






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 		_snake_case  (  A__   ,    A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    int			     =							StableDiffusionInpaintPipeline
			_lowercase		:    str			     =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_lowercase		:    Any			     =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_lowercase		:    List[str]			     =							frozenset(
			    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_lowercase		:    int			     =							frozenset([]					)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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		,					)
						SCREAMING_SNAKE_CASE						= PNDMScheduler(skip_prk_steps=a)
						torch.manual_seed(0)
						SCREAMING_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		,					sample_size=128		,					)
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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		,					)
						SCREAMING_SNAKE_CASE						= CLIPTextModel(a)
						SCREAMING_SNAKE_CASE						= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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






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

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

						assert image.shape == (1, 64, 64, 3)
						SCREAMING_SNAKE_CASE						= np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76])

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







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3)



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






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png')
						SCREAMING_SNAKE_CASE						= load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')
						SCREAMING_SNAKE_CASE						= load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy')

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

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

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

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






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

						SCREAMING_SNAKE_CASE						= 'stabilityai/stable-diffusion-2-inpainting'
						SCREAMING_SNAKE_CASE						= 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()

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

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

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







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						SCREAMING_SNAKE_CASE						= 'stabilityai/stable-diffusion-2-inpainting'
						SCREAMING_SNAKE_CASE						= PNDMScheduler.from_pretrained(a		,					subfolder='scheduler')
						SCREAMING_SNAKE_CASE						= 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()

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

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

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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




