


'''simple docstring'''
import qiskit


def     a   (    __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				qiskit.Aer.get_backend('''aer_simulator'''     )

					# Create a Quantum Circuit acting on the q register
					UpperCamelCase__     :int							      =				qiskit.QuantumCircuit(_lowerCamelCase				,      _lowerCamelCase     )

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

					# Execute the circuit on the simulator
					UpperCamelCase__     :str							      =				qiskit.execute(_lowerCamelCase				,      _lowerCamelCase				,      shots=1000     )

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


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



'''simple docstring'''
import torch
from torch import nn
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=1					,     UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				n_token
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Dict							      =				d_proj

							UpperCamelCase__     :Dict							      =				cutoffs + [n_token]
							UpperCamelCase__     :Union[str, Any]							      =				[0] + self.cutoffs
							UpperCamelCase__     :Any							      =				div_val

							UpperCamelCase__     :int							      =				self.cutoffs[0]
							UpperCamelCase__     :List[Any]							      =				len(self.cutoffs							) - 1
							UpperCamelCase__     :List[Any]							      =				self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												UpperCamelCase__     :Any							      =				nn.Parameter(torch.zeros(self.n_clusters					,     self.d_embed							)							)
												UpperCamelCase__     :Optional[Any]							      =				nn.Parameter(torch.zeros(self.n_clusters							)							)

							UpperCamelCase__     :Union[str, Any]							      =				nn.ModuleList()
							UpperCamelCase__     :str							      =				nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs							)							):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)
																	else:
																						self.out_projs.append(UpperCamelCase_							)

												self.out_layers.append(nn.Linear(UpperCamelCase_					,     UpperCamelCase_							)							)
							else:
												for i in range(len(self.cutoffs							)							):
																	UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	UpperCamelCase__     :Dict							      =				d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)

																	self.out_layers.append(nn.Linear(UpperCamelCase_					,     r_idx - l_idx							)							)

							UpperCamelCase__     :Tuple							      =				keep_order




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if proj is None:
												UpperCamelCase__     :List[str]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												UpperCamelCase__     :Any							      =				nn.functional.linear(UpperCamelCase_					,     proj.t().contiguous()							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if labels is not None:
												# Shift so that tokens < n predict n
												UpperCamelCase__     :Optional[Any]							      =				hidden[..., :-1, :].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				labels[..., 1:].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				hidden.view(-1					,     hidden.size(-1							)							)
												UpperCamelCase__     :str							      =				labels.view(-1							)
												if hidden.size(0							) != labels.size(0							):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''							)
							else:
												UpperCamelCase__     :int							      =				hidden.view(-1					,     hidden.size(-1							)							)

							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												if labels is not None:
																	UpperCamelCase__     :int							      =				labels != -100
																	UpperCamelCase__     :List[Any]							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)
																	UpperCamelCase__     :str							      =				(
																	    -nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)[mask].gather(1					,     labels[mask].unsqueeze(1							)							).squeeze(1							)
																	)
												else:
																	UpperCamelCase__     :Dict							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :str							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :Optional[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :str							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :List[str]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				weights[0], biases[0], self.out_projs[0]

												UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												if labels is None:
																	UpperCamelCase__     :Union[str, Any]							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												else:
																	UpperCamelCase__     :Any							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)

												UpperCamelCase__     :Any							      =				0
												UpperCamelCase__     :str							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						UpperCamelCase__     :Any							      =				(labels >= l_idx) & (labels < r_idx)
																						UpperCamelCase__     :int							      =				mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						UpperCamelCase__     :Tuple							      =				labels.index_select(0					,     UpperCamelCase_							) - l_idx
																						UpperCamelCase__     :str							      =				head_logprob.index_select(0					,     UpperCamelCase_							)
																						UpperCamelCase__     :int							      =				hidden.index_select(0					,     UpperCamelCase_							)
																	else:
																						UpperCamelCase__     :Dict							      =				hidden

																	if i == 0:
																						if labels is not None:
																											UpperCamelCase__     :Any							      =				head_logprob_i.gather(1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)
																						UpperCamelCase__     :Any							      =				self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											UpperCamelCase__     :List[Any]							      =				head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											UpperCamelCase__     :Dict							      =				logprob_i

																	if labels is not None:
																						if (hasattr(self					,     '''keep_order'''							) and self.keep_order) or keep_order:
																											out.index_copy_(0					,     UpperCamelCase_					,     -logprob_i							)
																						else:
																											out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
																						offset += logprob_i.size(0							)

							return out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[Any]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												return nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :Union[str, Any]							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :str							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :Dict							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :Optional[Any]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				weights[0], biases[0], self.out_projs[0]
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												UpperCamelCase__     :int							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												UpperCamelCase__     :int							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						UpperCamelCase__     :str							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :List[str]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Tuple							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

																						UpperCamelCase__     :Optional[Any]							      =				head_logprob[:, -i] + tail_logprob_i
																						UpperCamelCase__     :Union[str, Any]							      =				logprob_i

												return out