"""Co4E — node-graph workflow engine (ported from nova-platform's Flow feature).

A Co4E *workflow* is a graph of step nodes joined by edges. Steps run in
topological **waves** (all nodes at the same depth run together); a *parallel*
node fans out into one stage per sub-agent plus an optional join stage the wave
after. Each node names an agent persona (built-in or custom), optional attached
skills, a model, a permission preset and instructions; at run time these compile
into per-stage prompts fed to the agent, with each wave's outputs threaded into
the next wave's prompts.

This module is PURE PYTHON (no Qt) so the model, store, wave computation and
stage compilation are all unit-testable. UI lives in ``ui/co4e_*`` and the
runner that actually calls the provider lives in ``core/co4e_runner.py``.

Persistence: one JSON file per workflow under ``~/.cowork_local/co4e/workflows``
and one per custom agent under ``~/.cowork_local/co4e/agents``. Skills reuse the
existing ``core/skills.py`` registry.
"""
from __future__ import annotations

import json
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Dict, List, Optional

from ..config import CONFIG_DIR

CO4E_DIR = CONFIG_DIR / "co4e"
WORKFLOWS_DIR = CO4E_DIR / "workflows"
AGENTS_DIR = CO4E_DIR / "agents"

# ---- enums ---------------------------------------------------------------
STEP_IDLE, STEP_PENDING, STEP_RUNNING, STEP_DONE, STEP_ERROR, STEP_PLANNED = (
    "idle", "pending", "running", "done", "error", "planned")

PERMISSION_PRESETS = ("inherit", "read-only", "standard", "full")
# preset -> allowed tool names (None = all tools; enforced by run_cowork's
# allowed_tools filter — update_plan is always allowed on top of these since it
# only drives the plan panel and touches nothing). "save_file" is Cowork's way
# of writing a deliverable, so it belongs to "standard" (write) but NOT "read-only".
PRESET_SCOPES: Dict[str, Optional[List[str]]] = {
    "inherit": None,
    "read-only": ["read_file", "list_dir", "fetch_url"],
    "standard": ["read_file", "list_dir", "write_file", "edit_file", "save_file", "fetch_url"],
    "full": None,
}

# auto   — each step's agent plans then executes automatically (default)
# plan   — read-only: each step only drafts a plan, nothing is written
# manual — step-by-step: run one step at a time, review, then advance ("Next step")
RUN_MODES = ("auto", "plan", "manual")


def slugify(value: str) -> str:
    s = "".join(c if (c.isalnum() or c in "-_") else "-" for c in (value or "").strip().lower())
    return "-".join(filter(None, s.split("-"))) or "step"


# ---- data model ----------------------------------------------------------
@dataclass
class SubAgent:
    """One concurrent worker inside a parallel node."""
    agent: str = ""          # palette agent NAME (built-in or custom)
    instructions: str = ""   # per-sub-agent extra instructions


@dataclass
class Step:
    """A node's persona/config (mirrors nova StepNodeData)."""
    variant: str = "step"            # "step" | "parallel"
    label: str = "New Step"
    agent_slug: str = "custom-step"
    role: str = "AGENT"              # UPPERCASE badge
    icon: str = ""                   # line-icon name ("" = role default)
    instructions: str = ""
    context: str = ""                # extra background/info injected into the prompt
    model: str = ""                  # "" = active provider's default model
    # Auto self-check each step before advancing (Claude-CLI-style quality gate) —
    # ON by default so a flow verifies (and fixes) each step's work automatically.
    self_verify: bool = True
    max_verify_rounds: int = 1
    permission_preset: str = "full"  # steps default to full workspace access
    skills: List[str] = field(default_factory=list)      # registry skill NAMES
    attachments: List[str] = field(default_factory=list)  # local file paths fed to the step
    sub_agents: List[SubAgent] = field(default_factory=list)  # parallel only

    @property
    def is_parallel(self) -> bool:
        return self.variant == "parallel"


@dataclass
class Node:
    id: str
    x: float = 0.0
    y: float = 0.0
    data: Step = field(default_factory=Step)


@dataclass
class Edge:
    id: str
    source: str
    target: str


@dataclass
class Workflow:
    id: str
    name: str = "Untitled flow"
    is_template: bool = False
    nodes: List[Node] = field(default_factory=list)
    edges: List[Edge] = field(default_factory=list)


@dataclass
class CustomAgent:
    """A persisted custom Co4E agent persona (mirrors nova CustomFlowAgent)."""
    id: str
    name: str = ""
    role: str = "AGENT"
    instructions: str = ""
    context: str = ""                # extra background/info injected into the prompt
    model: str = ""
    permission_preset: str = "full"
    icon: str = ""
    skills: List[str] = field(default_factory=list)
    attachments: List[str] = field(default_factory=list)  # local file paths fed to the agent


# ---- (de)serialization ---------------------------------------------------
def step_from_dict(d: dict) -> Step:
    d = dict(d or {})
    subs = d.pop("sub_agents", None) or []
    known = Step().__dict__.keys()
    step = Step(**{k: v for k, v in d.items() if k in known})
    step.sub_agents = [
        SubAgent(agent=s.get("agent", ""), instructions=s.get("instructions", ""))
        if isinstance(s, dict) else SubAgent(agent=str(s))
        for s in subs
    ]
    return step


def node_from_dict(d: dict) -> Node:
    return Node(id=str(d.get("id", "")), x=float(d.get("x", 0) or 0),
                y=float(d.get("y", 0) or 0), data=step_from_dict(d.get("data", {})))


def workflow_from_dict(d: dict) -> Workflow:
    return Workflow(
        id=str(d.get("id", "")),
        name=d.get("name", "Untitled flow"),
        is_template=bool(d.get("is_template", False)),
        nodes=[node_from_dict(n) for n in d.get("nodes", [])],
        edges=[Edge(id=str(e.get("id", "")), source=str(e.get("source", "")),
                    target=str(e.get("target", ""))) for e in d.get("edges", [])],
    )


def workflow_to_dict(wf: Workflow) -> dict:
    return {
        "id": wf.id, "name": wf.name, "is_template": wf.is_template,
        "nodes": [{"id": n.id, "x": n.x, "y": n.y, "data": _step_dict(n.data)} for n in wf.nodes],
        "edges": [asdict(e) for e in wf.edges],
    }


def _step_dict(step: Step) -> dict:
    d = asdict(step)
    # asdict already turns sub_agents into list[dict]
    return d


def agent_to_dict(a: CustomAgent) -> dict:
    return asdict(a)


def agent_from_dict(d: dict) -> CustomAgent:
    known = CustomAgent(id="").__dict__.keys()
    d = {k: v for k, v in (d or {}).items() if k in known}
    d.setdefault("id", "")
    a = CustomAgent(**d)
    a.skills = list(a.skills or [])
    a.attachments = list(a.attachments or [])
    return a


# ---- id minting (no time/random — deterministic counter per process) -----
_counter = {"n": 0}


def _mint_id(prefix: str) -> str:
    _counter["n"] += 1
    return f"{prefix}_{_counter['n']:06d}"


def new_node_id() -> str:
    return _mint_id("node")


def new_edge_id(source: str, target: str) -> str:
    return f"e_{source}__{target}"


def new_workflow(name: str = "Untitled flow") -> Workflow:
    return Workflow(id=_mint_id("wf"), name=name)


def new_custom_agent(name: str = "") -> CustomAgent:
    return CustomAgent(id=_mint_id("agent"), name=name)


# ---- workflow store ------------------------------------------------------
def workflows_dir() -> Path:
    return WORKFLOWS_DIR


def list_workflows(directory: Optional[Path] = None) -> List[Workflow]:
    directory = directory or WORKFLOWS_DIR
    if not directory.exists():
        return []
    out: List[Workflow] = []
    for path in sorted(directory.glob("*.json")):
        try:
            out.append(workflow_from_dict(json.loads(path.read_text(encoding="utf-8"))))
        except (OSError, json.JSONDecodeError, TypeError, ValueError):
            continue
    return out


def save_workflow(wf: Workflow, directory: Optional[Path] = None) -> Path:
    directory = directory or WORKFLOWS_DIR
    directory.mkdir(parents=True, exist_ok=True)
    path = directory / f"{wf.id}.json"
    path.write_text(json.dumps(workflow_to_dict(wf), ensure_ascii=False, indent=2), encoding="utf-8")
    return path


def get_workflow(wf_id: str, directory: Optional[Path] = None) -> Optional[Workflow]:
    directory = directory or WORKFLOWS_DIR
    path = directory / f"{wf_id}.json"
    if not path.exists():
        return None
    try:
        return workflow_from_dict(json.loads(path.read_text(encoding="utf-8")))
    except (OSError, json.JSONDecodeError, TypeError, ValueError):
        return None


def duplicate_workflow(wf: Workflow, directory: Optional[Path] = None) -> Workflow:
    """Save a deep copy of ``wf`` under a fresh id and a "… (copy)" name, so it can
    be run in parallel with (or diverge from) the original. Node/edge ids are kept
    — they're only unique *within* a workflow, and each run gets its own id."""
    import copy as _copy

    dup = Workflow(
        id=_mint_id("wf"),
        name=f"{wf.name} ({tr_copy_suffix()})",
        nodes=[_copy.deepcopy(n) for n in wf.nodes],
        edges=[_copy.deepcopy(e) for e in wf.edges],
        is_template=False,
    )
    save_workflow(dup, directory)
    return dup


def tr_copy_suffix() -> str:
    """Localised 'copy' suffix — kept tiny + import-safe (no hard i18n dependency
    at module import time)."""
    try:
        from ..i18n import tr
        return tr("co4e.copy_suffix")
    except Exception:  # noqa: BLE001
        return "copy"


def delete_workflow(wf_id: str, directory: Optional[Path] = None) -> None:
    directory = directory or WORKFLOWS_DIR
    path = directory / f"{wf_id}.json"
    if path.exists():
        try:
            path.unlink()
        except OSError:
            pass


# ---- custom-agent store --------------------------------------------------
def agents_dir() -> Path:
    return AGENTS_DIR


def list_custom_agents(directory: Optional[Path] = None) -> List[CustomAgent]:
    directory = directory or AGENTS_DIR
    if not directory.exists():
        return []
    out: List[CustomAgent] = []
    for path in sorted(directory.glob("*.json")):
        try:
            out.append(agent_from_dict(json.loads(path.read_text(encoding="utf-8"))))
        except (OSError, json.JSONDecodeError, TypeError, ValueError):
            continue
    return out


def save_custom_agent(agent: CustomAgent, directory: Optional[Path] = None) -> Path:
    directory = directory or AGENTS_DIR
    directory.mkdir(parents=True, exist_ok=True)
    path = directory / f"{agent.id}.json"
    path.write_text(json.dumps(agent_to_dict(agent), ensure_ascii=False, indent=2), encoding="utf-8")
    return path


def delete_custom_agent(agent_id: str, directory: Optional[Path] = None) -> None:
    directory = directory or AGENTS_DIR
    path = directory / f"{agent_id}.json"
    if path.exists():
        try:
            path.unlink()
        except OSError:
            pass


# ---- wave computation ----------------------------------------------------
def compute_waves(nodes: List[Node], edges: List[Edge]) -> Dict[str, int]:
    """node id -> wave index (longest path from a root). Ignores edges that
    reference unknown nodes; cycles are broken defensively (a node never waits
    on itself transitively past the node count)."""
    ids = {n.id for n in nodes}
    preds: Dict[str, List[str]] = {n.id: [] for n in nodes}
    for e in edges:
        if e.source in ids and e.target in ids:
            preds[e.target].append(e.source)

    wave: Dict[str, int] = {}
    limit = len(nodes) + 1

    def depth(nid: str, seen: frozenset) -> int:
        if nid in wave:
            return wave[nid]
        if nid in seen or len(seen) > limit:
            return 0
        ps = preds.get(nid, [])
        w = 0 if not ps else 1 + max(depth(p, seen | {nid}) for p in ps)
        wave[nid] = w
        return w

    for n in nodes:
        depth(n.id, frozenset())
    return wave


def connected_component_count(nodes: List[Node], edges: List[Edge]) -> int:
    """Weakly-connected component count — >1 warns a flow is accidentally split."""
    ids = {n.id for n in nodes}
    parent = {n.id: n.id for n in nodes}

    def find(x):
        while parent[x] != x:
            parent[x] = parent[parent[x]]
            x = parent[x]
        return x

    def union(a, b):
        ra, rb = find(a), find(b)
        if ra != rb:
            parent[ra] = rb

    for e in edges:
        if e.source in ids and e.target in ids:
            union(e.source, e.target)
    return len({find(n.id) for n in nodes})


# ---- run-stage compilation ----------------------------------------------
@dataclass
class RunStage:
    id: str              # node id, or "<node>__p<i>" / "<node>__pjoin"
    node_id: str         # which canvas node this stage maps back onto
    wave: int
    prompt: str
    model: str = ""
    scope: Optional[List[str]] = None
    self_verify: bool = False
    max_verify_rounds: int = 1


PLAN_MODE_PREAMBLE = (
    "PLAN MODE — do NOT execute anything or change any files. Produce a concise, "
    "numbered plan of what you WOULD do for this step, then stop.\n\n")


def build_skills_block(skills: List[str], skill_map: Dict[str, str]) -> str:
    parts = []
    for name in skills or []:
        content = (skill_map.get(name) or "").strip()
        if content:
            parts.append(f'--- Skill "{name}" ---\n{content}')
    if not parts:
        return ""
    return ("\nThis agent carries the following attached skills (reusable "
            "instruction packs):\n" + "\n\n".join(parts) + "\n")


def _shared_prompt_parts(step: Step, skill_map: Dict[str, str], extra_context: str) -> str:
    parts = []
    if step.instructions.strip():
        parts.append(step.instructions.strip())
    # Extra step/agent context (free-text background the user added in config) —
    # injected so the agent has more information to carry out its task.
    if getattr(step, "context", "").strip():
        parts.append("Additional context:\n" + step.context.strip())
    block = build_skills_block(step.skills, skill_map)
    if block:
        parts.append(block)
    if extra_context:
        parts.append(extra_context)
    return "\n\n".join(parts)


def build_step_prompt(step: Step, skill_map: Dict[str, str], extra_context: str = "") -> str:
    head = f'You are the {step.role} agent for the workflow step "{step.label}".'
    body = _shared_prompt_parts(step, skill_map, extra_context)
    return f"{head}\n{body}".strip()


def build_subagent_prompt(step: Step, sub: SubAgent, peers: List[str],
                          skill_map: Dict[str, str], extra_context: str = "") -> str:
    peer_txt = ", ".join(p for p in peers if p) or "peers"
    head = (f'You are the "{sub.agent}" agent working concurrently (in parallel with '
            f'{peer_txt}) on the workflow step "{step.label}". Stay within your own scope.')
    parts = [head]
    if sub.instructions.strip():
        parts.append(sub.instructions.strip())
    shared = _shared_prompt_parts(step, skill_map, extra_context)
    if shared:
        parts.append(shared)
    return "\n\n".join(parts).strip()


def build_join_prompt(step: Step, skill_map: Dict[str, str], extra_context: str = "") -> str:
    head = (f'You are the coordinator for the parallel step "{step.label}". Consolidate the '
            f"outputs of the sub-agents (provided above as prior outputs) into one coherent result.")
    body = _shared_prompt_parts(step, skill_map, extra_context)
    return f"{head}\n{body}".strip()


def compile_run_stages(nodes: List[Node], edges: List[Edge],
                       extra_context: Dict[str, str] = None,
                       plan_mode: bool = False,
                       skill_map: Dict[str, str] = None) -> List[RunStage]:
    """Compile canvas nodes into ordered RunStages. ``extra_context`` maps a
    node id to text (upstream outputs) to append to that node's prompt."""
    extra_context = extra_context or {}
    skill_map = skill_map or {}
    waves = compute_waves(nodes, edges)
    stages: List[RunStage] = []

    def finalize(prompt: str, preset: str) -> tuple:
        scope = PRESET_SCOPES.get(preset)
        if plan_mode:
            prompt = PLAN_MODE_PREAMBLE + prompt
            scope = PRESET_SCOPES["read-only"]
        return prompt, scope

    for node in nodes:
        step = node.data
        w = waves.get(node.id, 0)
        ctx = extra_context.get(node.id, "")
        if step.is_parallel and step.sub_agents:
            peers = [s.agent for s in step.sub_agents]
            for i, sub in enumerate(step.sub_agents):
                prompt = build_subagent_prompt(step, sub, peers, skill_map, ctx)
                prompt, scope = finalize(prompt, step.permission_preset)
                stages.append(RunStage(
                    id=f"{node.id}__p{i}", node_id=node.id, wave=w, prompt=prompt,
                    model=step.model, scope=scope))
            if step.instructions.strip():
                prompt, scope = finalize(build_join_prompt(step, skill_map), step.permission_preset)
                stages.append(RunStage(
                    id=f"{node.id}__pjoin", node_id=node.id, wave=w + 1, prompt=prompt,
                    model=step.model, scope=scope,
                    self_verify=step.self_verify, max_verify_rounds=max(1, step.max_verify_rounds)))
        else:
            prompt, scope = finalize(build_step_prompt(step, skill_map, ctx), step.permission_preset)
            stages.append(RunStage(
                id=node.id, node_id=node.id, wave=w, prompt=prompt, model=step.model, scope=scope,
                self_verify=step.self_verify, max_verify_rounds=max(1, step.max_verify_rounds)))
    stages.sort(key=lambda s: s.wave)
    return stages


def stage_node_id(stage_id: str) -> str:
    """Map a stage id back onto its canvas node ('<node>__p2' -> '<node>')."""
    for sep in ("__pjoin", "__p"):
        if sep in stage_id:
            return stage_id.split(sep, 1)[0]
    return stage_id