import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 					A							(    unittest.TestCase					):


			@property
			def       lowercase_				(self					:						str		)						->    Union[str, Any]:
									"""simple docstring"""



									torch.manual_seed(0		)
									UpperCAmelCase__								=  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")	,  )
									return model





			def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
									"""simple docstring"""



									UpperCAmelCase__								=  self.dummy_uncond_unet
									UpperCAmelCase__								=  KarrasVeScheduler()

									UpperCAmelCase__								=  KarrasVePipeline(unet=__lowerCAmelCase	,  scheduler=__lowerCAmelCase		)
									pipe.to(__lowerCAmelCase		)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

									UpperCAmelCase__								=  torch.manual_seed(0		)
									UpperCAmelCase__								=  pipe(num_inference_steps=2	,  generator=__lowerCAmelCase	,  output_type="numpy"		).images

									UpperCAmelCase__								=  torch.manual_seed(0		)
									UpperCAmelCase__								=  pipe(num_inference_steps=2	,  generator=__lowerCAmelCase	,  output_type="numpy"	,  return_dict=__lowerCAmelCase		)[0]

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

									assert image.shape == (1, 3_2, 3_2, 3)
									UpperCAmelCase__								=  np.array([0.0, 1.0, 0.0, 0.0, 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					):


			def       lowercase_				(self					:						Any		)						->    List[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  "google/ncsnpp-celebahq-256"
									UpperCAmelCase__								=  UNetaDModel.from_pretrained(__lowerCAmelCase		)
									UpperCAmelCase__								=  KarrasVeScheduler()

									UpperCAmelCase__								=  KarrasVePipeline(unet=__lowerCAmelCase	,  scheduler=__lowerCAmelCase		)
									pipe.to(__lowerCAmelCase		)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

									UpperCAmelCase__								=  torch.manual_seed(0		)
									UpperCAmelCase__								=  pipe(num_inference_steps=2_0	,  generator=__lowerCAmelCase	,  output_type="numpy"		).images

									UpperCAmelCase__								=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 2_5_6, 2_5_6, 3)
									UpperCAmelCase__								=  np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]		)

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




from __future__ import annotations




def        lowerCAmelCase_			(					__A,  __A,  __A,  )					->    tuple:







      '''simple docstring'''


      if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
            raise ValueError("You cannot supply more or less than 2 values"							)
      elif electron_conc < 0:
            raise ValueError("Electron concentration cannot be negative in a semiconductor"							)
      elif hole_conc < 0:
            raise ValueError("Hole concentration cannot be negative in a semiconductor"							)
      elif intrinsic_conc < 0:
            raise ValueError(
                "Intrinsic concentration cannot be negative in a semiconductor"							)
      elif electron_conc == 0:
            return (
                "electron_conc",
                intrinsic_conc**2 / hole_conc,
            )
      elif hole_conc == 0:
            return (
                "hole_conc",
                intrinsic_conc**2 / electron_conc,
            )
      elif intrinsic_conc == 0:
            return (
                "intrinsic_conc",
                (electron_conc * hole_conc) ** 0.5,
            )
      else:
            return (-1, -1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



