"""
Pipeline composition config — source of truth for graph topology.

Defines what stages run, in what order, with what branching.
Belongs to a profile. Persisted as JSONB.

The execution strategy (serial, parallel, distributed) is separate —
the runner reads this config and flattens it into a sequence for now.
"""

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional


@dataclass
class StageRef:
    """Reference to a stage in the pipeline graph."""
    name: str                          # stage name (matches StageDefinition.name)
    branch: str = "trunk"              # which branch this belongs to
    execution_target: str = "local"    # local | gpu | lambda | gcp


@dataclass
class Edge:
    """Connection between stages in the graph."""
    source: str        # stage name
    target: str        # stage name
    condition: str = ""  # empty = unconditional, otherwise a routing rule key


@dataclass
class PipelineConfig:
    """
    Pipeline graph topology + routing rules.

    Holder model — stages/edges define the graph shape,
    routing_rules is a JSONB blob for decision tree logic.
    """
    name: str
    profile_name: str
    stages: List[StageRef] = field(default_factory=list)
    edges: List[Edge] = field(default_factory=list)
    routing_rules: Dict[str, Any] = field(default_factory=dict)


PIPELINE_CONFIG_VIEWS = [StageRef, Edge, PipelineConfig]