# Inference dependencies for learned skills (XPolicyLab policies) and
# perception, installed under constraints.txt. Training-only packages
# (lightning, deepspeed, wandb, decord) are left out.

# Xiaomi-Robotics-1 (XPolicyLab/policy/Xiaomi_Robotics_1). Upstream asks for
# torch 2.8 / transformers 4.57.1 / numpy 2.1; inference runs on the pinned core.
# flash-attn is not installed; the policy runs with SDPA attention.
accelerate==1.11.0
liger-kernel==0.6.5
omegaconf
hydra-core
# mmengine is left out: its own requirement on opencv-python would add a second
# cv2 next to opencv-python-headless (install it with --no-deps where needed).
addict
rich
termcolor
yapf

# G0.5 / GalaxeaVLA (XPolicyLab/policy/G05), inference only, installed under constraints.txt.
# Upstream pyproject pins torch 2.7.1 / transformers 4.57.1 / websockets 16 / python<3.11 plus
# training and sim packages (deepspeed, wandb, sapien, robosuite, ...). Inference runs on the
# pinned core. flash-attn / flash-attn-4 / causal-conv1d are not installed: the vision tower uses
# SDPA and the conv1d uses the torch fallback. The G05 package itself is installed with
#   pip install --no-deps --ignore-requires-python -e XPolicyLab/policy/G05/G05
# (its requires-python is <3.11; the code runs on 3.11).
GitPython==3.1.62               # g05.utils.data.normalizer does `from git import Repo` at import time
gitdb==4.0.12                   # GitPython dependency
smmap==5.0.3                    # gitdb dependency
rootutils==1.0.7                # G05 scripts/serve_policy.py (setup, build_obs_dict) imports it at module level
flash-linear-attention==0.5.2   # Triton Gated-DeltaNet kernels for the Qwen3.5 linear-attention layers (linear_attn_backend=fla); a slow torch fallback exists without it
fla-core==0.5.2                 # flash-linear-attention dependency

# SAM 3 (segment_text): runtime imports of the sam3 package, which is installed
# with --no-deps from its checkout (services/sam3.toml). decord (video) is stubbed by
# scripts/serve_sam3.py because only sam3's training loaders import it.
timm==1.0.30
ftfy==6.1.1
iopath==0.1.10
pycocotools==2.0.11             # imported by sam3's data module at import time

# Local development models: scripts/serve_vlm.py --quantize 4bit (NF4) fits a
# 32B vision-language model on one 24 GB GPU.
bitsandbytes==0.50.2

# OpenWAM (XPolicyLab/policy/OpenWAM/OpenWAM), inference only, for predict_future.
# Upstream pyproject asks for transformers>=5.5 and websockets<17; inference uses only
# AutoTokenizer / ContextManagers from transformers and runs on the pinned core. The
# package itself is installed with `pip install --no-deps -e .../OpenWAM/OpenWAM`.
diffusers==0.38.0
modelscope==1.40.1            # imported by the Wan backbone loader at import time
pandas==3.0.6                 # imported by openwam.dataloader at import time
pyarrow==25.0.1               # ditto

# Simate-beta (XPolicyLab/policy/Simate_beta, the simate service): its tokenizer.
sentencepiece==0.2.2
