






"""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 		_a       (						lowercase_    ,					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 __lowercase    (   self)	-> Dict:




          '''simple docstring'''




          torch.manual_seed(0)
          torch.manual_seed(0)
          lowercase__:    Optional[Any]        =      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__:    Optional[int]        =      RePaintScheduler()
          lowercase__:    Tuple        =      {"unet": unet, "scheduler": scheduler}
          return components





   def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_=0)	-> List[Any]:




          '''simple docstring'''




          if str(UpperCAmelCase_).startswith("mps"):
                 lowercase__:    Union[str, Any]        =      torch.manual_seed(UpperCAmelCase_)
          else:
                 lowercase__:    int        =      torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)
          lowercase__:    Tuple        =      np.random.RandomState(UpperCAmelCase_).standard_normal((1, 3, 32, 32))
          lowercase__:    int        =      torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_			,				dtype=torch.floataa)
          lowercase__:    int        =      (image > 0).to(device=UpperCAmelCase_			,				dtype=torch.floataa)
          lowercase__:    Tuple        =      {
              "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 __lowercase    (   self)	-> Optional[int]:




          '''simple docstring'''




          lowercase__:    Any        =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          lowercase__:    Optional[int]        =      self.get_dummy_components()
          lowercase__:    Any        =      RePaintPipeline(**UpperCAmelCase_)
          lowercase__:    Dict        =      sd_pipe.to(UpperCAmelCase_)
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

          lowercase__:    Optional[Any]        =      self.get_dummy_inputs(UpperCAmelCase_)
          lowercase__:    int        =      sd_pipe(**UpperCAmelCase_).images
          lowercase__:    Optional[int]        =      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase__:    Tuple        =      np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74])

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





   @skip_mps
   def __lowercase    (   self)	-> str:




          '''simple docstring'''




          return super().test_save_load_local()





   @unittest.skip("non-deterministic pipeline")
   def __lowercase    (   self)	-> int:




          '''simple docstring'''




          return super().test_inference_batch_single_identical()





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




          '''simple docstring'''




          return super().test_dict_tuple_outputs_equivalent()





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




          '''simple docstring'''




          return super().test_save_load_optional_components()





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




          '''simple docstring'''




          return super().test_attention_slicing_forward_pass()







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





   '''simple docstring'''






   def __lowercase    (   self)	-> Union[str, Any]:




          '''simple docstring'''




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





   def __lowercase    (   self)	-> int:




          '''simple docstring'''




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

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

          lowercase__:    Union[str, Any]        =      RePaintPipeline(unet=UpperCAmelCase_			,				scheduler=UpperCAmelCase_).to(UpperCAmelCase_)
          repaint.set_progress_bar_config(disable=UpperCAmelCase_)
          repaint.enable_attention_slicing()

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

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














"""simple docstring"""





def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       if len(snake_case_		) == 1 and series[0] != 0:
              return True
       lowercase__:    str        =      []
       lowercase__:    Optional[int]        =      len(snake_case_		)
       for i in range(0						,       snake_case_		):
              if series[i] == 0:
                     raise ValueError("Input series cannot have 0 as an element"		)
              rec_series.append(1 / series[i]		)
       lowercase__:    Optional[int]        =      rec_series[1] - rec_series[0]
       for index in range(2						,       snake_case_		):
              if rec_series[index] - rec_series[index - 1] != common_diff:
                     return False
       return True






def     A(  snake_case_		):
       """simple docstring"""





       if not isinstance(snake_case_						,       snake_case_		):
              raise ValueError("Input series is not valid, valid series - [2, 4, 6]"		)
       if len(snake_case_		) == 0:
              raise ValueError("Input list must be a non empty list"		)
       lowercase__:    Optional[int]        =      0
       for val in series:
              answer += 1 / val
       return len(snake_case_		) / answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()






