# Copyright (c) ONNX Project Contributors

# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import itertools
import math
import random
import unittest
from typing import Any

import ml_dtypes
import numpy as np
import numpy.typing as npt
import parameterized
import pytest

from onnx import (
    AttributeProto,
    GraphProto,
    ModelProto,
    OptionalProto,
    SequenceProto,
    TensorProto,
    TypeProto,
    checker,
    defs,
    helper,
    numpy_helper,
)


def _pack_4bit(array: np.ndarray) -> npt.NDArray[np.uint8]:
    """Convert a numpy array to flatten, packed int4/uint4. Elements must be in the correct range."""
    # Create a 1D copy
    array_flat = array.ravel().view(np.uint8).copy()
    size = array.size
    odd_sized = size % 2 == 1
    if odd_sized:
        array_flat.resize([size + 1], refcheck=False)
    array_flat &= 0x0F
    array_flat[1::2] <<= 4
    return array_flat[0::2] | array_flat[1::2]


def _pack_2bit(array: np.ndarray) -> npt.NDArray[np.uint8]:
    """Convert a numpy array to flatten, packed int2/uint2. Elements must be in the correct range."""
    # Create a 1D copy
    array_flat = array.ravel().view(np.uint8).copy()
    size = array.size
    pad_len = size % 4
    if pad_len:
        array_flat.resize([size + (4 - pad_len)], refcheck=False)
    array_flat &= 0x03
    array_flat[1::4] <<= 2
    array_flat[2::4] <<= 4
    array_flat[3::4] <<= 6
    return array_flat[0::4] | array_flat[1::4] | array_flat[2::4] | array_flat[3::4]  # type: ignore[return-type]


class TestHelperAttributeFunctions(unittest.TestCase):
    def test_attr_float(self) -> None:
        # float
        attr = helper.make_attribute("float", 1.0)
        self.assertEqual(attr.name, "float")
        self.assertEqual(attr.f, 1.0)
        checker.check_attribute(attr)
        # float with scientific
        attr = helper.make_attribute("float", 1e10)
        self.assertEqual(attr.name, "float")
        self.assertEqual(attr.f, 1e10)
        checker.check_attribute(attr)

    def test_attr_int(self) -> None:
        # integer
        attr = helper.make_attribute("int", 3)
        self.assertEqual(attr.name, "int")
        self.assertEqual(attr.i, 3)
        checker.check_attribute(attr)
        # long integer
        attr = helper.make_attribute("int", 5)
        self.assertEqual(attr.name, "int")
        self.assertEqual(attr.i, 5)
        checker.check_attribute(attr)
        # octinteger
        attr = helper.make_attribute("int", 0o1701)
        self.assertEqual(attr.name, "int")
        self.assertEqual(attr.i, 0o1701)
        checker.check_attribute(attr)
        # hexinteger
        attr = helper.make_attribute("int", 0x1701)
        self.assertEqual(attr.name, "int")
        self.assertEqual(attr.i, 0x1701)
        checker.check_attribute(attr)

    def test_attr_doc_string(self) -> None:
        attr = helper.make_attribute("a", "value")
        self.assertEqual(attr.name, "a")
        self.assertEqual(attr.doc_string, "")
        attr = helper.make_attribute("a", "value", "doc")
        self.assertEqual(attr.name, "a")
        self.assertEqual(attr.doc_string, "doc")

    def test_attr_string(self) -> None:
        # bytes
        attr = helper.make_attribute("str", b"test")
        self.assertEqual(attr.name, "str")
        self.assertEqual(attr.s, b"test")
        checker.check_attribute(attr)
        # unspecified
        attr = helper.make_attribute("str", "test")
        self.assertEqual(attr.name, "str")
        self.assertEqual(attr.s, b"test")
        checker.check_attribute(attr)
        # unicode
        attr = helper.make_attribute("str", "test")
        self.assertEqual(attr.name, "str")
        self.assertEqual(attr.s, b"test")
        checker.check_attribute(attr)
        # empty str
        attr = helper.make_attribute("str", "")
        self.assertEqual(attr.name, "str")
        self.assertEqual(helper.get_attribute_value(attr), b"")
        checker.check_attribute(attr)

    def test_attr_repeated_float(self) -> None:
        attr = helper.make_attribute("floats", [1.0, 2.0])
        self.assertEqual(attr.name, "floats")
        self.assertEqual(list(attr.floats), [1.0, 2.0])
        checker.check_attribute(attr)

    def test_attr_repeated_int(self) -> None:
        attr = helper.make_attribute("ints", [1, 2])
        self.assertEqual(attr.name, "ints")
        self.assertEqual(list(attr.ints), [1, 2])
        checker.check_attribute(attr)

    def test_attr_repeated_mixed_floats_and_ints(self) -> None:
        attr = helper.make_attribute("mixed", [1, 2, 3.0, 4.5])
        self.assertEqual(attr.name, "mixed")
        self.assertEqual(list(attr.floats), [1.0, 2.0, 3.0, 4.5])
        checker.check_attribute(attr)

    def test_attr_repeated_str(self) -> None:
        attr = helper.make_attribute("strings", ["str1", "str2"])
        self.assertEqual(attr.name, "strings")
        self.assertEqual(list(attr.strings), [b"str1", b"str2"])
        checker.check_attribute(attr)

    def test_attr_repeated_tensor_proto(self) -> None:
        tensors = [
            helper.make_tensor(
                name="a", data_type=TensorProto.FLOAT, dims=(1,), vals=np.ones(1)
            ),
            helper.make_tensor(
                name="b", data_type=TensorProto.FLOAT, dims=(1,), vals=np.ones(1)
            ),
        ]
        attr = helper.make_attribute("tensors", tensors)
        self.assertEqual(attr.name, "tensors")
        self.assertEqual(list(attr.tensors), tensors)
        checker.check_attribute(attr)

    def test_attr_sparse_tensor_proto(self) -> None:
        dense_shape = [3, 3]
        sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
        values_tensor = helper.make_tensor(
            name="sparse_values",
            data_type=TensorProto.FLOAT,
            dims=[len(sparse_values)],
            vals=np.array(sparse_values).astype(np.float32),
            raw=False,
        )

        linear_indices = [2, 3, 5]
        indices_tensor = helper.make_tensor(
            name="indices",
            data_type=TensorProto.INT64,
            dims=[len(linear_indices)],
            vals=np.array(linear_indices).astype(np.int64),
            raw=False,
        )
        sparse_tensor = helper.make_sparse_tensor(
            values_tensor, indices_tensor, dense_shape
        )

        attr = helper.make_attribute("sparse_attr", sparse_tensor)
        self.assertEqual(attr.name, "sparse_attr")
        checker.check_sparse_tensor(helper.get_attribute_value(attr))
        checker.check_attribute(attr)

    def test_attr_sparse_tensor_repeated_protos(self) -> None:
        dense_shape = [3, 3]
        sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
        values_tensor = helper.make_tensor(
            name="sparse_values",
            data_type=TensorProto.FLOAT,
            dims=[len(sparse_values)],
            vals=np.array(sparse_values).astype(np.float32),
            raw=False,
        )

        linear_indices = [2, 3, 5]
        indices_tensor = helper.make_tensor(
            name="indices",
            data_type=TensorProto.INT64,
            dims=[len(linear_indices)],
            vals=np.array(linear_indices).astype(np.int64),
            raw=False,
        )
        sparse_tensor = helper.make_sparse_tensor(
            values_tensor, indices_tensor, dense_shape
        )

        repeated_sparse = [sparse_tensor, sparse_tensor]
        attr = helper.make_attribute("sparse_attrs", repeated_sparse)
        self.assertEqual(attr.name, "sparse_attrs")
        checker.check_attribute(attr)
        for s in helper.get_attribute_value(attr):
            checker.check_sparse_tensor(s)

    def test_attr_repeated_graph_proto(self) -> None:
        graphs = [GraphProto(), GraphProto()]
        graphs[0].name = "a"
        graphs[1].name = "b"
        attr = helper.make_attribute("graphs", graphs)
        self.assertEqual(attr.name, "graphs")
        self.assertEqual(list(attr.graphs), graphs)
        checker.check_attribute(attr)

    def test_attr_type_proto(self) -> None:
        # type_proto
        type_proto = TypeProto()
        attr = helper.make_attribute("type_proto", type_proto)
        self.assertEqual(attr.name, "type_proto")
        self.assertEqual(attr.tp, type_proto)
        self.assertEqual(attr.type, AttributeProto.TYPE_PROTO)
        # type_protos
        types = [TypeProto(), TypeProto()]
        attr = helper.make_attribute("type_protos", types)

        self.assertEqual(attr.name, "type_protos")
        self.assertEqual(list(attr.type_protos), types)
        self.assertEqual(attr.type, AttributeProto.TYPE_PROTOS)

    def test_attr_empty_list(self) -> None:
        attr = helper.make_attribute("empty", [], attr_type=AttributeProto.STRINGS)
        self.assertEqual(attr.type, AttributeProto.STRINGS)
        self.assertEqual(len(attr.strings), 0)
        self.assertRaises(ValueError, helper.make_attribute, "empty", [])

    def test_attr_mismatch(self) -> None:
        with self.assertRaisesRegex(TypeError, "Inferred attribute type 'FLOAT'"):
            helper.make_attribute("test", 6.4, attr_type=AttributeProto.STRING)

    def test_is_attr_legal(self) -> None:
        # no name, no field
        attr = AttributeProto()
        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
        # name, but no field
        attr = AttributeProto()
        attr.name = "test"
        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
        # name, with two fields
        attr = AttributeProto()
        attr.name = "test"
        attr.f = 1.0
        attr.i = 2
        self.assertRaises(checker.ValidationError, checker.check_attribute, attr)

    def test_is_attr_legal_verbose(self) -> None:
        def _set(
            attr: AttributeProto,
            type_: AttributeProto.AttributeType,
            var: str,
            value: Any,
        ) -> None:
            setattr(attr, var, value)
            attr.type = type_

        def _extend(
            attr: AttributeProto,
            type_: AttributeProto.AttributeType,
            var: list[Any],
            value: Any,
        ) -> None:
            var.extend(value)
            attr.type = type_

        SET_ATTR = [
            (lambda attr: _set(attr, AttributeProto.FLOAT, "f", 1.0)),
            (lambda attr: _set(attr, AttributeProto.INT, "i", 1)),
            (lambda attr: _set(attr, AttributeProto.STRING, "s", b"str")),
            (
                lambda attr: _extend(
                    attr, AttributeProto.FLOATS, attr.floats, [1.0, 2.0]
                )
            ),
            (lambda attr: _extend(attr, AttributeProto.INTS, attr.ints, [1, 2])),
            (
                lambda attr: _extend(
                    attr, AttributeProto.STRINGS, attr.strings, [b"a", b"b"]
                )
            ),
        ]
        # Randomly set one field, and the result should be legal.
        for _i in range(100):
            attr = AttributeProto()
            attr.name = "test"
            random.choice(SET_ATTR)(attr)
            checker.check_attribute(attr)
        # Randomly set two fields, and then ensure helper function catches it.
        for _i in range(100):
            attr = AttributeProto()
            attr.name = "test"
            for func in random.sample(SET_ATTR, 2):
                func(attr)
            self.assertRaises(checker.ValidationError, checker.check_attribute, attr)


class TestHelperNodeFunctions(unittest.TestCase):
    def test_node_no_arg(self) -> None:
        self.assertTrue(defs.has("Relu"))
        node_def = helper.make_node("Relu", ["X"], ["Y"], name="test")
        self.assertEqual(node_def.op_type, "Relu")
        self.assertEqual(node_def.name, "test")
        self.assertEqual(list(node_def.input), ["X"])
        self.assertEqual(list(node_def.output), ["Y"])

    def test_attr_doc_string(self) -> None:
        node_def = helper.make_node("Relu", ["X"], ["Y"], name="test", doc_string="doc")
        self.assertEqual(node_def.doc_string, "doc")

    def test_node_with_arg(self) -> None:
        self.assertTrue(defs.has("Relu"))
        # Note: Relu actually does not need an arg, but let's
        # test it.
        node_def = helper.make_node("Relu", ["X"], ["Y"], arg_value=1)
        self.assertEqual(node_def.op_type, "Relu")
        self.assertEqual(list(node_def.input), ["X"])
        self.assertEqual(list(node_def.output), ["Y"])
        self.assertEqual(len(node_def.attribute), 1)
        self.assertEqual(node_def.attribute[0], helper.make_attribute("arg_value", 1))

    def test_node_domain(self) -> None:
        node_def = helper.make_node(
            "Relu", ["X"], ["Y"], name="test", doc_string="doc", domain="test.domain"
        )
        self.assertEqual(node_def.domain, "test.domain")

    def test_graph(self) -> None:
        node_def1 = helper.make_node("Relu", ["X"], ["Y"])
        node_def2 = helper.make_node("Add", ["X", "Y"], ["Z"])
        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])]
        graph = helper.make_graph(
            [node_def1, node_def2],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
            doc_string=None,
            value_info=value_info,
        )
        self.assertEqual(graph.name, "test")
        self.assertEqual(len(graph.node), 2)
        self.assertEqual(graph.node[0], node_def1)
        self.assertEqual(graph.node[1], node_def2)
        self.assertEqual(graph.doc_string, "")
        self.assertEqual(graph.value_info[0], value_info[0])

    def test_graph_docstring(self) -> None:
        graph = helper.make_graph([], "my graph", [], [], None, "my docs")
        self.assertEqual(graph.name, "my graph")
        self.assertEqual(graph.doc_string, "my docs")

    def test_model(self) -> None:
        node_def = helper.make_node("Relu", ["X"], ["Y"])
        graph_def = helper.make_graph(
            [node_def],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
            [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
        )
        self.assertRaises(AttributeError, helper.make_model, graph_def, xxx=1)
        model_def = helper.make_model(graph_def, producer_name="test")
        self.assertEqual(model_def.producer_name, "test")

    def test_model_docstring(self) -> None:
        graph = helper.make_graph([], "my graph", [], [])
        model_def = helper.make_model(graph, doc_string="test")
        # models may have their own documentation, but don't have a name
        # their name is the domain-qualified name of the underlying graph.
        self.assertFalse(hasattr(model_def, "name"))
        self.assertEqual(model_def.doc_string, "test")

    def test_model_metadata_props(self) -> None:
        graph = helper.make_graph([], "my graph", [], [])
        model_def = helper.make_model(graph, doc_string="test")
        helper.set_model_props(
            model_def, {"Title": "my graph", "Keywords": "test;graph"}
        )
        checker.check_model(model_def)
        helper.set_model_props(
            model_def, {"Title": "my graph", "Keywords": "test;graph"}
        )
        checker.check_model(model_def)  # helper replaces, so no dupe

        dupe = model_def.metadata_props.add()
        dupe.key = "Title"
        dupe.value = "Other"
        self.assertRaises(checker.ValidationError, checker.check_model, model_def)

    def test_model_irversion(self) -> None:
        def mk_model(opset_versions: list[tuple[str, int]]) -> ModelProto:
            graph = helper.make_graph([], "my graph", [], [])
            return helper.make_model_gen_version(
                graph,
                opset_imports=[helper.make_opsetid(*pair) for pair in opset_versions],
            )

        def test(opset_versions: list[tuple[str, int]], ir_version: int) -> None:
            model = mk_model(opset_versions)
            self.assertEqual(model.ir_version, ir_version)

        # opset version 9 requires minimum ir_version 4
        test([("", 9)], 4)
        test([("", 10)], 5)
        test([("", 11)], 6)
        test([("", 12)], 7)
        test([("", 13)], 7)
        test([("", 14)], 7)
        test([("", 15)], 8)
        test([("", 16)], 8)
        test([("", 17)], 8)
        test([("", 18)], 8)
        test([("", 19)], 9)
        test([("", 20)], 9)
        test([("", 21)], 10)
        test([("", 22)], 10)
        test([("", 23)], 11)
        test([("", 24)], 12)
        # standard opset can be referred to using empty-string or "ai.onnx"
        test([("ai.onnx", 9)], 4)
        test([("ai.onnx.ml", 2)], 6)
        test([("ai.onnx.ml", 3)], 8)
        test([("ai.onnx.ml", 4)], 9)
        test([("ai.onnx.ml", 5)], 10)
        test([("ai.onnx.training", 1)], 7)
        # helper should pick *max* IR version required from all opsets specified.
        test([("", 10), ("ai.onnx.ml", 2)], 6)
        self.assertRaises(ValueError, mk_model, [("", 100)])


class TestHelperTensorFunctions(unittest.TestCase):
    def test_make_string_tensor(self) -> None:
        string_list = [s.encode("utf-8") for s in ["Amy", "Billy", "Cindy", "David"]]
        tensor = helper.make_tensor(
            name="test",
            data_type=TensorProto.STRING,
            dims=(2, 2),
            vals=string_list,
            raw=False,
        )
        self.assertEqual(string_list, list(tensor.string_data))

    def test_make_bfloat16_tensor(self) -> None:
        # numpy doesn't support bf16, so we have to compute the correct result manually
        np_array = np.array(
            [
                [1.0, 2.0],
                [3.0, 4.0],
                [0.099853515625, 0.099365234375],
                [0.0998535081744, 0.1],
                [np.nan, np.inf],
            ],
            dtype=np.float32,
        )

        tensor = helper.make_tensor(
            name="test",
            data_type=TensorProto.BFLOAT16,
            dims=np_array.shape,
            vals=np_array,
        )
        self.assertEqual(tensor.name, "test")
        np.testing.assert_equal(
            numpy_helper.to_array(tensor).view(np.uint16),
            np_array.astype(ml_dtypes.bfloat16).view(np.uint16),
        )

    def test_make_float8e4m3fn_tensor(self) -> None:
        y = helper.make_tensor(
            "zero_point", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 50000, 10.1]
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array([0, 0.5, 1, 448, 10], dtype=ml_dtypes.float8_e4m3fn)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e4m3fnuz_tensor(self) -> None:
        y = helper.make_tensor(
            "zero_point",
            TensorProto.FLOAT8E4M3FNUZ,
            [7],
            [0, 0.5, 1, 50000, 10.1, -0.00001, 0.00001],
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array([0, 0.5, 1, 240, 10, 0, 0], dtype=ml_dtypes.float8_e4m3fnuz)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e5m2_tensor(self) -> None:
        y = helper.make_tensor(
            "zero_point", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 50000, 96]
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array([0, 0.5, 1, 49152, 96], dtype=ml_dtypes.float8_e5m2)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e5m2fnuz_tensor(self) -> None:
        y = helper.make_tensor(
            "zero_point",
            TensorProto.FLOAT8E5M2FNUZ,
            [7],
            [0, 0.5, 1, 50000, 96, -0.0000001, 0.0000001],
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array(
            [0, 0.5, 1, 49152, 96, 0, 0], dtype=ml_dtypes.float8_e5m2fnuz
        )
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_bfloat16_tensor_raw(self) -> None:
        array = np.array(
            [
                [1.0, 2.0],
                [3.0, 4.0],
                [0.099853515625, 0.099365234375],
                [0.0998535081744, 0.1],
                [np.nan, np.inf],
            ],
            dtype=ml_dtypes.bfloat16,
        ).view(np.uint16)

        tensor = helper.make_tensor(
            name="test",
            data_type=TensorProto.BFLOAT16,
            dims=array.shape,
            vals=numpy_helper.tobytes_little_endian(array),
            raw=True,
        )
        np.testing.assert_allclose(numpy_helper.to_array(tensor).view(np.uint16), array)

    def test_make_float8e4m3fn_tensor_raw(self) -> None:
        expected = np.array([0, 0.5, 1, 448, 10], dtype=ml_dtypes.float8_e4m3fn)
        packed_values = expected.tobytes()
        y = helper.make_tensor(
            name="test",
            data_type=TensorProto.FLOAT8E4M3FN,
            dims=list(expected.shape),
            vals=packed_values,
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e4m3fnuz_tensor_raw(self) -> None:
        expected = np.array([0, 0.5, 1, 240, 10], dtype=np.float32)
        f8 = expected.astype(ml_dtypes.float8_e4m3fnuz)
        packed_values = f8.tobytes()
        y = helper.make_tensor(
            name="test",
            data_type=TensorProto.FLOAT8E4M3FNUZ,
            dims=list(expected.shape),
            vals=packed_values,
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(
            ynp.view(np.uint8),
            expected.astype(ml_dtypes.float8_e4m3fnuz).view(np.uint8),
        )

    def test_make_float8e5m2_tensor_raw(self) -> None:
        expected = np.array([0, 0.5, 1, 49152, 10], dtype=ml_dtypes.float8_e5m2)
        packed_values = expected.tobytes()
        y = helper.make_tensor(
            name="test",
            data_type=TensorProto.FLOAT8E5M2,
            dims=list(expected.shape),
            vals=packed_values,
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e5m2fnuz_tensor_raw(self) -> None:
        expected = np.array([0, 0.5, 1, 49152, 10], dtype=ml_dtypes.float8_e5m2fnuz)
        packed_values = expected.tobytes()
        y = helper.make_tensor(
            name="test",
            data_type=TensorProto.FLOAT8E5M2FNUZ,
            dims=list(expected.shape),
            vals=packed_values,
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), expected.view(np.uint8))

    def test_make_float8e8m0_tensor(self) -> None:
        y = helper.make_tensor(
            "scale",
            TensorProto.FLOAT8E8M0,
            [7],
            [0, 0.124, 1.0, 1.5, 2.0, 2.1, np.finfo(np.float32).max],
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array([0, 124, 127, 128, 128, 129, 254], dtype=np.uint8)
        np.testing.assert_equal(ynp.view(np.uint8), expected)

    def test_make_float8e8m0_tensor_raw(self) -> None:
        e8m0_raw = np.array([120, 124, 127, 128, 129, 140], dtype=np.uint8)
        packed_values = e8m0_raw.tobytes()
        y = helper.make_tensor(
            name="test",
            data_type=TensorProto.FLOAT8E8M0,
            dims=list(e8m0_raw.shape),
            vals=packed_values,
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array(
            [0.0078125, 0.125, 1.0, 2.0, 4.0, 8192], dtype=ml_dtypes.float8_e8m0fnu
        )
        np.testing.assert_equal(
            ynp.view(np.uint8),
            expected.view(np.uint8),
        )

    @parameterized.parameterized.expand(
        itertools.product(
            (TensorProto.UINT4, TensorProto.INT4),
            ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
        )
    )
    def test_make_4bit_tensor(self, dtype, dims) -> None:
        type_range = {
            TensorProto.UINT4: (0, 15),
            TensorProto.INT4: (-8, 7),
        }
        data = np.random.randint(
            type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
        )
        y = helper.make_tensor("y", dtype, data.shape, data)

        # Check the expected size of int32_data in bytes
        expected_data_size = math.ceil(np.prod(data.shape) / 2.0)
        actual_data_size = len(bytes(y.int32_data))
        np.testing.assert_equal(actual_data_size, expected_data_size)

        # Check the expected data values.
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp, data)

    @parameterized.parameterized.expand(
        itertools.product(
            ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
        )
    )
    def test_4bit_tensor_size(self, dims) -> None:
        # A bug caused negative int4 values to inflate tensor size.
        # So, test negative values here.
        num_elems = np.prod(dims)
        data = np.array([-4] * num_elems, dtype=np.int8).reshape(dims)
        y = helper.make_tensor("y", TensorProto.INT4, data.shape, data)

        # Check the expected size of int32_data in bytes
        expected_data_size = math.ceil(num_elems / 2.0)
        actual_data_size = len(bytes(y.int32_data))
        np.testing.assert_equal(actual_data_size, expected_data_size)

    @parameterized.parameterized.expand(
        itertools.product(
            (TensorProto.UINT4, TensorProto.INT4), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
        )
    )
    def test_make_4bit_raw_tensor(self, dtype, dims) -> None:
        data = np.random.randint(0, high=16, size=dims, dtype=np.uint8)
        packed_data = _pack_4bit(data)

        y = helper.make_tensor(
            "packed_int4", dtype, dims, packed_data.tobytes(), raw=True
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), data)

    def test_make_float4e2m1_raw_tensor(self) -> None:
        data = np.array([0, 0.5, 1, 240, 10, -2], dtype=ml_dtypes.float4_e2m1fn)
        expected = data.view(np.uint8)
        packed_data = _pack_4bit(expected)
        y = helper.make_tensor(
            "packed_fp4e2m1",
            TensorProto.FLOAT4E2M1,
            data.shape,
            packed_data.tobytes(),
            raw=True,
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), expected)

    @parameterized.parameterized.expand(
        itertools.product(
            (TensorProto.UINT2, TensorProto.INT2),
            ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
        )
    )
    def test_make_2bit_tensor(self, dtype, dims) -> None:
        type_range = {
            TensorProto.UINT2: (0, 3),
            TensorProto.INT2: (-2, 1),
        }
        data = np.random.randint(
            type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
        )
        y = helper.make_tensor("y", dtype, data.shape, data)

        # Check the expected size of int32_data in bytes
        expected_data_size = math.ceil(np.prod(data.shape) / 4.0)
        actual_data_size = len(bytes(y.int32_data))
        np.testing.assert_equal(actual_data_size, expected_data_size)

        # Check the expected data values.
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp, data)

    @parameterized.parameterized.expand(
        itertools.product(
            ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
        )
    )
    def test_2bit_tensor_size(self, dims) -> None:
        # A bug caused negative int2 values to inflate tensor size.
        # So, test negative values here.
        num_elems = np.prod(dims)
        data = np.array([-2] * num_elems, dtype=np.int8).reshape(dims)
        y = helper.make_tensor("y", TensorProto.INT2, data.shape, data)

        # Check the expected size of int32_data in bytes
        expected_data_size = math.ceil(num_elems / 4.0)
        actual_data_size = len(bytes(y.int32_data))
        np.testing.assert_equal(actual_data_size, expected_data_size)

    @parameterized.parameterized.expand(
        itertools.product(
            (TensorProto.UINT2, TensorProto.INT2), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
        )
    )
    def test_make_2bit_raw_tensor(self, dtype, dims) -> None:
        data = np.random.randint(0, high=4, size=dims, dtype=np.uint8)
        packed_data = _pack_2bit(data)

        y = helper.make_tensor(
            "packed_int2", dtype, dims, packed_data.tobytes(), raw=True
        )
        ynp = numpy_helper.to_array(y)
        np.testing.assert_equal(ynp.view(np.uint8), data)

    def test_make_float4e2m1_tensor(self) -> None:
        y = helper.make_tensor(
            "zero_point",
            TensorProto.FLOAT4E2M1,
            [7],
            [0, 0.5, 1, 50000, -0.6, -100, -5],
        )
        ynp = numpy_helper.to_array(y)
        expected = np.array(
            [0, 0.5, 1, 6, -0.5, -6, -4], dtype=ml_dtypes.float4_e2m1fn
        ).view(np.uint8)
        np.testing.assert_equal(ynp.view(np.uint8), expected)

    def test_make_sparse_tensor(self) -> None:
        values = [1.1, 2.2, 3.3, 4.4, 5.5]
        values_tensor = helper.make_tensor(
            name="test", data_type=TensorProto.FLOAT, dims=(5,), vals=values
        )
        indices = [1, 3, 5, 7, 9]
        indices_tensor = helper.make_tensor(
            name="test_indices", data_type=TensorProto.INT64, dims=(5,), vals=indices
        )
        dense_shape = [10]
        sparse = helper.make_sparse_tensor(values_tensor, indices_tensor, dense_shape)
        self.assertEqual(sparse.values, values_tensor)
        self.assertEqual(sparse.indices, indices_tensor)
        self.assertEqual(sparse.dims, dense_shape)

    def test_make_tensor_value_info(self) -> None:
        vi = helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 4))
        checker.check_value_info(vi)

        # scalar value
        vi = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ())
        checker.check_value_info(vi)

    def test_make_sparse_tensor_value_info(self) -> None:
        vi = helper.make_sparse_tensor_value_info("X", TensorProto.FLOAT, (2, 3))
        checker.check_value_info(vi)

        # scalar value
        vi = helper.make_sparse_tensor_value_info("Y", TensorProto.FLOAT, ())
        checker.check_value_info(vi)


class TestHelperOptionalAndSequenceFunctions(unittest.TestCase):
    def test_make_optional(self) -> None:
        values = [1.1, 2.2, 3.3, 4.4, 5.5]
        values_tensor = helper.make_tensor(
            name="test", data_type=TensorProto.FLOAT, dims=(5,), vals=values
        )
        optional = helper.make_optional(
            name="test", elem_type=OptionalProto.TENSOR, value=values_tensor
        )
        self.assertEqual(optional.name, "test")
        self.assertEqual(optional.elem_type, OptionalProto.TENSOR)
        self.assertEqual(optional.tensor_value, values_tensor)

        # Test Sequence
        values_sequence = helper.make_sequence(
            name="test",
            elem_type=SequenceProto.TENSOR,
            values=[values_tensor, values_tensor],
        )
        optional = helper.make_optional(
            name="test", elem_type=OptionalProto.SEQUENCE, value=values_sequence
        )
        self.assertEqual(optional.name, "test")
        self.assertEqual(optional.elem_type, OptionalProto.SEQUENCE)
        self.assertEqual(optional.sequence_value, values_sequence)

        # Test None
        optional_none = helper.make_optional(
            name="test", elem_type=OptionalProto.UNDEFINED, value=None
        )
        self.assertEqual(optional_none.name, "test")
        self.assertEqual(optional_none.elem_type, OptionalProto.UNDEFINED)
        self.assertFalse(optional_none.HasField("tensor_value"))

    def test_make_optional_value_info(self) -> None:
        tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=[5])
        tensor_val_into = helper.make_value_info(
            name="test", type_proto=tensor_type_proto
        )
        optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
        optional_val_info = helper.make_value_info(
            name="test", type_proto=optional_type_proto
        )

        self.assertEqual(optional_val_info.name, "test")
        self.assertTrue(optional_val_info.type.optional_type)
        self.assertEqual(
            optional_val_info.type.optional_type.elem_type, tensor_val_into.type
        )

        # Test Sequence
        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
        optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
        optional_val_info = helper.make_value_info(
            name="test", type_proto=optional_type_proto
        )

        self.assertEqual(optional_val_info.name, "test")
        self.assertTrue(optional_val_info.type.optional_type)
        sequence_value_info = helper.make_value_info(
            name="test", type_proto=tensor_type_proto
        )
        self.assertEqual(
            optional_val_info.type.optional_type.elem_type.sequence_type.elem_type,
            sequence_value_info.type,
        )

    def test_make_sequence_value_info(self) -> None:
        tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=None)
        sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
        sequence_val_info = helper.make_value_info(
            name="test", type_proto=sequence_type_proto
        )
        sequence_val_info_prim = helper.make_tensor_sequence_value_info(
            name="test", elem_type=2, shape=None
        )

        self.assertEqual(sequence_val_info, sequence_val_info_prim)


class TestPrintableGraph(unittest.TestCase):
    def test_initializer_with_matching_graph_input(self) -> None:
        add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]

        graph = helper.make_graph(
            [add],
            "test",
            [
                helper.make_tensor_value_info("X", TensorProto.FLOAT, [1]),
                helper.make_tensor_value_info("Y_Initializer", TensorProto.FLOAT, [1]),
            ],  # inputs
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])],  # outputs
            [
                helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])
            ],  # initializers
            doc_string=None,
            value_info=value_info,
        )

        graph_str = helper.printable_graph(graph)
        self.assertTrue(
            """) optional inputs with matching initializers (
  %Y_Initializer[FLOAT, 1]"""
            in graph_str,
            graph_str,
        )

    def test_initializer_no_matching_graph_input(self) -> None:
        add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
        value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]

        graph = helper.make_graph(
            [add],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [1])],  # inputs
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])],  # outputs
            [
                helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])
            ],  # initializers
            doc_string=None,
            value_info=value_info,
        )

        graph_str = helper.printable_graph(graph)
        self.assertTrue(
            """) initializers (
  %Y_Initializer[FLOAT, 1]"""
            in graph_str,
            graph_str,
        )

    def test_unknown_dimensions(self) -> None:
        graph = helper.make_graph(
            [helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])],
            "test",
            [helper.make_tensor_value_info("X", TensorProto.FLOAT, [None])],  # inputs
            [helper.make_tensor_value_info("Z", TensorProto.FLOAT, [None])],  # outputs
            [
                helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])
            ],  # initializers
            doc_string=None,
        )
        model = helper.make_model(graph)
        checker.check_model(model)

        graph_str = helper.printable_graph(graph)
        self.assertIn("X[FLOAT, ?]", graph_str)


@pytest.mark.parametrize(
    "tensor_dtype",
    [t for t in helper.get_all_tensor_dtypes() if t != TensorProto.STRING],
    ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype),
)
def test_make_tensor_vals(tensor_dtype: int) -> None:
    np_type = helper.tensor_dtype_to_np_dtype(tensor_dtype)
    if tensor_dtype in {
        TensorProto.UINT8,
        TensorProto.UINT16,
        TensorProto.UINT32,
        TensorProto.UINT64,
    }:
        # Avoid "RuntimeWarning: invalid value encountered in cast" when using
        # astype() for negative floats.
        np_array = numpy_helper.create_random_int((2, 3), np_type)
    else:
        np_array = np.random.randn(2, 3)
    np_array = np_array.astype(np_type)
    tensor = helper.make_tensor(
        name="test", data_type=tensor_dtype, dims=np_array.shape, vals=np_array
    )
    roundtrip_array = numpy_helper.to_array(tensor)
    if tensor_dtype in {
        TensorProto.FLOAT8E5M2FNUZ,
        TensorProto.FLOAT8E5M2,
        TensorProto.FLOAT8E4M3FNUZ,
        TensorProto.FLOAT8E4M3FN,
        TensorProto.BFLOAT16,
        TensorProto.FLOAT8E8M0,
    }:
        # There is a bug in ml_dtypes that causes equality checks to fail for these dtypes
        # See https://github.com/jax-ml/ml_dtypes/issues/301
        assert roundtrip_array.shape == np_array.shape
        assert roundtrip_array.dtype == np_array.dtype
        assert roundtrip_array.tobytes() == np_array.tobytes()
    else:
        np.testing.assert_equal(np_array, roundtrip_array)


@pytest.mark.parametrize(
    "tensor_dtype",
    [t for t in helper.get_all_tensor_dtypes() if t != TensorProto.STRING],
    ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype),
)
@pytest.mark.parametrize(
    "vals_as_bytes",
    [True, False],
    ids=["vals_as_bytes", "vals_as_nparray"],
)
def test_make_tensor_raw(tensor_dtype: int, vals_as_bytes: bool) -> None:
    np_type = helper.tensor_dtype_to_np_dtype(tensor_dtype)
    if tensor_dtype in {
        TensorProto.UINT8,
        TensorProto.UINT16,
        TensorProto.UINT32,
        TensorProto.UINT64,
    }:
        # Avoid "RuntimeWarning: invalid value encountered in cast" when using
        # astype() for negative floats.
        np_array = numpy_helper.create_random_int((2, 3), np_type)
    else:
        np_array = np.random.randn(2, 3)
    np_array = np_array.astype(np_type)

    if vals_as_bytes:
        np_array_intermediate = np_array

        if tensor_dtype in {
            TensorProto.FLOAT4E2M1,
            TensorProto.INT4,
            TensorProto.UINT4,
        }:
            np_array_intermediate = _pack_4bit(np_array)
        if tensor_dtype in {
            TensorProto.INT2,
            TensorProto.UINT2,
        }:
            np_array_intermediate = _pack_2bit(np_array)

        vals = numpy_helper.tobytes_little_endian(np_array_intermediate)
    else:
        vals = np_array

    tensor = helper.make_tensor(
        name="test",
        data_type=tensor_dtype,
        dims=np_array.shape,
        vals=vals,
        raw=True,
    )
    roundtrip_array = numpy_helper.to_array(tensor)
    if tensor_dtype in {
        TensorProto.FLOAT8E5M2FNUZ,
        TensorProto.FLOAT8E5M2,
        TensorProto.FLOAT8E4M3FNUZ,
        TensorProto.FLOAT8E4M3FN,
        TensorProto.BFLOAT16,
        TensorProto.FLOAT8E8M0,
    }:
        # There is a bug in ml_dtypes that causes equality checks to fail for these dtypes
        # See https://github.com/jax-ml/ml_dtypes/issues/301
        assert roundtrip_array.shape == np_array.shape
        assert roundtrip_array.dtype == np_array.dtype
        assert roundtrip_array.tobytes() == np_array.tobytes()
    else:
        np.testing.assert_equal(np_array, roundtrip_array)


class TestHelperMappingFunctions(unittest.TestCase):
    # TODO (#4554): remove these tests about catching warnings after the deprecation period
    # Test these new functions should not raise any deprecation warnings
    @pytest.mark.filterwarnings("error::DeprecationWarning")
    def test_tensor_dtype_to_np_dtype_not_throw_warning(self) -> None:
        _ = helper.tensor_dtype_to_np_dtype(TensorProto.FLOAT)

    @pytest.mark.filterwarnings("error::DeprecationWarning")
    def test_tensor_dtype_to_storage_tensor_dtype_not_throw_warning(self) -> None:
        _ = helper.tensor_dtype_to_storage_tensor_dtype(TensorProto.FLOAT)

    @pytest.mark.filterwarnings("error::DeprecationWarning")
    def test_tensor_dtype_to_field_not_throw_warning(self) -> None:
        _ = helper.tensor_dtype_to_field(TensorProto.FLOAT)

    @pytest.mark.filterwarnings("error::DeprecationWarning")
    def test_np_dtype_to_tensor_dtype_not_throw_warning(self) -> None:
        _ = helper.np_dtype_to_tensor_dtype(np.dtype("float32"))

    def test_tensor_dtype_to_np_dtype_bfloat16(self) -> None:
        self.assertEqual(
            helper.tensor_dtype_to_np_dtype(TensorProto.BFLOAT16), ml_dtypes.bfloat16
        )

    def test_tensor_dtype_to_storage_tensor_dtype_bfloat16(self) -> None:
        self.assertEqual(
            helper.tensor_dtype_to_storage_tensor_dtype(TensorProto.BFLOAT16),
            TensorProto.INT32,
        )

    def test_tensor_dtype_to_field_bfloat16(self) -> None:
        self.assertEqual(
            helper.tensor_dtype_to_field(TensorProto.BFLOAT16), "int32_data"
        )


class TestAttrTypeToStr(unittest.TestCase):
    @parameterized.parameterized.expand(
        [
            (AttributeProto.AttributeType.FLOAT, "FLOAT"),
            (AttributeProto.AttributeType.INT, "INT"),
            (AttributeProto.AttributeType.STRING, "STRING"),
            (AttributeProto.AttributeType.TENSOR, "TENSOR"),
            (AttributeProto.AttributeType.GRAPH, "GRAPH"),
            (AttributeProto.AttributeType.SPARSE_TENSOR, "SPARSE_TENSOR"),
            (AttributeProto.AttributeType.TYPE_PROTO, "TYPE_PROTO"),
            (AttributeProto.AttributeType.FLOATS, "FLOATS"),
            (AttributeProto.AttributeType.INTS, "INTS"),
            (AttributeProto.AttributeType.STRINGS, "STRINGS"),
            (AttributeProto.AttributeType.TENSORS, "TENSORS"),
            (AttributeProto.AttributeType.GRAPHS, "GRAPHS"),
            (AttributeProto.AttributeType.SPARSE_TENSORS, "SPARSE_TENSORS"),
            (AttributeProto.AttributeType.TYPE_PROTOS, "TYPE_PROTOS"),
        ]
    )
    def test_attr_type_to_str(self, attr_type, expected_str):
        result = helper._attr_type_to_str(attr_type)
        self.assertEqual(result, expected_str)

    def test_attr_type_to_str_undefined(self):
        result = helper._attr_type_to_str(9999)
        self.assertEqual(result, "UNDEFINED")


if __name__ == "__main__":
    unittest.main()
