"""GPU worker extension point."""

from __future__ import annotations

from pathlib import Path
from time import perf_counter

from zsgdp.gpu.transformers_client import TransformersClient
from zsgdp.gpu.vllm_client import VLLMClient
from zsgdp.logging_config import get_logger

logger = get_logger(__name__)


class GPUWorker:
    def __init__(self, config: dict):
        self.config = config

    def run(self, task: dict, *, dry_run: bool = True) -> dict:
        readiness = _task_readiness(task)
        base = {
            "dry_run": dry_run,
            "task_id": task.get("task_id"),
            "task_type": task.get("task_type"),
            "region_id": task.get("region_id"),
            "provider": task.get("provider") or self.config.get("gpu", {}).get("provider", "huggingface_spaces"),
            "space_name": task.get("space_name") or self.config.get("gpu", {}).get("space_name", "zeroshotGPU"),
            "backend": task.get("backend") or self.config.get("gpu", {}).get("backend", "transformers"),
            "model_role": task.get("model_role"),
            "model_id": task.get("model_id"),
            "readiness": readiness,
        }
        if dry_run:
            return {**base, "status": "ready_dry_run" if readiness["ready"] else "blocked_missing_inputs"}
        if not readiness["ready"]:
            logger.warning(
                "gpu_task_blocked",
                extra={
                    "task_id": task.get("task_id"),
                    "task_type": task.get("task_type"),
                    "missing_inputs": readiness.get("missing_inputs"),
                },
            )
            return {**base, "status": "blocked_missing_inputs"}

        started = perf_counter()
        client = self._client_for_task(task)
        result = client.execute_task(task)
        status = result.get("status", "execution_failed")
        elapsed = round(perf_counter() - started, 3)
        log_method = logger.info if status == "executed" else logger.warning
        log_method(
            "gpu_task_executed",
            extra={
                "task_id": task.get("task_id"),
                "task_type": task.get("task_type"),
                "model_id": task.get("model_id"),
                "backend": base["backend"],
                "status": status,
                "elapsed_seconds": elapsed,
            },
        )
        return {
            **base,
            "status": status,
            "output": result,
        }

    def run_batch(self, batch: dict, *, dry_run: bool = True) -> dict:
        task_results = [self.run(task, dry_run=dry_run) for task in batch.get("tasks", [])]
        ready_count = sum(1 for result in task_results if result["readiness"]["ready"])
        executed_count = sum(1 for result in task_results if result["status"] == "executed")
        failed_count = sum(
            1
            for result in task_results
            if result["readiness"]["ready"] and result["status"] not in {"ready_dry_run", "executed"}
        )
        return {
            "batch_id": batch.get("batch_id"),
            "task_type": batch.get("task_type"),
            "provider": batch.get("provider"),
            "space_name": batch.get("space_name"),
            "backend": batch.get("backend"),
            "model_role": batch.get("model_role"),
            "model_id": batch.get("model_id"),
            "task_count": len(task_results),
            "ready_count": ready_count,
            "blocked_count": len(task_results) - ready_count,
            "executed_count": executed_count,
            "failed_count": failed_count,
            "status": "dry_run_complete" if dry_run else _batch_status(task_results),
            "results": task_results,
        }

    def _client_for_task(self, task: dict):
        backend = str(task.get("backend") or self.config.get("gpu", {}).get("backend", "transformers"))
        model_config = _model_config_for_task(self.config, task)
        model_id = task.get("model_id") or model_config.get("model_id")
        if backend == "vllm":
            gpu = self.config.get("gpu", {})
            endpoint = model_config.get("endpoint") or gpu.get("vllm_endpoint") or gpu.get("endpoint")
            return VLLMClient(endpoint=endpoint, model_id=model_id, api_key=gpu.get("api_key"))
        return TransformersClient(model_id=model_id, model_config=model_config)


def _task_readiness(task: dict) -> dict:
    missing: list[str] = []
    image_path = task.get("image_path")
    if task.get("task_type") in {"ocr_page", "table_vlm_repair", "figure_description", "vlm_route_repair"}:
        if image_path and not Path(str(image_path)).exists():
            missing.append("image_path")
        if not image_path and task.get("task_type") != "vlm_route_repair":
            missing.append("image_path")
    if not task.get("doc_id"):
        missing.append("doc_id")
    if not task.get("page_nums"):
        missing.append("page_nums")
    return {
        "ready": not missing,
        "missing_inputs": missing,
        "image_path_exists": bool(image_path and Path(str(image_path)).exists()),
    }


def _model_config_for_task(config: dict, task: dict) -> dict:
    if isinstance(task.get("model_config"), dict) and task["model_config"]:
        return dict(task["model_config"])
    role = task.get("model_role") or "vlm"
    models = config.get("gpu", {}).get("models", {})
    model_config = models.get(role, {}) if isinstance(models, dict) else {}
    return dict(model_config) if isinstance(model_config, dict) else {}


def _batch_status(task_results: list[dict]) -> str:
    if not task_results:
        return "execute_complete"
    if all(result["status"] == "executed" for result in task_results):
        return "execute_complete"
    if any(result["status"] == "executed" for result in task_results):
        return "execute_partial"
    if all(not result["readiness"]["ready"] for result in task_results):
        return "blocked_missing_inputs"
    return "execute_failed"