


from math import log

from scipy.constants import Boltzmann, physical_constants

A_								=   3_0_0  # TEMPERATURE (unit = K)







def 				UpperCAmelCase					(  UpperCAmelCase					,UpperCAmelCase					,UpperCAmelCase					,)->		List[Any]:







					'''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()



'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



