
"""simple docstring"""



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


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.bpe.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCAmelCase__          = {
    'camembert-base': 512,
}

UpperCAmelCase__          = '▁'

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					["""input_ids""", """attention_mask"""]







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			int="<s>"    ,      _lowerCamelCase			:			Union[str, Any]="</s>"    ,      _lowerCamelCase			:			str="</s>"    ,      _lowerCamelCase			:			Dict="<s>"    ,      _lowerCamelCase			:			int="<unk>"    ,      _lowerCamelCase			:			int="<pad>"    ,      _lowerCamelCase			:			Union[str, Any]="<mask>"    ,      _lowerCamelCase			:			Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]    ,      _lowerCamelCase			:			Optional[Dict[str, Any]] = None    ,      **_lowerCamelCase			:			List[Any]    ,      ):
       # Mask token behave like a normal word, i.e. include the space before it
       _snake_case					     =       AddedToken(_lowerCamelCase    ,      lstrip=_lowerCamelCase    ,      rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase    ,      _lowerCamelCase			) else mask_token

       _snake_case					     =       {} 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    ,      )
       _snake_case					     =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(str(_lowerCamelCase			)			)
       _snake_case					     =       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>
       _snake_case					     =       {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
       _snake_case					     =       len(self.fairseq_tokens_to_ids			)
       _snake_case					     =       len(self.sp_model			) + len(self.fairseq_tokens_to_ids			)
       _snake_case					     =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):

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







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):
       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      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [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      lowercase	(							self			:			List[Any]			):
       return len(self.fairseq_tokens_to_ids			) + len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str			):
       return self.sp_model.encode(_lowerCamelCase    ,      out_type=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			int			):
       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      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Optional[int]			):
       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      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       []
       _snake_case					     =       ''''''
       _snake_case					     =       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
           _snake_case					     =       True
           _snake_case					     =       []
         else:
           current_sub_tokens.append(_lowerCamelCase			)
           _snake_case					     =       False
       out_string += self.sp_model.decode(_lowerCamelCase			)
       return out_string.strip()







     def __getstate__(							self			:			str			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			Dict    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       d

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

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







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
         return
       _snake_case					     =       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:
           _snake_case					     =       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)}"
 )



