


"""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_			(				UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





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




	def 						UpperCAmelCase_   (				self    :       int     ) ->			int:
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)    ,    layers_per_block=2    ,    sample_size=3_2    ,    in_channels=9    ,    out_channels=4    ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,    cross_attention_dim=3_2    ,    attention_head_dim=(2, 4)    ,    use_linear_projection=UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       PNDMScheduler(skip_prk_steps=UpperCAmelCase__     )
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       AutoencoderKL(
						    block_out_channels=[3_2, 6_4]    ,    in_channels=3    ,    out_channels=3    ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,    latent_channels=4    ,    sample_size=1_2_8    ,    )
						torch.manual_seed(0     )
						__SCREAMING_SNAKE_CASE		     =       CLIPTextConfig(
						    bos_token_id=0    ,    eos_token_id=2    ,    hidden_size=3_2    ,    intermediate_size=3_7    ,    layer_norm_eps=1E-05    ,    num_attention_heads=4    ,    num_hidden_layers=5    ,    pad_token_id=1    ,    vocab_size=1_0_0_0    ,    hidden_act="gelu"    ,    projection_dim=5_1_2    ,    )
						__SCREAMING_SNAKE_CASE		     =       CLIPTextModel(UpperCAmelCase__     )
						__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 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       Union[str, Any]=0     ) ->			Any:
						# 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, 3_2, 3_2)    ,    rng=random.Random(UpperCAmelCase__     )     ).to(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       image.cpu().permute(0    ,    2    ,    3    ,    1     )[0]
						__SCREAMING_SNAKE_CASE		     =       Image.fromarray(np.uinta(UpperCAmelCase__     )     ).convert("RGB"     ).resize((6_4, 6_4)     )
						__SCREAMING_SNAKE_CASE		     =       Image.fromarray(np.uinta(image + 4     )     ).convert("RGB"     ).resize((6_4, 6_4)     )
						if str(UpperCAmelCase__     ).startswith("mps"     ):
											__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       torch.Generator(device=UpperCAmelCase__     ).manual_seed(UpperCAmelCase__     )
						__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 						UpperCAmelCase_   (				self    :       int     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       "cpu"  # ensure determinism for the device-dependent torch.Generator
						__SCREAMING_SNAKE_CASE		     =       self.get_dummy_components()
						__SCREAMING_SNAKE_CASE		     =       StableDiffusionInpaintPipeline(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       sd_pipe.to(UpperCAmelCase__     )
						sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__     )

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

						assert image.shape == (1, 6_4, 6_4, 3)
						__SCREAMING_SNAKE_CASE		     =       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    :       List[Any]     ) ->			List[str]:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3     )




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

	"""simple docstring"""





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




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Optional[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.npy"     )

						__SCREAMING_SNAKE_CASE		     =       "stabilityai/stable-diffusion-2-inpainting"
						__SCREAMING_SNAKE_CASE		     =       StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase__    ,    safety_checker=UpperCAmelCase__     )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )
						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=UpperCAmelCase__    ,    image=UpperCAmelCase__    ,    mask_image=UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    output_type="np"    ,    )
						__SCREAMING_SNAKE_CASE		     =       output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image     ).max() < 9E-3




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			Dict:
						__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(
						    UpperCAmelCase__    ,    torch_dtype=torch.floataa    ,    safety_checker=UpperCAmelCase__    ,    )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )
						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=UpperCAmelCase__    ,    image=UpperCAmelCase__    ,    mask_image=UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    output_type="np"    ,    )
						__SCREAMING_SNAKE_CASE		     =       output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image     ).max() < 5E-1




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			str:
						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(UpperCAmelCase__    ,    subfolder="scheduler"     )
						__SCREAMING_SNAKE_CASE		     =       StableDiffusionInpaintPipeline.from_pretrained(
						    UpperCAmelCase__    ,    safety_checker=UpperCAmelCase__    ,    scheduler=UpperCAmelCase__    ,    torch_dtype=torch.floataa    ,    )
						pipe.to(UpperCAmelCase__     )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__     )
						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=UpperCAmelCase__    ,    image=UpperCAmelCase__    ,    mask_image=UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    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 * 1_0**9




"""simple docstring"""







from __future__ import annotations

import math




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       int     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       size
						# approximate the overall size of segment tree with given value
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						# create array to store lazy update
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]
						__SCREAMING_SNAKE_CASE		     =       [0 for i in range(0    ,    4 * size     )]  # flag for lazy update




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       int     ) ->			int:
						return idx * 2 + 1




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       list[int]     ) ->			None:
						if left_element == right_element:
											__SCREAMING_SNAKE_CASE		     =       a[left_element - 1]
						else:
											__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
											self.build(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											self.build(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       max(
											    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			bool:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True

						if right_element < a or left_element > b:
											return True
						if left_element >= a and right_element <= b:
											__SCREAMING_SNAKE_CASE		     =       val
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       val
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
											return True
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						self.update(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						self.update(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       max(
						    self.segment_tree[self.left(UpperCAmelCase__     )]    ,    self.segment_tree[self.right(UpperCAmelCase__     )]     )
						return True




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       int     ) ->			int | float:
						if self.flag[idx] is True:
											__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
											__SCREAMING_SNAKE_CASE		     =       False
											if left_element != right_element:
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       self.lazy[idx]
																__SCREAMING_SNAKE_CASE		     =       True
																__SCREAMING_SNAKE_CASE		     =       True
						if right_element < a or left_element > b:
											return -math.inf
						if left_element >= a and right_element <= b:
											return self.segment_tree[idx]
						__SCREAMING_SNAKE_CASE		     =       (left_element + right_element) // 2
						__SCREAMING_SNAKE_CASE		     =       self.query(self.left(UpperCAmelCase__     )    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.query(self.right(UpperCAmelCase__     )    ,    mid + 1    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
						return max(UpperCAmelCase__    ,    UpperCAmelCase__     )




	def __str__(				self    :       int     ) ->			str:
						return str([self.query(1    ,    1    ,    self.size    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ) for i in range(1    ,    self.size + 1     )]     )


if __name__ == "__main__":
		a__       :							Tuple        =  [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
		a__       :							Dict        =  1_5
		a__       :							int        =  SegmentTree(size)
		segt.build(1, 1, size, A)
		print(segt.query(1, 1, size, 4, 6))
		print(segt.query(1, 1, size, 7, 1_1))
		print(segt.query(1, 1, size, 7, 1_2))
		segt.update(1, 1, size, 1, 3, 1_1_1)
		print(segt.query(1, 1, size, 1, 1_5))
		segt.update(1, 1, size, 7, 8, 2_3_5)
		print(segt)
