


"""simple docstring"""






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_		(      UpperCAmelCase_							,	unittest.TestCase ):





				"""simple docstring"""







				_lowerCAmelCase   :   int   				=       PaintByExamplePipeline
				_lowerCAmelCase   :   List[str]   				=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
				_lowerCAmelCase   :   Tuple   				=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				_lowerCAmelCase   :   List[Any]   				=       frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



				def 	lowerCAmelCase__		(    self  ):
								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					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							,		)
								SCREAMING_SNAKE_CASE__  				=					PNDMScheduler(skip_prk_steps=_lowercase  )
								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					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  )
								SCREAMING_SNAKE_CASE__  				=					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							,		)
								SCREAMING_SNAKE_CASE__  				=					PaintByExampleImageEncoder(_lowercase							,		proj_size=32  )
								SCREAMING_SNAKE_CASE__  				=					CLIPImageProcessor(crop_size=32							,		size=32  )

								SCREAMING_SNAKE_CASE__  				=					{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'image_encoder': image_encoder,
								    'safety_checker': None,
								    'feature_extractor': feature_extractor,
								}
								return components



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					np.array(image.convert("RGB"  )  )
								SCREAMING_SNAKE_CASE__  				=					image[None].transpose(0							,		3							,		1							,		2  )
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_lowercase  ).to(dtype=torch.floataa  ) / 127.5 - 1.0
								return image



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__="cpu"							,		UpperCAmelCase__=0  ):
								# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
								SCREAMING_SNAKE_CASE__  				=					floats_tensor((1, 3, 32, 32)							,		rng=random.Random(_lowercase  )  ).to(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					image.cpu().permute(0							,		2							,		3							,		1  )[0]
								SCREAMING_SNAKE_CASE__  				=					Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  ).resize((64, 64)  )
								SCREAMING_SNAKE_CASE__  				=					Image.fromarray(np.uinta(image + 4  )  ).convert("RGB"  ).resize((64, 64)  )
								SCREAMING_SNAKE_CASE__  				=					Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  ).resize((32, 32)  )

								if str(_lowercase  ).startswith("mps"  ):
												SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(_lowercase  )
								else:
												SCREAMING_SNAKE_CASE__  				=					torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					{
								    '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 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								SCREAMING_SNAKE_CASE__  				=					PaintByExamplePipeline(**_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					pipe.to("cpu"  )
								pipe.set_progress_bar_config(disable=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					self.get_dummy_inputs()
								SCREAMING_SNAKE_CASE__  				=					pipe(**_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					output.images

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

								assert image.shape == (1, 64, 64, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]  )

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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					'cpu'
								SCREAMING_SNAKE_CASE__  				=					self.get_dummy_inputs()
								inputs.pop("mask_image"  )
								SCREAMING_SNAKE_CASE__  				=					self.convert_to_pt(inputs.pop("image"  )  )
								SCREAMING_SNAKE_CASE__  				=					image.clamp(0							,		1  ) / 2

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

								SCREAMING_SNAKE_CASE__  				=					pipe(image=_lowercase							,		mask_image=mask_image[:, 0]							,		**_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					output.images

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

								SCREAMING_SNAKE_CASE__  				=					Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  )
								SCREAMING_SNAKE_CASE__  				=					Image.fromarray(np.uinta(_lowercase  )  ).convert("RGB"  )

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

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






				def 	lowerCAmelCase__		(    self  ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3  )


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





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






				def 	lowerCAmelCase__		(    self  ):
								# make sure here that pndm scheduler skips prk
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/dog_in_bucket.png"  )
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/mask.png"  )
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/panda.jpg"  )

								SCREAMING_SNAKE_CASE__  				=					PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"  )
								SCREAMING_SNAKE_CASE__  				=					pipe.to(_lowercase  )
								pipe.set_progress_bar_config(disable=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(321  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    image=_lowercase							,		mask_image=_lowercase							,		example_image=_lowercase							,		generator=_lowercase							,		guidance_scale=5.0							,		num_inference_steps=50							,		output_type="np"							,		)

								SCREAMING_SNAKE_CASE__  				=					output.images

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

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]  )

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








'''simple docstring'''

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : List[str]   	=      ArgumentParser(
						    description=(
						        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
						    )				)

						# Optional arguments for the launch helper
						parser.add_argument('--num_cores'    ,      type=a    ,      default=1    ,      help='Number of TPU cores to use (1 or 8).'				)

						# positional
						parser.add_argument(
						    'training_script'    ,      type=a    ,      help=(
						        'The full path to the single TPU training '
						        'program/script to be launched in parallel, '
						        'followed by all the arguments for the '
						        'training script'
						    )    ,      )

						# rest from the training program
						parser.add_argument('training_script_args'    ,      nargs=a				)

						return parser.parse_args()






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      parse_args()

						# Import training_script as a module.
						lowercase_   : str   	=      Path(args.training_script				)
						sys.path.append(str(script_fpath.parent.resolve()				)				)
						lowercase_   : int   	=      script_fpath.stem
						lowercase_   : List[Any]   	=      importlib.import_module(a				)

						# Patch sys.argv
						lowercase_   : List[str]   	=      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

						xmp.spawn(mod._mp_fn    ,      args=()    ,      nprocs=args.num_cores				)


if __name__ == "__main__":
						main()
