# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0

import sys
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

import numpy as np

import onnx
from onnx.backend.test.case.test_case import TestCase
from onnx.backend.test.case.utils import import_recursive
from onnx.onnx_pb import (
    AttributeProto,
    FunctionProto,
    GraphProto,
    ModelProto,
    NodeProto,
    TensorProto,
    TypeProto,
)

_NodeTestCases = []
_TargetOpType = None


def _rename_edges_helper(
    internal_node: NodeProto,
    rename_helper: Callable[[str], str],
    attribute_map: Dict[str, AttributeProto],
    prefix: str,
) -> NodeProto:
    new_node = NodeProto()
    new_node.CopyFrom(internal_node)
    new_node.ClearField("input")
    new_node.ClearField("output")
    new_node.ClearField("attribute")
    for internal_name in internal_node.input:
        new_node.input.append(rename_helper(internal_name))
    for internal_name in internal_node.output:
        new_node.output.append(rename_helper(internal_name))
    for attr in internal_node.attribute:
        if attr.HasField("ref_attr_name"):
            if attr.ref_attr_name in attribute_map:
                new_attr = AttributeProto()
                new_attr.CopyFrom(attribute_map[attr.ref_attr_name])  # type: ignore
                new_attr.name = attr.name
                new_node.attribute.extend([new_attr])
        else:
            new_attr = AttributeProto()
            new_attr.CopyFrom(attr)
            if attr.type == AttributeProto.GRAPH:
                new_graph = new_attr.g
                sg_rename = {}
                for in_desc in new_graph.input:
                    sg_rename[in_desc.name] = in_desc.name = prefix + in_desc.name
                for out_desc in new_graph.output:
                    sg_rename[out_desc.name] = out_desc.name = prefix + out_desc.name
                for init_desc in new_graph.initializer:
                    sg_rename[init_desc.name] = init_desc.name = prefix + init_desc.name
                for sparse_init_desc in new_graph.sparse_initializer:
                    sg_rename[
                        sparse_init_desc.values.name
                    ] = sparse_init_desc.values.name = (
                        prefix + sparse_init_desc.values.name
                    )
                for sparse_init_desc in new_graph.sparse_initializer:
                    sg_rename[
                        sparse_init_desc.indices.name
                    ] = sparse_init_desc.indices.name = (
                        prefix + sparse_init_desc.indices.name
                    )

                def subgraph_rename_helper(name: str) -> Any:
                    if name in sg_rename:  # noqa: B023
                        return sg_rename[name]  # noqa: B023
                    return rename_helper(name)

                new_nodes = [
                    _rename_edges_helper(
                        node_desc, subgraph_rename_helper, attribute_map, prefix
                    )
                    for node_desc in new_graph.node
                ]
                new_graph.ClearField("node")
                new_graph.node.extend(new_nodes)
            new_node.attribute.extend([new_attr])
    return new_node


# FIXME(TMVector): Any reason we can't get rid of this and use the C++ helper directly?
def function_expand_helper(
    node: NodeProto, function_proto: FunctionProto, op_prefix: str
) -> List[NodeProto]:
    io_names_map = {}
    attribute_map = {a.name: a for a in node.attribute}

    for idx in range(len(function_proto.input)):
        io_names_map[function_proto.input[idx]] = (
            node.input[idx] if idx in range(len(node.input)) else ""
        )

    for idx in range(len(function_proto.output)):
        # Even if the node has been created with optional outputs missing, we
        # can't assume that the function body handles this correctly, such as in
        # the case that output is also an intermediate value.
        # So we only add a name mapping if the output is present. An internal
        # name will be generated if the missing output is used, the same as any
        # other internal tensor.
        if idx in range(len(node.output)) and node.output[idx] != "":
            io_names_map[function_proto.output[idx]] = node.output[idx]

    def rename_helper(internal_name: str) -> Any:
        if internal_name in io_names_map:
            return io_names_map[internal_name]
        elif internal_name == "":
            return ""
        return op_prefix + internal_name

    new_node_list = [
        _rename_edges_helper(internal_node, rename_helper, attribute_map, op_prefix)
        for internal_node in function_proto.node
    ]
    return new_node_list


def function_testcase_helper(
    node: NodeProto, input_types: List[TypeProto], name: str
) -> Tuple[List[Tuple[List[NodeProto], Any]], int]:
    test_op = node.op_type
    op_prefix = test_op + "_" + name + "_expanded_function_"
    schema = onnx.defs.get_schema(test_op, domain=node.domain)

    # an op schema may have several functions, each for one opset version
    # opset versions include the op's since_version and other opset versions
    # if it is needed to define the op for a opset version other than the op's since_version.
    function_protos = []
    for opset_version in schema.function_opset_versions:  # type: ignore
        function_proto_str = schema.get_function_with_opset_version(opset_version)  # type: ignore
        function_proto = FunctionProto()
        function_proto.ParseFromString(function_proto_str)
        function_protos.append(function_proto)
    for opset_version in schema.context_dependent_function_opset_versions:  # type: ignore
        function_proto_str = schema.get_context_dependent_function_with_opset_version(  # type: ignore
            opset_version,
            node.SerializeToString(),
            [t.SerializeToString() for t in input_types],
        )
        function_proto = FunctionProto()
        function_proto.ParseFromString(function_proto_str)
        function_protos.append(function_proto)

    expanded_tests = []
    for function_proto in function_protos:
        for attr in schema.attributes:
            if attr in [a.name for a in node.attribute]:
                continue
            if schema.attributes[attr].default_value:
                node.attribute.extend([schema.attributes[attr].default_value])

        # function_proto.attributes
        node_list = function_expand_helper(node, function_proto, op_prefix)
        expanded_tests.append((node_list, function_proto.opset_import))

    return expanded_tests, schema.since_version


def _extract_value_info(
    input: Union[List[Any], np.ndarray, None],
    name: str,
    type_proto: Optional[TypeProto] = None,
) -> onnx.ValueInfoProto:
    if type_proto is None:
        if input is None:
            raise NotImplementedError(
                "_extract_value_info: both input and type_proto arguments cannot be None."
            )
        elif isinstance(input, list):
            elem_type = onnx.helper.np_dtype_to_tensor_dtype(input[0].dtype)
            shape = None
            tensor_type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
            type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
        elif isinstance(input, TensorProto):
            elem_type = input.data_type
            shape = tuple(input.dims)
            type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)
        else:
            elem_type = onnx.helper.np_dtype_to_tensor_dtype(input.dtype)
            shape = input.shape
            type_proto = onnx.helper.make_tensor_type_proto(elem_type, shape)

    return onnx.helper.make_value_info(name, type_proto)


def _make_test_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto:
    latest_onnx_version, latest_ml_version, latest_training_version = onnx.helper.VERSION_TABLE[-1][2:5]  # type: ignore
    if "opset_imports" in kwargs:
        for opset in kwargs["opset_imports"]:
            # If the test model uses an unreleased opset version (latest_version+1),
            # directly use make_model to create a model with the latest ir version
            if (
                (
                    (opset.domain in {"", "ai.onnx"})
                    and opset.version == latest_onnx_version + 1
                )
                or (
                    opset.domain == "ai.onnx.ml"
                    and opset.version == latest_ml_version + 1
                )
                or (
                    (
                        opset.domain
                        in {"ai.onnx.training version", "ai.onnx.preview.training"}
                    )
                    and opset.version == latest_training_version + 1
                )
            ):
                return onnx.helper.make_model(graph, **kwargs)
    # Otherwise, find and use the corresponding ir version according to given opset version
    return onnx.helper.make_model_gen_version(graph, **kwargs)


# In the case of ops with optional inputs and outputs, node_op.input and node_op.output indicate
# which inputs/outputs are present and which are omitted. However, the parameter inputs
# and outputs of this function include values only for inputs/outputs that are present.
# E.g., for an op with 3 inputs, if the second parameter is optional and we wish to omit it,
# node_op.inputs would look like ["Param1", "", "Param3"], while inputs would look like
# [input-1-value, input-3-value]
# Instead of creating model with latest version, it now generates models for since_version by default.
# Thus it can make every model uses the same opset version after every opset change.
# Besides, user can specify "use_max_opset_version" to generate models for
# the latest opset vesion that supports before targeted opset version
def expect(
    node_op: onnx.NodeProto,
    inputs: Sequence[Union[np.ndarray, TensorProto]],
    outputs: Sequence[Union[np.ndarray, TensorProto]],
    name: str,
    **kwargs: Any,
) -> None:
    # skip if the node_op's op_type is not same as the given one
    if _TargetOpType and node_op.op_type != _TargetOpType:
        return

    # in case node_op is modified
    node = deepcopy(node_op)
    present_inputs = [x for x in node.input if (x != "")]
    present_outputs = [x for x in node.output if (x != "")]
    input_type_protos = [None] * len(inputs)
    if "input_type_protos" in kwargs:
        input_type_protos = kwargs["input_type_protos"]
        del kwargs["input_type_protos"]
    output_type_protos = [None] * len(outputs)
    if "output_type_protos" in kwargs:
        output_type_protos = kwargs["output_type_protos"]
        del kwargs["output_type_protos"]
    inputs_vi = [
        _extract_value_info(arr, arr_name, input_type)
        for arr, arr_name, input_type in zip(inputs, present_inputs, input_type_protos)
    ]
    outputs_vi = [
        _extract_value_info(arr, arr_name, output_type)
        for arr, arr_name, output_type in zip(
            outputs, present_outputs, output_type_protos
        )
    ]
    graph = onnx.helper.make_graph(
        nodes=[node], name=name, inputs=inputs_vi, outputs=outputs_vi
    )
    kwargs["producer_name"] = "backend-test"

    if "opset_imports" not in kwargs:
        # To make sure the model will be produced with the same opset_version after opset changes
        # By default, it uses since_version as opset_version for produced models
        produce_opset_version = onnx.defs.get_schema(
            node.op_type, domain=node.domain
        ).since_version
        kwargs["opset_imports"] = [
            onnx.helper.make_operatorsetid(node.domain, produce_opset_version)
        ]

    model = _make_test_model_gen_version(graph, **kwargs)

    _NodeTestCases.append(
        TestCase(
            name=name,
            model_name=name,
            url=None,
            model_dir=None,
            model=model,
            data_sets=[(inputs, outputs)],
            kind="node",
            rtol=1e-3,
            atol=1e-7,
        )
    )

    # Create list of types for node.input, filling a default TypeProto for missing inputs:
    # E.g. merge(["x", "", "y"], [x-value-info, y-value-info]) will return [x-type, default-type, y-type]
    def merge(
        node_inputs: List[str], present_value_info: List[onnx.ValueInfoProto]
    ) -> List[TypeProto]:
        if node_inputs:
            if node_inputs[0] != "":
                return [
                    present_value_info[0].type,
                    *merge(node_inputs[1:], present_value_info[1:]),
                ]
            else:
                return [TypeProto(), *merge(node_inputs[1:], present_value_info)]
        return []

    merged_types = merge(list(node.input), inputs_vi)
    (
        expanded_tests,
        since_version,
    ) = function_testcase_helper(node, merged_types, name)
    for expanded_function_nodes, func_opset_import in expanded_tests:
        kwargs["producer_name"] = "backend-test"

        # TODO: if kwargs["opset_imports"] already exists, only generate test case for the opset version.
        # replace opset versions with what are specified in function proto
        if "opset_imports" not in kwargs:
            kwargs["opset_imports"] = func_opset_import
        else:
            for opset_import in func_opset_import:
                matches = [
                    opset
                    for opset in kwargs["opset_imports"]
                    if opset.domain == opset_import.domain
                ]
                if matches:
                    matches[0].version = opset_import.version
                else:
                    kwargs["opset_imports"].append(opset_import)

        onnx_ai_opset_version = ""
        if "opset_imports" in kwargs:
            onnx_ai_opset_imports = [
                oi for oi in kwargs["opset_imports"] if oi.domain in ("", "ai.onnx")
            ]
            if len(onnx_ai_opset_imports) == 1:
                onnx_ai_opset_version = onnx_ai_opset_imports[0].version

        function_test_name = name + "_expanded"
        if onnx_ai_opset_version and onnx_ai_opset_version != since_version:
            function_test_name += f"_ver{onnx_ai_opset_version}"
        graph = onnx.helper.make_graph(
            nodes=expanded_function_nodes,
            name=function_test_name,
            inputs=inputs_vi,
            outputs=outputs_vi,
        )
        model = _make_test_model_gen_version(graph, **kwargs)
        _NodeTestCases.append(
            TestCase(
                name=function_test_name,
                model_name=function_test_name,
                url=None,
                model_dir=None,
                model=model,
                data_sets=[(inputs, outputs)],
                kind="node",
                rtol=1e-3,
                atol=1e-7,
            )
        )


def collect_testcases(op_type: str) -> List[TestCase]:
    """Collect node test cases"""
    # only keep those tests related to this operator
    global _TargetOpType
    _TargetOpType = op_type

    import_recursive(sys.modules[__name__])
    return _NodeTestCases
