


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__				(			__lowerCamelCase,    unittest.TestCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Optional[Any]  					=	PaintByExamplePipeline
						__UpperCAmelCase   :		Optional[int]  					=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						__UpperCAmelCase   :		str  					=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						__UpperCAmelCase   :		Tuple  					=	frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




						def 					_UpperCamelCase			(   self     ):
							torch.manual_seed(0     )
							lowerCamelCase_			:							str      =       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				,							)
							lowerCamelCase_			:							Tuple      =       PNDMScheduler(skip_prk_steps=a_     )
							torch.manual_seed(0     )
							lowerCamelCase_			:							Dict      =       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     )
							lowerCamelCase_			:							Optional[Any]      =       CLIPVisionConfig(
							    hidden_size=32				,							projection_dim=32				,							intermediate_size=37				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							image_size=32				,							patch_size=4				,							)
							lowerCamelCase_			:							str      =       PaintByExampleImageEncoder(a_				,							proj_size=32     )
							lowerCamelCase_			:							str      =       CLIPImageProcessor(crop_size=32				,							size=32     )

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




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							List[str]      =       np.array(image.convert("RGB"     )     )
							lowerCamelCase_			:							int      =       image[None].transpose(0				,							3				,							1				,							2     )
							lowerCamelCase_			:							Any      =       torch.from_numpy(a_     ).to(dtype=torch.floataa     ) / 1_27.5 - 1.0
							return image




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

							if str(a_     ).startswith("mps"     ):
								lowerCamelCase_			:							Union[str, Any]      =       torch.manual_seed(a_     )
							else:
								lowerCamelCase_			:							List[Any]      =       torch.Generator(device=a_     ).manual_seed(a_     )
							lowerCamelCase_			:							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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[Any]      =       self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCamelCase_			:							Optional[Any]      =       PaintByExamplePipeline(**a_     )
							lowerCamelCase_			:							Any      =       pipe.to("cpu"     )
							pipe.set_progress_bar_config(disable=a_     )

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

							lowerCamelCase_			:							int      =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowerCamelCase_			:							List[Any]      =       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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[Any]      =       "cpu"
							lowerCamelCase_			:							Tuple      =       self.get_dummy_inputs()
							inputs.pop("mask_image"     )
							lowerCamelCase_			:							Union[str, Any]      =       self.convert_to_pt(inputs.pop("image"     )     )
							lowerCamelCase_			:							str      =       image.clamp(0				,							1     ) / 2

							# make sure here that pndm scheduler skips prk
							lowerCamelCase_			:							Optional[Any]      =       PaintByExamplePipeline(**self.get_dummy_components()     )
							lowerCamelCase_			:							int      =       pipe.to(a_     )
							pipe.set_progress_bar_config(disable=a_     )

							lowerCamelCase_			:							List[Any]      =       pipe(image=a_				,							mask_image=mask_image[:, 0]				,							**a_     )
							lowerCamelCase_			:							Dict      =       output.images

							lowerCamelCase_			:							Optional[int]      =       image.cpu().permute(0				,							2				,							3				,							1     )[0]
							lowerCamelCase_			:							Union[str, Any]      =       mask_image.cpu().permute(0				,							2				,							3				,							1     )[0]

							lowerCamelCase_			:							List[str]      =       Image.fromarray(np.uinta(a_     )     ).convert("RGB"     )
							lowerCamelCase_			:							Union[str, Any]      =       Image.fromarray(np.uinta(a_     )     ).convert("RGB"     )

							lowerCamelCase_			:							int      =       pipe(**self.get_dummy_inputs()     )
							lowerCamelCase_			:							Any      =       output.images

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




						def 					_UpperCamelCase			(   self     ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





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






						"""simple docstring"""




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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Any      =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"     )
							lowerCamelCase_			:							Union[str, Any]      =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"     )
							lowerCamelCase_			:							Dict      =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"     )

							lowerCamelCase_			:							Optional[int]      =       PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"     )
							lowerCamelCase_			:							Tuple      =       pipe.to(a_     )
							pipe.set_progress_bar_config(disable=a_     )

							lowerCamelCase_			:							List[str]      =       torch.manual_seed(321     )
							lowerCamelCase_			:							Dict      =       pipe(
							    image=a_				,							mask_image=a_				,							example_image=a_				,							generator=a_				,							guidance_scale=5.0				,							num_inference_steps=50				,							output_type="np"				,							)

							lowerCamelCase_			:							Optional[int]      =       output.images

							lowerCamelCase_			:							List[str]      =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							lowerCamelCase_			:							Dict      =       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





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


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

