


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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 	lowerCamelCase_       (				lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				StableDiffusionInpaintPipeline
					__UpperCAmelCase            =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					__UpperCAmelCase            =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__UpperCAmelCase            =				frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					__UpperCAmelCase            =				frozenset([]				)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								torch.manual_seed(0						)
								__lowercase 					=   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4) ,       layers_per_block=2 ,       sample_size=3_2 ,       in_channels=9 ,       out_channels=4 ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,       cross_attention_dim=3_2 ,       attention_head_dim=(2, 4) ,       use_linear_projection=snake_case_ ,       )
								__lowercase 					=   PNDMScheduler(skip_prk_steps=snake_case_						)
								torch.manual_seed(0						)
								__lowercase 					=   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 ,       sample_size=1_2_8 ,       )
								torch.manual_seed(0						)
								__lowercase 					=   CLIPTextConfig(
								    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       intermediate_size=3_7 ,       layer_norm_eps=1e-0_5 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       hidden_act='''gelu''' ,       projection_dim=5_1_2 ,       )
								__lowercase 					=   CLIPTextModel(snake_case_						)
								__lowercase 					=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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






					def A      (				self ,       snake_case_ ,       snake_case_=0						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(snake_case_						)						).to(snake_case_						)
								__lowercase 					=   image.cpu().permute(0 ,       2 ,       3 ,       1						)[0]
								__lowercase 					=   Image.fromarray(np.uinta(snake_case_						)						).convert('''RGB'''						).resize((6_4, 6_4)						)
								__lowercase 					=   Image.fromarray(np.uinta(image + 4						)						).convert('''RGB'''						).resize((6_4, 6_4)						)
								if str(snake_case_						).startswith('''mps'''						):
											__lowercase 					=   torch.manual_seed(snake_case_						)
								else:
											__lowercase 					=   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
								__lowercase 					=   {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': init_image,
								    '''mask_image''': mask_image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}
								return inputs






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__lowercase 					=   self.get_dummy_components()
								__lowercase 					=   StableDiffusionInpaintPipeline(**snake_case_						)
								__lowercase 					=   sd_pipe.to(snake_case_						)
								sd_pipe.set_progress_bar_config(disable=snake_case_						)

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

								assert image.shape == (1, 6_4, 6_4, 3)
								__lowercase 					=   np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

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




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



					'''simple docstring'''







					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''						)
								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''						)
								__lowercase 					=   load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
								    '''/yellow_cat_sitting_on_a_park_bench.npy'''						)

								__lowercase 					=   '''stabilityai/stable-diffusion-2-inpainting'''
								__lowercase 					=   StableDiffusionInpaintPipeline.from_pretrained(snake_case_ ,       safety_checker=snake_case_						)
								pipe.to(snake_case_						)
								pipe.set_progress_bar_config(disable=snake_case_						)
								pipe.enable_attention_slicing()

								__lowercase 					=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

								__lowercase 					=   torch.manual_seed(0						)
								__lowercase 					=   pipe(
								    prompt=snake_case_ ,       image=snake_case_ ,       mask_image=snake_case_ ,       generator=snake_case_ ,       output_type='''np''' ,       )
								__lowercase 					=   output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image						).max() < 9e-3






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''						)
								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''						)
								__lowercase 					=   load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
								    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''						)

								__lowercase 					=   '''stabilityai/stable-diffusion-2-inpainting'''
								__lowercase 					=   StableDiffusionInpaintPipeline.from_pretrained(
								    snake_case_ ,       torch_dtype=torch.floataa ,       safety_checker=snake_case_ ,       )
								pipe.to(snake_case_						)
								pipe.set_progress_bar_config(disable=snake_case_						)
								pipe.enable_attention_slicing()

								__lowercase 					=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

								__lowercase 					=   torch.manual_seed(0						)
								__lowercase 					=   pipe(
								    prompt=snake_case_ ,       image=snake_case_ ,       mask_image=snake_case_ ,       generator=snake_case_ ,       output_type='''np''' ,       )
								__lowercase 					=   output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image						).max() < 5e-1






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

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

								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/sd2-inpaint/init_image.png'''						)
								__lowercase 					=   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''						)

								__lowercase 					=   '''stabilityai/stable-diffusion-2-inpainting'''
								__lowercase 					=   PNDMScheduler.from_pretrained(snake_case_ ,       subfolder='''scheduler'''						)
								__lowercase 					=   StableDiffusionInpaintPipeline.from_pretrained(
								    snake_case_ ,       safety_checker=snake_case_ ,       scheduler=snake_case_ ,       torch_dtype=torch.floataa ,       )
								pipe.to(snake_case_						)
								pipe.set_progress_bar_config(disable=snake_case_						)
								pipe.enable_attention_slicing(1						)
								pipe.enable_sequential_cpu_offload()

								__lowercase 					=   '''Face of a yellow cat, high resolution, sitting on a park bench'''

								__lowercase 					=   torch.manual_seed(0						)
								__lowercase 					=   pipe(
								    prompt=snake_case_ ,       image=snake_case_ ,       mask_image=snake_case_ ,       generator=snake_case_ ,       num_inference_steps=2 ,       output_type='''np''' ,       )

								__lowercase 					=   torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.6_5 * 1_0**9






import unittest

from knapsack import knapsack as k
class 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   0
								__lowercase 					=   [0]
								__lowercase 					=   [0]
								__lowercase 					=   len(snake_case_						)
								self.assertEqual(k.knapsack(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						) ,       0						)

								__lowercase 					=   [6_0]
								__lowercase 					=   [1_0]
								__lowercase 					=   len(snake_case_						)
								self.assertEqual(k.knapsack(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						) ,       0						)






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   3
								__lowercase 					=   [1, 2, 3]
								__lowercase 					=   [3, 2, 1]
								__lowercase 					=   len(snake_case_						)
								self.assertEqual(k.knapsack(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						) ,       5						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   5_0
								__lowercase 					=   [6_0, 1_0_0, 1_2_0]
								__lowercase 					=   [1_0, 2_0, 3_0]
								__lowercase 					=   len(snake_case_						)
								self.assertEqual(k.knapsack(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						) ,       2_2_0						)


if __name__ == "__main__":
	unittest.main()


