






"""simple docstring"""


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 SPIECE_UNDERLINE, logging


_UpperCamelCase       :					str									=		logging.get_logger(__name__)

_UpperCamelCase       :					int									=		{'vocab_file': 'spiece.model'}

_UpperCamelCase       :					Dict									=		{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_UpperCamelCase       :					Optional[Any]									=		{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_UpperCamelCase       :					str									=		0
_UpperCamelCase       :					Optional[Any]									=		1
_UpperCamelCase       :					List[Any]									=		2
_UpperCamelCase       :					str									=		3
_UpperCamelCase       :					Optional[Any]									=		4






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  VOCAB_FILES_NAMES
    __magic_name__					  =  PRETRAINED_VOCAB_FILES_MAP
    __magic_name__					  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__					  =  '''left'''





    def __init__(							self			:      Tuple       ,	A			:      List[Any]       ,	A			:      Optional[Any]=False       ,	A			:      int=True       ,	A			:      Union[str, Any]=False       ,	A			:      Union[str, Any]="<s>"       ,	A			:      List[str]="</s>"       ,	A			:      List[Any]="<unk>"       ,	A			:      Dict="<sep>"       ,	A			:      Optional[Any]="<pad>"       ,	A			:      Optional[int]="<cls>"       ,	A			:      List[str]="<mask>"       ,	A			:      Tuple=["<eop>", "<eod>"]       ,	A			:      Optional[Dict[str, Any]] = None       ,	**A			:      Optional[Any]       ,	):


      '''simple docstring'''




      a  :    Union[str, Any]       =     AddedToken(A       ,	lstrip=A       ,	rstrip=A    ) if isinstance(A       ,	A    ) else mask_token

      a  :    List[str]       =     {} if sp_model_kwargs is None else sp_model_kwargs

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

      a  :    int       =     3

      a  :    str       =     do_lower_case
      a  :    List[str]       =     remove_space
      a  :    List[str]       =     keep_accents
      a  :    Union[str, Any]       =     vocab_file

      a  :    List[str]       =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(A    )





    @property
    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      return len(self.sp_model    )





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      a  :    Union[str, Any]       =     {self.convert_ids_to_tokens(A    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab





    def __getstate__(							self			:      Optional[Any]    ):


      '''simple docstring'''




      a  :    Union[str, Any]       =     self.__dict__.copy()
      a  :    Any       =     None
      return state





    def __setstate__(							self			:      str       ,	A			:      Union[str, Any]    ):


      '''simple docstring'''




      a  :    int       =     d

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

      a  :    Optional[Any]       =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(self.vocab_file    )





    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	A			:      Dict    ):


      '''simple docstring'''




      if self.remove_space:
        a  :    Tuple       =     ' '.join(inputs.strip().split()    )
      else:
        a  :    Union[str, Any]       =     inputs
      a  :    Optional[int]       =     outputs.replace('``'       ,	'"'    ).replace('\'\''       ,	'"'    )

      if not self.keep_accents:
        a  :    List[str]       =     unicodedata.normalize('NFKD'       ,	A    )
        a  :    Tuple       =     ''.join([c for c in outputs if not unicodedata.combining(A    )]    )
      if self.do_lower_case:
        a  :    Tuple       =     outputs.lower()

      return outputs





    def        lowerCamelCase__   (							self			:      str       ,	A			:      str    ):


      '''simple docstring'''




      a  :    Union[str, Any]       =     self.preprocess_text(A    )
      a  :    Dict       =     self.sp_model.encode(A       ,	out_type=A    )
      a  :    Tuple       =     []
      for piece in pieces:
        if len(A    ) > 1 and piece[-1] == str(','    ) and piece[-2].isdigit():
          a  :    Optional[int]       =     self.sp_model.EncodeAsPieces(piece[:-1].replace(A       ,	''    )    )
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
            if len(cur_pieces[0]    ) == 1:
              a  :    Union[str, Any]       =     cur_pieces[1:]
            else:
              a  :    int       =     cur_pieces[0][1:]
          cur_pieces.append(piece[-1]    )
          new_pieces.extend(A    )
        else:
          new_pieces.append(A    )

      return new_pieces





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      Optional[Any]    ):


      '''simple docstring'''




      return self.sp_model.PieceToId(A    )





    def        lowerCamelCase__   (							self			:      List[str]       ,	A			:      List[str]    ):


      '''simple docstring'''




      return self.sp_model.IdToPiece(A    )





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      List[str]    ):


      '''simple docstring'''




      a  :    Optional[Any]       =     ''.join(A    ).replace(A       ,	' '    ).strip()
      return out_string





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      List[int]       ,	A			:      bool = False       ,	A			:      bool = None       ,	A			:      bool = True       ,	**A			:      Tuple       ,	):


      '''simple docstring'''




      a  :    Optional[Any]       =     kwargs.pop('use_source_tokenizer'       ,	A    )

      a  :    int       =     self.convert_ids_to_tokens(A       ,	skip_special_tokens=A    )

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      a  :    Optional[int]       =     []
      a  :    int       =     []
      for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
          continue
        if token in self.added_tokens_encoder:
          if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(A    )    )
            a  :    Union[str, Any]       =     []
          sub_texts.append(A    )
        else:
          current_sub_text.append(A    )
      if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(A    )    )

      # Mimic the behavior of the Rust tokenizer:
      # By default, there are no spaces between special tokens
      a  :    List[Any]       =     ''.join(A    )

      a  :    Optional[int]       =     (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
        a  :    List[Any]       =     self.clean_up_tokenization(A    )
        return clean_text
      else:
        return text





    def        lowerCamelCase__   (							self			:      List[str]       ,	A			:      List[int]       ,	A			:      Optional[List[int]] = None    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      List[int]       ,	A			:      Optional[List[int]] = None       ,	A			:      bool = False    ):


      '''simple docstring'''




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

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





    def        lowerCamelCase__   (							self			:      int       ,	A			:      List[int]       ,	A			:      Optional[List[int]] = None    ):


      '''simple docstring'''




      a  :    int       =     [self.sep_token_id]
      a  :    str       =     [2]

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





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      str       ,	A			:      Optional[str] = None    ):


      '''simple docstring'''




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

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

      return (out_vocab_file,)












"""simple docstring"""


import math

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




def     snake_case						(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 > 1_0:
    raise ValueError('number of qubits too large to simulate(>10).'   )

  a  :    str       =     QuantumRegister(A_			,    'qr'   )
  a  :    List[Any]       =     ClassicalRegister(A_			,    'cr'   )

  a  :    Optional[Any]       =     QuantumCircuit(A_			,    A_   )

  a  :    Optional[Any]       =     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  :    Optional[int]       =     Aer.get_backend('qasm_simulator'   )
  a  :    Any       =     execute(A_			,    A_			,    shots=1_0_0_0_0   )

  return job.result().get_counts(A_   )


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




