





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 				a			(      __lowercase   ,__lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     StableDiffusionInpaintPipeline
				SCREAMING_SNAKE_CASE__     :   str		      =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				SCREAMING_SNAKE_CASE__     :   Any		      =     frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     frozenset([]			)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				int           =   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=_lowerCAmelCase			, )
										__SCREAMING_SNAKE_CASE:				int           =   PNDMScheduler(skip_prk_steps=_lowerCAmelCase					)
										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   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:				str           =   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:				Union[str, Any]           =   CLIPTextModel(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   floats_tensor((1, 3, 32, 32)			, rng=random.Random(_lowerCAmelCase					)					).to(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   image.cpu().permute(0			, 2			, 3			, 1					)[0]
										__SCREAMING_SNAKE_CASE:				int           =   Image.fromarray(np.uinta(_lowerCAmelCase					)					).convert('''RGB'''					).resize((64, 64)					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((64, 64)					)
										if str(_lowerCAmelCase					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.manual_seed(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    '''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					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				str           =   StableDiffusionInpaintPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   sd_pipe.to(_lowerCAmelCase					)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   sd_pipe(**_lowerCAmelCase					).images
										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										__SCREAMING_SNAKE_CASE:				Dict           =   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					):

										"""simple docstring"""




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





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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/sd2-inpaint/init_image.png'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
										__SCREAMING_SNAKE_CASE:				str           =   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:				Union[str, Any]           =   '''stabilityai/stable-diffusion-2-inpainting'''
										__SCREAMING_SNAKE_CASE:				int           =   StableDiffusionInpaintPipeline.from_pretrained(_lowerCAmelCase			, safety_checker=_lowerCAmelCase					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										pipe.enable_attention_slicing()

										__SCREAMING_SNAKE_CASE:				Any           =   '''Face of a yellow cat, high resolution, sitting on a park bench'''

										__SCREAMING_SNAKE_CASE:				Tuple           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   pipe(
										    prompt=_lowerCAmelCase			, image=_lowerCAmelCase			, mask_image=_lowerCAmelCase			, generator=_lowerCAmelCase			, output_type='''np'''			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   output.images[0]

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/sd2-inpaint/init_image.png'''					)
										__SCREAMING_SNAKE_CASE:				Any           =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''					)
										__SCREAMING_SNAKE_CASE:				str           =   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:				Dict           =   '''stabilityai/stable-diffusion-2-inpainting'''
										__SCREAMING_SNAKE_CASE:				Any           =   StableDiffusionInpaintPipeline.from_pretrained(
										    _lowerCAmelCase			, torch_dtype=torch.floataa			, safety_checker=_lowerCAmelCase			, )
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										pipe.enable_attention_slicing()

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''Face of a yellow cat, high resolution, sitting on a park bench'''

										__SCREAMING_SNAKE_CASE:				Any           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pipe(
										    prompt=_lowerCAmelCase			, image=_lowerCAmelCase			, mask_image=_lowerCAmelCase			, generator=_lowerCAmelCase			, output_type='''np'''			, )
										__SCREAMING_SNAKE_CASE:				str           =   output.images[0]

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

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

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''stabilityai/stable-diffusion-2-inpainting'''
										__SCREAMING_SNAKE_CASE:				Any           =   PNDMScheduler.from_pretrained(_lowerCAmelCase			, subfolder='''scheduler'''					)
										__SCREAMING_SNAKE_CASE:				int           =   StableDiffusionInpaintPipeline.from_pretrained(
										    _lowerCAmelCase			, safety_checker=_lowerCAmelCase			, scheduler=_lowerCAmelCase			, torch_dtype=torch.floataa			, )
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										pipe.enable_attention_slicing(1					)
										pipe.enable_sequential_cpu_offload()

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''Face of a yellow cat, high resolution, sitting on a park bench'''

										__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe(
										    prompt=_lowerCAmelCase			, image=_lowerCAmelCase			, mask_image=_lowerCAmelCase			, generator=_lowerCAmelCase			, num_inference_steps=2			, output_type='''np'''			, )

										__SCREAMING_SNAKE_CASE:				List[str]           =   torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 10**9










def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



