#!/usr/bin/env python3
"""Generate Python tool models from the Java backend's OpenAPI spec (SwaggerDoc.json).

Uses datamodel-code-generator to convert OpenAPI request schemas to Pydantic models.
Run via:
    task engine:tool-models
"""

from __future__ import annotations

import argparse
import json
from collections.abc import Iterable
from dataclasses import dataclass
from pathlib import Path
from typing import Any

from datamodel_code_generator import InputFileType, PythonVersion, generate
from datamodel_code_generator.enums import DataModelType
from datamodel_code_generator.format import Formatter
from referencing import Registry, Resource
from referencing.jsonschema import DRAFT202012

# Fields inherited from PDFFile base class — not tool parameters.
BASE_CLASS_FIELDS = frozenset({"fileInput", "fileId"})

_ENGINE_ROOT = Path(__file__).resolve().parents[1]

_FILE_HEADER = (
    "# AUTO-GENERATED FILE. DO NOT EDIT.\n"
    "# Generated by scripts/generate_tool_models.py from Java OpenAPI spec (SwaggerDoc.json).\n"
    "# ruff: noqa: E501"
)


@dataclass
class ToolSpec:
    path: str
    enum_name: str
    class_name: str


@dataclass
class DiscoveryResult:
    tools: list[ToolSpec]
    combined_schema: dict[str, Any]


class ToolDiscovery:
    """Discovers tool endpoints from an OpenAPI spec and builds a combined JSON Schema."""

    # Namespaces exposed to the LLM as callable tools. Largely matches ``InternalApiClient.java``.
    # Note: ``/api/v1/filter/`` is intentionally excluded because those APIs are for pipeline processing,
    #  not tool execution.
    ALLOWED_PATH_PREFIXES = (
        "/api/v1/general/",
        "/api/v1/misc/",
        "/api/v1/security/",
        "/api/v1/convert/",
    )

    def __init__(self, spec: dict[str, Any]):
        resource = Resource.from_contents(spec, default_specification=DRAFT202012)
        self.resolver = Registry().with_resource("", resource).resolver()
        self.spec = spec

    def discover(self) -> DiscoveryResult:
        tools: list[ToolSpec] = []
        defs: dict[str, Any] = {}
        used_enum: set[str] = set()
        used_class: set[str] = set()

        for path, path_item in sorted(self.spec.get("paths", {}).items()):
            if "{" in path or not any(path.startswith(p) for p in self.ALLOWED_PATH_PREFIXES):
                continue
            body_props = self._get_request_properties(path_item) or {}
            query_props = self._get_query_parameters(path_item)
            # Body properties win on name collision — body is the canonical param source
            # for the existing tools; query params are additive.
            properties = {**query_props, **body_props}
            if not properties:
                continue
            clean_props = self._filter_properties(properties)
            if not clean_props:
                continue

            enum_name = _deduplicate(_path_to_enum_name(path), used_enum)
            class_name = _deduplicate(_path_to_class_name(path), used_class)

            defs[class_name] = {"type": "object", "properties": clean_props}
            tools.append(ToolSpec(path, enum_name, class_name))

        self._inline_component_refs(defs)

        combined_schema: dict[str, Any] = {
            "$defs": defs,
            "anyOf": [{"$ref": f"#/$defs/{t.class_name}"} for t in tools],
        }
        return DiscoveryResult(tools=tools, combined_schema=combined_schema)

    def _inline_component_refs(self, defs: dict[str, Any]) -> None:
        """Pull every component transitively referenced from tool param schemas into ``defs``
        and rewrite the refs from ``#/components/schemas/X`` to ``#/$defs/X``.

        Without this, nested refs (e.g. ``list[RedactionArea]``) are unresolvable when the
        combined schema is handed to datamodel-code-generator, producing ``RootModel[Any]``
        shells that downstream JSON-schema strict-mode transformers reject.
        """
        schemas = self.spec.get("components", {}).get("schemas", {})
        queue: list[object] = list(defs.values())
        while queue:
            for name in _rewrite_refs(queue.pop()):
                if name not in defs and name in schemas:
                    defs[name] = schemas[name]
                    queue.append(schemas[name])

    def _resolve_ref(self, schema: dict[str, Any]) -> dict[str, Any]:
        if "$ref" in schema:
            return self.resolver.lookup(schema["$ref"]).contents
        return schema

    def _get_request_properties(self, path_item: dict[str, Any]) -> dict[str, Any] | None:
        post = path_item.get("post")
        if not post:
            return None
        content = post.get("requestBody", {}).get("content", {})
        for media_type in ("multipart/form-data", "application/json"):
            if media_type in content:
                schema = content[media_type].get("schema")
                if schema:
                    return self._resolve_ref(schema).get("properties")
        return None

    def _get_query_parameters(self, path_item: dict[str, Any]) -> dict[str, Any]:
        """Extract query parameters as a property map — AI tools expose their main
        inputs (e.g. ``prompt``, ``tolerance``) here rather than in the request body,
        and a handful of converters use query strings alongside multipart files.
        """
        post = path_item.get("post") or {}
        props: dict[str, Any] = {}
        for param in post.get("parameters") or []:
            if param.get("in") != "query":
                continue
            name = param.get("name")
            schema = param.get("schema")
            if not name or not schema:
                continue
            resolved = dict(self._resolve_ref(schema))
            if "description" not in resolved and param.get("description"):
                resolved["description"] = param["description"]
            props[name] = resolved
        return props

    def _filter_properties(self, properties: dict[str, Any]) -> dict[str, Any]:
        """Remove base-class fields and binary upload fields, resolving any $refs."""
        clean: dict[str, Any] = {}
        for name, prop in properties.items():
            if name in BASE_CLASS_FIELDS:
                continue
            prop = self._resolve_ref(prop)
            if prop.get("type") == "string" and prop.get("format") == "binary":
                continue
            clean[name] = prop
        return clean


_COMPONENT_REF_PREFIX = "#/components/schemas/"


def _rewrite_refs(obj: object) -> Iterable[str]:
    """Rewrite ``#/components/schemas/X`` refs to ``#/$defs/X`` in place, yielding each
    component name encountered so the caller can pull referenced schemas into ``$defs``.
    """
    if isinstance(obj, dict):
        ref = obj.get("$ref")
        if isinstance(ref, str) and ref.startswith(_COMPONENT_REF_PREFIX):
            name = ref.removeprefix(_COMPONENT_REF_PREFIX)
            obj["$ref"] = "#/$defs/" + name
            yield name
        for value in obj.values():
            yield from _rewrite_refs(value)
    elif isinstance(obj, list):
        for value in obj:
            yield from _rewrite_refs(value)


def _tool_name_segments(path: str) -> str:
    """Extract a descriptive name from the endpoint path.

    Converters use two segments (e.g. /api/v1/convert/cbr/pdf → cbr-to-pdf).
    Other tools use the last segment (e.g. /api/v1/misc/compress-pdf → compress-pdf).
    """
    parts = path.rstrip("/").split("/")
    if "/api/v1/convert/" in path and len(parts) >= 6:
        return f"{parts[-2]}-to-{parts[-1]}"
    return parts[-1]


def _path_to_enum_name(path: str) -> str:
    return _tool_name_segments(path).replace("-", "_").upper()


def _path_to_class_name(path: str) -> str:
    return "".join(p.capitalize() for p in _tool_name_segments(path).split("-")) + "Params"


def _deduplicate(name: str, used: set[str]) -> str:
    """Return name, appending 2, 3, ... if already in used. Adds result to used."""
    candidate = name
    n = 2
    while candidate in used:
        candidate = f"{name}{n}"
        n += 1
    used.add(candidate)
    return candidate


def generate_models_code(combined_schema: dict[str, Any]) -> str:
    """Run datamodel-code-generator once on the combined schema."""
    code = generate(
        input_=json.dumps(combined_schema, sort_keys=True),
        input_file_type=InputFileType.JsonSchema,
        output_model_type=DataModelType.PydanticV2BaseModel,
        target_python_version=PythonVersion.PY_313,
        snake_case_field=True,
        base_class="stirling.models.base.ApiModel",
        field_constraints=True,
        no_alias=True,
        set_default_enum_member=True,
        additional_imports=["enum.StrEnum"],
        enable_version_header=False,
        custom_file_header=_FILE_HEADER,
        formatters=[Formatter.RUFF_FORMAT, Formatter.RUFF_CHECK],
        settings_path=_ENGINE_ROOT / "pyproject.toml",
    )
    return str(code or "")


def write_output(out_path: Path, tools: list[ToolSpec], models_code: str) -> None:
    union_lines = ["type ParamToolModel = ("]
    for i, tool in enumerate(tools):
        prefix = "    | " if i > 0 else "    "
        union_lines.append(f"{prefix}{tool.class_name}")
    union_lines.append(")")
    union_lines.append("type ParamToolModelType = type[ParamToolModel]")

    enum_lines = [
        "class ToolEndpoint(StrEnum):",
        *(f'    {t.enum_name} = "{t.path}"' for t in tools),
    ]

    ops_lines = [
        "OPERATIONS: dict[ToolEndpoint, ParamToolModelType] = {",
        *(f"    ToolEndpoint.{t.enum_name}: {t.class_name}," for t in tools),
        "}",
    ]

    parts = [models_code, "\n", *union_lines, "\n", *enum_lines, "\n", *ops_lines, ""]
    out_path.write_text("\n".join(parts), encoding="utf-8")


def main() -> None:
    parser = argparse.ArgumentParser(description="Generate Python tool models from Java OpenAPI spec")
    parser.add_argument("--spec", required=True, help="Path to SwaggerDoc.json")
    parser.add_argument("--output", required=True, help="Path to output tool_models.py")
    args = parser.parse_args()

    spec_path = Path(args.spec)
    if not spec_path.exists():
        raise SystemExit(f"OpenAPI spec not found at {spec_path}\nRun 'task engine:tool-models' to generate it.")
    output_path = Path(args.output)

    with open(spec_path, encoding="utf-8") as f:
        spec = json.load(f)

    result = ToolDiscovery(spec).discover()
    models_code = generate_models_code(result.combined_schema)
    write_output(output_path, result.tools, models_code)

    print(f"Generated {len(result.tools)} tool models from {spec_path.name}")
    for tool in result.tools:
        print(f"  {tool.enum_name}: {tool.path} -> {tool.class_name}")


if __name__ == "__main__":
    main()