






from __future__ import annotations

from enum import Enum





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       List[str] 					= 24
		UpperCAmelCase_							:       str 					= 21
		UpperCAmelCase_							:       List[str] 					= 18
		UpperCAmelCase_							:       Dict 					= 15
		UpperCAmelCase_							:       Tuple 					= 12
		UpperCAmelCase_							:       Optional[int] 					= 9
		UpperCAmelCase_							:       List[Any] 					= 6
		UpperCAmelCase_							:       Any 					= 3
		UpperCAmelCase_							:       List[Any] 					= 2
		UpperCAmelCase_							:       Dict 					= 1
		UpperCAmelCase_							:       List[str] 					= -1
		UpperCAmelCase_							:       List[str] 					= -2
		UpperCAmelCase_							:       Any 					= -3
		UpperCAmelCase_							:       Any 					= -6
		UpperCAmelCase_							:       Dict 					= -9
		UpperCAmelCase_							:       int 					= -12
		UpperCAmelCase_							:       List[Any] 					= -15
		UpperCAmelCase_							:       List[Any] 					= -18
		UpperCAmelCase_							:       Dict 					= -21
		UpperCAmelCase_							:       Optional[Any] 					= -24





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Optional[int] 					= 8
		UpperCAmelCase_							:       List[Any] 					= 7
		UpperCAmelCase_							:       List[str] 					= 6
		UpperCAmelCase_							:       Any 					= 5
		UpperCAmelCase_							:       Tuple 					= 4
		UpperCAmelCase_							:       int 					= 3
		UpperCAmelCase_							:       Union[str, Any] 					= 2
		UpperCAmelCase_							:       str 					= 1



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     ):


		'''simple docstring'''

		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							SIUnit[known_prefix.lower()]
		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							SIUnit[unknown_prefix.lower()]
		__A    =							known_amount * (
		    10 ** (known_prefix.value - unknown_prefix.value)
		)
		return unknown_amount



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     ):


		'''simple docstring'''

		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							BinaryUnit[known_prefix.lower()]
		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							BinaryUnit[unknown_prefix.lower()]
		__A    =							known_amount * (
		    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
		)
		return unknown_amount


if __name__ == "__main__":
					import doctest

					doctest.testmod()





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 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= 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_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   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        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]						)

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





    @skip_mps
    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       return super().test_save_load_local()





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





       '''simple docstring'''
       return super().test_inference_batch_single_identical()





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return super().test_save_load_optional_components()





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       return super().test_attention_slicing_forward_pass()





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






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def        _lowerCamelCase			(						self					: List[str]						):





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

       UpperCAmelCase_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

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

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image						).mean() < 1E-2