


'''simple docstring'''






import math

import qiskit






def 		_A			(lowerCAmelCase__					:int = 1			,  lowerCAmelCase__					:int = 1			,  lowerCAmelCase__					:int = 1						)				->					qiskit.result.counts.Counts:




					'''simple docstring'''



					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
					_a      =							qiskit.QuantumRegister(4			,  'qr'						)
					_a      =							qiskit.ClassicalRegister(2			,  'cr'						)
					# list the entries
					_a      =							[input_a, input_a, carry_in]

					_a      =							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

					_a      =							qiskit.Aer.get_backend('aer_simulator'						)
					_a      =							qiskit.execute(lowerCAmelCase__			,  lowerCAmelCase__			,  shots=10_00						)

					return job.result().get_counts(lowerCAmelCase__						)


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





'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_   :      Union[str, Any]				    =      logging.get_logger(__name__)

a_   :      Union[str, Any]				    =      {"vocab_file": "spiece.model"}

a_   :      List[Any]				    =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

a_   :      Dict				    =      {"bert_for_seq_generation": 5_1_2}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		[]
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<::::>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			sep_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							vocab_file

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.sp_model.get_piece_size()
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def __getstate__(	self  )   ->							Any:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							int:
      _a      =							d

      # for backward compatibility
      if not hasattr(self ,			'sp_model_kwargs'  ):
           _a      =							{}

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							self.sp_model.IdToPiece(__magic_name__  )
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							[]
      _a      =							''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)

