





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      A_	(a_					,							unittest.TestCase      ):
 UpperCAmelCase__ =      PaintByExamplePipeline
 UpperCAmelCase__ =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__ =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__ =      frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 def 					_lowercase    (   self		):






    '''simple docstring'''







    torch.manual_seed(0		)
    UpperCAmelCase       		=    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							, )
    UpperCAmelCase       		=    PNDMScheduler(skip_prk_steps=lowercase__		)
    torch.manual_seed(0		)
    UpperCAmelCase       		=    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							, )
    torch.manual_seed(0		)
    UpperCAmelCase       		=    CLIPVisionConfig(
        hidden_size=3_2							, projection_dim=3_2							, intermediate_size=3_7							, layer_norm_eps=1E-05							, num_attention_heads=4							, num_hidden_layers=5							, image_size=3_2							, patch_size=4							, )
    UpperCAmelCase       		=    PaintByExampleImageEncoder(lowercase__							, proj_size=3_2		)
    UpperCAmelCase       		=    CLIPImageProcessor(crop_size=3_2							, size=3_2		)

    UpperCAmelCase       		=    {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''image_encoder''': image_encoder,
        '''safety_checker''': None,
        '''feature_extractor''': feature_extractor,
    }
    return components
 def 					_lowercase    (   self							, _A		):






    '''simple docstring'''







    UpperCAmelCase       		=    np.array(image.convert('''RGB'''		)		)
    UpperCAmelCase       		=    image[None].transpose(0							, 3							, 1							, 2		)
    UpperCAmelCase       		=    torch.from_numpy(lowercase__		).to(dtype=torch.floataa		) / 1_27.5 - 1.0
    return image
 def 					_lowercase    (   self							, _A="cpu"							, _A=0		):






    '''simple docstring'''







    UpperCAmelCase       		=    floats_tensor((1, 3, 3_2, 3_2)							, rng=random.Random(lowercase__		)		).to(lowercase__		)
    UpperCAmelCase       		=    image.cpu().permute(0							, 2							, 3							, 1		)[0]
    UpperCAmelCase       		=    Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((6_4, 6_4)		)
    UpperCAmelCase       		=    Image.fromarray(np.uinta(image + 4		)		).convert('''RGB'''		).resize((6_4, 6_4)		)
    UpperCAmelCase       		=    Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((3_2, 3_2)		)

    if str(lowercase__		).startswith('''mps'''		):
       UpperCAmelCase       		=    torch.manual_seed(lowercase__		)
    else:
       UpperCAmelCase       		=    torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
    UpperCAmelCase       		=    {
        '''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 					_lowercase    (   self		):






    '''simple docstring'''







    UpperCAmelCase       		=    self.get_dummy_components()

    # make sure here that pndm scheduler skips prk
    UpperCAmelCase       		=    PaintByExamplePipeline(**lowercase__		)
    UpperCAmelCase       		=    pipe.to('''cpu'''		)
    pipe.set_progress_bar_config(disable=lowercase__		)

    UpperCAmelCase       		=    self.get_dummy_inputs()
    UpperCAmelCase       		=    pipe(**lowercase__		)
    UpperCAmelCase       		=    output.images

    UpperCAmelCase       		=    image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    UpperCAmelCase       		=    np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]		)

    assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
 def 					_lowercase    (   self		):






    '''simple docstring'''







    UpperCAmelCase       		=    '''cpu'''
    UpperCAmelCase       		=    self.get_dummy_inputs()
    inputs.pop('''mask_image'''		)
    UpperCAmelCase       		=    self.convert_to_pt(inputs.pop('''image'''		)		)
    UpperCAmelCase       		=    image.clamp(0							, 1		) / 2

    # make sure here that pndm scheduler skips prk
    UpperCAmelCase       		=    PaintByExamplePipeline(**self.get_dummy_components()		)
    UpperCAmelCase       		=    pipe.to(lowercase__		)
    pipe.set_progress_bar_config(disable=lowercase__		)

    UpperCAmelCase       		=    pipe(image=lowercase__							, mask_image=mask_image[:, 0]							, **lowercase__		)
    UpperCAmelCase       		=    output.images

    UpperCAmelCase       		=    image.cpu().permute(0							, 2							, 3							, 1		)[0]
    UpperCAmelCase       		=    mask_image.cpu().permute(0							, 2							, 3							, 1		)[0]

    UpperCAmelCase       		=    Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		)
    UpperCAmelCase       		=    Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		)

    UpperCAmelCase       		=    pipe(**self.get_dummy_inputs()		)
    UpperCAmelCase       		=    output.images

    assert out_a.shape == (1, 6_4, 6_4, 3)
    assert np.abs(out_a.flatten() - out_a.flatten()		).max() < 5E-2







 def 					_lowercase    (   self		):






    '''simple docstring'''







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

@slow
@require_torch_gpu
class      A_	(unittest.TestCase      ):
 def 					_lowercase    (   self		):






    '''simple docstring'''







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







 def 					_lowercase    (   self		):






    '''simple docstring'''







    UpperCAmelCase       		=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/paint_by_example/dog_in_bucket.png'''		)
    UpperCAmelCase       		=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/paint_by_example/mask.png'''		)
    UpperCAmelCase       		=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/paint_by_example/panda.jpg'''		)

    UpperCAmelCase       		=    PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''		)
    UpperCAmelCase       		=    pipe.to(lowercase__		)
    pipe.set_progress_bar_config(disable=lowercase__		)

    UpperCAmelCase       		=    torch.manual_seed(3_2_1		)
    UpperCAmelCase       		=    pipe(
        image=lowercase__							, mask_image=lowercase__							, example_image=lowercase__							, generator=lowercase__							, guidance_scale=5.0							, num_inference_steps=5_0							, output_type='''np'''							, )

    UpperCAmelCase       		=    output.images

    UpperCAmelCase       		=    image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    UpperCAmelCase       		=    np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]		)

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










import math





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__ = 6008_5147_5143			)  ->    int:







			'''simple docstring'''

			try:
						UpperCAmelCase       		=    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.'''			)
			UpperCAmelCase       		=    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			):
												UpperCAmelCase       		=    n // i
												break
									elif is_prime(UpperCamelCase__			):
												UpperCAmelCase       		=    i
			return max_number


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



