

'''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     __lowerCAmelCase					(   __SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						unittest.TestCase			):






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





       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            torch.manual_seed(0					)
            _UpperCamelCase       =	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=_SCREAMING_SNAKE_CASE	,			)
            _UpperCamelCase       =	PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE					)
            torch.manual_seed(0					)
            _UpperCamelCase       =	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					)
            _UpperCamelCase       =	CLIPTextConfig(
                bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			hidden_act='''gelu'''	,			projection_dim=512	,			)
            _UpperCamelCase       =	CLIPTextModel(_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase       =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

            _UpperCamelCase       =	{
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "safety_checker": None,
                "feature_extractor": None,
            }
            return components





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]=0					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	floats_tensor((1, 3, 32, 32)	,			rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase       =	image.cpu().permute(0	,			2	,			3	,			1					)[0]
            _UpperCamelCase       =	Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE					)					).convert('''RGB'''					).resize((64, 64)					)
            _UpperCamelCase       =	Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((64, 64)					)
            if str(_SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
                 _UpperCamelCase       =	torch.manual_seed(_SCREAMING_SNAKE_CASE					)
            else:
                 _UpperCamelCase       =	torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase       =	{
                "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 snake_case__						(							self       :		Dict					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
            _UpperCamelCase       =	self.get_dummy_components()
            _UpperCamelCase       =	StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE					)
            _UpperCamelCase       =	sd_pipe.to(_SCREAMING_SNAKE_CASE					)
            sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

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

            assert image.shape == (1, 64, 64, 3)
            _UpperCamelCase       =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]					)

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







       def snake_case__						(							self       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




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

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






       """simple docstring"""





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

            '''simple docstring'''




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





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	"stabilityai/stable-diffusion-2-inpainting"
            _UpperCamelCase       =	StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,			safety_checker=_SCREAMING_SNAKE_CASE					)
            pipe.to(_SCREAMING_SNAKE_CASE					)
            pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
            pipe.enable_attention_slicing()

            _UpperCamelCase       =	"Face of a yellow cat, high resolution, sitting on a park bench"

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

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





       def snake_case__						(							self       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




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

            _UpperCamelCase       =	"stabilityai/stable-diffusion-2-inpainting"
            _UpperCamelCase       =	StableDiffusionInpaintPipeline.from_pretrained(
                _SCREAMING_SNAKE_CASE	,			torch_dtype=torch.floataa	,			safety_checker=_SCREAMING_SNAKE_CASE	,			)
            pipe.to(_SCREAMING_SNAKE_CASE					)
            pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
            pipe.enable_attention_slicing()

            _UpperCamelCase       =	"Face of a yellow cat, high resolution, sitting on a park bench"

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

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







       def snake_case__						(							self       :		List[Any]					)   ->					List[str]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	"Face of a yellow cat, high resolution, sitting on a park bench"

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

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










'''simple docstring'''

from __future__ import annotations







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



