




import os
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


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

snake_case_ : Any    		=       {"vocab_file": "sentencepiece.bpe.model"}

snake_case_ : Optional[int]    		=       {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

snake_case_ : int    		=       {
    "camembert-base": 512,
}

snake_case_ : Dict    		=       "▁"







class __snake_case			(			A_		):
   UpperCAmelCase__  :    Dict          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    List[str]          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    List[str]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__  :    Union[str, Any]          =  ['''input_ids''', '''attention_mask''']







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						int="<s>"					,						_snake_case		:						Union[str, Any]="</s>"					,						_snake_case		:						str="</s>"					,						_snake_case		:						Dict="<s>"					,						_snake_case		:						int="<unk>"					,						_snake_case		:						int="<pad>"					,						_snake_case		:						Union[str, Any]="<mask>"					,						_snake_case		:						Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]					,						_snake_case		:						Optional[Dict[str, Any]] = None					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							AddedToken(_lowerCamelCase					,						lstrip=_lowerCamelCase					,						rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase					,						_lowerCamelCase) else mask_token

        UpperCAmelCase_          =							{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_lowerCamelCase					,						eos_token=_lowerCamelCase					,						unk_token=_lowerCamelCase					,						sep_token=_lowerCamelCase					,						cls_token=_lowerCamelCase					,						pad_token=_lowerCamelCase					,						mask_token=_lowerCamelCase					,						additional_special_tokens=_lowerCamelCase					,						sp_model_kwargs=self.sp_model_kwargs					,						**_lowerCamelCase					,						)
        UpperCAmelCase_          =							spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(_lowerCamelCase))
        UpperCAmelCase_          =							vocab_file
        # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
        # sentencepiece vocabulary (this is the case for <s> and </s>
        UpperCAmelCase_          =							{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
        UpperCAmelCase_          =							len(self.fairseq_tokens_to_ids)
        UpperCAmelCase_          =							len(self.sp_model) + len(self.fairseq_tokens_to_ids)
        UpperCAmelCase_          =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						List[int]					,						_snake_case		:						Optional[List[int]] = None):

        """simple docstring"""


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







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

        """simple docstring"""


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

        if token_ids_a is None:
             return [1] + ([0] * len(_lowerCamelCase)) + [1]
        return [1] + ([0] * len(_lowerCamelCase)) + [1, 1] + ([0] * len(_lowerCamelCase)) + [1]







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						List[int]					,						_snake_case		:						Optional[List[int]] = None):

        """simple docstring"""


        UpperCAmelCase_          =							[self.sep_token_id]
        UpperCAmelCase_          =							[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 + sep + token_ids_a + sep) * [0]







   @property
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        return len(self.fairseq_tokens_to_ids) + len(self.sp_model)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						str):

        """simple docstring"""


        return self.sp_model.encode(_lowerCamelCase					,						out_type=_lowerCamelCase)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						int):

        """simple docstring"""


        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        elif self.sp_model.PieceToId(_lowerCamelCase) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
        return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        UpperCAmelCase_          =							''''''
        UpperCAmelCase_          =							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(_lowerCamelCase) + token
                  UpperCAmelCase_          =							True
                  UpperCAmelCase_          =							[]
             else:
                  current_sub_tokens.append(_lowerCamelCase)
                  UpperCAmelCase_          =							False
        out_string += self.sp_model.decode(_lowerCamelCase)
        return out_string.strip()







   def __getstate__(      self		:						str):

        """simple docstring"""


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







   def __setstate__(      self		:						Dict					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							d

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

        UpperCAmelCase_          =							spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						str					,						_snake_case		:						Optional[str] = None):

        """simple docstring"""


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

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

        return (out_vocab_file,)








"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 3		)     ->					qiskit.result.counts.Counts:
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    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(__lowerCamelCase		) != 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).'''		)

  _snake_case					     =       QuantumRegister(__lowerCamelCase ,					'''qr'''		)
  _snake_case					     =       ClassicalRegister(__lowerCamelCase ,					'''cr'''		)

  _snake_case					     =       QuantumCircuit(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       number_of_qubits

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

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

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

  return job.result().get_counts(__lowerCamelCase		)


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



