import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort







@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case       (		unittest.TestCase	):




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )




   @property
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ort.SessionOptions()
     SCREAMING_SNAKE_CASE__    							=   False
     return options




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/in_paint/overture-creations-5sI6fQgYIuo.png"""   )
     SCREAMING_SNAKE_CASE__    							=   load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""   )
     SCREAMING_SNAKE_CASE__    							=   load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""   )

     # using the PNDM scheduler by default
     SCREAMING_SNAKE_CASE__    							=   OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         """CompVis/stable-diffusion-v1-4""" ,			revision="""onnx""" ,			safety_checker=_lowercase ,			feature_extractor=_lowercase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
     pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   """A red cat sitting on a park bench"""

     SCREAMING_SNAKE_CASE__    							=   np.random.RandomState(0   )
     SCREAMING_SNAKE_CASE__    							=   pipe(
         prompt=_lowercase ,			image=_lowercase ,			mask_image=_lowercase ,			strength=0.75 ,			guidance_scale=7.5 ,			num_inference_steps=15 ,			generator=_lowercase ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image   ).max() < 1E-2

from math import log

from scipy.constants import Boltzmann, physical_constants

__lowerCamelCase    :				int							     =	300  # TEMPERATURE (unit = K)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  )							->      float:







  """simple docstring"""





  if donor_conc <= 0:
    raise ValueError("""Donor concentration should be positive"""			)
  elif acceptor_conc <= 0:
    raise ValueError("""Acceptor concentration should be positive"""			)
  elif intrinsic_conc <= 0:
    raise ValueError("""Intrinsic concentration should be positive"""			)
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        """Donor concentration should be greater than intrinsic concentration"""			)
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        """Acceptor concentration should be greater than intrinsic concentration"""			)
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
