


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      List[Any]    				=				params

										lowerCAmelCase_ :      Union[str, Any]    				=				np.array(__lowercase							)
										lowerCAmelCase_ :      List[Any]    				=				np.array([len(__lowercase							) for t in data]							)

										self.check()
										self.remove_long_sequences()
										self.remove_empty_sequences()
										self.remove_unknown_sequences()
										self.check()
										self.print_statistics()




						def __getitem__(				self   ,						__lowercase							)					->    List[Any]:
										return (self.token_ids[index], self.lengths[index])




						def __len__(				self							)					->    Union[str, Any]:
										return len(self.lengths							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										assert len(self.token_ids							) == len(self.lengths							)
										assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Union[str, Any]    				=				self.params.max_model_input_size
										lowerCAmelCase_ :      Optional[int]    				=				self.lengths > max_len
										logger.info(f"""Splitting {sum(__lowercase							)} too long sequences."""							)

										def divide_chunks(__lowercase   ,						__lowercase							):
														return [l[i : i + n] for i in range(0   ,						len(__lowercase							)   ,						__lowercase							)]

										lowerCAmelCase_ :      Union[str, Any]    				=				[]
										lowerCAmelCase_ :      Union[str, Any]    				=				[]
										if self.params.mlm:
														lowerCAmelCase_ :      Dict    				=				self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
										else:
														lowerCAmelCase_ :      int    				=				self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

										for seq_, len_ in zip(self.token_ids   ,						self.lengths							):
														assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
														if len_ <= max_len:
																		new_tok_ids.append(seq_							)
																		new_lengths.append(len_							)
														else:
																		lowerCAmelCase_ :      Optional[int]    				=				[]
																		for sub_s in divide_chunks(seq_   ,						max_len - 2							):
																						if sub_s[0] != cls_id:
																										lowerCAmelCase_ :      Dict    				=				np.insert(__lowercase   ,						0   ,						__lowercase							)
																						if sub_s[-1] != sep_id:
																										lowerCAmelCase_ :      Dict    				=				np.insert(__lowercase   ,						len(__lowercase							)   ,						__lowercase							)
																						assert len(__lowercase							) <= max_len
																						assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																						sub_seqs.append(__lowercase							)

																		new_tok_ids.extend(__lowercase							)
																		new_lengths.extend([len(__lowercase							) for l in sub_seqs]							)

										lowerCAmelCase_ :      str    				=				np.array(__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				np.array(__lowercase							)




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      Union[str, Any]    				=				len(self							)
										lowerCAmelCase_ :      List[Any]    				=				self.lengths > 1_1
										lowerCAmelCase_ :      Dict    				=				self.token_ids[indices]
										lowerCAmelCase_ :      Tuple    				=				self.lengths[indices]
										lowerCAmelCase_ :      Any    				=				len(self							)
										logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""							)




						def 						lowercase_ (				self							)					->    Tuple:







										if "unk_token" not in self.params.special_tok_ids:
														return
										else:
														lowerCAmelCase_ :      int    				=				self.params.special_tok_ids['unk_token']
										lowerCAmelCase_ :      str    				=				len(self							)
										lowerCAmelCase_ :      List[str]    				=				np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
										lowerCAmelCase_ :      int    				=				(unk_occs / self.lengths) < 0.5
										lowerCAmelCase_ :      List[str]    				=				self.token_ids[indices]
										lowerCAmelCase_ :      Optional[Any]    				=				self.lengths[indices]
										lowerCAmelCase_ :      Union[str, Any]    				=				len(self							)
										logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""							)




						def 						lowercase_ (				self							)					->    Dict:







										if not self.params.is_master:
														return
										logger.info(f"""{len(self							)} sequences"""							)
										# data_len = sum(self.lengths)
										# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
										# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

										# unk_idx = self.params.special_tok_ids['unk_token']
										# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
										# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




						def 						lowercase_ (				self   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Optional[Any]    				=				[t[0] for t in batch]
										lowerCAmelCase_ :      List[str]    				=				[t[1] for t in batch]
										assert len(__lowercase							) == len(__lowercase							)

										# Max for paddings
										lowerCAmelCase_ :      List[str]    				=				max(__lowercase							)

										# Pad token ids
										if self.params.mlm:
														lowerCAmelCase_ :      str    				=				self.params.special_tok_ids['pad_token']
										else:
														lowerCAmelCase_ :      Optional[int]    				=				self.params.special_tok_ids['unk_token']
										lowerCAmelCase_ :      Tuple    				=				[list(t.astype(__lowercase							)							) + [pad_idx] * (max_seq_len_ - len(__lowercase							)) for t in token_ids]
										assert len(tk_							) == len(__lowercase							)
										assert all(len(__lowercase							) == max_seq_len_ for t in tk_							)

										lowerCAmelCase_ :      Union[str, Any]    				=				torch.tensor(tk_							)  # (bs, max_seq_len_)
										lowerCAmelCase_ :      List[str]    				=				torch.tensor(__lowercase							)  # (bs)
										return tk_t, lg_t



import math

import qiskit



def 			lowerCAmelCase (		lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
				if (
				    isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				    or isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				    or isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				):
								raise TypeError('''inputs must be integers.''' )

				if (input_a < 0) or (input_a < 0) or (carry_in < 0):
								raise ValueError('''inputs must be positive.''' )

				if (
				    (math.floor(lowerCAmelCase_ ) != input_a)
				    or (math.floor(lowerCAmelCase_ ) != input_a)
				    or (math.floor(lowerCAmelCase_ ) != carry_in)
				):
								raise ValueError('''inputs must be exact integers.''' )

				if (input_a > 2) or (input_a > 2) or (carry_in > 2):
								raise ValueError('''inputs must be less or equal to 2.''' )

				# build registers
				lowerCAmelCase_ :      str    				=				qiskit.QuantumRegister(4 ,   '''qr''' )
				lowerCAmelCase_ :      str    				=				qiskit.ClassicalRegister(2 ,   '''cr''' )
				# list the entries
				lowerCAmelCase_ :      Any    				=				[input_a, input_a, carry_in]

				lowerCAmelCase_ :      int    				=				qiskit.QuantumCircuit(lowerCAmelCase_ ,   lowerCAmelCase_ )

				for i in range(0 ,   3 ):
								if entry[i] == 2:
												quantum_circuit.h(lowerCAmelCase_ )  # for hadamard entries
								elif entry[i] == 1:
												quantum_circuit.x(lowerCAmelCase_ )  # for 1 entries
								elif entry[i] == 0:
												quantum_circuit.i(lowerCAmelCase_ )  # for 0 entries

    # build the circuit
				quantum_circuit.ccx(0 ,   1 ,   3 )  # ccx = toffoli gate
				quantum_circuit.cx(0 ,   1 )
				quantum_circuit.ccx(1 ,   2 ,   3 )
				quantum_circuit.cx(1 ,   2 )
				quantum_circuit.cx(0 ,   1 )

				quantum_circuit.measure([2, 3] ,   lowerCAmelCase_ )  # measure the last two qbits

				lowerCAmelCase_ :      Tuple    				=				qiskit.Aer.get_backend('''aer_simulator''' )
				lowerCAmelCase_ :      Union[str, Any]    				=				qiskit.execute(lowerCAmelCase_ ,   lowerCAmelCase_ ,   shots=1_000 )

				return job.result().get_counts(lowerCAmelCase_ )


if __name__ == "__main__":
						print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")