

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a_      =     False






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			pass






@nightly
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			List[str]						)				->							List[Any]:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
				# remove text_unet
				pipe.remove_unused_weights()
				pipe.to(__snake_case						)
				pipe.set_progress_bar_config(disable=__snake_case						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							"A painting of a squirrel eating a burger "
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(
				    prompt=__snake_case   ,   generator=__snake_case   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type="numpy"						).images

				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__snake_case						)
					SCREAMING_SNAKE_CASE :   Any					  =							VersatileDiffusionTextToImagePipeline.from_pretrained(__snake_case						)
				pipe.to(__snake_case						)
				pipe.set_progress_bar_config(disable=__snake_case						)

				SCREAMING_SNAKE_CASE :   int					  =							generator.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							pipe(
				    prompt=__snake_case   ,   generator=__snake_case   ,   guidance_scale=7.5   ,   num_inference_steps=2   ,   output_type="numpy"						).images

				assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							VersatileDiffusionTextToImagePipeline.from_pretrained(
				    "shi-labs/versatile-diffusion"   ,   torch_dtype=torch.floataa						)
				pipe.to(__snake_case						)
				pipe.set_progress_bar_config(disable=__snake_case						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger "
				SCREAMING_SNAKE_CASE :   int					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							pipe(
				    prompt=__snake_case   ,   generator=__snake_case   ,   guidance_scale=7.5   ,   num_inference_steps=50   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Dict					  =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

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






'''simple docstring'''




from __future__ import annotations

_SCREAMING_SNAKE_CASE							     =			1.6021E-19  # units = C





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  tuple[str, float]:
   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()

