import math

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







def       A__   (						snake_case_			:		int = 3   ):
 if isinstance(snake_case_							,					snake_case_   ):
  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(snake_case_   ) != 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).'''   )

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   QuantumRegister(snake_case_							,					'''qr'''   )
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   ClassicalRegister(snake_case_							,					'''cr'''   )

 SCREAMING_SNAKE_CASE__: str=   QuantumCircuit(snake_case_							,					snake_case_   )

 SCREAMING_SNAKE_CASE__: Dict=   number_of_qubits

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

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

 # measure all the qubits
 quantum_circuit.measure(snake_case_							,					snake_case_   )
 # simulate with 10000 shots
 SCREAMING_SNAKE_CASE__: List[Any]=   Aer.get_backend('''qasm_simulator'''   )
 SCREAMING_SNAKE_CASE__: int=   execute(snake_case_							,					snake_case_							,					shots=10_000   )

 return job.result().get_counts(snake_case_   )


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


"""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


__lowerCAmelCase       :			List[str]   	=logging.get_logger(__name__)

__lowerCAmelCase       :			str   	={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase       :			Dict   	={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

__lowerCAmelCase       :			Tuple   	={
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

__lowerCAmelCase       :			Optional[int]   	="""▁"""





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Tuple				    =      VOCAB_FILES_NAMES
      snake_case__     :  Optional[Any]				    =      PRETRAINED_VOCAB_FILES_MAP
      snake_case__     :  Dict				    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__     :  Optional[Any]				    =      ['input_ids', 'attention_mask']

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="<unk>"						,			__lowerCAmelCase="<pad>"						,			__lowerCAmelCase="<mask>"						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else mask_token

            lowercase								=      {} 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						,			sp_model_kwargs=self.sp_model_kwargs						,			**__lowerCAmelCase						,			)

            lowercase								=      vocab_file
            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(__lowerCAmelCase		)		)

            lowercase								=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

            lowercase								=      len(self.sp_model		) - 1
            lowercase								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [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 							A__			(		self		):







            """simple docstring"""



            return len(self.sp_model		)

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowercase								=      self.sp_model.PieceToId(__lowerCAmelCase		)

            return spm_id if spm_id else self.unk_token_id

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      []
            lowercase								=      """"""
            lowercase								=      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
                        lowercase								=      True
                        lowercase								=      []
                  else:
                        current_sub_tokens.append(__lowerCAmelCase		)
                        lowercase								=      False
            out_string += self.sp_model.decode(__lowerCAmelCase		)
            return out_string.strip()

      def __getstate__(		self		):







            """simple docstring"""



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

      def __setstate__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      d

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

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







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if not os.path.isdir(__lowerCAmelCase		):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
                  return
            lowercase								=      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:
                        lowercase								=      self.sp_model.serialized_model_proto()
                        fi.write(__lowerCAmelCase		)

            return (out_vocab_file,)



