"""Project configuration dataclasses and config I/O helpers.

Per module_taxonomy_refactor_20260627 Phase 3b, the project context
(ProjectContext + 5 sub-dataclasses) and config I/O helpers moved
from src/models.py to this module.

Per the 4-criteria decision rule:
 - C1 (cross-system usage >= 3 systems): YES (project_manager, aggregate,
   api_hooks, app_controller, gui_2, orchestrator_pm, tests)
 - C2 (state machine / lifecycle): NO (just config; no state transitions)
 - C3 (test file already exists): YES (test_project_context_20260627.py)
 - C4 (substantial size): YES (ProjectContext + 5 sub + 3 helpers + 60+ lines)

Therefore: DEDICATED FILE = src/project.py
"""
from __future__ import annotations

import re
import tomllib

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

from src.paths        import get_config_path
from src.type_aliases import Metadata


# --------------------------------------------------------------------------- ProjectContext

@dataclass(frozen=True, slots=True)
class ProjectMeta:
 name:           str  = ""
 summary_only:   bool = False
 execution_mode: str  = "standard"


@dataclass(frozen=True, slots=True)
class ProjectOutput:
 namespace:  str = "project"
 output_dir: str = ""


@dataclass(frozen=True, slots=True)
class ProjectFiles:
 base_dir: str           = ""
 paths:    tuple[str, ...] = ()


@dataclass(frozen=True, slots=True)
class ProjectScreenshots:
 base_dir: str           = "."
 paths:    tuple[str, ...] = ()


@dataclass(frozen=True, slots=True)
class ProjectDiscussion:
 roles:   tuple[str, ...] = ()
 history: tuple[str, ...] = ()


@dataclass(frozen=True, slots=True)
class ProjectContext:
 """Typed return type for project_manager.flat_config(). Replaces the dict[str, Any] that flat_config() returned. Per conductor/tracks/cruft_elimination_20260627/SPEC_CORRECTION_phase_2.md."""
 project:         ProjectMeta        = field(default_factory=ProjectMeta)
 output:          ProjectOutput      = field(default_factory=ProjectOutput)
 files:           ProjectFiles       = field(default_factory=ProjectFiles)
 screenshots:     ProjectScreenshots = field(default_factory=ProjectScreenshots)
 context_presets: Metadata           = field(default_factory=dict)
 discussion:      ProjectDiscussion  = field(default_factory=ProjectDiscussion)

 def to_dict(self) -> Metadata:
  return {
   "project": {
    "name":           self.project.name,
    "summary_only":   self.project.summary_only,
    "execution_mode": self.project.execution_mode,
   },
   "output": {
    "namespace":  self.output.namespace,
    "output_dir": self.output.output_dir,
   },
   "files": {
    "base_dir": self.files.base_dir,
    "paths":    list(self.files.paths),
   },
   "screenshots": {
    "base_dir": self.screenshots.base_dir,
    "paths":    list(self.screenshots.paths),
   },
   "context_presets": dict(self.context_presets),
   "discussion": {
    "roles":   list(self.discussion.roles),
    "history": list(self.discussion.history),
   },
  }

 def __getitem__(self, key: str) -> Any:
  return self.to_dict()[key]

 def get(self, key: str, default: Any = None) -> Any:
  return self.to_dict().get(key, default)


EMPTY_PROJECT_CONTEXT: ProjectContext = ProjectContext()


# --------------------------------------------------------------------------- Config IO helpers

def _clean_nones(data: Any) -> Any:
 if isinstance(data, dict):
  return {k: _clean_nones(v) for k, v in data.items() if v is not None}
 elif isinstance(data, list):
  return [_clean_nones(v) for v in data if v is not None]
 return data


def load_config_from_disk() -> Metadata:
 """
 Re-read the global config.toml from disk and return the parsed
 dict. The single source of truth for the in-memory config is
 the AppController's self.config attribute; this function is the
 disk I/O primitive that the controller owns. Direct callers in
 src/ are an architectural smell (bypassing the state owner) and
 will be flagged by scripts/audit_no_models_config_io.py.
 [C: src/app_controller.py:AppController.load_config, src/app_controller.py:AppController.__init__]
 """
 with open(get_config_path(), "rb") as f:
  return tomllib.load(f)


def save_config_to_disk(config: Metadata) -> None:
 # tomli_w is loaded on-demand (sub-track 2 of startup_speedup_20260606).
 # If it's already in sys.modules (e.g. warmed up or loaded by a prior
 # call), the import is a fast lookup; otherwise it's a cold load paid
 # only when the user actually saves config.
 import tomli_w
 config = _clean_nones(config)
 with open(get_config_path(), "wb") as f:
  tomli_w.dump(config, f)


# --------------------------------------------------------------------------- History utilities

def parse_history_entries(history_strings: list[str], roles: list[str]) -> list[Metadata]:
 import re
 from src import thinking_parser
 entries = []
 for raw in history_strings:
  ts   = ""
  rest = raw
  if rest.startswith("@"):
   nl = rest.find("\n")
   if nl != -1:
    ts   = rest[1:nl]
    rest = rest[nl + 1:]
  known = roles or ["User", "AI", "Vendor API", "System"]
  role_pat = re.compile(r"^(" + "|".join(re.escape(r) for r in known) + r"):", re.IGNORECASE)
  match = role_pat.match(rest)
  role = match.group(1) if match else "User"
  if match:
   content = rest[match.end():].strip()
  else:
   content = rest
  entry_obj = {"role": role, "content": content, "collapsed": True, "ts": ts}
  if role == "AI" and ("<thinking>" in content or "<thought>" in content or "Thinking:" in content):
   segments, parsed_content = thinking_parser.parse_thinking_trace(content)
   if segments:
    entry_obj["content"] = parsed_content
    entry_obj["thinking_segments"] = [{"content": s.content, "marker": s.marker} for s in segments]
  entries.append(entry_obj)
 return entries