text: import numpy as np
  import pytest
  
  import pandas as pd
  import pandas._testing as tm
  from pandas.core.arrays import FloatingArray
  
  
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy(box):
      con = pd.Series if box else pd.array
  
      # default (with or without missing values) -> object dtype
      arr = con([0.1, 0.2, 0.3], dtype="Float64")
      result = arr.to_numpy()
      expected = np.array([0.1, 0.2, 0.3], dtype="object")
      tm.assert_numpy_array_equal(result, expected)
  
      arr = con([0.1, 0.2, None], dtype="Float64")
      result = arr.to_numpy()
      expected = np.array([0.1, 0.2, pd.NA], dtype="object")
      tm.assert_numpy_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_float(box):
      con = pd.Series if box else pd.array
  
      # no missing values -> can convert to float, otherwise raises
      arr = con([0.1, 0.2, 0.3], dtype="Float64")
      result = arr.to_numpy(dtype="float64")
      expected = np.array([0.1, 0.2, 0.3], dtype="float64")
      tm.assert_numpy_array_equal(result, expected)
  
      arr = con([0.1, 0.2, None], dtype="Float64")
      with pytest.raises(ValueError, match="cannot convert to 'float64'-dtype"):
          result = arr.to_numpy(dtype="float64")
  
      # need to explicitly specify na_value
      result = arr.to_numpy(dtype="float64", na_value=np.nan)
      expected = np.array([0.1, 0.2, np.nan], dtype="float64")
      tm.assert_numpy_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_int(box):
      con = pd.Series if box else pd.array
  
      # no missing values -> can convert to int, otherwise raises
      arr = con([1.0, 2.0, 3.0], dtype="Float64")
      result = arr.to_numpy(dtype="int64")
      expected = np.array([1, 2, 3], dtype="int64")
      tm.assert_numpy_array_equal(result, expected)
  
      arr = con([1.0, 2.0, None], dtype="Float64")
      with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
          result = arr.to_numpy(dtype="int64")
  
      # automatic casting (floors the values)
      arr = con([0.1, 0.9, 1.1], dtype="Float64")
      result = arr.to_numpy(dtype="int64")
      expected = np.array([0, 0, 1], dtype="int64")
      tm.assert_numpy_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_na_value(box):
      con = pd.Series if box else pd.array
  
      arr = con([0.0, 1.0, None], dtype="Float64")
      result = arr.to_numpy(dtype=object, na_value=None)
      expected = np.array([0.0, 1.0, None], dtype="object")
      tm.assert_numpy_array_equal(result, expected)
  
      result = arr.to_numpy(dtype=bool, na_value=False)
      expected = np.array([False, True, False], dtype="bool")
      tm.assert_numpy_array_equal(result, expected)
  
      result = arr.to_numpy(dtype="int64", na_value=-99)
      expected = np.array([0, 1, -99], dtype="int64")
      tm.assert_numpy_array_equal(result, expected)
  
  
  def test_to_numpy_na_value_with_nan():
      # array with both NaN and NA -> only fill NA with `na_value`
      arr = FloatingArray(np.array([0.0, np.nan, 0.0]), np.array([False, False, True]))
      result = arr.to_numpy(dtype="float64", na_value=-1)
      expected = np.array([0.0, np.nan, -1.0], dtype="float64")
      tm.assert_numpy_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"])
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_dtype(box, dtype):
      con = pd.Series if box else pd.array
      arr = con([0.0, 1.0], dtype="Float64")
  
      result = arr.to_numpy(dtype=dtype)
      expected = np.array([0, 1], dtype=dtype)
      tm.assert_numpy_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"])
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_na_raises(box, dtype):
      con = pd.Series if box else pd.array
      arr = con([0.0, 1.0, None], dtype="Float64")
      with pytest.raises(ValueError, match=dtype):
          arr.to_numpy(dtype=dtype)
  
  
  @pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
  def test_to_numpy_string(box, dtype):
      con = pd.Series if box else pd.array
      arr = con([0.0, 1.0, None], dtype="Float64")
  
      result = arr.to_numpy(dtype="str")
      expected = np.array([0.0, 1.0, pd.NA], dtype=f"{tm.ENDIAN}U32")
      tm.assert_numpy_array_equal(result, expected)
  
  
  def test_to_numpy_copy():
      # to_numpy can be zero-copy if no missing values
      arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
      result = arr.to_numpy(dtype="float64")
      result[0] = 10
      tm.assert_extension_array_equal(arr, pd.array([10, 0.2, 0.3], dtype="Float64"))
  
      arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
      result = arr.to_numpy(dtype="float64", copy=True)
      result[0] = 10
      tm.assert_extension_array_equal(arr, pd.array([0.1, 0.2, 0.3], dtype="Float64"))
  