





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase=5				)				-> Dict:

							"""simple docstring"""



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


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

UpperCAmelCase_							=				"""Le camembert est <mask> :)"""




print(fill_mask(masked_input, model, tokenizer, topk=3))






import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0]				,       [0]				)

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(lowercase				)


if __name__ == "__main__":
							print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')