



'''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 				lowercase			(    _lowerCamelCase   ,   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 _snake_case					(				self				)						->  Optional[int]:
        torch.manual_seed(0				)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[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    :		List[str]     	=					RePaintScheduler()
        _UpperCAmelCase    :		List[str]     	=					{"""unet""": unet, """scheduler""": scheduler}
        return components







 def _snake_case					(				self    ,a_    ,a_=0				)						->  Dict:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		Optional[Any]     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[str]     	=					torch.Generator(device=a_				).manual_seed(a_				)
        _UpperCAmelCase    :		int     	=					np.random.RandomState(a_				).standard_normal((1, 3, 32, 32)				)
        _UpperCAmelCase    :		Tuple     	=					torch.from_numpy(a_				).to(device=a_    ,dtype=torch.floataa				)
        _UpperCAmelCase    :		int     	=					(image > 0).to(device=a_    ,dtype=torch.floataa				)
        _UpperCAmelCase    :		Optional[int]     	=					{
            """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 _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		Union[str, Any]     	=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase    :		Tuple     	=					self.get_dummy_components()
        _UpperCAmelCase    :		Optional[int]     	=					RePaintPipeline(**a_				)
        _UpperCAmelCase    :		Tuple     	=					sd_pipe.to(a_				)
        sd_pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_inputs(a_				)
        _UpperCAmelCase    :		Any     	=					sd_pipe(**a_				).images
        _UpperCAmelCase    :		Optional[Any]     	=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCAmelCase    :		Optional[Any]     	=					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 _snake_case					(				self				)						->  Any:
        return super().test_save_load_local()







 @unittest.skip("""non-deterministic pipeline"""				)
 def _snake_case					(				self				)						->  Tuple:
        return super().test_inference_batch_single_identical()







 @skip_mps
 def _snake_case					(				self				)						->  Dict:
        return super().test_dict_tuple_outputs_equivalent()







 @skip_mps
 def _snake_case					(				self				)						->  List[str]:
        return super().test_save_load_optional_components()







 @skip_mps
 def _snake_case					(				self				)						->  Dict:
        return super().test_attention_slicing_forward_pass()







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


 """simple docstring"""


 def _snake_case					(				self				)						->  Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		int     	=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
            """repaint/celeba_hq_256.png"""				)
        _UpperCAmelCase    :		Any     	=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""				)
        _UpperCAmelCase    :		int     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
            """repaint/celeba_hq_256_result.npy"""				)

        _UpperCAmelCase    :		List[Any]     	=					"""google/ddpm-ema-celebahq-256"""
        _UpperCAmelCase    :		Tuple     	=					UNetaDModel.from_pretrained(a_				)
        _UpperCAmelCase    :		Tuple     	=					RePaintScheduler.from_pretrained(a_				)

        _UpperCAmelCase    :		Dict     	=					RePaintPipeline(unet=a_    ,scheduler=a_				).to(a_				)
        repaint.set_progress_bar_config(disable=a_				)
        repaint.enable_attention_slicing()

        _UpperCAmelCase    :		List[str]     	=					torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					repaint(
            a_    ,a_    ,num_inference_steps=250    ,eta=0.0    ,jump_length=10    ,jump_n_sample=10    ,generator=a_    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		int     	=					output.images[0]

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








'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						float:





       '''simple docstring'''


       return a + b


if __name__ == "__main__":
   A_				:					Tuple							=      5
   A_				:					Tuple							=      6
   print(f"""The sum of {a} + {b} is {add(a, b)}""")



