




'''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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Any			   =      StableDiffusionImageVariationPipeline
 __snake_case	:     Optional[int]			   =      IMAGE_VARIATION_PARAMS
 __snake_case	:     int			   =      IMAGE_VARIATION_BATCH_PARAMS
 __snake_case	:     Any			   =      frozenset([]				)
 # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case	:     Union[str, Any]			   =      frozenset([]				)



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        torch.manual_seed(0 )
        lowercase  :		Optional[Any]						=      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   ,		)
        lowercase  :		Dict						=      PNDMScheduler(skip_prk_steps=A )
        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   ,		)
        torch.manual_seed(0 )
        lowercase  :		Any						=      CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1E-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        lowercase  :		int						=      CLIPVisionModelWithProjection(A )
        lowercase  :		Dict						=      CLIPImageProcessor(crop_size=32   ,		size=32 )

        lowercase  :		Optional[Any]						=      {
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''feature_extractor''': feature_extractor,
            '''safety_checker''': None,
        }
        return components



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A=0 ):






        '''simple docstring'''




        lowercase  :		Tuple						=      floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(A ) )
        lowercase  :		Any						=      image.cpu().permute(0   ,		2   ,		3   ,		1 )[0]
        lowercase  :		List[Any]						=      Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((32, 32) )
        if str(A ).startswith('''mps''' ):
               lowercase  :		List[str]						=      torch.manual_seed(A )
        else:
               lowercase  :		List[Any]						=      torch.Generator(device=A ).manual_seed(A )
        lowercase  :		int						=      {
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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

        assert image.shape == (1, 64, 64, 3)
        lowercase  :		Optional[Any]						=      np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1] )

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase  :		Union[str, Any]						=      self.get_dummy_components()
        lowercase  :		Tuple						=      StableDiffusionImageVariationPipeline(**A )
        lowercase  :		str						=      sd_pipe.to(A )
        sd_pipe.set_progress_bar_config(disable=A )

        lowercase  :		Dict						=      self.get_dummy_inputs(A )
        lowercase  :		List[Any]						=      2 * [inputs['''image''']]
        lowercase  :		List[str]						=      sd_pipe(**A )

        lowercase  :		Optional[Any]						=      output.images

        lowercase  :		Tuple						=      image[-1, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)
        lowercase  :		Optional[Any]						=      np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9] )

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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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






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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A="cpu"   ,		A=torch.floataa   ,		A=0 ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      torch.Generator(device=A ).manual_seed(A )
        lowercase  :		Optional[Any]						=      load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
            '''/stable_diffusion_imgvar/input_image_vermeer.png''' )
        lowercase  :		int						=      np.random.RandomState(A ).standard_normal((1, 4, 64, 64) )
        lowercase  :		Any						=      torch.from_numpy(A ).to(device=A   ,		dtype=A )
        lowercase  :		Any						=      {
            '''image''': init_image,
            '''latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 3,
            '''guidance_scale''': 7.5,
            '''output_type''': '''numpy''',
        }
        return inputs



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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

        assert image.shape == (1, 512, 512, 3)
        lowercase  :		Any						=      np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9] )
        assert np.abs(image_slice - expected_slice ).max() < 6E-3



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      0

        def callback_fn(A   ,		A   ,		A ) -> None:
               lowercase  :		Optional[int]						=      True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      lowercase  :		str						=      latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowercase  :		List[Any]						=      latents[0, -3:, -3:, -1]
                      lowercase  :		Tuple						=      np.array(
                          [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9] )

                      assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
               elif step == 2:
                      lowercase  :		Dict						=      latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowercase  :		Union[str, Any]						=      latents[0, -3:, -3:, -1]
                      lowercase  :		str						=      np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3] )

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

        lowercase  :		Optional[int]						=      False

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

        lowercase  :		int						=      self.get_inputs(A   ,		dtype=torch.floataa )
        pipe(**A   ,		callback=A   ,		callback_steps=1 )
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Dict						=      '''fusing/sd-image-variations-diffusers'''
        lowercase  :		str						=      StableDiffusionImageVariationPipeline.from_pretrained(
            A   ,		safety_checker=A   ,		torch_dtype=torch.floataa )
        lowercase  :		Optional[Any]						=      pipe.to(A )
        pipe.set_progress_bar_config(disable=A )
        pipe.enable_attention_slicing(1 )
        pipe.enable_sequential_cpu_offload()

        lowercase  :		Any						=      self.get_inputs(A   ,		dtype=torch.floataa )
        lowercase  :		Tuple						=      pipe(**A )

        lowercase  :		Optional[int]						=      torch.cuda.max_memory_allocated()
        # make sure that less than 2.6 GB is allocated
        assert mem_bytes < 2.6 * 10**9






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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A="cpu"   ,		A=torch.floataa   ,		A=0 ):






        '''simple docstring'''




        lowercase  :		List[str]						=      torch.Generator(device=A ).manual_seed(A )
        lowercase  :		int						=      load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
            '''/stable_diffusion_imgvar/input_image_vermeer.png''' )
        lowercase  :		List[str]						=      np.random.RandomState(A ).standard_normal((1, 4, 64, 64) )
        lowercase  :		Union[str, Any]						=      torch.from_numpy(A ).to(device=A   ,		dtype=A )
        lowercase  :		List[str]						=      {
            '''image''': init_image,
            '''latents''': latents,
            '''generator''': generator,
            '''num_inference_steps''': 50,
            '''guidance_scale''': 7.5,
            '''output_type''': '''numpy''',
        }
        return inputs



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''' )
        sd_pipe.to(A )
        sd_pipe.set_progress_bar_config(disable=A )

        lowercase  :		Optional[int]						=      self.get_inputs(A )
        lowercase  :		str						=      sd_pipe(**A ).images[0]

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





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''' )
        lowercase  :		List[Any]						=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config )
        sd_pipe.to(A )
        sd_pipe.set_progress_bar_config(disable=A )

        lowercase  :		str						=      self.get_inputs(A )
        lowercase  :		Union[str, Any]						=      25
        lowercase  :		Dict						=      sd_pipe(**A ).images[0]

        lowercase  :		Union[str, Any]						=      load_numpy(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
            '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy''' )
        lowercase  :		Tuple						=      np.abs(expected_image - image ).max()
        assert max_diff < 1E-3










'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




