"""
Pipeline node functions — one per stage.

Each node: reads state, gets config from profile dict, runs stage logic,
emits transitions, returns output dict.
"""

from __future__ import annotations

import os

from core.detect import emit
from core.detect.models import CropContext, PipelineStats
from core.detect.profile import get_profile, get_stage_config, build_vlm_prompt, aggregate_detections
from core.detect.stages.models import (
    DetectionConfig,
    FieldSegmentationConfig,
    FrameExtractionConfig,
    OCRConfig,
    RegionAnalysisConfig,
    ResolverConfig,
    SceneFilterConfig,
)
from core.detect.state import DetectState
from core.detect.stages.frame_extractor import extract_frames
from core.detect.stages.scene_filter import scene_filter
from core.detect.stages.field_segmentation import run_field_segmentation
from core.detect.stages.edge_detector import detect_edge_regions
from core.detect.stages.yolo_detector import detect_objects
from core.detect.stages.preprocess import preprocess_regions
from core.detect.stages.ocr_stage import run_ocr
from core.detect.stages.brand_resolver import resolve_brands
from core.detect.stages.vlm_local import escalate_vlm
from core.detect.stages.vlm_cloud import escalate_cloud
from core.detect.stages.aggregator import compile_report
from core.detect.tracing import trace_node, flush as flush_traces

from .events import emit_transition

INFERENCE_URL = os.environ.get("INFERENCE_URL")  # None = local mode

NODES = [
    "extract_frames",
    "filter_scenes",
    "field_segmentation",
    "detect_edges",
    "detect_objects",
    "preprocess",
    "run_ocr",
    "match_brands",
    "escalate_vlm",
    "escalate_cloud",
    "compile_report",
]


def _load_profile(state: DetectState) -> dict:
    """Load profile dict, apply config overrides if present."""
    name = state.get("profile_name", "soccer_broadcast")
    profile = get_profile(name)

    overrides = state.get("config_overrides")
    if overrides:
        # Merge overrides into a copy of the profile configs
        merged_configs = dict(profile.get("configs", {}))
        for stage_name, stage_overrides in overrides.items():
            if stage_name in merged_configs:
                merged_configs[stage_name] = {**merged_configs[stage_name], **stage_overrides}
            else:
                merged_configs[stage_name] = stage_overrides
        profile = {**profile, "configs": merged_configs}

    return profile


def _emit(state, node, status):
    emit_transition(state, node, status, NODES)


# --- Node functions ---

def node_extract_frames(state: DetectState) -> dict:
    job_id = state.get("job_id", "")
    if job_id and not emit._run_context:
        emit.set_run_context(run_id=job_id, parent_job_id=job_id, run_type="initial")

    source_asset_id = state.get("source_asset_id")
    if source_asset_id and not state.get("session_brands"):
        from core.detect.stages.brand_resolver import build_session_dict
        session_brands = build_session_dict(source_asset_id)
        state["session_brands"] = session_brands

    _emit(state, "extract_frames", "running")

    with trace_node(state, "extract_frames") as span:
        profile = _load_profile(state)
        config = FrameExtractionConfig(**get_stage_config(profile, "extract_frames"))
        frames = extract_frames(state["video_path"], config, job_id=job_id)
        span.set_output({"frames_extracted": len(frames)})

    # Cache frames on the timeline for reuse across jobs and UI
    timeline_id = state.get("timeline_id")
    if timeline_id:
        from core.detect.checkpoint.frames import cache_frames, cache_exists
        if not cache_exists(timeline_id):
            cache_frames(timeline_id, frames)
            from core.detect.checkpoint.storage import update_timeline_status
            update_timeline_status(timeline_id, "cached", frame_count=len(frames))

    _emit(state, "extract_frames", "done")
    return {"frames": frames, "stats": PipelineStats(frames_extracted=len(frames))}


def node_filter_scenes(state: DetectState) -> dict:
    _emit(state, "filter_scenes", "running")

    with trace_node(state, "filter_scenes") as span:
        profile = _load_profile(state)
        config = SceneFilterConfig(**get_stage_config(profile, "filter_scenes"))
        frames = state.get("frames", [])
        kept = scene_filter(frames, config, job_id=state.get("job_id"))
        span.set_output({"frames_in": len(frames), "frames_kept": len(kept)})

    stats = state.get("stats", PipelineStats())
    stats.frames_after_scene_filter = len(kept)

    _emit(state, "filter_scenes", "done")
    return {"filtered_frames": kept, "stats": stats}


def node_field_segmentation(state: DetectState) -> dict:
    _emit(state, "field_segmentation", "running")

    with trace_node(state, "field_segmentation") as span:
        profile = _load_profile(state)
        config = FieldSegmentationConfig(**get_stage_config(profile, "field_segmentation"))
        frames = state.get("filtered_frames", [])
        job_id = state.get("job_id")

        result = run_field_segmentation(frames, config, inference_url=INFERENCE_URL, job_id=job_id)
        span.set_output({
            "frames": len(frames),
            "avg_coverage": sum(result["field_coverage"].values()) / max(len(result["field_coverage"]), 1),
        })

    _emit(state, "field_segmentation", "done")
    return {
        "field_masks": result["field_masks"],
        "field_boundaries": result["field_boundaries"],
        "field_coverage": result["field_coverage"],
    }


def node_detect_edges(state: DetectState) -> dict:
    _emit(state, "detect_edges", "running")

    with trace_node(state, "detect_edges") as span:
        profile = _load_profile(state)
        config = RegionAnalysisConfig(**get_stage_config(profile, "detect_edges"))
        frames = state.get("filtered_frames", [])
        field_masks = state.get("field_masks", {})
        job_id = state.get("job_id")

        regions = detect_edge_regions(
            frames, config, inference_url=INFERENCE_URL, job_id=job_id,
            field_masks=field_masks,
        )
        total = sum(len(r) for r in regions.values())
        span.set_output({"frames": len(frames), "edge_regions": total})

    stats = state.get("stats", PipelineStats())
    stats.cv_regions_detected = total

    _emit(state, "detect_edges", "done")
    return {"edge_regions_by_frame": regions, "stats": stats}


def node_detect_objects(state: DetectState) -> dict:
    _emit(state, "detect_objects", "running")

    with trace_node(state, "detect_objects") as span:
        profile = _load_profile(state)
        config = DetectionConfig(**get_stage_config(profile, "detect_objects"))
        frames = state.get("filtered_frames", [])
        job_id = state.get("job_id")

        all_boxes = detect_objects(frames, config, inference_url=INFERENCE_URL, job_id=job_id)
        total_regions = sum(len(boxes) for boxes in all_boxes.values())
        span.set_output({"frames": len(frames), "regions_detected": total_regions})

    stats = state.get("stats", PipelineStats())
    stats.regions_detected = total_regions

    _emit(state, "detect_objects", "done")
    return {"boxes_by_frame": all_boxes, "stats": stats}


def node_preprocess(state: DetectState) -> dict:
    _emit(state, "preprocess", "running")

    with trace_node(state, "preprocess") as span:
        profile = _load_profile(state)
        prep_config = get_stage_config(profile, "preprocess")
        frames = state.get("filtered_frames", [])
        boxes = state.get("boxes_by_frame", {})
        job_id = state.get("job_id")

        do_contrast = prep_config.get("contrast", True)
        do_deskew = prep_config.get("deskew", False)
        do_binarize = prep_config.get("binarize", False)

        preprocessed = preprocess_regions(
            frames, boxes,
            do_contrast=do_contrast,
            do_deskew=do_deskew,
            do_binarize=do_binarize,
            inference_url=INFERENCE_URL,
            job_id=job_id,
        )
        span.set_output({"regions_preprocessed": len(preprocessed)})

    _emit(state, "preprocess", "done")
    return {"preprocessed_crops": preprocessed}


def node_run_ocr(state: DetectState) -> dict:
    _emit(state, "run_ocr", "running")

    with trace_node(state, "run_ocr") as span:
        profile = _load_profile(state)
        config = OCRConfig(**get_stage_config(profile, "run_ocr"))
        frames = state.get("filtered_frames", [])
        boxes = state.get("boxes_by_frame", {})
        job_id = state.get("job_id")

        candidates = run_ocr(frames, boxes, config, inference_url=INFERENCE_URL, job_id=job_id)
        span.set_output({"regions_in": sum(len(b) for b in boxes.values()), "text_candidates": len(candidates)})

    stats = state.get("stats", PipelineStats())
    stats.regions_resolved_by_ocr = len(candidates)

    _emit(state, "run_ocr", "done")
    return {"text_candidates": candidates, "stats": stats}


def node_match_brands(state: DetectState) -> dict:
    _emit(state, "match_brands", "running")

    with trace_node(state, "match_brands") as span:
        profile = _load_profile(state)
        config = ResolverConfig(**get_stage_config(profile, "match_brands"))
        candidates = state.get("text_candidates", [])
        session_brands = state.get("session_brands", {})
        job_id = state.get("job_id")
        source_asset_id = state.get("source_asset_id")

        matched, unresolved = resolve_brands(
            candidates, config,
            session_brands=session_brands,
            source_asset_id=source_asset_id,
            content_type=profile["name"], job_id=job_id,
        )
        span.set_output({"matched": len(matched), "unresolved": len(unresolved)})

    _emit(state, "match_brands", "done")
    return {"detections": matched, "unresolved_candidates": unresolved}


def node_escalate_vlm(state: DetectState) -> dict:
    _emit(state, "escalate_vlm", "running")

    with trace_node(state, "escalate_vlm") as span:
        profile = _load_profile(state)
        vlm_config = get_stage_config(profile, "escalate_vlm")
        vlm_template = vlm_config.get("vlm_prompt_template", "Identify the brand in this image.")
        candidates = state.get("unresolved_candidates", [])
        job_id = state.get("job_id")

        vlm_prompt_fn = lambda ctx: build_vlm_prompt(ctx, vlm_template)

        vlm_matched, still_unresolved = escalate_vlm(
            candidates,
            vlm_prompt_fn=vlm_prompt_fn,
            inference_url=INFERENCE_URL,
            content_type=profile["name"],
            source_asset_id=state.get("source_asset_id"),
            job_id=job_id,
        )

        stats = state.get("stats", PipelineStats())
        stats.regions_escalated_to_local_vlm = len(candidates)
        span.set_output({"candidates": len(candidates), "matched": len(vlm_matched),
                         "still_unresolved": len(still_unresolved)})

    existing = state.get("detections", [])

    vlm_skipped = os.environ.get("SKIP_VLM", "").strip() == "1"
    _emit(state, "escalate_vlm", "skipped" if vlm_skipped else "done")
    return {
        "detections": existing + vlm_matched,
        "unresolved_candidates": still_unresolved,
        "stats": stats,
    }


def node_escalate_cloud(state: DetectState) -> dict:
    _emit(state, "escalate_cloud", "running")

    with trace_node(state, "escalate_cloud") as span:
        profile = _load_profile(state)
        vlm_config = get_stage_config(profile, "escalate_vlm")
        vlm_template = vlm_config.get("vlm_prompt_template", "Identify the brand in this image.")
        candidates = state.get("unresolved_candidates", [])
        job_id = state.get("job_id")
        stats = state.get("stats", PipelineStats())

        vlm_prompt_fn = lambda ctx: build_vlm_prompt(ctx, vlm_template)

        cloud_matched = escalate_cloud(
            candidates,
            vlm_prompt_fn=vlm_prompt_fn,
            stats=stats,
            content_type=profile["name"],
            source_asset_id=state.get("source_asset_id"),
            job_id=job_id,
        )

        span.set_output({"candidates": len(candidates), "matched": len(cloud_matched),
                         "cloud_calls": stats.cloud_llm_calls,
                         "cost_usd": stats.estimated_cloud_cost_usd})

    existing = state.get("detections", [])

    cloud_skipped = os.environ.get("SKIP_CLOUD", "").strip() == "1"
    _emit(state, "escalate_cloud", "skipped" if cloud_skipped else "done")
    return {"detections": existing + cloud_matched, "stats": stats}


def node_compile_report(state: DetectState) -> dict:
    _emit(state, "compile_report", "running")

    with trace_node(state, "compile_report") as span:
        profile = _load_profile(state)
        detections = state.get("detections", [])
        stats = state.get("stats", PipelineStats())
        job_id = state.get("job_id")

        report = compile_report(
            detections=detections,
            stats=stats,
            video_source=state.get("video_path", ""),
            content_type=profile["name"],
            job_id=job_id,
        )

        span.set_output({"brands": len(report.brands), "detections": len(report.timeline)})

    flush_traces()
    _emit(state, "compile_report", "done")
    return {"report": report}


NODE_FUNCTIONS = [
    ("extract_frames", node_extract_frames),
    ("filter_scenes", node_filter_scenes),
    ("field_segmentation", node_field_segmentation),
    ("detect_edges", node_detect_edges),
    ("detect_objects", node_detect_objects),
    ("preprocess", node_preprocess),
    ("run_ocr", node_run_ocr),
    ("match_brands", node_match_brands),
    ("escalate_vlm", node_escalate_vlm),
    ("escalate_cloud", node_escalate_cloud),
    ("compile_report", node_compile_report),
]