




"""simple docstring"""

import qiskit






def 						_snake_case					( _snake_case							: int = 2							)   -> qiskit.result.counts.Counts:






      '''simple docstring'''


      _A        =       qubits

      # Using Aer's simulator
      _A        =       qiskit.Aer.get_backend('aer_simulator'							)

      # Creating a Quantum Circuit acting on the q register
      _A        =       qiskit.QuantumCircuit(_snake_case    ,  _snake_case							)

      # Adding a H gate on qubit 0 (now q0 in superposition)
      circuit.h(0							)

      for i in range(1    ,  _snake_case							):
            # Adding CX (CNOT) gate
            circuit.cx(i - 1    ,  _snake_case							)

      # Mapping the quantum measurement to the classical bits
      circuit.measure(list(range(_snake_case							)							)    ,  list(range(_snake_case							)							)							)

      # Now measuring any one qubit would affect other qubits to collapse
      # their super position and have same state as the measured one.

      # Executing the circuit on the simulator
      _A        =       qiskit.execute(_snake_case    ,  _snake_case    ,  shots=10_00							)

      return job.result().get_counts(_snake_case							)


if __name__ == "__main__":
       print(F'''Total count for various states are: {quantum_entanglement(3)}''')







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

