

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 		__magic_name__     (				SCREAMING_SNAKE_CASE__						,			unittest.TestCase				):

   '''simple docstring'''




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



   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     torch.manual_seed(0			)
     torch.manual_seed(0			)
     a__    =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__    =RePaintScheduler()
     a__    ={'''unet''': unet, '''scheduler''': scheduler}
     return components



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=0			)				->       int:






     """simple docstring"""






     if str(A__			).startswith('''mps'''			):
       a__    =torch.manual_seed(A__			)
     else:
       a__    =torch.Generator(device=A__			).manual_seed(A__			)
     a__    =np.random.RandomState(A__			).standard_normal((1, 3, 32, 32)			)
     a__    =torch.from_numpy(A__			).to(device=A__,       dtype=torch.floataa			)
     a__    =(image > 0).to(device=A__,       dtype=torch.floataa			)
     a__    ={
         '''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 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
     a__    =self.get_dummy_components()
     a__    =RePaintPipeline(**A__			)
     a__    =sd_pipe.to(A__			)
     sd_pipe.set_progress_bar_config(disable=A__			)

     a__    =self.get_dummy_inputs(A__			)
     a__    =sd_pipe(**A__			).images
     a__    =image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     a__    =np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]			)

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



   @skip_mps
   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     return super().test_save_load_local()



   @unittest.skip('''non-deterministic pipeline'''			)
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     return super().test_inference_batch_single_identical()



   @skip_mps
   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     return super().test_dict_tuple_outputs_equivalent()



   @skip_mps
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     return super().test_save_load_optional_components()



   @skip_mps
   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     return super().test_attention_slicing_forward_pass()







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

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """simple docstring"""






     a__    =load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256.png'''			)
     a__    =load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
     a__    =load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256_result.npy'''			)

     a__    ='''google/ddpm-ema-celebahq-256'''
     a__    =UNetaDModel.from_pretrained(A__			)
     a__    =RePaintScheduler.from_pretrained(A__			)

     a__    =RePaintPipeline(unet=A__,       scheduler=A__			).to(A__			)
     repaint.set_progress_bar_config(disable=A__			)
     repaint.enable_attention_slicing()

     a__    =torch.manual_seed(0			)
     a__    =repaint(
         A__,       A__,       num_inference_steps=250,       eta=0.0,       jump_length=10,       jump_n_sample=10,       generator=A__,       output_type='''np''',       )
     a__    =output.images[0]

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


def 			UpperCamelCase       (				lowerCAmelCase__ = 30						):
					'''simple docstring'''
					lowercase		      =		exponent + 2
					lowercase		      =		(1 + 5**0.5) / 2
					lowercase		      =		(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowerCAmelCase__						)


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




