(0							)
          lowercase       :							Any									=				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       :							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=1_000      ,       hidden_act="gelu"      ,       projection_dim=32      ,       )
          lowercase       :							str									=				CLIPTextModel(_a							)
          lowercase       :							List[str]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				CLIPTextModelWithProjection(_a							)
          lowercase       :							Union[str, Any]									=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      ,       local_files_only=_a							)

          lowercase       :							Any									=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_encoder_2": text_encoder_a,
              "tokenizer_2": tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          lowercase       :							Dict									=				floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(_a							)							).to(_a							)
          lowercase       :							List[Any]									=				image / 2 + 0.5
          if str(_a							).startswith("mps"							):
                 lowercase       :							Optional[int]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							str									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Optional[int]									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 5.0,
              "output_type": "numpy",
              "strength": 0.7_5,
          }
          return inputs
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase       :							Optional[int]									=				self.get_dummy_components()
          lowercase       :							str									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							List[Any]									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Optional[Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							Tuple									=				sd_pipe(**_a							).images
          lowercase       :							Dict									=				image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          lowercase       :							Tuple									=				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]							)

          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
   def      __magic_name__     (     self							):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.get_dummy_components()
          lowercase       :							Optional[Any]									=				StableDiffusionXLImgaImgPipeline(**_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          lowercase       :							Any									=				sd_pipe.to(_a							)
          sd_pipe.set_progress_bar_config(disable=_a							)

          # forward without prompt embeds
          lowercase       :							Union[str, Any]									=				self.get_dummy_inputs(_a							)
          lowercase       :							int									=				3 * ["this is a negative prompt"]
          lowercase       :							Any									=				negative_prompt
          lowercase       :							List[str]									=				3 * [inputs["prompt"]]

          lowercase       :							Optional[int]									=				sd_pipe(**_a							)
          lowercase       :							List[Any]									=				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          lowercase       :							Any									=				self.get_dummy_inputs(_a							)
          lowercase       :							List[str]									=				3 * ["this is a negative prompt"]
          lowercase       :							Optional[Any]									=				3 * [inputs.pop("prompt"							)]

          (
              (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       
          )       :							List[Any]									=				sd_pipe.encode_prompt(_a      ,       negative_prompt=_a							)

          lowercase       :							int									=				sd_pipe(
              **_a      ,       prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       pooled_prompt_embeds=_a      ,       negative_pooled_prompt_embeds=_a      ,       )
          lowercase       :							Dict									=				output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()							).max() < 1E-4


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self      ,       _a      ,       _a="cpu"      ,       _a=torch.floataa      ,       _a=0							):
          lowercase       :							Optional[int]									=				torch.Generator(device=_a							).manual_seed(_a							)
          lowercase       :							Any									=				np.random.RandomState(_a							).standard_normal((1, 4, 64, 64)							)
          lowercase       :							List[str]									=				torch.from_numpy(_a							).to(device=_a      ,       dtype=_a							)
          lowercase       :							List[Any]									=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs

   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"							)
          pipe.to(_a							)
          pipe.set_progress_bar_config(disable=_a							)

          lowercase       :							Any									=				self.get_inputs(_a							)
          lowercase       :							int									=				pipe(**_a							).images
          lowercase       :							Any									=				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          lowercase       :							Optional[Any]									=				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]							)
          assert np.abs(image_slice - expected_slice							).max() < 7E-3


