# Tier-2 e2e-runner image — Jetson Orin Nano (JetPack 6.x, L4T R36.x).
#
# AZ-615: companion image to `tests/e2e/Dockerfile` (Colima/Tier-1 smoke
# harness) that runs the full Reality Gate — including C3 matcher + C7
# inference — against a CUDA-capable GPU.
#
# Hardware contract (operator-confirmed, 2026-05-17):
#   * Jetson Orin Nano, JetPack 6.2.2+b24, L4T R36.5.0
#   * nvidia-container-toolkit ≥ 1.16
#   * `docker run --runtime=nvidia ... nvidia-smi` returns the GPU
#
# Image layout mirrors the Colima Dockerfile (so AC-4 AST scan + bind
# mounts work the same way):
#   /opt/pyproject.toml
#   /opt/src/gps_denied_onboard/...     (SUT package, editable install)
#   /opt/tests/...                      (bind-mounted from host)
#   /opt/_docs/00_problem/input_data/   (bind-mounted from host)
#
# Build context is the repo root (see `docker-compose.test.jetson.yml`
# → `services.e2e-runner.build.context`).
#
# BUILD HOST: this image MUST be built ON the Jetson — cross-building
# from x86 macOS produces images that miss Tegra-specific shared libs
# the nvidia-container-runtime later mounts at run time.

# ---------------------------------------------------------------------------
# Base — l4t-pytorch ships JetPack runtime + PyTorch wheel ready for `.cuda()`
#
# Tag selection: NGC publishes l4t-pytorch on a slight lag from L4T BSP
# releases. With BSP R36.5 on the device, the closest stable NGC tag at
# author time is `r36.4.0-pth2.3-py3`. NVIDIA containers are
# forward-compatible across one minor BSP (the container's userspace
# can be slightly older than the host's L4T kernel). If a `r36.5.0-*`
# tag is published, prefer it.
#
# Image lookup at run time: `docker manifest inspect nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3`
FROM nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3 AS runtime

ARG DEBIAN_FRONTEND=noninteractive
# System deps mirror tests/e2e/Dockerfile + the Jetson runtime stack:
#   * build-essential / libpq-dev / libspatialindex-dev — same as Colima
#   * python3-pip / python3-venv — l4t-pytorch ships python but not always venv
#   * libgl1 + libglib2.0-0 — OpenCV runtime libs (same reason as Colima)
#   * libpq5 + libspatialindex-c6 — runtime side of psycopg + rtree
# Note: CUDA / cuDNN / TensorRT come pre-baked in the base image — do NOT
# attempt to apt-install them (would conflict with the Tegra-specific libs
# the runtime mounts).
RUN apt-get update && apt-get install -y --no-install-recommends \
        ca-certificates \
        build-essential \
        libpq-dev \
        libspatialindex-dev \
        libpq5 \
        libspatialindex-c6 \
        libgl1 \
        libglib2.0-0 \
        python3-pip \
        python3-venv \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /opt

# Editable SUT install. Skipping the `[inference]` extra because PyTorch +
# torchvision are already provided by the l4t-pytorch base image with
# Tegra-specific CUDA builds; reinstalling them from PyPI would clobber
# the Tegra wheels with x86-compatible ones that lack the cuDNN / cuBLAS
# linkage required by Orin.
COPY pyproject.toml README.md ./
COPY src ./src

# `--break-system-packages` is needed because the l4t-pytorch base image
# uses an externally-managed Python environment (PEP 668). The alternative
# would be to layer a venv on top of the pre-installed torch, but that
# would shadow the Tegra-tuned torch wheel and break `.cuda()`. The image
# IS the environment; embracing system-pip is the path of least drift.
RUN pip3 install --no-cache-dir --break-system-packages -e ".[dev]"

# ENTRYPOINT mirrors the Colima Dockerfile — pytest discovers both
# `tests/e2e/replay/` (heavy tier2 ACs run with GPS_DENIED_TIER=2) and
# any future `tests/e2e/scenarios/` additions. Rootdir resolves to /opt
# via the COPY'd pyproject.toml so `from tests.e2e.replay._helpers import ...`
# works inside the test files.
ENTRYPOINT ["pytest", "-q", "/opt/tests/e2e/"]