codingutf8 2020 the hugging face team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license ensure torch utils pytree treats modeloutput subclasses as nodes and not leaves this is important for distributeddataparallel gradient synchronization with staticgraphtrue invalid test subclass of modeloutput where dataclass decorator is not used a float b optionalfloat none c optionalfloat none class modeloutputsubclasstesterunittest testcase def testdirectmodeloutputself check that direct usage of modeloutput instantiates without errors modeloutputa 1 1 def testsubclassnodataclassself check that a subclass of modeloutput without dataclass is invalid a valid subclass is inherently tested other unit tests above with self assertraisestypeerror modeloutputtestnodataclassa1 1 b2 2 c3 3 coding utf 8 2020 the hugging face team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license ensure torch utils _pytree treats modeloutput subclasses as nodes and not leaves this is important for distributeddataparallel gradient synchronization with static_graph true invalid test subclass of modeloutput where dataclass decorator is not used check that direct usage of modeloutput instantiates without errors check that a subclass of modeloutput without dataclass is invalid a valid subclass is inherently tested other unit tests above
import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.testing_utils import require_torch
from transformers.utils import ModelOutput


@dataclass
class ModelOutputTest(ModelOutput):
    a: float
    b: Optional[float] = None
    c: Optional[float] = None


class ModelOutputTester(unittest.TestCase):
    def test_get_attributes(self):
        x = ModelOutputTest(a=30)
        self.assertEqual(x.a, 30)
        self.assertIsNone(x.b)
        self.assertIsNone(x.c)
        with self.assertRaises(AttributeError):
            _ = x.d

    def test_index_with_ints_and_slices(self):
        x = ModelOutputTest(a=30, b=10)
        self.assertEqual(x[0], 30)
        self.assertEqual(x[1], 10)
        self.assertEqual(x[:2], (30, 10))
        self.assertEqual(x[:], (30, 10))

        x = ModelOutputTest(a=30, c=10)
        self.assertEqual(x[0], 30)
        self.assertEqual(x[1], 10)
        self.assertEqual(x[:2], (30, 10))
        self.assertEqual(x[:], (30, 10))

    def test_index_with_strings(self):
        x = ModelOutputTest(a=30, b=10)
        self.assertEqual(x["a"], 30)
        self.assertEqual(x["b"], 10)
        with self.assertRaises(KeyError):
            _ = x["c"]

        x = ModelOutputTest(a=30, c=10)
        self.assertEqual(x["a"], 30)
        self.assertEqual(x["c"], 10)
        with self.assertRaises(KeyError):
            _ = x["b"]

    def test_dict_like_properties(self):
        x = ModelOutputTest(a=30)
        self.assertEqual(list(x.keys()), ["a"])
        self.assertEqual(list(x.values()), [30])
        self.assertEqual(list(x.items()), [("a", 30)])
        self.assertEqual(list(x), ["a"])

        x = ModelOutputTest(a=30, b=10)
        self.assertEqual(list(x.keys()), ["a", "b"])
        self.assertEqual(list(x.values()), [30, 10])
        self.assertEqual(list(x.items()), [("a", 30), ("b", 10)])
        self.assertEqual(list(x), ["a", "b"])

        x = ModelOutputTest(a=30, c=10)
        self.assertEqual(list(x.keys()), ["a", "c"])
        self.assertEqual(list(x.values()), [30, 10])
        self.assertEqual(list(x.items()), [("a", 30), ("c", 10)])
        self.assertEqual(list(x), ["a", "c"])

        with self.assertRaises(Exception):
            x = x.update({"d": 20})
        with self.assertRaises(Exception):
            del x["a"]
        with self.assertRaises(Exception):
            _ = x.pop("a")
        with self.assertRaises(Exception):
            _ = x.setdefault("d", 32)

    def test_set_attributes(self):
        x = ModelOutputTest(a=30)
        x.a = 10
        self.assertEqual(x.a, 10)
        self.assertEqual(x["a"], 10)

    def test_set_keys(self):
        x = ModelOutputTest(a=30)
        x["a"] = 10
        self.assertEqual(x.a, 10)
        self.assertEqual(x["a"], 10)

    def test_instantiate_from_dict(self):
        x = ModelOutputTest({"a": 30, "b": 10})
        self.assertEqual(list(x.keys()), ["a", "b"])
        self.assertEqual(x.a, 30)
        self.assertEqual(x.b, 10)

    def test_instantiate_from_iterator(self):
        x = ModelOutputTest([("a", 30), ("b", 10)])
        self.assertEqual(list(x.keys()), ["a", "b"])
        self.assertEqual(x.a, 30)
        self.assertEqual(x.b, 10)

        with self.assertRaises(ValueError):
            _ = ModelOutputTest([("a", 30), (10, 10)])

        x = ModelOutputTest(a=(30, 30))
        self.assertEqual(list(x.keys()), ["a"])
        self.assertEqual(x.a, (30, 30))

    @require_torch
    def test_torch_pytree(self):
        
        
        import torch.utils._pytree as pytree

        x = ModelOutput({"a": 1.0, "c": 2.0})
        self.assertFalse(pytree._is_leaf(x))

        x = ModelOutputTest(a=1.0, c=2.0)
        self.assertFalse(pytree._is_leaf(x))

        expected_flat_outs = [1.0, 2.0]
        expected_tree_spec = pytree.TreeSpec(
            ModelOutputTest, (ModelOutputTest, ["a", "c"]), [pytree.LeafSpec(), pytree.LeafSpec()]
        )

        actual_flat_outs, actual_tree_spec = pytree.tree_flatten(x)
        self.assertEqual(expected_flat_outs, actual_flat_outs)
        self.assertEqual(expected_tree_spec, actual_tree_spec)

        unflattened_x = pytree.tree_unflatten(actual_flat_outs, actual_tree_spec)
        self.assertEqual(x, unflattened_x)


class ModelOutputTestNoDataclass(ModelOutput):
    

    a: float
    b: Optional[float] = None
    c: Optional[float] = None


class ModelOutputSubclassTester(unittest.TestCase):
    def test_direct_model_output(self):
        
        ModelOutput({"a": 1.1})

    def test_subclass_no_dataclass(self):
        
        
        with self.assertRaises(TypeError):
            ModelOutputTestNoDataclass(a=1.1, b=2.2, c=3.3)