"""DriftShield — an OpenEnv benchmark that trains LLM agents to survive
production runtime failures (prompt injection, schema drift, poisoned memory,
lying tools).

The Python package is still importable as ``support_ops_env`` for backwards
compatibility with the live HF Space URL and existing notebooks. New code
should prefer the ``DriftShield*`` aliases below.
"""

from .client import SupportOpsEnv
from .models import SupportOpsAction, SupportOpsObservation, SupportOpsState
from .tasks import (
    DIFFICULTY_ORDER,
    DRIFTSHIELD_TASK_IDS,
    DRIFTSHIELD_TIERS,
    TASK_IDS,
    expand_curriculum_with_mastery,
    get_curriculum_task_ids,
    get_task_spec,
    list_task_specs,
    load_curriculum_state,
)

# DriftShield-branded aliases — preferred for new code.
DriftShieldEnv = SupportOpsEnv
DriftShieldAction = SupportOpsAction
DriftShieldObservation = SupportOpsObservation
DriftShieldState = SupportOpsState

__all__ = [
    "DIFFICULTY_ORDER",
    "DRIFTSHIELD_TASK_IDS",
    "DRIFTSHIELD_TIERS",
    "DriftShieldAction",
    "DriftShieldEnv",
    "DriftShieldObservation",
    "DriftShieldState",
    "SupportOpsAction",
    "SupportOpsEnv",
    "SupportOpsObservation",
    "SupportOpsState",
    "TASK_IDS",
    "expand_curriculum_with_mastery",
    "get_curriculum_task_ids",
    "get_task_spec",
    "list_task_specs",
    "load_curriculum_state",
]


def get_training_utils():
    """Lazy-import training utilities from :mod:`support_ops_env.train`.

    Returns a dict with: ``SYSTEM_PROMPT``, ``rollout_once``,
    ``format_observation``, ``format_history``, ``parse_tool_calls``,
    ``apply_chat_template``, ``reward_total``, ``reward_fields``,
    ``reward_reply``, ``reward_grounding``, ``plot_rewards``,
    ``patch_trl_vllm_compat``, ``require_vllm_trl_colocate_safe``.

    Example (Colab)::

        from support_ops_env import get_training_utils

        tu = get_training_utils()
        SYSTEM_PROMPT = tu["SYSTEM_PROMPT"]
        rollout_once = tu["rollout_once"]
    """
    from . import train as _train

    return {
        "SYSTEM_PROMPT": _train.SYSTEM_PROMPT,
        "rollout_once": _train.rollout_once,
        "format_observation": _train.format_observation,
        "format_history": _train.format_history,
        "parse_tool_calls": _train.parse_tool_calls,
        "apply_chat_template": _train.apply_chat_template,
        "reward_total": _train.reward_total,
        "reward_fields": _train.reward_fields,
        "reward_reply": _train.reward_reply,
        "reward_grounding": _train.reward_grounding,
        "plot_rewards": _train.plot_rewards,
        "patch_trl_vllm_compat": _train.patch_trl_vllm_compat,
        "require_vllm_trl_colocate_safe": _train.require_vllm_trl_colocate_safe,
    }


def get_gemma4_training_utils():
    """Lazy-import the Gemma 4 training utilities from :mod:`support_ops_env.train_gemma4`.

    This path uses TRL's ``environment_factory`` API (the newer pattern shipped
    with Gemma 4's CARLA reference). Install with::

        pip install -e ".[gemma]"

    Returns a dict with: ``SYSTEM_PROMPT``, ``SupportOpsToolEnv``, ``reward_total``,
    ``reward_fields``, ``reward_reply``, ``reward_merge``.

    Example (Colab)::

        from support_ops_env import get_gemma4_training_utils

        g4 = get_gemma4_training_utils()
        ToolEnv = g4["SupportOpsToolEnv"]
        ToolEnv._env_url = "https://<you>-support-ops-env.hf.space"
    """
    from . import train_gemma4 as _g4

    return {
        "SYSTEM_PROMPT": _g4.SYSTEM_PROMPT,
        "SupportOpsToolEnv": _g4.SupportOpsToolEnv,
        "reward_total": _g4.reward_total,
        "reward_investigation": _g4.reward_investigation,
        "reward_routing": _g4.reward_routing,
        "reward_reply": _g4.reward_reply,
        "reward_groundedness": _g4.reward_groundedness,
        # Back-compat aliases (older cells may still import these).
        "reward_fields": _g4.reward_routing,
        "reward_merge": _g4.reward_investigation,
    }