





"""simple docstring"""







import math

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







def     A__			(       __lowerCamelCase = 3					):





  """simple docstring"""


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

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

  _lowerCAmelCase	      =					QuantumCircuit(_snake_case,		_snake_case					)

  _lowerCAmelCase	      =					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
  _lowerCAmelCase	      =					Aer.get_backend('qasm_simulator'					)
  _lowerCAmelCase	      =					execute(_snake_case,		_snake_case,		shots=1_0_0_0_0					)

  return job.result().get_counts(_snake_case					)


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









"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

