



'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @property
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      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''')				,     )
      return model



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.dummy_uncond_unet
      _a											=						PNDMScheduler()

      _a											=						PNDMPipeline(unet=A				,     scheduler=A )
      pndm.to(A )
      pndm.set_progress_bar_config(disable=A )

      _a											=						torch.manual_seed(0 )
      _a											=						pndm(generator=A				,     num_inference_steps=20				,     output_type='''numpy''' ).images

      _a											=						torch.manual_seed(0 )
      _a											=						pndm(generator=A				,     num_inference_steps=20				,     output_type='''numpy'''				,     return_dict=A )[0]

      _a											=						image[0, -3:, -3:, -1]
      _a											=						image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _a											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






@slow
@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						'''google/ddpm-cifar10-32'''

      _a											=						UNetaDModel.from_pretrained(A )
      _a											=						PNDMScheduler()

      _a											=						PNDMPipeline(unet=A				,     scheduler=A )
      pndm.to(A )
      pndm.set_progress_bar_config(disable=A )
      _a											=						torch.manual_seed(0 )
      _a											=						pndm(generator=A				,     output_type='''numpy''' ).images

      _a											=						image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _a											=						np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2








'''simple docstring'''





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowercase_	 =						typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowercase_	 =						typing.Union[np.floataa, int, float]  # noqa: UP007




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return np.sqrt(np.sum((np.asarray(__A) - np.asarray(__A)) ** 2))




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return sum((va - va) ** 2 for va, va in zip(__A						,			__A)) ** (1 / 2)


if __name__ == "__main__":




  def lowerCAmelCase						():



       """simple docstring"""



       from timeit import timeit

       print('''Without Numpy''')
       print(
           timeit(
               '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
       print('''With Numpy''')
       print(
           timeit(
               '''euclidean_distance([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
  benchmark()



