'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      str       =							StableDiffusionInpaintPipeline
		lowercase   :      Dict       =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		lowercase   :      str       =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowercase   :      Optional[int]       =							frozenset(
		    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		lowercase   :      Any       =							frozenset([])






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								torch.manual_seed(0				)
								_lowerCAmelCase										=UNetaDConditionModel(
								    block_out_channels=(32, 64)		,						layers_per_block=2		,						sample_size=32		,						in_channels=9		,						out_channels=4		,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,						cross_attention_dim=32		,						attention_head_dim=(2, 4)		,						use_linear_projection=__A		,						)
								_lowerCAmelCase										=PNDMScheduler(skip_prk_steps=__A				)
								torch.manual_seed(0				)
								_lowerCAmelCase										=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				)
								_lowerCAmelCase										=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=1000		,						hidden_act='gelu'		,						projection_dim=512		,						)
								_lowerCAmelCase										=CLIPTextModel(__A				)
								_lowerCAmelCase										=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A=0				) ->     Tuple:
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								_lowerCAmelCase										=floats_tensor((1, 3, 32, 32)		,						rng=random.Random(__A				)				).to(__A				)
								_lowerCAmelCase										=image.cpu().permute(0		,						2		,						3		,						1				)[0]
								_lowerCAmelCase										=Image.fromarray(np.uinta(__A				)				).convert('RGB'				).resize((64, 64)				)
								_lowerCAmelCase										=Image.fromarray(np.uinta(image + 4				)				).convert('RGB'				).resize((64, 64)				)
								if str(__A				).startswith('mps'				):
														_lowerCAmelCase										=torch.manual_seed(__A				)
								else:
														_lowerCAmelCase										=torch.Generator(device=__A				).manual_seed(__A				)
								_lowerCAmelCase										={
								    '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 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										='cpu'  # ensure determinism for the device-dependent torch.Generator
								_lowerCAmelCase										=self.get_dummy_components()
								_lowerCAmelCase										=StableDiffusionInpaintPipeline(**__A				)
								_lowerCAmelCase										=sd_pipe.to(__A				)
								sd_pipe.set_progress_bar_config(disable=__A				)

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

								assert image.shape == (1, 64, 64, 3)
								_lowerCAmelCase										=np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]				)

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






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3				)

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







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'				)
								_lowerCAmelCase										=load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
								    '/yellow_cat_sitting_on_a_park_bench.npy'				)

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

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

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

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 9E-3






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'				)
								_lowerCAmelCase										=load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
								    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'				)

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

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

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

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 5E-1






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

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

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

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

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

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 10**9






'''simple docstring'''

import unittest

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







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=0
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)

								_lowerCAmelCase										=[60]
								_lowerCAmelCase										=[10]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=3
								_lowerCAmelCase										=[1, 2, 3]
								_lowerCAmelCase										=[3, 2, 1]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						5				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=50
								_lowerCAmelCase										=[60, 100, 120]
								_lowerCAmelCase										=[10, 20, 30]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						220				)


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





