#!/usr/bin/env python

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

import os
from typing import IO, Any, Dict, List, Sequence

from onnx import AttributeProto, defs, load
from onnx.backend.test.case import collect_snippets
from onnx.backend.test.loader import load_model_tests
from onnx.backend.test.runner import Runner


def is_ml(schemas: Sequence[defs.OpSchema]) -> bool:
    return any(s.domain == "ai.onnx.ml" for s in schemas)


def gen_outlines(f: IO[Any], ml: bool) -> None:
    f.write("# Test Coverage Report")
    if ml:
        f.write(" (ONNX-ML Operators)\n")
    else:
        f.write(" (ONNX Core Operators)\n")
    f.write("## Outlines\n")
    f.write("* [Node Test Coverage](#node-test-coverage)\n")
    f.write("* [Model Test Coverage](#model-test-coverage)\n")
    f.write("* [Overall Test Coverage](#overall-test-coverage)\n")


common_covered: Sequence[str] = []
experimental_covered: Sequence[str] = []


def gen_node_test_coverage(
    schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
    global common_covered
    global experimental_covered
    generators = set(
        {
            "Multinomial",
            "RandomNormal",
            "RandomNormalLike",
            "RandomUniform",
            "RandomUniformLike",
        }
    )
    node_tests = collect_snippets()
    common_covered = sorted(
        s.name
        for s in schemas
        if s.name in node_tests
        and s.support_level == defs.OpSchema.SupportType.COMMON
        and (s.domain == "ai.onnx.ml") == ml
    )
    common_no_cover = sorted(
        s.name
        for s in schemas
        if s.name not in node_tests
        and s.support_level == defs.OpSchema.SupportType.COMMON
        and (s.domain == "ai.onnx.ml") == ml
    )
    common_generator = sorted(name for name in common_no_cover if name in generators)
    experimental_covered = sorted(
        s.name
        for s in schemas
        if s.name in node_tests
        and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
        and (s.domain == "ai.onnx.ml") == ml
    )
    experimental_no_cover = sorted(
        s.name
        for s in schemas
        if s.name not in node_tests
        and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
        and (s.domain == "ai.onnx.ml") == ml
    )
    experimental_generator = sorted(
        name for name in experimental_no_cover if name in generators
    )
    num_common = len(common_covered) + len(common_no_cover) - len(common_generator)
    num_experimental = (
        len(experimental_covered)
        + len(experimental_no_cover)
        - len(experimental_generator)
    )
    f.write("# Node Test Coverage\n")
    f.write("## Summary\n")
    if num_common:
        f.write(
            f"Node tests have covered {len(common_covered)}/{num_common} "
            f"({len(common_covered) / float(num_common) * 100:.2f}%, {len(common_generator)} "
            f"generators excluded) common operators.\n\n"
        )
    else:
        f.write("Node tests have covered 0/0 (N/A) common operators. \n\n")
    if num_experimental:
        f.write(
            "Node tests have covered {}/{} ({:.2f}%, {} generators excluded) "
            "experimental operators.\n\n".format(
                len(experimental_covered),
                num_experimental,
                (len(experimental_covered) / float(num_experimental) * 100),
                len(experimental_generator),
            )
        )
    else:
        f.write("Node tests have covered 0/0 (N/A) experimental operators.\n\n")
    titles = [
        "&#x1F49A;Covered Common Operators",
        "&#x1F494;No Cover Common Operators",
        "&#x1F49A;Covered Experimental Operators",
        "&#x1F494;No Cover Experimental Operators",
    ]
    all_lists = [
        common_covered,
        common_no_cover,
        experimental_covered,
        experimental_no_cover,
    ]
    for t in titles:
        f.write(f"* [{t[9:]}](#{t[9:].lower().replace(' ', '-')})\n")
    f.write("\n")
    for t, l in zip(titles, all_lists):  # noqa: E741
        f.write(f"## {t}\n")
        for s in l:
            f.write(f"### {s}")
            if s in node_tests:
                f.write(
                    f"\nThere are {len(node_tests[s])} test cases, listed as following:\n"
                )
                for summary, code in sorted(node_tests[s]):
                    f.write("<details>\n")
                    f.write(f"<summary>{summary}</summary>\n\n")
                    f.write(f"```python\n{code}\n```\n\n")
                    f.write("</details>\n")
            else:
                if s in generators:
                    f.write(" (random generator operator)\n")
                else:
                    f.write(" (call for test cases)\n")
            f.write("\n\n")
        f.write("<br/>\n\n")


def gen_model_test_coverage(
    schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
    f.write("# Model Test Coverage\n")
    # Process schemas
    schema_dict = {}
    for schema in schemas:
        schema_dict[schema.name] = schema
    # Load models from each model test using Runner.prepare_model_data
    # Need to grab associated nodes
    attrs: Dict[str, Dict[str, List[Any]]] = {}
    model_paths: List[Any] = []
    for rt in load_model_tests(kind="real"):
        if rt.url.startswith("onnx/backend/test/data/light/"):
            # testing local files
            model_name = os.path.normpath(
                os.path.join(os.path.dirname(__file__), "..", "..", "..", rt.url)
            )
            if not os.path.exists(model_name):
                raise FileNotFoundError(f"Unable to find model {model_name!r}.")
            model_paths.append(model_name)
        else:
            model_dir = Runner.prepare_model_data(rt)
            model_paths.append(os.path.join(model_dir, "model.onnx"))
    model_paths.sort()
    model_written = False
    for model_pb_path in model_paths:
        model = load(model_pb_path)
        if ml:
            ml_present = False
            for opset in model.opset_import:
                if opset.domain == "ai.onnx.ml":
                    ml_present = True
            if not ml_present:
                continue
            else:
                model_written = True
        f.write(f"## {model.graph.name}\n")
        # Deconstruct model
        num_covered = 0
        for node in model.graph.node:
            if node.op_type in common_covered or node.op_type in experimental_covered:
                num_covered += 1
                # Add details of which nodes are/aren't covered
                # Iterate through and store each node's attributes
                for attr in node.attribute:
                    if node.op_type not in attrs:
                        attrs[node.op_type] = {}
                    if attr.name not in attrs[node.op_type]:
                        attrs[node.op_type][attr.name] = []
                    if attr.type == AttributeProto.FLOAT:
                        if attr.f not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.f)
                    elif attr.type == AttributeProto.INT:
                        if attr.i not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.i)
                    elif attr.type == AttributeProto.STRING:
                        if attr.s not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.s)
                    elif attr.type == AttributeProto.TENSOR:
                        if attr.t not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.t)
                    elif attr.type == AttributeProto.GRAPH:
                        if attr.g not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.g)
                    elif attr.type == AttributeProto.FLOATS:
                        if attr.floats not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.floats)
                    elif attr.type == AttributeProto.INTS:
                        if attr.ints not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.ints)
                    elif attr.type == AttributeProto.STRINGS:
                        if attr.strings not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.strings)
                    elif attr.type == AttributeProto.TENSORS:
                        if attr.tensors not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.tensors)
                    elif attr.type == AttributeProto.GRAPHS:
                        if attr.graphs not in attrs[node.op_type][attr.name]:
                            attrs[node.op_type][attr.name].append(attr.graphs)
        f.write(
            f"\n{model.graph.name} has {num_covered} nodes. "
            f"Of these, {len(model.graph.node)} are covered by node tests "
            f"({100.0 * float(num_covered) / float(len(model.graph.node))}%)\n\n\n"
        )
        # Iterate through attrs, print
        f.write("<details>\n")
        f.write("<summary>nodes</summary>\n\n")
        for op in sorted(attrs):
            f.write("<details>\n")
            # Get total number of attributes for node schema
            f.write(
                f"<summary>{op}: {len(attrs[op])} out of {len(schema_dict[op].attributes)} attributes covered</summary>\n\n"
            )
            for attribute in sorted(schema_dict[op].attributes):
                if attribute in attrs[op]:
                    f.write(f"{attribute}: {len(attrs[op][attribute])}\n")
                else:
                    f.write(f"{attribute}: 0\n")
            f.write("</details>\n")
        f.write("</details>\n\n\n")
    if not model_written and ml:
        f.write("No model tests present for selected domain\n")


def gen_overall_test_coverage(
    schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
    f.write("# Overall Test Coverage\n")
    f.write("## To be filled.\n")


def gen_spdx(f: IO[Any]) -> None:
    f.write("<!--- SPDX-License-Identifier: Apache-2.0 -->\n")


def main() -> None:
    base_dir = os.path.dirname(
        os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
    )
    docs_dir = os.path.join(base_dir, "docs")
    schemas = defs.get_all_schemas()

    has_ml = is_ml(schemas)
    fname = os.path.join(docs_dir, "TestCoverage.md")
    with open(fname, "w+", newline="", encoding="utf-8") as f:  # type: ignore
        gen_spdx(f)
        gen_outlines(f, False)
        gen_node_test_coverage(schemas, f, False)
        gen_model_test_coverage(schemas, f, False)
        gen_overall_test_coverage(schemas, f, False)

    if has_ml:
        fname = os.path.join(docs_dir, "TestCoverage-ml.md")
        with open(fname, "w+", newline="", encoding="utf-8") as f:  # type: ignore
            gen_spdx(f)
            gen_outlines(f, True)
            gen_node_test_coverage(schemas, f, True)
            gen_model_test_coverage(schemas, f, True)
            gen_overall_test_coverage(schemas, f, True)


if __name__ == "__main__":
    main()
