"""simple docstring"""







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 							__snake_case(					A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			RePaintPipeline
    _UpperCAmelCase       =			IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    _UpperCAmelCase       =			IMAGE_INPAINTING_BATCH_PARAMS
    _UpperCAmelCase       =			False
    def 						_a							(  self	):




         '''simple docstring'''



         torch.manual_seed(0	)
         torch.manual_seed(0	)
         __A					:					Optional[Any]								=		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')					,       )
         __A					:					Dict								=		RePaintScheduler()
         __A					:					List[str]								=		{'unet': unet, 'scheduler': scheduler}
         return components
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					str								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					Any								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
         __A					:					List[str]								=		np.random.RandomState(__lowerCamelCase	).standard_normal((1, 3, 32, 32)	)
         __A					:					Any								=		torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase					,       dtype=torch.floataa	)
         __A					:					int								=		(image > 0).to(device=__lowerCamelCase					,       dtype=torch.floataa	)
         __A					:					List[str]								=		{
             '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	):




         '''simple docstring'''



         __A					:					Tuple								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
         __A					:					Optional[int]								=		self.get_dummy_components()
         __A					:					int								=		RePaintPipeline(**__lowerCamelCase	)
         __A					:					Union[str, Any]								=		sd_pipe.to(__lowerCamelCase	)
         sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

         __A					:					int								=		self.get_dummy_inputs(__lowerCamelCase	)
         __A					:					List[str]								=		sd_pipe(**__lowerCamelCase	).images
         __A					:					Optional[Any]								=		image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __A					:					Optional[int]								=		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3
    @skip_mps
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         return super().test_inference_batch_single_identical()
    @skip_mps
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         return super().test_save_load_optional_components()

    @skip_mps
    def 						_a							(  self	):




         '''simple docstring'''



         return super().test_attention_slicing_forward_pass()







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


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



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

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[int]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
             'repaint/celeba_hq_256.png'	)
         __A					:					List[Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'	)
         __A					:					int								=		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
             'repaint/celeba_hq_256_result.npy'	)

         __A					:					str								=		'google/ddpm-ema-celebahq-256'
         __A					:					List[Any]								=		UNetaDModel.from_pretrained(__lowerCamelCase	)
         __A					:					Dict								=		RePaintScheduler.from_pretrained(__lowerCamelCase	)

         __A					:					Union[str, Any]								=		RePaintPipeline(unet=__lowerCamelCase					,       scheduler=__lowerCamelCase	).to(__lowerCamelCase	)
         repaint.set_progress_bar_config(disable=__lowerCamelCase	)
         repaint.enable_attention_slicing()

         __A					:					Optional[int]								=		torch.manual_seed(0	)
         __A					:					List[Any]								=		repaint(
             __lowerCamelCase					,       __lowerCamelCase					,       num_inference_steps=250					,       eta=0.0					,       jump_length=10					,       jump_n_sample=10					,       generator=__lowerCamelCase					,       output_type='np'					,       )
         __A					:					Optional[Any]								=		output.images[0]

         assert image.shape == (256, 256, 3)
         assert np.abs(expected_image - image	).mean() < 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 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       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=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

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

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[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()



