"""
Plan orchestration tasks.

Coordinates the full 3-phase execution:
1. Analyze inputs
2. Generate plan
3. Execute steps level by level

Uses IPFS-backed cache for durability.
"""

import json
import logging
import os
from pathlib import Path
from typing import Dict, List, Optional

from celery import current_task, group, chain

# Import from the Celery app
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from celery_app import app
from claiming import get_claimer
from cache_manager import get_cache_manager

# Import artdag modules
try:
    from artdag import Cache
    from artdag.analysis import Analyzer, AnalysisResult
    from artdag.planning import RecipePlanner, ExecutionPlan, Recipe
except ImportError:
    Cache = None
    Analyzer = None
    AnalysisResult = None
    RecipePlanner = None
    ExecutionPlan = None
    Recipe = None

from .execute import execute_step

logger = logging.getLogger(__name__)

# Cache directories
CACHE_DIR = Path(os.environ.get('CACHE_DIR', str(Path.home() / ".artdag" / "cache")))
ANALYSIS_CACHE_DIR = CACHE_DIR / 'analysis'
PLAN_CACHE_DIR = CACHE_DIR / 'plans'


@app.task(bind=True, name='tasks.run_plan')
def run_plan(
    self,
    plan_json: str,
    run_id: Optional[str] = None,
) -> dict:
    """
    Execute a complete execution plan.

    Runs steps level by level, with parallel execution within each level.
    Results are stored in IPFS-backed cache.

    Args:
        plan_json: JSON-serialized ExecutionPlan
        run_id: Optional run ID for tracking

    Returns:
        Dict with execution results
    """
    if ExecutionPlan is None:
        raise ImportError("artdag.planning not available")

    plan = ExecutionPlan.from_json(plan_json)
    cache_mgr = get_cache_manager()

    logger.info(f"Executing plan {plan.plan_id[:16]}... ({len(plan.steps)} steps)")

    # Build initial cache_ids mapping (step_id -> cache_id)
    cache_ids = {}
    for step in plan.steps:
        cache_ids[step.step_id] = step.cache_id

    # Also map input hashes
    for name, cid in plan.input_hashes.items():
        cache_ids[name] = cid

    # Group steps by level
    steps_by_level = plan.get_steps_by_level()
    max_level = max(steps_by_level.keys()) if steps_by_level else 0

    results_by_step = {}
    total_cached = 0
    total_executed = 0

    for level in range(max_level + 1):
        level_steps = steps_by_level.get(level, [])
        if not level_steps:
            continue

        logger.info(f"Executing level {level}: {len(level_steps)} steps")

        # Check which steps need execution
        steps_to_run = []

        for step in level_steps:
            # Check if cached
            cached_path = cache_mgr.get_by_cid(step.cache_id)
            if cached_path:
                results_by_step[step.step_id] = {
                    "status": "cached",
                    "cache_id": step.cache_id,
                    "output_path": str(cached_path),
                }
                total_cached += 1
            else:
                steps_to_run.append(step)

        if not steps_to_run:
            logger.info(f"Level {level}: all steps cached")
            continue

        # Build input cache_ids for this level
        level_cache_ids = dict(cache_ids)

        # Execute steps in parallel
        tasks = [
            execute_step.s(step.to_json(), plan.plan_id, level_cache_ids)
            for step in steps_to_run
        ]

        job = group(tasks)
        async_results = job.apply_async()

        # Wait for completion
        try:
            step_results = async_results.get(timeout=3600)
        except Exception as e:
            logger.error(f"Level {level} execution failed: {e}")
            return {
                "status": "failed",
                "error": str(e),
                "level": level,
                "results": results_by_step,
                "run_id": run_id,
            }

        # Process results
        for result in step_results:
            step_id = result.get("step_id")
            cache_id = result.get("cache_id")

            results_by_step[step_id] = result
            cache_ids[step_id] = cache_id

            if result.get("status") in ("completed", "cached", "completed_by_other"):
                total_executed += 1
            elif result.get("status") == "failed":
                logger.error(f"Step {step_id} failed: {result.get('error')}")
                return {
                    "status": "failed",
                    "error": f"Step {step_id} failed: {result.get('error')}",
                    "level": level,
                    "results": results_by_step,
                    "run_id": run_id,
                }

    # Get final output
    output_step = plan.get_step(plan.output_step)
    output_cache_id = output_step.cache_id if output_step else None
    output_path = None
    output_ipfs_cid = None
    output_name = plan.output_name

    if output_cache_id:
        output_path = cache_mgr.get_by_cid(output_cache_id)
        output_ipfs_cid = cache_mgr.get_ipfs_cid(output_cache_id)

    # Build list of all outputs with their names and artifacts
    all_outputs = []
    for step in plan.steps:
        step_result = results_by_step.get(step.step_id, {})
        step_outputs = step_result.get("outputs", [])

        # If no outputs in result, build from step definition
        if not step_outputs and step.outputs:
            for output_def in step.outputs:
                output_cache_path = cache_mgr.get_by_cid(output_def.cache_id)
                output_ipfs = cache_mgr.get_ipfs_cid(output_def.cache_id) if output_cache_path else None
                all_outputs.append({
                    "name": output_def.name,
                    "step_id": step.step_id,
                    "step_name": step.name,
                    "cache_id": output_def.cache_id,
                    "media_type": output_def.media_type,
                    "path": str(output_cache_path) if output_cache_path else None,
                    "ipfs_cid": output_ipfs,
                    "status": "cached" if output_cache_path else "missing",
                })
        else:
            for output in step_outputs:
                all_outputs.append({
                    **output,
                    "step_id": step.step_id,
                    "step_name": step.name,
                    "status": "completed",
                })

    return {
        "status": "completed",
        "run_id": run_id,
        "plan_id": plan.plan_id,
        "plan_name": plan.name,
        "recipe_name": plan.recipe_name,
        "output_name": output_name,
        "output_cache_id": output_cache_id,
        "output_path": str(output_path) if output_path else None,
        "output_ipfs_cid": output_ipfs_cid,
        "total_steps": len(plan.steps),
        "cached": total_cached,
        "executed": total_executed,
        "results": results_by_step,
        "outputs": all_outputs,
    }


def _extract_analysis_from_recipe(compiled_recipe) -> List[Dict]:
    """
    Extract analysis nodes from a compiled recipe.

    Finds all (analyze ...) nodes and returns their configurations.
    Analysis nodes are identified by type "ANALYZE" or by having
    an "analyze" config key.
    """
    analysis_nodes = []
    nodes = compiled_recipe.nodes

    if isinstance(nodes, dict):
        nodes = list(nodes.values())

    for node in nodes:
        node_type = node.get("type", "").upper()
        config = node.get("config", {})

        # Check if this is an analysis node
        if node_type == "ANALYZE" or config.get("analyze"):
            analysis_nodes.append({
                "node_id": node.get("id"),
                "input_ref": config.get("input") or config.get("source"),
                "feature": config.get("feature") or config.get("analyze"),
                "config": config,
            })

    return analysis_nodes


@app.task(bind=True, name='tasks.run_recipe')
def run_recipe(
    self,
    recipe_sexp: str,
    input_hashes: Dict[str, str],
    run_id: Optional[str] = None,
) -> dict:
    """
    Run a complete recipe through all phases.

    The recipe S-expression declares what analysis is needed.
    Analysis nodes in the recipe are executed first, then their
    outputs are used to generate the execution plan.

    1. Parse: Compile recipe S-expression
    2. Analyze: Run analysis nodes from recipe
    3. Plan: Generate execution plan using analysis results
    4. Execute: Run the plan

    Args:
        recipe_sexp: Recipe S-expression content
        input_hashes: Mapping from input name to content hash
        run_id: Optional run ID for tracking

    Returns:
        Dict with final results
    """
    # Import S-expression compiler
    try:
        from artdag.sexp import compile_string
    except ImportError:
        raise ImportError("artdag.sexp not available")

    if Analyzer is None:
        raise ImportError("artdag.analysis not available")

    cache_mgr = get_cache_manager()

    logger.info(f"Running recipe with {len(input_hashes)} inputs")

    # Phase 1: Parse recipe
    logger.info("Phase 1: Parsing recipe S-expression...")

    try:
        compiled = compile_string(recipe_sexp)
    except Exception as e:
        return {"status": "failed", "error": f"Recipe parse error: {e}"}

    logger.info(f"Parsed recipe: {compiled.name}")

    # Phase 2: Run analysis nodes from recipe
    logger.info("Phase 2: Running analysis from recipe...")

    analysis_nodes = _extract_analysis_from_recipe(compiled)
    logger.info(f"Found {len(analysis_nodes)} analysis nodes in recipe")

    ANALYSIS_CACHE_DIR.mkdir(parents=True, exist_ok=True)
    analyzer = Analyzer(cache_dir=ANALYSIS_CACHE_DIR)

    analysis_results = {}
    for analysis_node in analysis_nodes:
        input_ref = analysis_node["input_ref"]
        feature = analysis_node["feature"]
        node_id = analysis_node["node_id"]

        # Resolve input reference to content hash
        cid = input_hashes.get(input_ref)
        if not cid:
            logger.warning(f"Analysis node {node_id}: input '{input_ref}' not in input_hashes")
            continue

        path = cache_mgr.get_by_cid(cid)
        if not path:
            logger.warning(f"Analysis node {node_id}: content {cid[:16]}... not in cache")
            continue

        try:
            # Run analysis for the specific feature
            features = [feature] if feature else ["beats", "energy"]
            result = analyzer.analyze(
                input_hash=cid,
                features=features,
                input_path=Path(path),
            )
            # Store result keyed by node_id so plan can reference it
            analysis_results[node_id] = result
            # Also store by cid for compatibility
            analysis_results[cid] = result
            logger.info(f"Analysis {node_id}: feature={feature}, tempo={result.tempo}")
        except Exception as e:
            logger.warning(f"Analysis failed for {node_id}: {e}")

    logger.info(f"Completed {len(analysis_results)} analysis results")

    # Phase 3: Generate plan
    logger.info("Phase 3: Generating execution plan...")

    # Use the S-expression planner if available
    try:
        from artdag.sexp.planner import create_plan
        plan = create_plan(compiled, inputs=input_hashes)
    except ImportError:
        # Fall back to legacy planner
        if RecipePlanner is None:
            raise ImportError("No planner available")
        recipe = Recipe.from_dict(compiled.to_dict())
        planner = RecipePlanner(use_tree_reduction=True)
        plan = planner.plan(
            recipe=recipe,
            input_hashes=input_hashes,
            analysis=analysis_results,
        )

    logger.info(f"Generated plan with {len(plan.steps)} steps")

    # Save plan as S-expression through cache manager (goes to IPFS)
    import tempfile

    plan_content = plan.to_sexp_string() if hasattr(plan, 'to_sexp_string') else plan.to_json()
    plan_suffix = ".sexp" if hasattr(plan, 'to_sexp_string') else ".json"

    with tempfile.NamedTemporaryFile(delete=False, suffix=plan_suffix, mode="w") as tmp:
        tmp.write(plan_content)
        tmp_path = Path(tmp.name)

    # Store in cache (content-addressed, auto-pins to IPFS)
    # Plan is just another node output - no special treatment needed
    cached, plan_ipfs_cid = cache_mgr.put(tmp_path, node_type="plan", move=True)
    plan_cache_id = plan_ipfs_cid or cached.cid  # Prefer IPFS CID
    logger.info(f"Plan cached: cid={plan_cache_id}, ipfs={plan_ipfs_cid}")

    # Phase 4: Execute
    logger.info("Phase 4: Executing plan...")

    result = run_plan(plan.to_json(), run_id=run_id)

    return {
        "status": result.get("status"),
        "run_id": run_id,
        "recipe": compiled.name,
        "plan_id": plan.plan_id,
        "plan_cache_id": plan_cache_id,
        "plan_ipfs_cid": plan_ipfs_cid,
        "output_path": result.get("output_path"),
        "output_cache_id": result.get("output_cache_id"),
        "output_ipfs_cid": result.get("output_ipfs_cid"),
        "analysis_count": len(analysis_results),
        "total_steps": len(plan.steps),
        "cached": result.get("cached", 0),
        "executed": result.get("executed", 0),
        "error": result.get("error"),
    }


@app.task(bind=True, name='tasks.generate_plan')
def generate_plan(
    self,
    recipe_sexp: str,
    input_hashes: Dict[str, str],
) -> dict:
    """
    Generate an execution plan without executing it.

    Useful for:
    - Previewing what will be executed
    - Checking cache status
    - Debugging recipe issues

    Args:
        recipe_sexp: Recipe S-expression content
        input_hashes: Mapping from input name to content hash

    Returns:
        Dict with plan details
    """
    try:
        from artdag.sexp import compile_string
    except ImportError:
        raise ImportError("artdag.sexp not available")

    if Analyzer is None:
        raise ImportError("artdag.analysis not available")

    cache_mgr = get_cache_manager()

    # Parse recipe
    try:
        compiled = compile_string(recipe_sexp)
    except Exception as e:
        return {"status": "failed", "error": f"Recipe parse error: {e}"}

    # Extract and run analysis nodes from recipe
    analysis_nodes = _extract_analysis_from_recipe(compiled)

    ANALYSIS_CACHE_DIR.mkdir(parents=True, exist_ok=True)
    analyzer = Analyzer(cache_dir=ANALYSIS_CACHE_DIR)

    analysis_results = {}
    for analysis_node in analysis_nodes:
        input_ref = analysis_node["input_ref"]
        feature = analysis_node["feature"]
        node_id = analysis_node["node_id"]

        cid = input_hashes.get(input_ref)
        if not cid:
            continue

        path = cache_mgr.get_by_cid(cid)
        if path:
            try:
                features = [feature] if feature else ["beats", "energy"]
                result = analyzer.analyze(
                    input_hash=cid,
                    features=features,
                    input_path=Path(path),
                )
                analysis_results[node_id] = result
                analysis_results[cid] = result
            except Exception as e:
                logger.warning(f"Analysis failed for {node_id}: {e}")

    # Generate plan
    try:
        from artdag.sexp.planner import create_plan
        plan = create_plan(compiled, inputs=input_hashes)
    except ImportError:
        if RecipePlanner is None:
            raise ImportError("No planner available")
        recipe = Recipe.from_dict(compiled.to_dict())
        planner = RecipePlanner(use_tree_reduction=True)
        plan = planner.plan(
            recipe=recipe,
            input_hashes=input_hashes,
            analysis=analysis_results,
        )

    # Check cache status for each step
    steps_status = []
    for step in plan.steps:
        cached = cache_mgr.has_content(step.cache_id)
        steps_status.append({
            "step_id": step.step_id,
            "node_type": step.node_type,
            "cache_id": step.cache_id,
            "level": step.level,
            "cached": cached,
        })

    cached_count = sum(1 for s in steps_status if s["cached"])

    return {
        "status": "planned",
        "recipe": compiled.name,
        "plan_id": plan.plan_id,
        "total_steps": len(plan.steps),
        "cached_steps": cached_count,
        "pending_steps": len(plan.steps) - cached_count,
        "steps": steps_status,
        "plan_json": plan.to_json(),
    }