



"""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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  Dict     )->					List[str]:
								torch.manual_seed(0     )
								lowerCamelCase__		:		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'''),  )
								return model




					def 		snake_case       (       self							:  int     )->					Optional[int]:
								lowerCamelCase__		:		Optional[int]			=self.dummy_uncond_unet
								lowerCamelCase__		:		Tuple			=PNDMScheduler()

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

								lowerCamelCase__		:		Union[str, Any]			=torch.manual_seed(0     )
								lowerCamelCase__		:		int			=pndm(generator=lowerCamelCase,  num_inference_steps=20,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		Optional[Any]			=torch.manual_seed(0     )
								lowerCamelCase__		:		Any			=pndm(generator=lowerCamelCase,  num_inference_steps=20,  output_type='''numpy''',  return_dict=lowerCamelCase     )[0]

								lowerCamelCase__		:		List[str]			=image[0, -3:, -3:, -1]
								lowerCamelCase__		:		Tuple			=image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Optional[Any]			=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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								lowerCamelCase__		:		Union[str, Any]			='''google/ddpm-cifar10-32'''

								lowerCamelCase__		:		List[str]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=PNDMScheduler()

								lowerCamelCase__		:		str			=PNDMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								pndm.to(lowerCamelCase     )
								pndm.set_progress_bar_config(disable=lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=torch.manual_seed(0     )
								lowerCamelCase__		:		Tuple			=pndm(generator=lowerCamelCase,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		List[str]			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		int			=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









"""simple docstring"""






from __future__ import annotations

_lowercase : Dict      						=						1.6_021E-19  # units = C






def      snake_case__   (		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		__lowerCamelCase				:    float							,		):
			"""simple docstring"""
			if (conductivity, electron_conc, mobility).count(0	) != 1:
						raise ValueError('''You cannot supply more or less than 2 values'''	)
			elif conductivity < 0:
						raise ValueError('''Conductivity cannot be negative'''	)
			elif electron_conc < 0:
						raise ValueError('''Electron concentration cannot be negative'''	)
			elif mobility < 0:
						raise ValueError('''mobility cannot be negative'''	)
			elif conductivity == 0:
						return (
						    "conductivity",
						    mobility * electron_conc * ELECTRON_CHARGE,
						)
			elif electron_conc == 0:
						return (
						    "electron_conc",
						    conductivity / (mobility * ELECTRON_CHARGE),
						)
			else:
						return (
						    "mobility",
						    conductivity / (electron_conc * ELECTRON_CHARGE),
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




