"""OpenAI-shape bridge shared by Hermes' ACP clients.

ACP has no OpenAI-style ``tools``/``tool_calls`` channel, so Hermes' tool
schemas travel INTO the prompt as text (:func:`render_tool_bridge_sections`) and
calls are parsed back OUT of the response text (:func:`extract_tool_calls_from_text`).
Clients differ only in WHICH tools they forward (``allowlist``): a CLI with no
tools of its own forwards everything; an autonomous agent with its own
read/edit/execute tools forwards only Hermes' agent-level tools, since
re-offering overlapping ones makes Hermes redo finished work.
"""

from __future__ import annotations

import json
import re
from types import SimpleNamespace
from typing import Any, Iterable

from openai.types.chat.chat_completion_message_tool_call import (
    ChatCompletionMessageToolCall,
    Function,
)

TOOL_CALL_BLOCK_RE = re.compile(r"<tool_call>\s*(\{.*?\})\s*</tool_call>", re.DOTALL)
TOOL_CALL_JSON_RE = re.compile(
    r"\{\s*\"id\"\s*:\s*\"[^\"]+\"\s*,\s*\"type\"\s*:\s*\"function\"\s*,\s*\"function\"\s*:\s*\{.*?\}\s*\}",
    re.DOTALL,
)

TOOL_CALL_CONTRACT = (
    "Available tools (OpenAI function schema). "
    "When using a tool, emit ONLY <tool_call>{...}</tool_call> with one JSON object "
    "containing id/type/function{name,arguments}. arguments must be a JSON string."
)

__all__ = [
    "TOOL_CALL_BLOCK_RE",
    "TOOL_CALL_JSON_RE",
    "TOOL_CALL_CONTRACT",
    "StreamChunks",
    "build_openai_tool_call",
    "tool_specs_from_openai_tools",
    "render_tool_bridge_sections",
    "extract_tool_calls_from_text",
    "completion_to_stream_chunks",
]


class StreamChunks(list):
    """Chunk list that also carries response-level attributes (e.g. ``hermes_projected_messages``)
    Hermes reads off the ``create`` result; a plain list would drop them on the stream path."""


def completion_to_stream_chunks(completion: SimpleNamespace) -> StreamChunks:
    """Re-shape a one-shot ACP response as OpenAI stream chunks (data chunk + usage chunk).

    Response-level attributes other than choices/usage/model are copied onto the result.
    """
    choice = completion.choices[0]
    message = choice.message
    tool_call_deltas = None
    if message.tool_calls:
        tool_call_deltas = [
            SimpleNamespace(
                index=index,
                id=getattr(tool_call, "id", None),
                type=getattr(tool_call, "type", "function"),
                function=SimpleNamespace(
                    name=getattr(tool_call.function, "name", None),
                    arguments=getattr(tool_call.function, "arguments", None),
                ),
            )
            for index, tool_call in enumerate(message.tool_calls)
        ]
    delta = SimpleNamespace(
        role="assistant",
        content=message.content or None,
        tool_calls=tool_call_deltas,
        reasoning_content=getattr(message, "reasoning_content", None),
        reasoning=getattr(message, "reasoning", None),
    )
    data_chunk = SimpleNamespace(
        choices=[SimpleNamespace(index=0, delta=delta, finish_reason=choice.finish_reason)],
        model=completion.model,
        usage=None,
    )
    usage_chunk = SimpleNamespace(choices=[], model=completion.model, usage=completion.usage)
    chunks = StreamChunks([data_chunk, usage_chunk])
    for key, value in vars(completion).items():
        if key not in ("choices", "usage", "model"):
            setattr(chunks, key, value)
    return chunks


def build_openai_tool_call(*, call_id: str, name: str, arguments: str) -> ChatCompletionMessageToolCall:
    """Build an OpenAI-compatible tool-call object for downstream handling."""
    return ChatCompletionMessageToolCall(
        id=call_id,
        call_id=call_id,
        response_item_id=None,
        type="function",
        function=Function(name=name, arguments=arguments),
    )


def tool_specs_from_openai_tools(
    tools: list[dict[str, Any]] | None,
    *,
    allowlist: Iterable[str] | None = None,
) -> list[dict[str, Any]]:
    """Flatten OpenAI ``tools`` into ``{name, description, parameters}`` specs; malformed entries are skipped."""
    allowed = {str(n).strip() for n in allowlist} if allowlist is not None else None
    specs: list[dict[str, Any]] = []
    for t in tools or []:
        fn = t.get("function") or {} if isinstance(t, dict) else None
        if not isinstance(fn, dict):
            continue
        name = fn.get("name")
        if not isinstance(name, str) or not name.strip():
            continue
        name = name.strip()
        if allowed is not None and name not in allowed:
            continue
        specs.append({"name": name, "description": fn.get("description", ""), "parameters": fn.get("parameters", {})})
    return specs


def render_tool_bridge_sections(
    tools: list[dict[str, Any]] | None,
    tool_choice: Any = None,
    *,
    allowlist: Iterable[str] | None = None,
) -> list[str]:
    """Prompt sections carrying the forwarded tool schemas + choice hint (empty list when neither applies)."""
    specs = tool_specs_from_openai_tools(tools, allowlist=allowlist)
    sections: list[str] = []
    if specs:
        sections.append(TOOL_CALL_CONTRACT + "\n" + json.dumps(specs, ensure_ascii=False))
    if tool_choice is not None:
        sections.append(f"Tool choice hint: {json.dumps(tool_choice, ensure_ascii=False)}")
    return sections


def extract_tool_calls_from_text(text: str) -> tuple[list[ChatCompletionMessageToolCall], str]:
    """Pull ``<tool_call>`` blocks out of an ACP response.

    Returns ``(tool_calls, cleaned_text)`` with the consumed blocks removed so the
    assistant message doesn't show raw JSON. Bare-JSON fallback runs only when no
    XML block parsed.
    """
    if not isinstance(text, str) or not text.strip():
        return [], ""

    extracted: list[ChatCompletionMessageToolCall] = []
    consumed_spans: list[tuple[int, int]] = []

    def _try_add_tool_call(raw_json: str) -> None:
        try:
            obj = json.loads(raw_json)
        except Exception:
            return
        fn = obj.get("function") if isinstance(obj, dict) else None
        if not isinstance(fn, dict):
            return
        fn_name = fn.get("name")
        if not isinstance(fn_name, str) or not fn_name.strip():
            return
        fn_args = fn.get("arguments", "{}")
        if not isinstance(fn_args, str):
            fn_args = json.dumps(fn_args, ensure_ascii=False)
        call_id = obj.get("id")
        if not isinstance(call_id, str) or not call_id.strip():
            call_id = f"acp_call_{len(extracted)+1}"
        extracted.append(build_openai_tool_call(call_id=call_id, name=fn_name.strip(), arguments=fn_args))

    for m in TOOL_CALL_BLOCK_RE.finditer(text):
        _try_add_tool_call(m.group(1))
        consumed_spans.append((m.start(), m.end()))
    if not extracted:
        for m in TOOL_CALL_JSON_RE.finditer(text):
            _try_add_tool_call(m.group(0))
            consumed_spans.append((m.start(), m.end()))
    if not consumed_spans:
        return extracted, text.strip()

    consumed_spans.sort()
    merged: list[tuple[int, int]] = []
    for start, end in consumed_spans:
        if not merged or start > merged[-1][1]:
            merged.append((start, end))
        else:
            merged[-1] = (merged[-1][0], max(merged[-1][1], end))

    parts: list[str] = []
    cursor = 0
    for start, end in merged:
        if cursor < start:
            parts.append(text[cursor:start])
        cursor = max(cursor, end)
    if cursor < len(text):
        parts.append(text[cursor:])
    cleaned = "\n".join(p.strip() for p in parts if p and p.strip()).strip()
    return extracted, cleaned