



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 	__lowerCAmelCase							(SCREAMING_SNAKE_CASE__					,unittest.TestCase  ):


					'''simple docstring'''


					a__        =     PaintByExamplePipeline
					a__        =     IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					a__        =     IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a__        =     frozenset([]  )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







					def 		_a (      self   ):




										"""simple docstring"""





										torch.manual_seed(0   )
										snake_case_						:List[Any]								=						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    ,       )
										snake_case_						:List[str]								=						PNDMScheduler(skip_prk_steps=a   )
										torch.manual_seed(0   )
										snake_case_						: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    ,       )
										torch.manual_seed(0   )
										snake_case_						:List[str]								=						CLIPVisionConfig(
										    hidden_size=32    ,       projection_dim=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-0_5    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       image_size=32    ,       patch_size=4    ,       )
										snake_case_						:List[Any]								=						PaintByExampleImageEncoder(a    ,       proj_size=32   )
										snake_case_						:List[str]								=						CLIPImageProcessor(crop_size=32    ,       size=32   )

										snake_case_						:List[Any]								=						{
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "image_encoder": image_encoder,
										    "safety_checker": None,
										    "feature_extractor": feature_extractor,
										}
										return components







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						np.array(image.convert("RGB"   )   )
										snake_case_						:List[Any]								=						image[None].transpose(0    ,       3    ,       1    ,       2   )
										snake_case_						:Optional[int]								=						torch.from_numpy(a   ).to(dtype=torch.floataa   ) / 1_27.5 - 1.0
										return image







					def 		_a (      self    ,       a="cpu"    ,       a=0   ):




										"""simple docstring"""





										snake_case_						:Union[str, Any]								=						floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(a   )   ).to(a   )
										snake_case_						:Any								=						image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]
										snake_case_						:str								=						Image.fromarray(np.uinta(a   )   ).convert("RGB"   ).resize((64, 64)   )
										snake_case_						:List[Any]								=						Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
										snake_case_						:Dict								=						Image.fromarray(np.uinta(a   )   ).convert("RGB"   ).resize((32, 32)   )

										if str(a   ).startswith("mps"   ):
															snake_case_						:List[str]								=						torch.manual_seed(a   )
										else:
															snake_case_						:Optional[Any]								=						torch.Generator(device=a   ).manual_seed(a   )
										snake_case_						:str								=						{
										    "example_image": example_image,
										    "image": init_image,
										    "mask_image": mask_image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										snake_case_						:Optional[int]								=						PaintByExamplePipeline(**a   )
										snake_case_						:List[str]								=						pipe.to("cpu"   )
										pipe.set_progress_bar_config(disable=a   )

										snake_case_						:Tuple								=						self.get_dummy_inputs()
										snake_case_						:Union[str, Any]								=						pipe(**a   )
										snake_case_						:Union[str, Any]								=						output.images

										snake_case_						:Union[str, Any]								=						image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										snake_case_						:str								=						np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						"cpu"
										snake_case_						:int								=						self.get_dummy_inputs()
										inputs.pop("mask_image"   )
										snake_case_						:str								=						self.convert_to_pt(inputs.pop("image"   )   )
										snake_case_						:Dict								=						image.clamp(0    ,       1   ) / 2

										# make sure here that pndm scheduler skips prk
										snake_case_						:Dict								=						PaintByExamplePipeline(**self.get_dummy_components()   )
										snake_case_						:str								=						pipe.to(a   )
										pipe.set_progress_bar_config(disable=a   )

										snake_case_						:Dict								=						pipe(image=a    ,       mask_image=mask_image[:, 0]    ,       **a   )
										snake_case_						:Dict								=						output.images

										snake_case_						:List[str]								=						image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]
										snake_case_						:Tuple								=						mask_image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]

										snake_case_						:int								=						Image.fromarray(np.uinta(a   )   ).convert("RGB"   )
										snake_case_						:Optional[Any]								=						Image.fromarray(np.uinta(a   )   ).convert("RGB"   )

										snake_case_						:Any								=						pipe(**self.get_dummy_inputs()   )
										snake_case_						:Union[str, Any]								=						output.images

										assert out_a.shape == (1, 64, 64, 3)
										assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2







					def 		_a (      self   ):




										"""simple docstring"""





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



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


					'''simple docstring'''


					def 		_a (      self   ):




										"""simple docstring"""





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







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/paint_by_example/dog_in_bucket.png"   )
										snake_case_						:int								=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/paint_by_example/mask.png"   )
										snake_case_						:str								=						load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/paint_by_example/panda.jpg"   )

										snake_case_						:Dict								=						PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
										snake_case_						:Tuple								=						pipe.to(a   )
										pipe.set_progress_bar_config(disable=a   )

										snake_case_						:List[str]								=						torch.manual_seed(3_21   )
										snake_case_						:str								=						pipe(
										    image=a    ,       mask_image=a    ,       example_image=a    ,       generator=a    ,       guidance_scale=5.0    ,       num_inference_steps=50    ,       output_type="np"    ,       )

										snake_case_						:Any								=						output.images

										snake_case_						:str								=						image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										snake_case_						:List[str]								=						np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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






"""simple docstring"""






def      A      (			_A,     _A							):




					"""simple docstring"""
					if n == 0:
										return 1

					elif n % 2 == 1:
										return binary_exponentiation(_A,     n - 1							) * a

					else:
										snake_case_						:Optional[int]								=						binary_exponentiation(_A,     n / 2							)
										return b * b


if __name__ == "__main__":
				try:
								__UpperCAmelCase      :      str   					=					int(input('Enter Base : ').strip())
								__UpperCAmelCase      :      Any   					=					int(input('Enter Power : ').strip())
				except ValueError:
								print('Invalid literal for integer')

				__UpperCAmelCase      :      Optional[int]   					=					binary_exponentiation(BASE, POWER)
				print(F'''{BASE}^({POWER}) : {RESULT}''')

