

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers.generation import (
           MaxLengthCriteria,
           MaxNewTokensCriteria,
           MaxTimeCriteria,
           StoppingCriteriaList,
           validate_stopping_criteria,
       )







@require_torch
class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   def  _UpperCAmelCase (				self,    lowercase_       )							->    Dict:






     """simple docstring"""






     a__  =3
     a__  =250

     a__  =ids_tensor((batch_size, length),    lowercase_       )
     a__  =torch.ones((batch_size, length),    device=lowercase_,    dtype=torch.float       ) / length
     return input_ids, scores



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__,      a__  =self._get_tensors(5       )

     a__  =StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10       ),
             MaxTimeCriteria(max_time=0.1       ),
         ]       )

     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(9       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(10       )
     self.assertTrue(criteria(lowercase_,    lowercase_       )       )



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =MaxLengthCriteria(max_length=10       )

     a__,      a__  =self._get_tensors(5       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(9       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(10       )
     self.assertTrue(criteria(lowercase_,    lowercase_       )       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =MaxNewTokensCriteria(start_length=5,    max_new_tokens=5       )

     a__,      a__  =self._get_tensors(5       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(9       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__,      a__  =self._get_tensors(10       )
     self.assertTrue(criteria(lowercase_,    lowercase_       )       )

     a__  =StoppingCriteriaList([criteria]       )
     self.assertEqual(criteria_list.max_length,    10       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__,      a__  =self._get_tensors(5       )

     a__  =MaxTimeCriteria(max_time=0.1       )
     self.assertFalse(criteria(lowercase_,    lowercase_       )       )

     a__  =MaxTimeCriteria(max_time=0.1,    initial_timestamp=time.time() - 0.2       )
     self.assertTrue(criteria(lowercase_,    lowercase_       )       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       ),    10       )

     with self.assertWarns(lowercase_       ):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       ),    11       )

     a__  =validate_stopping_criteria(StoppingCriteriaList(),    11       )

     self.assertEqual(len(lowercase_       ),    1       )



import math

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




def 			UpperCAmelCase__				(			_A      : int = 3							):





  '''simple docstring'''







  if isinstance(_A	,      _A							):
    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(_A							) != 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).'''							)

  a__  =QuantumRegister(_A	,      '''qr'''							)
  a__  =ClassicalRegister(_A	,      '''cr'''							)

  a__  =QuantumCircuit(_A	,      _A							)

  a__  =number_of_qubits

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

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

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

  return job.result().get_counts(_A							)


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