

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase           =					logging.get_logger(__name__)
lowerCamelCase           =					{'''vocab_file''': '''spiece.model'''}

lowerCamelCase           =					{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

lowerCamelCase           =					{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

lowerCamelCase           =					'''▁'''







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	str    =   VOCAB_FILES_NAMES
   lowerCamelCase__ :	str    =   PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__ :	str    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



   def __init__(				self,    lowercase_,    lowercase_=True,    lowercase_=True,    lowercase_=False,    lowercase_="[CLS]",    lowercase_="[SEP]",    lowercase_="<unk>",    lowercase_="[SEP]",    lowercase_="<pad>",    lowercase_="[CLS]",    lowercase_="[MASK]",    lowercase_ = None,    **lowercase_,    )							->    None:






     """simple docstring"""






     a__  =(
         AddedToken(lowercase_,    lstrip=lowercase_,    rstrip=lowercase_,    normalized=lowercase_       )
         if isinstance(lowercase_,    lowercase_       )
         else mask_token
     )

     a__  ={} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=lowercase_,    remove_space=lowercase_,    keep_accents=lowercase_,    bos_token=lowercase_,    eos_token=lowercase_,    unk_token=lowercase_,    sep_token=lowercase_,    pad_token=lowercase_,    cls_token=lowercase_,    mask_token=lowercase_,    sp_model_kwargs=self.sp_model_kwargs,    **lowercase_,    )

     a__  =do_lower_case
     a__  =remove_space
     a__  =keep_accents
     a__  =vocab_file

     a__  =spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(lowercase_       )



   @property
   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     return len(self.sp_model       )



   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     a__  ={self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab



   def __getstate__(				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.__dict__.copy()
     a__  =None
     return state



   def __setstate__(				self,    lowercase_       )							->    Any:






     """simple docstring"""






     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,    lowercase_       )							->    int:






     """simple docstring"""






     if self.remove_space:
       a__  =''' '''.join(inputs.strip().split()       )
     else:
       a__  =inputs
     a__  =outputs.replace('''``''',    '''"'''       ).replace('''\'\'''',    '''"'''       )

     if not self.keep_accents:
       a__  =unicodedata.normalize('''NFKD''',    lowercase_       )
       a__  =''''''.join([c for c in outputs if not unicodedata.combining(lowercase_       )]       )
     if self.do_lower_case:
       a__  =outputs.lower()

     return outputs



   def  _UpperCAmelCase (				self,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =self.preprocess_text(lowercase_       )
     a__  =self.sp_model.encode(lowercase_,    out_type=lowercase_       )
     a__  =[]
     for piece in pieces:
       if len(lowercase_       ) > 1 and piece[-1] == str(''','''       ) and piece[-2].isdigit():
         a__  =self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_,    ''''''       )       )
         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
           if len(cur_pieces[0]       ) == 1:
             a__  =cur_pieces[1:]
           else:
             a__  =cur_pieces[0][1:]
         cur_pieces.append(piece[-1]       )
         new_pieces.extend(lowercase_       )
       else:
         new_pieces.append(lowercase_       )

     return new_pieces



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






     return self.sp_model.PieceToId(lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    int:






     """simple docstring"""






     return self.sp_model.IdToPiece(lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_       )							->    Tuple:






     """simple docstring"""






     a__  =[]
     a__  =''''''
     a__  =False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(lowercase_       ) + token
         a__  =True
         a__  =[]
       else:
         current_sub_tokens.append(lowercase_       )
         a__  =False
     out_string += self.sp_model.decode(lowercase_       )
     return out_string.strip()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    List[int]:






     """simple docstring"""






     a__  =[self.sep_token_id]
     a__  =[self.cls_token_id]
     if token_ids_a is None:
       return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None,    lowercase_ = False       )							->    List[int]:






     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowercase_,    token_ids_a=lowercase_,    already_has_special_tokens=lowercase_       )

     if token_ids_a is not None:
       return [1] + ([0] * len(lowercase_       )) + [1] + ([0] * len(lowercase_       )) + [1]
     return [1] + ([0] * len(lowercase_       )) + [1]



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    List[int]:






     """simple docstring"""






     a__  =[self.sep_token_id]
     a__  =[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep       ) * [0]
     return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = None       )							->    Tuple[str]:






     """simple docstring"""






     if not os.path.isdir(lowercase_       ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
       return
     a__  =os.path.join(
         lowercase_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

     return (out_vocab_file,)



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def 			UpperCAmelCase__				(			_A      : int = 3							):





  '''simple docstring'''







  if isinstance(_A	,      _A							):
    raise TypeError('''number of qubits must be a integer.'''							)
  if number_of_qubits <= 0:
    raise ValueError('''number of qubits must be > 0.'''							)
  if math.floor(_A							) != number_of_qubits:
    raise ValueError('''number of qubits must be exact integer.'''							)
  if number_of_qubits > 10:
    raise ValueError('''number of qubits too large to simulate(>10).'''							)

  a__  =QuantumRegister(_A	,      '''qr'''							)
  a__  =ClassicalRegister(_A	,      '''cr'''							)

  a__  =QuantumCircuit(_A	,      _A							)

  a__  =number_of_qubits

  for i in range(_A							):
    quantum_circuit.h(number_of_qubits - i - 1							)
    counter -= 1
    for j in range(_A							):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)	,      _A	,      _A							)

  for k in range(number_of_qubits // 2							):
    quantum_circuit.swap(_A	,      number_of_qubits - k - 1							)

  # measure all the qubits
  quantum_circuit.measure(_A	,      _A							)
  # simulate with 10000 shots
  a__  =Aer.get_backend('''qasm_simulator'''							)
  a__  =execute(_A	,      _A	,      shots=1_00_00							)

  return job.result().get_counts(_A							)


if __name__ == "__main__":
       print(
           f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
       )
