






from __future__ import annotations

# Define the Gravitational Constant G and the function
SCREAMING_SNAKE_CASE       							=  6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2

def    _lowerCamelCase		(  __A			:							float						,						__A			:							float						,						__A			:							float						,						__A			:							float       )					->    Dict:
    _UpperCAmelCase :					Optional[Any]	     = mass_a * mass_a

    if (force, mass_a, mass_a, distance).count(0       ) != 1:
        raise ValueError('''One and only one argument must be 0'''       )
    if force < 0:
        raise ValueError('''Gravitational force can not be negative'''       )
    if distance < 0:
        raise ValueError('''Distance can not be negative'''       )
    if mass_a < 0 or mass_a < 0:
        raise ValueError('''Mass can not be negative'''       )
    if force == 0:
        _UpperCAmelCase :					Tuple	     = GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
        return {"force": force}
    elif mass_a == 0:
        _UpperCAmelCase :					List[str]	     = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_1": mass_a}
    elif mass_a == 0:
        _UpperCAmelCase :					Optional[int]	     = (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_2": mass_a}
    elif distance == 0:
        _UpperCAmelCase :					Optional[int]	     = (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
        return {"distance": distance}
    raise ValueError('''One and only one argument must be 0'''       )


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()












"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




