


'''simple docstring'''






from __future__ import annotations

__A :  Tuple    							=				8.988E9  # units = N * m^s * C^-2







def     UpperCamelCase_      (   A__ :       float							,   A__ :       float							,   A__ :       float							,   A__ :       float ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       abs(chargea * chargea )

  if (force, chargea, chargea, distance).count(0 ) != 1:
    raise ValueError("""One and only one argument must be 0""" )
  if distance < 0:
    raise ValueError("""Distance cannot be negative""" )
  if force == 0:
    lowerCAmelCase_      :				Tuple      =       COULOMBS_CONSTANT * charge_product / (distance**2)
    return {"force": force}
  elif chargea == 0:
    lowerCAmelCase_      :				Optional[Any]      =       abs(A__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
    return {"charge1": chargea}
  elif chargea == 0:
    lowerCAmelCase_      :				Tuple      =       abs(A__ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
    return {"charge2": chargea}
  elif distance == 0:
    lowerCAmelCase_      :				Tuple      =       (COULOMBS_CONSTANT * charge_product / abs(A__ )) ** 0.5
    return {"distance": distance}
  raise ValueError("""Exactly one argument must be 0""" )


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				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""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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



