



'''simple docstring'''






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						bool = False  )      ->   list[float]:
							if radian_mode:
														return [magnitude * cos(_lowerCAmelCase  ), magnitude * sin(_lowerCAmelCase  )]
							return [magnitude * cos(radians(_lowerCAmelCase  )  ), magnitude * sin(radians(_lowerCAmelCase  )  )]


def  snake_case_     (					_lowerCAmelCase  :						NDArray[floataa]   ,      _lowerCAmelCase  :						NDArray[floataa]   ,      _lowerCAmelCase  :						float = 10**-1  )      ->   bool:
							UpperCAmelCase :       NDArray[floataa] 							=		cross(_lowerCAmelCase   ,      _lowerCAmelCase  )
							UpperCAmelCase :       float 							=		sum(_lowerCAmelCase  )
							return abs(_lowerCAmelCase  ) < eps


if __name__ == "__main__":
						# Test to check if it works
						UpperCamelCase__:   List[Any]								=			array(
						    [
						        polar_force(718.4, 180 - 30),
						        polar_force(879.54, 45),
						        polar_force(100, -90),
						    ]
						)

						UpperCamelCase__:   NDArray[floataa]								=			array([[0, 0], [0, 0], [0, 0]])

						assert in_static_equilibrium(forces, location)

						# Problem 1 in image_data/2D_problems.jpg
						UpperCamelCase__:   Optional[Any]								=			array(
						    [
						        polar_force(30 * 9.81, 15),
						        polar_force(215, 180 - 45),
						        polar_force(264, 90 - 30),
						    ]
						)

						UpperCamelCase__:   int								=			array([[0, 0], [0, 0], [0, 0]])

						assert in_static_equilibrium(forces, location)

						# Problem in image_data/2D_problems_1.jpg
						UpperCamelCase__:   List[Any]								=			array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

						UpperCamelCase__:   List[str]								=			array([[0, 0], [6, 0], [10, 0], [12, 0]])

						assert in_static_equilibrium(forces, location)

						import doctest

						doctest.testmod()







"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=5	)							->		Union[str, Any]:


					"""simple docstring"""





					assert masked_input.count('''<mask>'''	) == 1
					_UpperCamelCase							 =  torch.tensor(tokenizer.encode(__snake_case,     add_special_tokens=__snake_case	)	).unsqueeze(0	)  # Batch size 1
					_UpperCamelCase							 =  model(__snake_case	)[0]  # The last hidden-state is the first element of the output tuple
					_UpperCamelCase							 =  (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
					_UpperCamelCase							 =  logits[0, masked_index, :]
					_UpperCamelCase							 =  logits.softmax(dim=0	)
					_UpperCamelCase ,  _UpperCamelCase							 =  prob.topk(k=__snake_case,     dim=0	)
					_UpperCamelCase							 =  ''' '''.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(__snake_case	)	)]	)
					_UpperCamelCase							 =  tokenizer.mask_token
					_UpperCamelCase							 =  []
					for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
										_UpperCamelCase							 =  predicted_token_bpe.replace('''\u2581''',     ''' '''	)
										if " {0}".format(__snake_case	) in masked_input:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(''' {0}'''.format(__snake_case	),     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
										else:
															topk_filled_outputs.append(
															    (
															        masked_input.replace(__snake_case,     __snake_case	),
															        values[index].item(),
															        predicted_token,
															    )	)
					return topk_filled_outputs


_a														=		CamembertTokenizer.from_pretrained("""camembert-base""")
_a														=		CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_a														=		"""Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))






