

'''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 lowerCamelCase      (						__lowerCAmelCase							,							__lowerCAmelCase							,							__lowerCAmelCase							,							unittest.TestCase							):
				snake_case_           =   StableDiffusionImageVariationPipeline
				snake_case_           =   IMAGE_VARIATION_PARAMS
				snake_case_           =   IMAGE_VARIATION_BATCH_PARAMS
				snake_case_           =   frozenset([]							)
				# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				snake_case_           =   frozenset([]							)

				def 				_lowerCamelCase						(					self   )						->       Any:
					torch.manual_seed(0   )
					snake_case							 =				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,	)
					snake_case							 =				PNDMScheduler(skip_prk_steps=lowercase_   )
					torch.manual_seed(0   )
					snake_case							 =				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   )
					snake_case							 =				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,	)
					snake_case							 =				CLIPVisionModelWithProjection(lowercase_   )
					snake_case							 =				CLIPImageProcessor(crop_size=32,	size=32   )

					snake_case							 =				{
					    'unet': unet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'image_encoder': image_encoder,
					    'feature_extractor': feature_extractor,
					    'safety_checker': None,
					}
					return components

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=0   )						->       Optional[int]:
					snake_case							 =				floats_tensor((1, 3, 32, 32),	rng=random.Random(lowercase_   )   )
					snake_case							 =				image.cpu().permute(0,	2,	3,	1   )[0]
					snake_case							 =				Image.fromarray(np.uinta(lowercase_   )   ).convert('RGB'   ).resize((32, 32)   )
					if str(lowercase_   ).startswith('mps'   ):
						snake_case							 =				torch.manual_seed(lowercase_   )
					else:
						snake_case							 =				torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
					snake_case							 =				{
					    'image': image,
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs

				def 				_lowerCamelCase						(					self   )						->       Any:
					snake_case							 =				'cpu'  # ensure determinism for the device-dependent torch.Generator
					snake_case							 =				self.get_dummy_components()
					snake_case							 =				StableDiffusionImageVariationPipeline(**lowercase_   )
					snake_case							 =				sd_pipe.to(lowercase_   )
					sd_pipe.set_progress_bar_config(disable=lowercase_   )

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

					assert image.shape == (1, 64, 64, 3)
					snake_case							 =				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 				_lowerCamelCase						(					self   )						->       Optional[int]:
					snake_case							 =				'cpu'  # ensure determinism for the device-dependent torch.Generator
					snake_case							 =				self.get_dummy_components()
					snake_case							 =				StableDiffusionImageVariationPipeline(**lowercase_   )
					snake_case							 =				sd_pipe.to(lowercase_   )
					sd_pipe.set_progress_bar_config(disable=lowercase_   )

					snake_case							 =				self.get_dummy_inputs(lowercase_   )
					snake_case							 =				2 * [inputs['image']]
					snake_case							 =				sd_pipe(**lowercase_   )

					snake_case							 =				output.images

					snake_case							 =				image[-1, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)
					snake_case							 =				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 				_lowerCamelCase						(					self   )						->       Tuple:
					super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







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

				def 				_lowerCamelCase						(					self   )						->       int:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_="cpu",	lowercase_=torch.floataa,	lowercase_=0   )						->       Tuple:
					snake_case							 =				torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
					snake_case							 =				load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
					snake_case							 =				np.random.RandomState(lowercase_   ).standard_normal((1, 4, 64, 64)   )
					snake_case							 =				torch.from_numpy(lowercase_   ).to(device=lowercase_,	dtype=lowercase_   )
					snake_case							 =				{
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs

				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					snake_case							 =				StableDiffusionImageVariationPipeline.from_pretrained(
					    'lambdalabs/sd-image-variations-diffusers',	safety_checker=lowercase_   )
					snake_case							 =				sd_pipe.to(lowercase_   )
					sd_pipe.set_progress_bar_config(disable=lowercase_   )

					snake_case							 =				self.get_inputs(lowercase_   )
					snake_case							 =				sd_pipe(**lowercase_   ).images
					snake_case							 =				image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					snake_case							 =				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 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					snake_case							 =				0

					def callback_fn(lowercase_,	lowercase_,	lowercase_   ) -> None:
						snake_case							 =				True
						nonlocal number_of_steps
						number_of_steps += 1
						if step == 1:
							snake_case							 =				latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							snake_case							 =				latents[0, -3:, -3:, -1]
							snake_case							 =				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:
							snake_case							 =				latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							snake_case							 =				latents[0, -3:, -3:, -1]
							snake_case							 =				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

					snake_case							 =				False

					snake_case							 =				StableDiffusionImageVariationPipeline.from_pretrained(
					    'fusing/sd-image-variations-diffusers',	safety_checker=lowercase_,	torch_dtype=torch.floataa,	)
					pipe.to(lowercase_   )
					pipe.set_progress_bar_config(disable=lowercase_   )
					pipe.enable_attention_slicing()

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






				def 				_lowerCamelCase						(					self   )						->       List[str]:
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					snake_case							 =				'fusing/sd-image-variations-diffusers'
					snake_case							 =				StableDiffusionImageVariationPipeline.from_pretrained(
					    lowercase_,	safety_checker=lowercase_,	torch_dtype=torch.floataa   )
					snake_case							 =				pipe.to(lowercase_   )
					pipe.set_progress_bar_config(disable=lowercase_   )
					pipe.enable_attention_slicing(1   )
					pipe.enable_sequential_cpu_offload()

					snake_case							 =				self.get_inputs(lowercase_,	dtype=torch.floataa   )
					snake_case							 =				pipe(**lowercase_   )

					snake_case							 =				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 lowerCamelCase      (						unittest.TestCase							):

				def 				_lowerCamelCase						(					self   )						->       Any:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_="cpu",	lowercase_=torch.floataa,	lowercase_=0   )						->       Dict:
					snake_case							 =				torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
					snake_case							 =				load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
					snake_case							 =				np.random.RandomState(lowercase_   ).standard_normal((1, 4, 64, 64)   )
					snake_case							 =				torch.from_numpy(lowercase_   ).to(device=lowercase_,	dtype=lowercase_   )
					snake_case							 =				{
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 50,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs

				def 				_lowerCamelCase						(					self   )						->       str:
					snake_case							 =				StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
					sd_pipe.to(lowercase_   )
					sd_pipe.set_progress_bar_config(disable=lowercase_   )

					snake_case							 =				self.get_inputs(lowercase_   )
					snake_case							 =				sd_pipe(**lowercase_   ).images[0]

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






				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					snake_case							 =				StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
					snake_case							 =				DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config   )
					sd_pipe.to(lowercase_   )
					sd_pipe.set_progress_bar_config(disable=lowercase_   )

					snake_case							 =				self.get_inputs(lowercase_   )
					snake_case							 =				25
					snake_case							 =				sd_pipe(**lowercase_   ).images[0]

					snake_case							 =				load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'   )
					snake_case							 =				np.abs(expected_image - image   ).max()
					assert max_diff < 1E-3








'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey

def     __magic_name__       ( A					,   A							)			->		Union[str, Any]:
	snake_case , snake_case							 =				img.shape[0], img.shape[1]

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

	def normalize(A							):
		return min(A					,   2_5_5							)

	for i in range(A							):
		for j in range(A							):
			snake_case							 =				int(to_grayscale(*img[i][j]							)							)
			snake_case							 =				[
			    normalize(A							),
			    normalize(greyscale + factor							),
			    normalize(greyscale + 2 * factor							),
			]

	return img


if __name__ == "__main__":
						# read original image
						lowerCAmelCase_						   =			{
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
						}

						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()





