

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 						lowercase     (					_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	RePaintPipeline
		a__       							=	IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
		a__       							=	PipelineTesterMixin.required_optional_params - {
		    '''latents''',
		    '''num_images_per_prompt''',
		    '''callback''',
		    '''callback_steps''',
		}
		a__       							=	IMAGE_INPAINTING_BATCH_PARAMS
		a__       							=	False


		def 		A__       (					self):
			torch.manual_seed(0)
			torch.manual_seed(0)
			_UpperCamelCase :		Dict								=    UNetaDModel(
			    block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=3						,							out_channels=3						,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,							)
			_UpperCamelCase :		int								=    RePaintScheduler()
			_UpperCamelCase :		Dict								=    {"unet": unet, "scheduler": scheduler}
			return components


		def 		A__       (					self						,							__snake_case						,							__snake_case=0):




			if str(lowerCamelCase__).startswith('mps'):
				_UpperCamelCase :		List[Any]								=    torch.manual_seed(lowerCamelCase__)
			else:
				_UpperCamelCase :		Any								=    torch.Generator(device=lowerCamelCase__).manual_seed(lowerCamelCase__)
			_UpperCamelCase :		List[Any]								=    np.random.RandomState(lowerCamelCase__).standard_normal((1, 3, 32, 32))
			_UpperCamelCase :		Optional[Any]								=    torch.from_numpy(lowerCamelCase__).to(device=lowerCamelCase__						,							dtype=torch.floataa)
			_UpperCamelCase :		List[str]								=    (image > 0).to(device=lowerCamelCase__						,							dtype=torch.floataa)
			_UpperCamelCase :		List[Any]								=    {
			    "image": image,
			    "mask_image": mask,
			    "generator": generator,
			    "num_inference_steps": 5,
			    "eta": 0.0,
			    "jump_length": 2,
			    "jump_n_sample": 2,
			    "output_type": "numpy",
			}
			return inputs


		def 		A__       (					self):
			_UpperCamelCase :		int								=    "cpu"  # ensure determinism for the device-dependent torch.Generator
			_UpperCamelCase :		Dict								=    self.get_dummy_components()
			_UpperCamelCase :		Tuple								=    RePaintPipeline(**lowerCamelCase__)
			_UpperCamelCase :		Tuple								=    sd_pipe.to(lowerCamelCase__)
			sd_pipe.set_progress_bar_config(disable=lowerCamelCase__)

			_UpperCamelCase :		Union[str, Any]								=    self.get_dummy_inputs(lowerCamelCase__)
			_UpperCamelCase :		Any								=    sd_pipe(**lowerCamelCase__).images
			_UpperCamelCase :		Tuple								=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			_UpperCamelCase :		Union[str, Any]								=    np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


		@skip_mps
		def 		A__       (					self):
			return super().test_save_load_local()


		@unittest.skip('non-deterministic pipeline')
		def 		A__       (					self):
			return super().test_inference_batch_single_identical()


		@skip_mps
		def 		A__       (					self):
			return super().test_dict_tuple_outputs_equivalent()


		@skip_mps
		def 		A__       (					self):
			return super().test_save_load_optional_components()






		@skip_mps
		def 		A__       (					self):
			return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		def 		A__       (					self):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






		def 		A__       (					self):
			_UpperCamelCase :		Any								=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png')
			_UpperCamelCase :		Dict								=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png')
			_UpperCamelCase :		Optional[Any]								=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy')

			_UpperCamelCase :		Tuple								=    "google/ddpm-ema-celebahq-256"
			_UpperCamelCase :		List[Any]								=    UNetaDModel.from_pretrained(lowerCamelCase__)
			_UpperCamelCase :		Union[str, Any]								=    RePaintScheduler.from_pretrained(lowerCamelCase__)

			_UpperCamelCase :		int								=    RePaintPipeline(unet=lowerCamelCase__						,							scheduler=lowerCamelCase__).to(lowerCamelCase__)
			repaint.set_progress_bar_config(disable=lowerCamelCase__)
			repaint.enable_attention_slicing()

			_UpperCamelCase :		int								=    torch.manual_seed(0)
			_UpperCamelCase :		Any								=    repaint(
			    lowerCamelCase__						,							lowerCamelCase__						,							num_inference_steps=2_50						,							eta=0.0						,							jump_length=10						,							jump_n_sample=10						,							generator=lowerCamelCase__						,							output_type='np'						,							)
			_UpperCamelCase :		Union[str, Any]								=    output.images[0]

			assert image.shape == (2_56, 2_56, 3)
			assert np.abs(expected_image - image).mean() < 1e-2








"""simple docstring"""






import math



def 			A__		(					_UpperCAmelCase    :      int	)							->	bool:

	'''simple docstring'''


	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5				,    int(math.sqrt(_UpperCAmelCase	) + 1	)				,    6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True



def 			A__		(					_UpperCAmelCase    :      int = 60_08_51_47_51_43	)							->	int:

	'''simple docstring'''


	try:
		snake_case__  :						List[Any]		=      int(_UpperCAmelCase	)
	except (TypeError, ValueError):
		raise TypeError("Parameter n must be int or castable to int."	)
	if n <= 0:
		raise ValueError("Parameter n must be greater than or equal to one."	)
	snake_case__  :						int		=      0
	if is_prime(_UpperCAmelCase	):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(_UpperCAmelCase	):
		return n
	for i in range(3				,    int(math.sqrt(_UpperCAmelCase	)	) + 1				,    2	):
		if n % i == 0:
			if is_prime(n // i	):
				snake_case__  :						int		=      n // i
				break
			elif is_prime(_UpperCAmelCase	):
				snake_case__  :						List[Any]		=      i
	return max_number


if __name__ == "__main__":
					print(f"{solution() = }")





