






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


      def _UpperCamelCase    (     self					:						Any    ):





            """simple docstring"""







            torch.manual_seed(0    )
            torch.manual_seed(0    )
            __snake_case   :     Optional[int]	      =UNetaDModel(
                block_out_channels=(3_2, 6_4)				, layers_per_block=2				, sample_size=3_2				, in_channels=3				, out_channels=3				, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				, )
            __snake_case   :     List[str]	      =RePaintScheduler()
            __snake_case   :     List[str]	      ={"""unet""": unet, """scheduler""": scheduler}
            return components


      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						Union[str, Any]				, a					:						List[Any]=0    ):





            """simple docstring"""







            if str(a_    ).startswith('''mps'''    ):
                  __snake_case   :     Optional[Any]	      =torch.manual_seed(a_    )
            else:
                  __snake_case   :     List[str]	      =torch.Generator(device=a_    ).manual_seed(a_    )
            __snake_case   :     int	      =np.random.RandomState(a_    ).standard_normal((1, 3, 3_2, 3_2)    )
            __snake_case   :     Tuple	      =torch.from_numpy(a_    ).to(device=a_				, dtype=torch.floataa    )
            __snake_case   :     int	      =(image > 0).to(device=a_				, dtype=torch.floataa    )
            __snake_case   :     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 _UpperCamelCase    (     self					:						Optional[int]    ):





            """simple docstring"""







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

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

            assert image.shape == (1, 3_2, 3_2, 3)
            __snake_case   :     Optional[Any]	      =np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]    )

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


      @skip_mps
      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            return super().test_save_load_local()


      @unittest.skip('''non-deterministic pipeline'''    )
      def _UpperCamelCase    (     self					:						Any    ):





            """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					:						List[str]    ):





            """simple docstring"""







            return super().test_save_load_optional_components()







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





            """simple docstring"""







            return super().test_attention_slicing_forward_pass()



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


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







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







      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







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

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

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

            __snake_case   :     List[str]	      =torch.manual_seed(0    )
            __snake_case   :     List[str]	      =repaint(
                a_				, a_				, num_inference_steps=2_5_0				, eta=0.0				, jump_length=1_0				, jump_n_sample=1_0				, generator=a_				, output_type='''np'''				, )
            __snake_case   :     int	      =output.images[0]

            assert image.shape == (2_5_6, 2_5_6, 3)
            assert np.abs(expected_image - image    ).mean() < 1e-2







'''simple docstring'''






def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



