






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      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =StableDiffusionInpaintPipeline
	a_         =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	a_         =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	a_         =frozenset(
	    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	a_         =frozenset([]	)

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						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=__UpperCAmelCase		,    )
							lowerCAmelCase__       =						PNDMScheduler(skip_prk_steps=__UpperCAmelCase       )
							torch.manual_seed(0       )
							lowerCAmelCase__       =						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       )
							lowerCAmelCase__       =						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		,    )
							lowerCAmelCase__       =						CLIPTextModel(__UpperCAmelCase       )
							lowerCAmelCase__       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=0       )->					List[str]:







							'''simple docstring'''





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







							'''simple docstring'''





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

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

							assert image.shape == (1, 64, 64, 3)
							lowerCAmelCase__       =						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       )->					Union[str, Any]:







							'''simple docstring'''





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





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

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





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

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

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

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

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

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





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

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
							lowerCAmelCase__       =						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()

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

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

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


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





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

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

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-2-inpainting"
							lowerCAmelCase__       =						PNDMScheduler.from_pretrained(__UpperCAmelCase		,    subfolder="scheduler"       )
							lowerCAmelCase__       =						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()

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

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=__UpperCAmelCase		,    image=__UpperCAmelCase		,    mask_image=__UpperCAmelCase		,    generator=__UpperCAmelCase		,    num_inference_steps=2		,    output_type="np"		,    )

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














def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1

						while left <= right:
												# avoid divided by 0 during interpolation
												if sorted_collection[left] == sorted_collection[right]:
																		if sorted_collection[left] == item:
																								return left
																		else:
																								return None

												lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
												    sorted_collection[right] - sorted_collection[left]
												)

												# out of range check
												if point < 0 or point >= len(UpperCamelCase_   ):
																		return None

												lowerCAmelCase__       =						sorted_collection[point]
												if current_item == item:
																		return point
												else:
																		if point < left:
																								lowerCAmelCase__       =						left
																								lowerCAmelCase__       =						point
																		elif point > right:
																								lowerCAmelCase__       =						right
																								lowerCAmelCase__       =						point
																		else:
																								if item < current_item:
																														lowerCAmelCase__       =						point - 1
																								else:
																														lowerCAmelCase__       =						point + 1
						return None


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																		return left
												else:
																		return None

						lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(UpperCamelCase_   ):
												return None

						if sorted_collection[point] == item:
												return point
						elif point < left:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						elif point > right:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												if sorted_collection[point] > item:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   point - 1   )
												else:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   point + 1    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						if collection != sorted(UpperCamelCase_   ):
												raise ValueError("Collection must be ascending sorted"   )
						return True


if __name__ == "__main__":
	import sys
	a_							=    0
	if debug == 1:
		a_							=    [10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
		a_							=    67

	a_							=    interpolation_search(collection, target)
	if result is not None:
		print(F"{target} found at positions: {result}")
	else:
		print('''Not found''')






