
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	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Tuple=       RePaintPipeline
    _a	:		int=       IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
    _a	:		List[str]=       PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    _a	:		List[Any]=       IMAGE_INPAINTING_BATCH_PARAMS
    _a	:		str=       False
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           torch.manual_seed(0   )
           torch.manual_seed(0   )
           lowercase							:  Dict 			=   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""")	,)
           lowercase							:  Dict 			=   RePaintScheduler()
           lowercase							:  int 			=   {"""unet""": unet, """scheduler""": scheduler}
           return components
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case=0   ):



           '''simple docstring'''







           if str(snake_case   ).startswith("""mps"""   ):
                  lowercase							:  Dict 			=   torch.manual_seed(snake_case   )
           else:
                  lowercase							:  Any 			=   torch.Generator(device=snake_case   ).manual_seed(snake_case   )
           lowercase							:  Optional[int] 			=   np.random.RandomState(snake_case   ).standard_normal((1, 3, 32, 32)   )
           lowercase							:  List[str] 			=   torch.from_numpy(snake_case   ).to(device=snake_case	,dtype=torch.floataa   )
           lowercase							:  Union[str, Any] 			=   (image > 0).to(device=snake_case	,dtype=torch.floataa   )
           lowercase							:  Any 			=   {
               """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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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

           lowercase							:  List[str] 			=   self.get_dummy_inputs(snake_case   )
           lowercase							:  List[Any] 			=   sd_pipe(**snake_case   ).images
           lowercase							:  str 			=   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowercase							:  str 			=   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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







           return super().test_save_load_optional_components()
    @skip_mps
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class __snake_case	(   unittest.TestCase  ):
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256.png"""   )
           lowercase							:  Dict 			=   load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""   )
           lowercase							:  Tuple 			=   load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
               """repaint/celeba_hq_256_result.npy"""   )

           lowercase							:  List[Any] 			=   """google/ddpm-ema-celebahq-256"""
           lowercase							:  str 			=   UNetaDModel.from_pretrained(snake_case   )
           lowercase							:  List[str] 			=   RePaintScheduler.from_pretrained(snake_case   )

           lowercase							:  Dict 			=   RePaintPipeline(unet=snake_case	,scheduler=snake_case   ).to(snake_case   )
           repaint.set_progress_bar_config(disable=snake_case   )
           repaint.enable_attention_slicing()

           lowercase							:  List[str] 			=   torch.manual_seed(0   )
           lowercase							:  Tuple 			=   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"""	,)
           lowercase							:  int 			=   output.images[0]

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


from __future__ import annotations







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					bool:
       lowercase							:  int
       lowercase							:  list[str] 			=   list(SCREAMING_SNAKE_CASE__       )
       lowercase			,    lowercase							:  Union[str, Any] 			=   _vector[:-1], int(_vector[-1]       )
       lowercase							:  list[int] 			=   [int(SCREAMING_SNAKE_CASE__       ) for digit in __vector]

       vector.reverse()
       for i, digit in enumerate(SCREAMING_SNAKE_CASE__       ):
              if i & 1 == 0:
                     lowercase							:  int 			=   digit * 2
                     if doubled > 9:
                            doubled -= 9
                     check_digit += doubled
              else:
                     check_digit += digit

       return check_digit % 10 == 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  assert is_luhn("""79927398713""")
  assert not is_luhn("""79927398714""")
