





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      __UpperCAmelCase	:       int    	=							None

__UpperCAmelCase	:       Tuple    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       List[str]    	=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase	:       Union[str, Any]    	=							{
    "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"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase	:       Tuple    	=							{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

__UpperCAmelCase	:       List[Any]    	=							"▁"







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					VOCAB_FILES_NAMES
       lowerCAmelCase__						=					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						=					["""input_ids""", """attention_mask"""]
       lowerCAmelCase__						=					BarthezTokenizer




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

         super().__init__(
             A							,    tokenizer_file=A							,    bos_token=A							,    eos_token=A							,    unk_token=A							,    sep_token=A							,    cls_token=A							,    pad_token=A							,    mask_token=A							,    **A							,    )

         __snake_case:						Union[str, Any]			  =						vocab_file
         __snake_case:						str			  =						False if not self.vocab_file else True




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

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case:						Optional[Any]			  =						[self.cls_token_id]
         __snake_case:						int			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   List[int]							,    A			:   Optional[List[int]] = None		):
         __snake_case:						int			  =						[self.sep_token_id]
         __snake_case:						Tuple			  =						[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]






       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   str							,    A			:   Optional[str] = None		):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
               """tokenizer."""		)

         if not os.path.isdir(A		):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
           return
         __snake_case:						List[Any]			  =						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		):
           copyfile(self.vocab_file							,    A		)

         return (out_vocab_file,)













import math

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







def       A__							(   SCREAMING_SNAKE_CASE__ = 3)       ->   qiskit.result.counts.Counts:
  if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_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(SCREAMING_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).""")

  __snake_case:						int			  =						QuantumRegister(SCREAMING_SNAKE_CASE__ , """qr""")
  __snake_case:						List[str]			  =						ClassicalRegister(SCREAMING_SNAKE_CASE__ , """cr""")

  __snake_case:						Optional[Any]			  =						QuantumCircuit(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple			  =						number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  # simulate with 10000 shots
  __snake_case:						Union[str, Any]			  =						Aer.get_backend("""qasm_simulator""")
  __snake_case:						Optional[Any]			  =						execute(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , shots=1_0000)

  return job.result().get_counts(SCREAMING_SNAKE_CASE__)


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






