






"""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      UpperCamelCase		(__snake_case						,						__snake_case						,						__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		StableDiffusionInpaintPipeline
						_SCREAMING_SNAKE_CASE  : str												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						_SCREAMING_SNAKE_CASE  : Tuple												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE  : List[Any]												=		frozenset(
						    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						_SCREAMING_SNAKE_CASE  : str												=		frozenset([]     )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[Any]:
										torch.manual_seed(0				)
										lowercase    :      str       = 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=__magic_name__			, )
										lowercase    :      List[str]       = PNDMScheduler(skip_prk_steps=__magic_name__				)
										torch.manual_seed(0				)
										lowercase    :      Optional[int]       = 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				)
										lowercase    :      Union[str, Any]       = 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=1_000			, hidden_act="""gelu"""			, projection_dim=512			, )
										lowercase    :      Tuple       = CLIPTextModel(__magic_name__				)
										lowercase    :      Union[str, Any]       = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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






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

										lowercase    :      Union[str, Any]       = self.get_dummy_inputs(__magic_name__				)
										lowercase    :      Dict       = sd_pipe(**__magic_name__				).images
										lowercase    :      Union[str, Any]       = image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowercase    :      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  :int				)      ->Any:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3				)


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






						def      __snake_case      (		self  :Union[str, Any]				)      ->Dict:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






						def      __snake_case      (		self  :int				)      ->Dict:
										lowercase    :      Optional[Any]       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""				)
										lowercase    :      List[Any]       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
										lowercase    :      Any       = load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
										    """/yellow_cat_sitting_on_a_park_bench.npy"""				)

										lowercase    :      List[str]       = """stabilityai/stable-diffusion-2-inpainting"""
										lowercase    :      Optional[int]       = StableDiffusionInpaintPipeline.from_pretrained(__magic_name__			, safety_checker=__magic_name__				)
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

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

										lowercase    :      Optional[int]       = torch.manual_seed(0				)
										lowercase    :      List[Any]       = pipe(
										    prompt=__magic_name__			, image=__magic_name__			, mask_image=__magic_name__			, generator=__magic_name__			, output_type="""np"""			, )
										lowercase    :      List[str]       = output.images[0]

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






						def      __snake_case      (		self  :Any				)      ->Dict:
										lowercase    :      Tuple       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""				)
										lowercase    :      Optional[int]       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
										lowercase    :      Union[str, Any]       = 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    :      Union[str, Any]       = """stabilityai/stable-diffusion-2-inpainting"""
										lowercase    :      Tuple       = StableDiffusionInpaintPipeline.from_pretrained(
										    __magic_name__			, torch_dtype=torch.floataa			, safety_checker=__magic_name__			, )
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing()

										lowercase    :      Any       = """Face of a yellow cat, high resolution, sitting on a park bench"""

										lowercase    :      List[Any]       = torch.manual_seed(0				)
										lowercase    :      Union[str, Any]       = pipe(
										    prompt=__magic_name__			, image=__magic_name__			, mask_image=__magic_name__			, generator=__magic_name__			, output_type="""np"""			, )
										lowercase    :      Optional[Any]       = output.images[0]

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






						def      __snake_case      (		self  :Optional[int]				)      ->List[str]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase    :      List[Any]       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""				)
										lowercase    :      Any       = load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)

										lowercase    :      str       = """stabilityai/stable-diffusion-2-inpainting"""
										lowercase    :      Any       = PNDMScheduler.from_pretrained(__magic_name__			, subfolder="""scheduler"""				)
										lowercase    :      List[Any]       = StableDiffusionInpaintPipeline.from_pretrained(
										    __magic_name__			, safety_checker=__magic_name__			, scheduler=__magic_name__			, torch_dtype=torch.floataa			, )
										pipe.to(__magic_name__				)
										pipe.set_progress_bar_config(disable=__magic_name__				)
										pipe.enable_attention_slicing(1				)
										pipe.enable_sequential_cpu_offload()

										lowercase    :      List[Any]       = """Face of a yellow cat, high resolution, sitting on a park bench"""

										lowercase    :      str       = torch.manual_seed(0				)
										lowercase    :      int       = pipe(
										    prompt=__magic_name__			, image=__magic_name__			, mask_image=__magic_name__			, generator=__magic_name__			, num_inference_steps=2			, output_type="""np"""			, )

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








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
