






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 		a_      (	snake_case_  ,						snake_case_  ,						snake_case_  ,						unittest.TestCase ):







      '''simple docstring'''

      lowerCamelCase__       :				List[str] =	StableDiffusionImageVariationPipeline
      lowerCamelCase__       :				int =	IMAGE_VARIATION_PARAMS
      lowerCamelCase__       :				Optional[int] =	IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase__       :				Dict =	frozenset([] )
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCamelCase__       :				List[str] =	frozenset([] )




      def 			a__							(self					):

       '''simple docstring'''



       torch.manual_seed(0					)
       lowerCamelCase__     :			List[str]     				=						UNetaDConditionModel(
           block_out_channels=(3_2, 6_4),     layers_per_block=2,     sample_size=3_2,     in_channels=4,     out_channels=4,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),     cross_attention_dim=3_2,     )
       lowerCamelCase__     :			List[str]     				=						PNDMScheduler(skip_prk_steps=lowerCamelCase_					)
       torch.manual_seed(0					)
       lowerCamelCase__     :			Dict     				=						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__     :			Dict     				=						CLIPVisionConfig(
           hidden_size=3_2,     projection_dim=3_2,     intermediate_size=3_7,     layer_norm_eps=1e-05,     num_attention_heads=4,     num_hidden_layers=5,     image_size=3_2,     patch_size=4,     )
       lowerCamelCase__     :			List[str]     				=						CLIPVisionModelWithProjection(lowerCamelCase_					)
       lowerCamelCase__     :			Any     				=						CLIPImageProcessor(crop_size=3_2,     size=3_2					)

       lowerCamelCase__     :			List[str]     				=						{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'image_encoder': image_encoder,
           'feature_extractor': feature_extractor,
           'safety_checker': None,
       }
       return components




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_=0					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						floats_tensor((1, 3, 3_2, 3_2),     rng=random.Random(lowerCamelCase_					)					)
       lowerCamelCase__     :			int     				=						image.cpu().permute(0,     2,     3,     1					)[0]
       lowerCamelCase__     :			Optional[Any]     				=						Image.fromarray(np.uinta(lowerCamelCase_					)					).convert('RGB'					).resize((3_2, 3_2)					)
       if str(lowerCamelCase_					).startswith('mps'					):
        lowerCamelCase__     :			List[str]     				=						torch.manual_seed(lowerCamelCase_					)
       else:
        lowerCamelCase__     :			Optional[int]     				=						torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						{
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			List[Any]     				=						'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase__     :			Optional[int]     				=						self.get_dummy_components()
       lowerCamelCase__     :			Union[str, Any]     				=						StableDiffusionImageVariationPipeline(**lowerCamelCase_					)
       lowerCamelCase__     :			int     				=						sd_pipe.to(lowerCamelCase_					)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

       lowerCamelCase__     :			Any     				=						self.get_dummy_inputs(lowerCamelCase_					)
       lowerCamelCase__     :			Tuple     				=						sd_pipe(**lowerCamelCase_					).images
       lowerCamelCase__     :			Union[str, Any]     				=						image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       lowerCamelCase__     :			List[Any]     				=						np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]					)

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




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase__     :			Dict     				=						self.get_dummy_components()
       lowerCamelCase__     :			List[str]     				=						StableDiffusionImageVariationPipeline(**lowerCamelCase_					)
       lowerCamelCase__     :			str     				=						sd_pipe.to(lowerCamelCase_					)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

       lowerCamelCase__     :			Any     				=						self.get_dummy_inputs(lowerCamelCase_					)
       lowerCamelCase__     :			Dict     				=						2 * [inputs['image']]
       lowerCamelCase__     :			Dict     				=						sd_pipe(**lowerCamelCase_					)

       lowerCamelCase__     :			List[str]     				=						output.images

       lowerCamelCase__     :			Union[str, Any]     				=						image[-1, -3:, -3:, -1]

       assert image.shape == (2, 6_4, 6_4, 3)
       lowerCamelCase__     :			Union[str, Any]     				=						np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]					)

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





      def 			a__							(self					):

       '''simple docstring'''



       super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_="cpu",     lowerCamelCase_=torch.floataa,     lowerCamelCase_=0					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[int]     				=						torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)
       lowerCamelCase__     :			Tuple     				=						load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/input_image_vermeer.png'					)
       lowerCamelCase__     :			Optional[int]     				=						np.random.RandomState(lowerCamelCase_					).standard_normal((1, 4, 6_4, 6_4)					)
       lowerCamelCase__     :			List[str]     				=						torch.from_numpy(lowerCamelCase_					).to(device=lowerCamelCase_,     dtype=lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						{
           'image': init_image,
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			List[str]     				=						StableDiffusionImageVariationPipeline.from_pretrained(
           'lambdalabs/sd-image-variations-diffusers',     safety_checker=lowerCamelCase_					)
       lowerCamelCase__     :			int     				=						sd_pipe.to(lowerCamelCase_					)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

       lowerCamelCase__     :			str     				=						self.get_inputs(lowerCamelCase_					)
       lowerCamelCase__     :			Any     				=						sd_pipe(**lowerCamelCase_					).images
       lowerCamelCase__     :			Optional[Any]     				=						image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       lowerCamelCase__     :			List[str]     				=						np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]					)
       assert np.abs(image_slice - expected_slice					).max() < 6e-3




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						0

       def callback_fn(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					) -> None:
        lowerCamelCase__     :			Optional[int]     				=						True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
         lowerCamelCase__     :			List[Any]     				=						latents.detach().cpu().numpy()
         assert latents.shape == (1, 4, 6_4, 6_4)
         lowerCamelCase__     :			Union[str, Any]     				=						latents[0, -3:, -3:, -1]
         lowerCamelCase__     :			int     				=						np.array(
             [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]					)

         assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2
        elif step == 2:
         lowerCamelCase__     :			int     				=						latents.detach().cpu().numpy()
         assert latents.shape == (1, 4, 6_4, 6_4)
         lowerCamelCase__     :			Dict     				=						latents[0, -3:, -3:, -1]
         lowerCamelCase__     :			Tuple     				=						np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]					)

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

       lowerCamelCase__     :			Optional[int]     				=						False

       lowerCamelCase__     :			Optional[Any]     				=						StableDiffusionImageVariationPipeline.from_pretrained(
           'fusing/sd-image-variations-diffusers',     safety_checker=lowerCamelCase_,     torch_dtype=torch.floataa,     )
       pipe.to(lowerCamelCase_					)
       pipe.set_progress_bar_config(disable=lowerCamelCase_					)
       pipe.enable_attention_slicing()

       lowerCamelCase__     :			List[Any]     				=						self.get_inputs(lowerCamelCase_,     dtype=torch.floataa					)
       pipe(**lowerCamelCase_,     callback=lowerCamelCase_,     callback_steps=1					)
       assert callback_fn.has_been_called
       assert number_of_steps == inputs["num_inference_steps"]





      def 			a__							(self					):

       '''simple docstring'''



       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowerCamelCase__     :			Tuple     				=						'fusing/sd-image-variations-diffusers'
       lowerCamelCase__     :			int     				=						StableDiffusionImageVariationPipeline.from_pretrained(
           lowerCamelCase_,     safety_checker=lowerCamelCase_,     torch_dtype=torch.floataa					)
       lowerCamelCase__     :			List[str]     				=						pipe.to(lowerCamelCase_					)
       pipe.set_progress_bar_config(disable=lowerCamelCase_					)
       pipe.enable_attention_slicing(1					)
       pipe.enable_sequential_cpu_offload()

       lowerCamelCase__     :			Tuple     				=						self.get_inputs(lowerCamelCase_,     dtype=torch.floataa					)
       lowerCamelCase__     :			Union[str, Any]     				=						pipe(**lowerCamelCase_					)

       lowerCamelCase__     :			Optional[int]     				=						torch.cuda.max_memory_allocated()
       # make sure that less than 2.6 GB is allocated
       assert mem_bytes < 2.6 * 1_0**9



@nightly
@require_torch_gpu
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_="cpu",     lowerCamelCase_=torch.floataa,     lowerCamelCase_=0					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						torch.Generator(device=lowerCamelCase_					).manual_seed(lowerCamelCase_					)
       lowerCamelCase__     :			str     				=						load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/input_image_vermeer.png'					)
       lowerCamelCase__     :			Optional[Any]     				=						np.random.RandomState(lowerCamelCase_					).standard_normal((1, 4, 6_4, 6_4)					)
       lowerCamelCase__     :			List[Any]     				=						torch.from_numpy(lowerCamelCase_					).to(device=lowerCamelCase_,     dtype=lowerCamelCase_					)
       lowerCamelCase__     :			Optional[int]     				=						{
           'image': init_image,
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 5_0,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'					)
       sd_pipe.to(lowerCamelCase_					)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

       lowerCamelCase__     :			Any     				=						self.get_inputs(lowerCamelCase_					)
       lowerCamelCase__     :			List[str]     				=						sd_pipe(**lowerCamelCase_					).images[0]

       lowerCamelCase__     :			Any     				=						load_numpy(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'					)
       lowerCamelCase__     :			Optional[Any]     				=						np.abs(expected_image - image					).max()
       assert max_diff < 1e-3





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'					)
       lowerCamelCase__     :			int     				=						DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config					)
       sd_pipe.to(lowerCamelCase_					)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

       lowerCamelCase__     :			Tuple     				=						self.get_inputs(lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						2_5
       lowerCamelCase__     :			Union[str, Any]     				=						sd_pipe(**lowerCamelCase_					).images[0]

       lowerCamelCase__     :			Optional[Any]     				=						load_numpy(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'					)
       lowerCamelCase__     :			Optional[int]     				=						np.abs(expected_image - image					).max()
       assert max_diff < 1e-3









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__				,     lowerCamelCase__     :			Optional[Any]     				=						img.shape[0], img.shape[1]

 def to_grayscale(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
  return 0.2_126 * red + 0.587 * green + 0.114 * blue

 def normalize(_lowerCamelCase				):
  return min(_lowerCamelCase			,   255				)

 for i in range(_lowerCamelCase				):
  for j in range(_lowerCamelCase				):
   lowerCamelCase__     :			List[Any]     				=						int(to_grayscale(*img[i][j]				)				)
   lowerCamelCase__     :			Optional[int]     				=						[
       normalize(_lowerCamelCase				),
       normalize(greyscale + factor				),
       normalize(greyscale + 2 * factor				),
   ]

 return img


if __name__ == "__main__":
      # read original image
      A_			: List[str]				=			{
          percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f"Original image with sepia (factor: {percentage})", img)

      waitKey(0)
      destroyAllWindows()

