fix(rl): enhance intervention handling in actor and learner

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

.github/workflows/benchmark_tests.yml

@@ -118,7 +118,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=pepijn223/smolvla_libero \
+                --policy.path=lerobot/smolvla_libero \
                 --env.type=libero \
                 --env.task=libero_spatial \
                 --eval.batch_size=1 \
@@ -147,7 +147,7 @@ jobs:
             --artifacts-dir /tmp/libero-artifacts \
             --env libero \
             --task libero_spatial \
-            --policy pepijn223/smolvla_libero
+            --policy lerobot/smolvla_libero
 
       - name: Upload Libero rollout video
         if: always()
@@ -270,7 +270,7 @@ jobs:
             bash -c "
               hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
               lerobot-eval \
-                --policy.path=pepijn223/smolvla_metaworld \
+                --policy.path=lerobot/smolvla_metaworld \
                 --env.type=metaworld \
                 --env.task=metaworld-push-v3 \
                 --eval.batch_size=1 \
@@ -299,7 +299,7 @@ jobs:
             --artifacts-dir /tmp/metaworld-artifacts \
             --env metaworld \
             --task metaworld-push-v3 \
-            --policy pepijn223/smolvla_metaworld
+            --policy lerobot/smolvla_metaworld
 
       - name: Upload MetaWorld rollout video
         if: always()
@@ -317,6 +317,115 @@ jobs:
           path: /tmp/metaworld-artifacts/metrics.json
           if-no-files-found: warn
 
+  # ── ROBOTWIN 2.0 ──────────────────────────────────────────────────────────
+  # Isolated image: full RoboTwin 2.0 stack — SAPIEN, mplib, CuRobo,
+  # pytorch3d, + simulation assets (~4 GB).
+  # Build takes ~20 min on first run; subsequent runs hit the layer cache.
+  # Requires an NVIDIA GPU runner with CUDA 12.1 drivers.
+  robotwin-integration-test:
+    name: RoboTwin 2.0 — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+      ROBOTWIN_POLICY: lerobot/smolvla_robotwin
+      ROBOTWIN_TASKS: beat_block_hammer,click_bell,handover_block,stack_blocks_two,click_alarmclock,open_microwave,adjust_bottle,lift_pot,stamp_seal,turn_switch
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        if: ${{ env.DOCKERHUB_USERNAME != '' }}
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+        env:
+          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
+      # Build the full-install image: SAPIEN, mplib, CuRobo, pytorch3d +
+      # simulation assets (~4 GB). Layer cache lives in the runner's local
+      # Docker daemon — reused across re-runs on the same machine.
+      - name: Build RoboTwin 2.0 benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.robotwin
+          push: false
+          load: true
+          tags: lerobot-benchmark-robotwin:ci
+          cache-from: type=local,src=/tmp/.buildx-cache-robotwin
+          cache-to: type=local,dest=/tmp/.buildx-cache-robotwin,mode=max
+
+      - name: Run RoboTwin 2.0 smoke eval (10 tasks, 1 episode each)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          # Named container (no --rm) so we can docker cp artifacts out.
+          docker run --name robotwin-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e ROBOTWIN_POLICY="${ROBOTWIN_POLICY}" \
+            -e ROBOTWIN_TASKS="${ROBOTWIN_TASKS}" \
+            lerobot-benchmark-robotwin:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              cd /opt/robotwin && lerobot-eval \
+                --policy.path=\"\$ROBOTWIN_POLICY\" \
+                --env.type=robotwin \
+                --env.task=\"\$ROBOTWIN_TASKS\" \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--rename_map={\"observation.images.head_camera\": \"observation.images.camera1\", \"observation.images.left_camera\": \"observation.images.camera2\", \"observation.images.right_camera\": \"observation.images.camera3\"}' \
+                --output_dir=/tmp/eval-artifacts
+              python /lerobot/scripts/ci/extract_task_descriptions.py \
+                --env robotwin \
+                --task \"\$ROBOTWIN_TASKS\" \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy RoboTwin artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/robotwin-artifacts
+          docker cp robotwin-eval:/tmp/eval-artifacts/. /tmp/robotwin-artifacts/ 2>/dev/null || true
+          docker rm -f robotwin-eval || true
+
+      - name: Parse RoboTwin eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/robotwin-artifacts \
+            --env robotwin \
+            --task "${ROBOTWIN_TASKS}" \
+            --policy "${ROBOTWIN_POLICY}"
+
+      - name: Upload RoboTwin rollout video
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: robotwin-rollout-video
+          path: /tmp/robotwin-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload RoboTwin eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: robotwin-metrics
+          path: /tmp/robotwin-artifacts/metrics.json
+          if-no-files-found: warn
+
   # ── ROBOCASA365 ──────────────────────────────────────────────────────────
   # Isolated image: robocasa + robosuite installed manually as editable
   # clones (no `lerobot[robocasa]` extra — robocasa's setup.py pins
@@ -416,3 +525,421 @@ jobs:
           name: robocasa-metrics
           path: /tmp/robocasa-artifacts/metrics.json
           if-no-files-found: warn
+
+  # ── ROBOCEREBRA ───────────────────────────────────────────────────────────
+  # Reuses the LIBERO simulator (libero_10 suite) with RoboCerebra camera
+  # defaults (image/wrist_image). The image is layered on
+  # huggingface/lerobot-gpu, which already ships [libero] as part of [all].
+  robocerebra-integration-test:
+    name: RoboCerebra — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        if: ${{ env.DOCKERHUB_USERNAME != '' }}
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+        env:
+          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
+      - name: Build RoboCerebra benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.robocerebra
+          push: false
+          load: true
+          tags: lerobot-benchmark-robocerebra:ci
+          cache-from: type=local,src=/tmp/.buildx-cache-robocerebra
+          cache-to: type=local,dest=/tmp/.buildx-cache-robocerebra,mode=max
+
+      - name: Run RoboCerebra smoke eval (1 episode)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          docker run --name robocerebra-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            -e LIBERO_DATA_FOLDER=/tmp/libero_data \
+            lerobot-benchmark-robocerebra:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=lerobot/smolvla_robocerebra \
+                --env.type=libero \
+                --env.task=libero_10 \
+                --env.fps=20 \
+                --env.obs_type=pixels_agent_pos \
+                --env.observation_height=256 \
+                --env.observation_width=256 \
+                '--env.camera_name_mapping={\"agentview_image\": \"image\", \"robot0_eye_in_hand_image\": \"wrist_image\"}' \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
+                --policy.empty_cameras=1 \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env libero --task libero_10 \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy RoboCerebra artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/robocerebra-artifacts
+          docker cp robocerebra-eval:/tmp/eval-artifacts/. /tmp/robocerebra-artifacts/ 2>/dev/null || true
+          docker rm -f robocerebra-eval || true
+
+      - name: Parse RoboCerebra eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/robocerebra-artifacts \
+            --env robocerebra \
+            --task libero_10 \
+            --policy lerobot/smolvla_robocerebra
+
+      - name: Upload RoboCerebra rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: robocerebra-rollout-video
+          path: /tmp/robocerebra-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload RoboCerebra eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: robocerebra-metrics
+          path: /tmp/robocerebra-artifacts/metrics.json
+          if-no-files-found: warn
+
+  # ── ROBOMME ───────────────────────────────────────────────────────────────
+  # Isolated image: mani-skill/SAPIEN/Vulkan chain with gymnasium and numpy
+  # overrides (robomme can't be a pyproject extra due to numpy<2 pin).
+  robomme-integration-test:
+    name: RoboMME — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+      ROBOMME_POLICY: lerobot/smolvla_robomme
+      ROBOMME_TASKS: PickXtimes,BinFill,StopCube,MoveCube,InsertPeg,SwingXtimes,VideoUnmask,ButtonUnmask,PickHighlight,PatternLock
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        if: ${{ env.DOCKERHUB_USERNAME != '' }}
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+        env:
+          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
+      - name: Build RoboMME benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.robomme
+          push: false
+          load: true
+          tags: lerobot-benchmark-robomme:ci
+
+      - name: Run RoboMME smoke eval (10 tasks, 1 episode each)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          docker run --name robomme-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            -e ROBOMME_POLICY="${ROBOMME_POLICY}" \
+            -e ROBOMME_TASKS="${ROBOMME_TASKS}" \
+            lerobot-benchmark-robomme:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=\"\$ROBOMME_POLICY\" \
+                --env.type=robomme \
+                --env.task=\"\$ROBOMME_TASKS\" \
+                --env.dataset_split=test \
+                --env.task_ids=[0] \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
+                --policy.empty_cameras=3 \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env robomme --task \"\$ROBOMME_TASKS\" \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy RoboMME artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/robomme-artifacts
+          docker cp robomme-eval:/tmp/eval-artifacts/. /tmp/robomme-artifacts/ 2>/dev/null || true
+          docker rm -f robomme-eval || true
+
+      - name: Parse RoboMME eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/robomme-artifacts \
+            --env robomme \
+            --task "${ROBOMME_TASKS}" \
+            --policy "${ROBOMME_POLICY}"
+
+      - name: Upload RoboMME rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: robomme-rollout-video
+          path: /tmp/robomme-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload RoboMME eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: robomme-metrics
+          path: /tmp/robomme-artifacts/metrics.json
+          if-no-files-found: warn
+
+  # ── LIBERO-plus ───────────────────────────────────────────────────────────
+  # Isolated image: LIBERO-plus fork cloned into /home/user_lerobot on top of
+  # huggingface/lerobot-gpu (see docker/Dockerfile.benchmark.libero_plus).
+  libero-plus-integration-test:
+    name: LIBERO-plus — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+      LIBERO_PLUS_SUITE: libero_spatial
+      LIBERO_PLUS_POLICY: lerobot/smolvla_libero_plus
+      LIBERO_PLUS_TASK_IDS: "[0,100,260,500,1000,1500,2000,2400]"
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        if: ${{ env.DOCKERHUB_USERNAME != '' }}
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+        env:
+          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
+      - name: Build LIBERO-plus benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.libero_plus
+          push: false
+          load: true
+          tags: lerobot-benchmark-libero-plus:ci
+          cache-from: type=local,src=/tmp/.buildx-cache-libero-plus
+          cache-to: type=local,dest=/tmp/.buildx-cache-libero-plus,mode=max
+
+      - name: Run LIBERO-plus smoke eval (1 episode)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          docker run --name libero-plus-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            -e LIBERO_PLUS_SUITE="${LIBERO_PLUS_SUITE}" \
+            -e LIBERO_PLUS_POLICY="${LIBERO_PLUS_POLICY}" \
+            -e LIBERO_PLUS_TASK_IDS="${LIBERO_PLUS_TASK_IDS}" \
+            lerobot-benchmark-libero-plus:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=\"\$LIBERO_PLUS_POLICY\" \
+                --env.type=libero_plus \
+                --env.task=\"\$LIBERO_PLUS_SUITE\" \
+                --env.task_ids=\"\$LIBERO_PLUS_TASK_IDS\" \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
+                --policy.empty_cameras=1 \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env libero_plus --task \"\$LIBERO_PLUS_SUITE\" \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy LIBERO-plus artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/libero-plus-artifacts
+          docker cp libero-plus-eval:/tmp/eval-artifacts/. /tmp/libero-plus-artifacts/ 2>/dev/null || true
+          docker rm -f libero-plus-eval || true
+
+      - name: Parse LIBERO-plus eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/libero-plus-artifacts \
+            --env libero_plus \
+            --task "${LIBERO_PLUS_SUITE}" \
+            --policy "${LIBERO_PLUS_POLICY}"
+
+      - name: Upload LIBERO-plus rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: libero-plus-rollout-video
+          path: /tmp/libero-plus-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload LIBERO-plus eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: libero-plus-metrics
+          path: /tmp/libero-plus-artifacts/metrics.json
+          if-no-files-found: warn
+
+  # ── VLABENCH ─────────────────────────────────────────────────────────────
+  # Isolated image: lerobot[vlabench] only (VLABench, mujoco==3.2.2, dm-control chain)
+  vlabench-integration-test:
+    name: VLABench — build image + 1-episode eval
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
+
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
+        with:
+          persist-credentials: false
+          lfs: true
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          cache-binary: false
+
+      - name: Login to Docker Hub
+        if: ${{ env.DOCKERHUB_USERNAME != '' }}
+        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
+        with:
+          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
+        env:
+          DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
+
+      - name: Build VLABench benchmark image
+        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
+        with:
+          context: .
+          file: docker/Dockerfile.benchmark.vlabench
+          push: false
+          load: true
+          tags: lerobot-benchmark-vlabench:ci
+          build-args: |
+            VLABENCH_ASSETS_REPO=lerobot/vlabench-assets
+
+      - name: Run VLABench smoke eval (10 tasks, 1 episode each)
+        if: env.HF_USER_TOKEN != ''
+        run: |
+          docker run --name vlabench-eval --gpus all \
+            --shm-size=4g \
+            -e HF_HOME=/tmp/hf \
+            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
+            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
+            -e MUJOCO_GL=egl \
+            lerobot-benchmark-vlabench:ci \
+            bash -c "
+              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
+              lerobot-eval \
+                --policy.path=lerobot/smolvla_vlabench \
+                --env.type=vlabench \
+                --env.task=select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
+                --eval.batch_size=1 \
+                --eval.n_episodes=1 \
+                --eval.use_async_envs=false \
+                --policy.device=cuda \
+                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.second_image\": \"observation.images.camera2\", \"observation.images.wrist_image\": \"observation.images.camera3\"}' \
+                --output_dir=/tmp/eval-artifacts
+              python scripts/ci/extract_task_descriptions.py \
+                --env vlabench \
+                --task select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
+                --output /tmp/eval-artifacts/task_descriptions.json
+            "
+
+      - name: Copy VLABench artifacts from container
+        if: always()
+        run: |
+          mkdir -p /tmp/vlabench-artifacts
+          docker cp vlabench-eval:/tmp/eval-artifacts/. /tmp/vlabench-artifacts/ 2>/dev/null || true
+          docker rm -f vlabench-eval || true
+
+      - name: Parse VLABench eval metrics
+        if: always()
+        run: |
+          python3 scripts/ci/parse_eval_metrics.py \
+            --artifacts-dir /tmp/vlabench-artifacts \
+            --env vlabench \
+            --task select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
+            --policy lerobot/smolvla_vlabench
+
+      - name: Upload VLABench rollout video
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: vlabench-rollout-video
+          path: /tmp/vlabench-artifacts/videos/
+          if-no-files-found: warn
+
+      - name: Upload VLABench eval metrics
+        if: always()
+        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
+        with:
+          name: vlabench-metrics
+          path: /tmp/vlabench-artifacts/metrics.json
+          if-no-files-found: warn

AGENTS.md

@@ -1,5 +1,7 @@
 This file provides guidance to AI agents when working with code in this repository.
 
+> **User-facing help → [`AGENT_GUIDE.md`](./AGENT_GUIDE.md)** (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands).
+
 ## Project Overview
 
 LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing.

AGENT_GUIDE.md

@@ -0,0 +1,410 @@
+# AGENT_GUIDE.md — LeRobot Helper for AI Agents & Users
+
+This file is a practical, copy-paste-friendly companion for any AI agent (Cursor, Claude, ChatGPT, Codex, etc.) helping a user work with LeRobot. It complements [`AGENTS.md`](./AGENTS.md) (dev/contributor context) with **user-facing guidance**: how to start, what to train, how long, how to record, and how to calibrate an SO-101.
+
+---
+
+## 1. Start here — ask the user first (MANDATORY)
+
+Before suggesting any command, an agent MUST ask the user at least these questions and wait for answers:
+
+1. **What's your goal?** (e.g. "teach my SO-101 to fold a cloth", "train a policy on an existing HF dataset", "contribute a PR", "understand the codebase")
+2. **What hardware do you have?**
+   - Robot: none / SO-100 / SO-101 / Koch / LeKiwi / Reachy / other
+   - Teleop: leader arm / phone / keyboard / gamepad / none
+   - Cameras: how many, resolution, fixed or moving?
+3. **What machine will you train on?**
+   - GPU model + VRAM (e.g. "laptop 3060 6 GB", "RTX 4090 24 GB", "A100 80 GB", "CPU only")
+   - OS: macOS / Linux / Windows
+4. **Skill level & time budget?** First time, some ML, experienced? Hours, days, a weekend?
+5. **Do you already have a dataset?** Yes (HF repo id?) / no / want to record one
+6. **How can I help right now?** (pick one concrete next step)
+
+Only after you have answers, propose a concrete path. If something is ambiguous, ask again rather than guessing. Bias toward **the simplest thing that works** for the user's hardware and goal.
+
+---
+
+## 2. LeRobot in 60 seconds
+
+LeRobot = **datasets + policies + envs + robot control**, unified by a small set of strong abstractions.
+
+- **`LeRobotDataset`** — episode-aware dataset (video or images + actions + state), loadable from the Hub or disk.
+- **Policies** (`ACT`, `Diffusion`, `SmolVLA`, `π0`, `π0.5`, `Wall-X`, `X-VLA`, `VQ-BeT`, `TD-MPC`, …) — all inherit `PreTrainedPolicy` and can be pushed/pulled from the Hub.
+- **Processors** — small composable transforms between dataset → policy → robot.
+- **Envs** (sim) and **Robots** (real) — same action/observation contract so code swaps cleanly.
+- **CLI** — `lerobot-record`, `lerobot-train`, `lerobot-eval`, `lerobot-teleoperate`, `lerobot-calibrate`, `lerobot-find-port`, `lerobot-setup-motors`, `lerobot-replay`.
+
+See [`AGENTS.md`](./AGENTS.md) for repo architecture.
+
+---
+
+## 3. Quickstart paths (pick one)
+
+### Path A — "I have an SO-101 and want my first trained policy"
+
+Go to §4 (SO-101 end-to-end), then §5 (data tips), then §6 (pick a policy — likely **ACT**), then §7 (how long), then §8 (eval).
+
+### Path B — "No hardware, I want to train on an existing dataset"
+
+Skip §4. Pick a policy in §6, pick a duration in §7, then run `lerobot-train` per §4.9 with a Hub `--dataset.repo_id` and an `--env.type` for eval. Finish with §8.
+
+### Path C — "I just want to understand the codebase"
+
+Read §2 above, then `AGENTS.md` "Architecture", then open `src/lerobot/policies/act/` and `src/lerobot/datasets/lerobot_dataset.py` as canonical examples.
+
+---
+
+## 4. SO-101 end-to-end cheat-sheet
+
+Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/source/il_robots.mdx`](./docs/source/il_robots.mdx). Minimum commands in order. Confirm arms are assembled + powered before issuing.
+
+**4.1 Install**
+
+```bash
+pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
+# pip install 'lerobot[all]'                # everything
+# pip install 'lerobot[aloha,pusht]'        # specific features
+# pip install 'lerobot[smolvla]'            # add SmolVLA deps
+git lfs install && git lfs pull
+hf auth login                               # required to push datasets/policies
+```
+
+Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
+
+**4.2 Find USB ports** — run once per arm, unplug when prompted.
+
+```bash
+lerobot-find-port
+```
+
+macOS: `/dev/tty.usbmodem...`; Linux: `/dev/ttyACM0` (may need `sudo chmod 666 /dev/ttyACM0`).
+
+**4.3 Setup motor IDs & baudrate** (one-time, per arm)
+
+```bash
+lerobot-setup-motors --robot.type=so101_follower --robot.port=<FOLLOWER_PORT>
+lerobot-setup-motors --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>
+```
+
+**4.4 Calibrate** — center all joints, press Enter, sweep each joint through its full range. The `id` is the calibration key — reuse it everywhere.
+
+```bash
+lerobot-calibrate --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower
+lerobot-calibrate --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>   --teleop.id=my_leader
+```
+
+**4.5 Teleoperate** (sanity check, no recording)
+
+```bash
+lerobot-teleoperate \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --display_data=true
+```
+
+> **Feetech timeout / comms error on SO-100 / SO-101?** Before touching software, check the **red motor LEDs** on the daisy chain.
+>
+> - **All steady red, gripper → base chain** → wiring OK.
+> - **One or more motors dark / chain stops mid-way** → wiring issue: reseat the 3-pin cables, check the controller-board power supply, and make sure each motor is fully clicked in.
+> - **LEDs blinking** → the motor is in an **error state**: usually overload (forcing a joint past its limit) **or wrong power supply voltage**. SO-100 / SO-101 ship in two variants — a **5 V / 7.4 V** build and a **12 V** build — they are NOT interchangeable. Using a 12 V PSU on a 5 V / 7.4 V arm (or vice-versa) will trip this error; confirm your motor variant before powering up.
+>
+> Most "timeout" errors are physical, not code.
+
+**4.6 Record a dataset** — keys: **→** next, **←** redo, **ESC** finish & upload.
+
+```bash
+HF_USER=$(NO_COLOR=1 hf auth whoami | awk -F': *' 'NR==1 {print $2}')
+
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>  --teleop.id=my_leader \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/my_task \
+  --dataset.single_task="<describe the task in one sentence>" \
+  --dataset.num_episodes=50 \
+  --dataset.episode_time_s=30 \
+  --dataset.reset_time_s=10 \
+  --display_data=true
+```
+
+**4.7 Visualize** — **always** do this before training. Look for missing frames, camera blur, unreachable targets, inconsistent object positions.
+After upload: https://huggingface.co/spaces/lerobot/visualize_dataset → paste `${HF_USER}/my_task`. Works for **any LeRobot-formatted Hub dataset** — use it to scout other datasets, inspect episode quality, or debug your own data before retraining.
+
+**4.8 Replay an episode** (sanity check)
+
+```bash
+lerobot-replay --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --dataset.repo_id=${HF_USER}/my_task --dataset.episode=0
+```
+
+**4.9 Train** (default: ACT — fastest, lowest memory). Apple silicon: `--policy.device=mps`. See §6/§7 for policy and duration.
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/my_task \
+  --policy.type=act \
+  --policy.device=cuda \
+  --output_dir=outputs/train/act_my_task \
+  --job_name=act_my_task \
+  --batch_size=8 \
+  --wandb.enable=true \
+  --policy.repo_id=${HF_USER}/act_my_task
+```
+
+**4.10 Evaluate on the real robot** — compare success rate to a teleoperated baseline.
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_my_task \
+  --dataset.single_task="<same task description as training>" \
+  --dataset.num_episodes=10 \
+  --policy.path=${HF_USER}/act_my_task
+```
+
+---
+
+## 5. Data collection tips (beginner → reliable policy)
+
+Good data beats clever models. Adopt these defaults and deviate only with evidence.
+
+### 5.1 Setup & ergonomics
+
+- **Fix the rig and cameras** before touching the software. If the rig vibrates or the operator gets frustrated, fix that first — more bad data won't help.
+- **Lighting matters more than resolution.** Diffuse, consistent light. Avoid moving shadows.
+- **"Can you do the task from the camera view alone?"** If no, your cameras are wrong. Fix before recording.
+- Enable **action interpolation** for rollouts when available for smoother trajectories.
+
+### 5.2 Practice before you record
+
+- Do 5–10 demos without recording. Build a deliberate, repeatable strategy.
+- Hesitant or inconsistent demos teach the model hesitation.
+
+### 5.3 Quality over speed
+
+Deliberate, high-quality execution beats fast sloppy runs. Optimize for speed only **after** strategy is dialed in — never trade quality for it.
+
+### 5.4 Consistency within and across episodes
+
+Same grasp, approach vector, and timing. Coherent strategies are much easier to learn than wildly varying movements.
+
+### 5.5 Start small, then extend (the golden rule)
+
+- **First 50 episodes = constrained version** of the task: one object, fixed position, fixed camera setup, one operator.
+- Train a quick ACT model. See what fails.
+- **Then add diversity** along one axis at a time: more positions → more lighting → more objects → more operators.
+- Don't try to collect the "perfect dataset" on day one. Iterate.
+
+### 5.6 Policy choice for beginners
+
+- **Laptop / first time / want results fast → ACT.** Works surprisingly well, trains fast even on a laptop GPU.
+- **Bigger GPU / language-conditioned / multi-task → SmolVLA.** Unfreezing the vision encoder (see §7) is a big win here.
+- Defer π0 / π0.5 / Wall-X / X-VLA until you have a proven ACT baseline and a 20+ GB GPU.
+
+### 5.7 Recommended defaults for your first task
+
+| Setting          | Value                                                                                                                                                 |
+| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Episodes         | **50** to start, scale to 100–300 after first training                                                                                                |
+| Episode length   | 20–45 s (shorter is fine for grasp/place)                                                                                                             |
+| Reset time       | 10 s                                                                                                                                                  |
+| FPS              | 30                                                                                                                                                    |
+| Cameras          | **2 cameras recommended**: 1 fixed front + 1 wrist. Multi-view often outperforms single-view. A single fixed camera also works to keep things simple. |
+| Task description | Short, specific, action-phrased sentence                                                                                                              |
+
+### 5.8 Troubleshooting signal
+
+- Policy fails at one specific stage → record 10–20 more episodes **targeting that stage**.
+- Policy flaps / oscillates → likely inconsistent demos, or need more training; re-record worst episodes (use **←** to redo).
+- Policy ignores the object → camera framing or lighting issue, not a model issue.
+
+See also: [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets#what-makes-a-good-dataset).
+
+---
+
+## 6. Which policy should I train?
+
+Match the policy to the user's **GPU memory** and **time budget**. Numbers below come from an internal profiling run (one training update per policy). They are **indicative only** — see caveats.
+
+### 6.1 Profiling snapshot (indicative)
+
+All policies typically train for **5–10 epochs** (see §7).
+
+| Policy      | Batch | Update (ms) | Peak GPU mem (GB) | Best for                                                                                         |
+| ----------- | ----: | ----------: | ----------------: | ------------------------------------------------------------------------------------------------ |
+| `act`       |     4 |    **83.9** |          **0.94** | First-time users, laptops, single-task. Fast and reliable.                                       |
+| `diffusion` |     4 |       168.6 |              4.94 | Multi-modal action distributions; needs mid-range GPU.                                           |
+| `smolvla`   |     1 |       357.8 |              3.93 | Language-conditioned, multi-task, small VLA. **Unfreeze vision encoder for big gains** (see §7). |
+| `xvla`      |     1 |       731.6 |             15.52 | Large VLA, multi-task.                                                                           |
+| `wall_x`    |     1 |       716.5 |             15.95 | Large VLA with world-model objective.                                                            |
+| `pi0`       |     1 |       940.3 |             15.50 | Strong large VLA baseline (Physical Intelligence).                                               |
+| `pi05`      |     1 |      1055.8 |             16.35 | Newer π policy; similar footprint to `pi0`.                                                      |
+
+**Critical caveats:**
+
+- **Optimizer:** measured with **SGD**. LeRobot's default is **AdamW**, which keeps extra optimizer state → **peak memory will be noticeably higher** with the default, especially for `pi0`, `pi05`, `wall_x`, `xvla`.
+- **Batch size:** the large policies were profiled at batch 1. In practice use a **larger batch** for stable training (see §7.4). Memory scales roughly linearly with batch.
+
+### 6.2 Decision rules
+
+- **< 8 GB VRAM (laptop, 3060, M-series Mac):** → `act`. Maybe `diffusion` if you have ~6–8 GB free.
+- **12–16 GB VRAM (4070/4080, A4000):** → `smolvla` with defaults, or `act`/`diffusion` with larger batch. `pi0`/`pi05`/`wall_x`/`xvla` feasible only with small batch + gradient accumulation.
+- **24+ GB VRAM (3090/4090/A5000):** → any policy. Prefer `smolvla` (unfrozen) for multi-task; `act` for single-task grasp-and-place (still often the best ROI). Could experiment with `pi0` or `pi05` or `xvla`
+- **80 GB (A100/H100):** → any, with healthy batch. `pi05`, `xvla`, `wall_x` become comfortable.
+- **CPU only:** → don't train here. Use Google Colab (see [`docs/source/notebooks.mdx`](./docs/source/notebooks.mdx)) or a rented GPU.
+
+---
+
+## 7. How long should I train?
+
+Robotics imitation learning usually converges in a **few epochs over the dataset**, not hundreds of thousands of raw steps. Think **epochs first**, then translate to steps.
+
+### 7.1 Rule of thumb
+
+- **Typical total: 5–10 epochs.** Start at 5, eval, then decide if more helps.
+- Very small datasets (< 30 episodes) may want slightly more epochs — but first, **collect more data**.
+- VLAs with a pretrained vision backbone typically need **fewer** epochs than training from scratch.
+
+### 7.2 Steps ↔ epochs conversion
+
+```
+total_frames     = sum of frames over all episodes      # e.g. 50 eps × 30 fps × 30 s ≈ 45,000
+steps_per_epoch  = ceil(total_frames / batch_size)
+total_steps      = epochs × steps_per_epoch
+```
+
+Examples for `--batch_size=8`:
+
+| Dataset size            |  Frames | Steps / epoch | 5 epochs | 10 epochs |
+| ----------------------- | ------: | ------------: | -------: | --------: |
+| 50 eps × 30 s @ 30 fps  |  45,000 |        ~5,625 |      28k |       56k |
+| 100 eps × 30 s @ 30 fps |  90,000 |       ~11,250 |      56k |      113k |
+| 300 eps × 30 s @ 30 fps | 270,000 |       ~33,750 |     169k |      338k |
+
+Pass the resulting total with `--steps=<N>`; eval at intermediate checkpoints (`outputs/train/.../checkpoints/`).
+
+### 7.3 Per-policy starting points (single-task, ~50 episodes)
+
+| Policy         | Batch | Steps (first run) | Notes                                                             |
+| -------------- | ----: | ----------------: | ----------------------------------------------------------------- |
+| `act`          |  8–16 |           30k–80k | Usually converges under 50k for single-task.                      |
+| `diffusion`    |  8–16 |          80k–150k | Benefits from longer training than ACT.                           |
+| `smolvla`      |   4–8 |           30k–80k | Pretrained VLM → converges fast.                                  |
+| `pi0` / `pi05` |   1–4 |           30k–80k | Memory-bound; use gradient accumulation for effective batch ≥ 16! |
+
+### 7.4 Batch size guidance
+
+- **Bigger batch is preferable** for stable gradients on teleop data.
+- If GPU memory is the bottleneck, use **gradient accumulation** to raise _effective_ batch without raising peak memory.
+- Scale **learning rate** gently with batch; most LeRobot defaults work fine for a 2–4× batch change.
+
+### 7.5 Scale LR schedule & checkpoints with `--steps`
+
+LeRobot's default schedulers (e.g. SmolVLA's cosine decay) use `scheduler_decay_steps=30_000`, which is sized for long training runs. When you shorten training (e.g. 5k–10k steps on a small dataset), **scale the scheduler down to match** — otherwise the LR stays near the peak and never decays. Same for checkpoint frequency.
+
+```bash
+lerobot-train ... \
+  --steps=5000 \
+  --policy.scheduler_decay_steps=5000 \
+  --save_freq=5000
+```
+
+Rule of thumb: set `scheduler_decay_steps ≈ steps`, and `save_freq` to whatever granularity you want for eval (e.g. every 1k–5k steps). Match `scheduler_warmup_steps` proportionally if your run is very short.
+
+### 7.6 SmolVLA: unfreeze the vision encoder for real gains
+
+SmolVLA ships with `freeze_vision_encoder=True`. Unfreezing usually **improves performance substantially** on specialized tasks, at the cost of more VRAM and slower steps. Enable with:
+
+```bash
+lerobot-train ... --policy.type=smolvla \
+  --policy.freeze_vision_encoder=false \
+  --policy.train_expert_only=false
+```
+
+### 7.7 Signals to stop / keep going
+
+- Train loss plateaus → stop, save a Hub checkpoint.
+- Train loss still dropping and you're under 10 epochs → keep going.
+
+---
+
+## 8. Evaluation & benchmarks
+
+Two flavors of evaluation:
+
+### 8.1 Real-robot eval (SO-101, etc.)
+
+Reuse `lerobot-record` with `--policy.path` to run the trained policy on-robot and save the run as an eval dataset. Convention: prefix the dataset with `eval_`.
+
+```bash
+lerobot-record \
+  --robot.type=so101_follower --robot.port=<FOLLOWER_PORT> --robot.id=my_follower \
+  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+  --dataset.repo_id=${HF_USER}/eval_my_task \
+  --dataset.single_task="<same task description used during training>" \
+  --dataset.num_episodes=10 \
+  --policy.path=${HF_USER}/act_my_task
+```
+
+Report success rate across episodes. Compare to a teleoperated baseline and to an earlier checkpoint to catch regressions.
+
+### 8.2 Sim-benchmark eval
+
+For policies trained on sim datasets (PushT, Aloha, LIBERO, MetaWorld, RoboCasa, …) use `lerobot-eval` against the matching `env.type`:
+
+```bash
+lerobot-eval \
+  --policy.path=${HF_USER}/diffusion_pusht \
+  --env.type=pusht \
+  --eval.n_episodes=50 \
+  --eval.batch_size=10 \
+  --policy.device=cuda
+```
+
+- Use `--policy.path=outputs/train/.../checkpoints/<step>/pretrained_model` for local checkpoints.
+- `--eval.n_episodes` should be ≥ 50 for a stable success-rate estimate.
+- Available envs live in `src/lerobot/envs/`. See [`docs/source/libero.mdx`](./docs/source/libero.mdx), [`metaworld.mdx`](./docs/source/metaworld.mdx), [`robocasa.mdx`](./docs/source/robocasa.mdx), [`vlabench.mdx`](./docs/source/vlabench.mdx) for specific benchmarks.
+- To add a new benchmark, see [`docs/source/adding_benchmarks.mdx`](./docs/source/adding_benchmarks.mdx) and [`envhub.mdx`](./docs/source/envhub.mdx).
+
+### 8.2b Dockerfiles for benchmark eval
+
+Benchmark envs have native dependencies that are painful to install locally. The repo ships **pre-baked Dockerfiles** for each supported benchmark — use these to run `lerobot-eval` in a reproducible environment:
+
+| Benchmark   | Dockerfile                                                                             |
+| ----------- | -------------------------------------------------------------------------------------- |
+| LIBERO      | [`docker/Dockerfile.benchmark.libero`](./docker/Dockerfile.benchmark.libero)           |
+| LIBERO+     | [`docker/Dockerfile.benchmark.libero_plus`](./docker/Dockerfile.benchmark.libero_plus) |
+| MetaWorld   | [`docker/Dockerfile.benchmark.metaworld`](./docker/Dockerfile.benchmark.metaworld)     |
+| RoboCasa    | [`docker/Dockerfile.benchmark.robocasa`](./docker/Dockerfile.benchmark.robocasa)       |
+| RoboCerebra | [`docker/Dockerfile.benchmark.robocerebra`](./docker/Dockerfile.benchmark.robocerebra) |
+| RoboMME     | [`docker/Dockerfile.benchmark.robomme`](./docker/Dockerfile.benchmark.robomme)         |
+| RoboTwin    | [`docker/Dockerfile.benchmark.robotwin`](./docker/Dockerfile.benchmark.robotwin)       |
+| VLABench    | [`docker/Dockerfile.benchmark.vlabench`](./docker/Dockerfile.benchmark.vlabench)       |
+
+Build and run (adapt to your benchmark):
+
+```bash
+docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-bench-robomme .
+docker run --gpus all --rm -it \
+  -v $HOME/.cache/huggingface:/root/.cache/huggingface \
+  lerobot-bench-robomme \
+  lerobot-eval --policy.path=<your_policy> --env.type=<env> --eval.n_episodes=50
+```
+
+See [`docker/README.md`](./docker/README.md) for base-image details.
+
+### 8.3 Target success rates
+
+Single-task grasp-and-place with 50 clean episodes: ACT should reach **> 70% success** on the training configuration. Less → data problem (see §5), not model problem. Expect a drop when generalizing to new positions — scale episodes or diversity to recover.
+
+---
+
+## 9. Further reading & resources
+
+- **Getting started:** [`installation.mdx`](./docs/source/installation.mdx) · [`il_robots.mdx`](./docs/source/il_robots.mdx) · [What makes a good dataset](https://huggingface.co/blog/lerobot-datasets)
+- **Per-policy docs:** browse [`docs/source/*.mdx`](./docs/source/) (policies, hardware, benchmarks, advanced training).
+- **Community:** [Discord](https://discord.com/invite/s3KuuzsPFb) · [Hub `LeRobot` tag](https://huggingface.co/datasets?other=LeRobot) · [Dataset visualizer](https://huggingface.co/spaces/lerobot/visualize_dataset)
+
+> Keep this file current. If you learn a rule that would prevent a class of user mistakes, add it here and in [`AGENTS.md`](./AGENTS.md).

docker/Dockerfile.benchmark.libero_plus

@@ -0,0 +1,84 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Benchmark image for LIBERO-plus integration tests.
+# Extends the nightly GPU image (which has lerobot[all]) with the LIBERO-plus
+# fork source + its 6.4 GB perturbation assets.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.libero_plus -t lerobot-benchmark-libero-plus .
+# Run:    docker run --gpus all --rm lerobot-benchmark-libero-plus lerobot-eval ...
+
+FROM huggingface/lerobot-gpu:latest
+ENV MUJOCO_GL=egl
+
+# unzip for the 6.4 GB assets.zip; the rest are LIBERO-plus build-time extras
+# (wand / ImageMagick / fontconfig) not in the nightly base.
+USER root
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+         unzip libexpat1 libfontconfig1-dev libmagickwand-dev \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+USER user_lerobot
+
+# robosuite==1.4.1 is mandatory (the fork uses `single_arm_env` removed in
+# v1.5+). The rest are LIBERO-plus runtime deps pulled from its setup.py.
+# We install these explicitly instead of via the [libero_plus] extra because
+# the extra's `libero @ git+...` dep installs as a namespace package and then
+# clone and PYTHONPATH-override it below.
+RUN uv pip install --no-cache \
+        "robosuite==1.4.1" \
+        "bddl==1.0.1" \
+        "easydict==1.13" \
+        "mujoco==3.7.0" \
+        "matplotlib==3.10.8" \
+        "Wand==0.6.13" \
+        "scikit-image==0.25.2" \
+        "gym==0.26.2"
+
+# Clone LIBERO-plus and make it importable as `libero`. The nightly base has
+# hf-libero (10 tasks) preinstalled via lerobot[libero]; uninstall it so
+# Python resolves `import libero` to the 2402-task LIBERO-plus module instead.
+# Pinned to the current upstream main SHA so benchmark builds stay reproducible.
+ARG LIBERO_PLUS_SHA=4976dc3
+ENV LIBERO_PLUS_ROOT=/home/user_lerobot/libero-plus/libero/libero
+RUN git clone https://github.com/sylvestf/LIBERO-plus.git /home/user_lerobot/libero-plus \
+    && git -C /home/user_lerobot/libero-plus checkout ${LIBERO_PLUS_SHA} \
+    && cd /home/user_lerobot/libero-plus && uv pip install --no-cache --no-deps -e "." \
+    && (uv pip uninstall hf-libero 2>/dev/null || true)
+ENV PYTHONPATH="/home/user_lerobot/libero-plus:${PYTHONPATH}"
+
+# Perturbation textures/scenes: bddl_base_domain.py resolves XMLs via
+# DIR_PATH/../assets (package-relative, ignoring ~/.libero/config.yaml). All
+# 2402 tasks reference files that ship only in Sylvest/LIBERO-plus's
+# assets.zip (6.4 GB) under a deep author-internal prefix — extract and
+# flatten it under ${LIBERO_PLUS_ROOT}/assets.
+RUN python -c "\
+from huggingface_hub import hf_hub_download; \
+hf_hub_download(repo_id='Sylvest/LIBERO-plus', repo_type='dataset', \
+                filename='assets.zip', local_dir='/tmp/libero-plus-dl')" \
+    && unzip -q /tmp/libero-plus-dl/assets.zip -d /tmp/libero-plus-dl/extract \
+    && ASSETS_DIR=$(find /tmp/libero-plus-dl/extract -type d -name assets | head -1) \
+    && mv "${ASSETS_DIR}" ${LIBERO_PLUS_ROOT}/assets \
+    && rm -rf /tmp/libero-plus-dl
+
+# Point ~/.libero/config.yaml at the clone so LIBERO-plus's imports are
+# non-interactive (it calls input() when the config is missing).
+RUN mkdir -p /home/user_lerobot/.libero \
+    && printf "assets: ${LIBERO_PLUS_ROOT}/assets\nbddl_files: ${LIBERO_PLUS_ROOT}/bddl_files\ndatasets: ${LIBERO_PLUS_ROOT}/../datasets\ninit_states: ${LIBERO_PLUS_ROOT}/init_files\n" \
+       > /home/user_lerobot/.libero/config.yaml
+
+# Overlay the PR's source code on top of the nightly image.
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robocerebra

@@ -0,0 +1,43 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Benchmark image for RoboCerebra integration tests.
+# RoboCerebra reuses LIBERO's simulator (libero_10 suite) with a different
+# rename_map, so this image is identical to the LIBERO benchmark image —
+# extends the nightly GPU base with LIBERO assets + the PR's source code.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.robocerebra -t lerobot-benchmark-robocerebra .
+# Run:    docker run --gpus all --rm lerobot-benchmark-robocerebra lerobot-eval ...
+
+FROM huggingface/lerobot-gpu:latest
+
+# Pre-download lerobot/libero-assets from HF Hub so nothing is fetched at
+# runtime (which times out on CI). Point the libero config at the cached path.
+# libero/libero/__init__.py calls input() when ~/.libero/config.yaml is missing,
+# so we write the config before any libero import can happen.
+RUN LIBERO_DIR=$(python -c \
+      "import importlib.util, os; s=importlib.util.find_spec('libero'); \
+       print(os.path.join(os.path.dirname(s.origin), 'libero'))") && \
+    mkdir -p /home/user_lerobot/.libero && \
+    python -c "\
+from huggingface_hub import snapshot_download; \
+snapshot_download(repo_id='lerobot/libero-assets', repo_type='dataset', \
+                  local_dir='/home/user_lerobot/.libero/assets')" && \
+    printf "assets: /home/user_lerobot/.libero/assets\nbddl_files: ${LIBERO_DIR}/bddl_files\ndatasets: ${LIBERO_DIR}/../datasets\ninit_states: ${LIBERO_DIR}/init_files\n" \
+    > /home/user_lerobot/.libero/config.yaml
+
+# Overlay the PR's source code on top of the nightly image.
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robomme

@@ -0,0 +1,56 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Benchmark image for RoboMME integration tests.
+# Extends the nightly GPU image (which has lerobot[all]) with Vulkan system
+# libs for ManiSkill/SAPIEN and the robomme extra. robomme isn't in [all]
+# because mani-skill hard-pins gymnasium==0.29.1 and numpy<2.0.0 which
+# conflict with lerobot's defaults; both are safe at runtime:
+#   - gymnasium 0.29.x has the same 5-tuple step() API as 1.x (since 0.26)
+#   - numpy 1.26.4 is API-compatible with lerobot's actual usage.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-benchmark-robomme .
+# Run:    docker run --gpus all --rm lerobot-benchmark-robomme lerobot-eval ...
+
+FROM huggingface/lerobot-gpu:latest
+
+# NVIDIA Container Toolkit: expose Vulkan driver capability for headless rendering.
+ENV NVIDIA_DRIVER_CAPABILITIES=all \
+    VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json
+
+# ManiSkill/SAPIEN's renderer needs Vulkan, which isn't in the base image.
+USER root
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+         libvulkan1 libvulkan-dev mesa-vulkan-drivers \
+    && mkdir -p /usr/share/vulkan/icd.d \
+    && echo '{"file_format_version":"1.0.0","ICD":{"library_path":"libGLX_nvidia.so.0","api_version":"1.3.0"}}' \
+       > /usr/share/vulkan/icd.d/nvidia_icd.json \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+USER user_lerobot
+
+# Install smolvla + av-dep via the PR's pyproject, then layer robomme on top
+# with gymnasium/numpy overrides. robomme isn't a pyproject extra because its
+# mani-skill pin conflicts with lerobot's base numpy>=2 (see pyproject.toml).
+COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
+RUN printf 'gymnasium==0.29.1\nnumpy==1.26.4\n' > /tmp/robomme_override.txt \
+    && uv pip install --no-cache --override /tmp/robomme_override.txt \
+         -e ".[smolvla,av-dep]" \
+         "robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main" \
+    && python -c "import robomme; print('robomme import OK')"
+
+# Overlay the PR's source code on top of the nightly image.
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]

docker/Dockerfile.benchmark.robotwin

@@ -0,0 +1,138 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Benchmark image for RoboTwin 2.0 integration tests.
+# Extends the nightly GPU image with the RoboTwin simulator stack:
+#   sapien/mplib/pytorch3d + NVlabs CuRobo + embodiments.zip + objects.zip
+# (~3.96 GB of assets; background_texture.zip ~11 GB skipped for smoke eval).
+#
+# Build: docker build -f docker/Dockerfile.benchmark.robotwin -t lerobot-benchmark-robotwin .
+# Run:   docker run --gpus all --rm lerobot-benchmark-robotwin \
+#            lerobot-eval --env.type=robotwin --env.task=beat_block_hammer ...
+
+FROM huggingface/lerobot-gpu:latest
+
+ENV NVIDIA_DRIVER_CAPABILITIES=all \
+    VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json \
+    ROBOTWIN_ROOT=/opt/robotwin
+
+# The nightly base is CUDA -base (no compiler, no Vulkan loader). CuRobo's
+# `pip install -e .` runs nvcc, and SAPIEN renders via Vulkan — add both.
+USER root
+# Pinned upstream SHA for reproducible benchmark runs. Bump when we need
+# an upstream fix; don't rely on `main` drift.
+ARG ROBOTWIN_SHA=0aeea2d669c0f8516f4d5785f0aa33ba812c14b4
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+         cuda-nvcc-12-4 cuda-cudart-dev-12-4 \
+         libvulkan1 vulkan-tools \
+    && mkdir -p /usr/share/vulkan/icd.d \
+    && echo '{"file_format_version":"1.0.0","ICD":{"library_path":"libGLX_nvidia.so.0","api_version":"1.3.0"}}' \
+       > /usr/share/vulkan/icd.d/nvidia_icd.json \
+    && git clone https://github.com/RoboTwin-Platform/RoboTwin.git ${ROBOTWIN_ROOT} \
+    && git -C ${ROBOTWIN_ROOT} checkout ${ROBOTWIN_SHA} \
+    && chown -R user_lerobot:user_lerobot ${ROBOTWIN_ROOT} \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+USER user_lerobot
+
+# RoboTwin runtime deps (av is already in the base via [av-dep]).
+RUN uv pip install --no-cache \
+        "sapien==3.0.0b1" "mplib==0.2.1" "transforms3d==0.4.2" "trimesh==4.4.3" \
+        "open3d==0.19.0" "imageio==2.34.2" termcolor zarr pydantic h5py
+
+# pytorch3d has no universal wheel; must be built from source (~10 min, cached).
+RUN uv pip install --no-cache --no-build-isolation \
+        "git+https://github.com/facebookresearch/pytorch3d.git@stable"
+
+# CuRobo — NVlabs motion generator; TORCH_CUDA_ARCH_LIST must be set or the
+# build aborts on an empty arch list. RoboTwin's own installer pins v0.7.8,
+# which still exposes the v1 API (`curobo.types.math`) that RoboTwin imports.
+ARG CUROBO_REF=v0.7.8
+RUN cd ${ROBOTWIN_ROOT}/envs \
+    && git clone --branch ${CUROBO_REF} --depth 1 https://github.com/NVlabs/curobo.git \
+    && cd curobo \
+    && TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0" \
+       uv pip install -e . --no-build-isolation --no-cache
+
+# Upstream patches (mirror RoboTwin's script/_install.sh).
+# These patches target the exact versions pinned above; re-check when upgrading.
+# mplib==0.2.1: drop a broken `or collide` clause in planner.py.
+#   Safe to remove once mplib > 0.2.1 ships with the fix upstream.
+# sapien==3.0.0b1: fix URDF loader encoding + .srdf extension check.
+#   Safe to remove once sapien > 3.0.0b1 ships with the fix upstream.
+RUN python - <<'EOF'
+import pathlib, re, site
+for d in site.getsitepackages():
+    p = pathlib.Path(d) / "mplib" / "planner.py"
+    if p.exists():
+        p.write_text(re.sub(r"\bor collide\b", "", p.read_text(), count=1))
+        print(f"mplib patch applied: {p}")
+    p = pathlib.Path(d) / "sapien" / "wrapper" / "urdf_loader.py"
+    if p.exists():
+        src = p.read_text().replace(
+            "with open(srdf_path) as f:", 'with open(srdf_path, encoding="utf-8") as f:'
+        ).replace('"srdf"', '".srdf"')
+        p.write_text(src)
+        print(f"sapien patch applied: {p}")
+EOF
+
+# Simulation assets from TianxingChen/RoboTwin2.0: embodiments (~220 MB) +
+# objects (~3.74 GB). background_texture (~11 GB) is intentionally skipped.
+# The dataset is public — no auth token needed.
+RUN python - <<'EOF'
+import os, pathlib, zipfile
+from huggingface_hub import hf_hub_download
+
+assets_dir = pathlib.Path(os.environ["ROBOTWIN_ROOT"]) / "assets"
+assets_dir.mkdir(parents=True, exist_ok=True)
+for fname in ("embodiments.zip", "objects.zip"):
+    local = hf_hub_download(
+        repo_id="TianxingChen/RoboTwin2.0",
+        repo_type="dataset",
+        filename=fname,
+        local_dir=str(assets_dir),
+    )
+    with zipfile.ZipFile(local, "r") as z:
+        z.extractall(str(assets_dir))
+    pathlib.Path(local).unlink()
+EOF
+
+WORKDIR ${ROBOTWIN_ROOT}
+RUN python script/update_embodiment_config_path.py
+
+ENV PYTHONPATH="${ROBOTWIN_ROOT}"
+
+# Fail the image build early if the CuRobo package layout regresses. Importing
+# RoboTwin's planner here is too eager because CuRobo constructs CUDA-backed
+# defaults at import time, while Docker builds don't have access to an NVIDIA
+# driver.
+RUN python - <<'EOF'
+from pathlib import Path
+
+from curobo.types.math import Pose
+
+planner_src = (Path("/opt/robotwin/envs/robot/planner.py")).read_text()
+assert "from curobo.types.math import Pose as CuroboPose" in planner_src
+
+print("CuRobo import OK:", Pose.__name__)
+print("RoboTwin planner import references curobo.types.math")
+EOF
+
+# Return to the lerobot source directory (set by base image) before overlaying.
+WORKDIR /lerobot
+
+# Overlay the PR's source code on top of the nightly image.
+COPY --chown=user_lerobot:user_lerobot . .
+
+CMD ["/bin/bash"]

docker/Dockerfile.benchmark.vlabench

@@ -0,0 +1,99 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Benchmark image for VLABench integration tests.
+# Extends the nightly GPU image with the PR's source code and VLABench setup.
+#
+# Build:  docker build -f docker/Dockerfile.benchmark.vlabench -t lerobot-benchmark-vlabench .
+# Run:    docker run --gpus all --rm lerobot-benchmark-vlabench lerobot-eval ...
+
+FROM huggingface/lerobot-gpu:latest
+
+# Install VLABench from GitHub (not on PyPI) and pin MuJoCo/dm-control.
+# Shallow-clone without submodule recursion (nested SSH-only submodules fail in CI).
+# Editable install (-e) because VLABench/utils/ has no __init__.py, so
+# find_packages() omits it from wheels; editable mode uses the source tree directly.
+# rrt-algorithms has the same packaging issue (rrt/ dir missing __init__.py).
+# Patch: constant.py calls os.listdir on ~100 asset/obj/meshes/* dirs at import
+# time. Guard the call so missing dirs return [] instead of crashing (in case
+# the asset download is partial).
+#
+# Pinned upstream SHAs for reproducible benchmark runs. Bump when you need
+# an upstream fix; don't rely on `main`/`develop` drift.
+ARG VLABENCH_SHA=cf588fe60c0c7282174fe979f5913170cfe69017
+ARG RRT_ALGORITHMS_SHA=e51d95ee489a225220d6ae2a764c4111f6ba7d85
+RUN git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench && \
+    git -C ~/VLABench checkout ${VLABENCH_SHA} && \
+    git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms && \
+    git -C ~/rrt-algorithms checkout ${RRT_ALGORITHMS_SHA} && \
+    python3 -c "\
+import pathlib; \
+p = pathlib.Path.home() / 'VLABench/VLABench/configs/constant.py'; \
+t = p.read_text(); \
+p.write_text(t.replace( \
+    'subdirs = os.listdir(xml_dir)', \
+    'if not os.path.isdir(xml_dir): return []\n    subdirs = os.listdir(xml_dir)'))" && \
+    uv pip install --no-cache -e ~/VLABench -e ~/rrt-algorithms \
+      mujoco==3.2.2 dm-control==1.0.22 \
+      open3d colorlog scikit-learn openai gdown
+
+# Download VLABench mesh assets. Task configs reference object meshes
+# (obj/meshes/fruit/, containers/basket/, tablewares/plates/, etc.); without
+# them the task builder picks from an empty mesh list and crashes with
+# IndexError at task-build time (random.choice([]) in config_manager.py).
+#
+# Preferred source: an HF Hub mirror. Set VLABENCH_ASSETS_REPO at build time
+# (e.g. --build-arg VLABENCH_ASSETS_REPO=lerobot/vlabench-assets) and we'll
+# snapshot_download the repo into VLABench's assets dir. This is the reliable
+# path for CI — Google Drive frequently returns HTTP 429 ("Too many users have
+# viewed or downloaded this file recently") on shared academic files.
+#
+# After download we *validate* that at least one XML exists under each
+# task-critical subtree and fail the build loudly if not. Silent-empty asset
+# dirs are the #1 cause of VLABench runtime crashes in CI, so we surface them
+# here rather than after a 10-minute eval build.
+#
+# Fallback: VLABench's own gdown-based script. Best-effort only.
+ARG VLABENCH_ASSETS_REPO=""
+RUN ASSETS_DIR="$HOME/VLABench/VLABench/assets" && \
+    if [ -n "${VLABENCH_ASSETS_REPO}" ]; then \
+        echo "Downloading VLABench assets from HF Hub: ${VLABENCH_ASSETS_REPO}" && \
+        uv pip install --no-cache "huggingface_hub[hf_xet]>=0.26" && \
+        python -c "from huggingface_hub import snapshot_download; \
+p = snapshot_download(repo_id='${VLABENCH_ASSETS_REPO}', repo_type='dataset', \
+    local_dir='${ASSETS_DIR}', allow_patterns=['obj/**', 'scenes/**']); \
+print('snapshot_download returned:', p)"; \
+    else \
+        echo "No VLABENCH_ASSETS_REPO set — falling back to gdown" && \
+        python ~/VLABench/scripts/download_assets.py --choice all; \
+    fi && \
+    python -c "\
+from pathlib import Path; \
+import sys; \
+root = Path('${ASSETS_DIR}'); \
+checks = ['obj/meshes/tablewares/plates', 'obj/meshes/containers/basket', 'obj/meshes/fruit', 'obj/meshes/containers/tray']; \
+failed = []; \
+print(f'Validating VLABench assets under {root}'); \
+[print(f'  {c}: {len(list((root/c).rglob(\"*.xml\")))} XMLs') for c in checks]; \
+[failed.append(c) for c in checks if not any((root/c).rglob('*.xml'))]; \
+sys.exit(f'Empty asset dirs (no *.xml): {failed}') if failed else print('All asset dirs populated.')"
+
+# Overlay the PR's source code on top of the nightly image.
+COPY --chown=user_lerobot:user_lerobot . .
+
+# Re-install lerobot editably so the new source (with VLABenchEnv registration
+# and updated obs handling) replaces the stale package baked into the nightly image.
+RUN uv pip install --no-cache --no-deps -e .
+
+CMD ["/bin/bash"]

docs/source/_toctree.yml

@@ -77,12 +77,22 @@
     title: Adding a New Benchmark
   - local: libero
     title: LIBERO
+  - local: libero_plus
+    title: LIBERO-plus
   - local: metaworld
     title: Meta-World
+  - local: robotwin
+    title: RoboTwin 2.0
   - local: robocasa
     title: RoboCasa365
+  - local: robocerebra
+    title: RoboCerebra
+  - local: robomme
+    title: RoboMME
   - local: envhub_isaaclab_arena
     title: NVIDIA IsaacLab Arena Environments
+  - local: vlabench
+    title: VLABench
   title: "Benchmarks"
 - sections:
   - local: introduction_processors

docs/source/libero_plus.mdx

@@ -0,0 +1,188 @@
+# LIBERO-plus
+
+LIBERO-plus is a **robustness benchmark** for Vision-Language-Action (VLA) models built on top of [LIBERO](./libero). It systematically stress-tests policies by applying **seven independent perturbation dimensions** to the original LIBERO task set, exposing failure modes that standard benchmarks miss.
+
+- Paper: [In-depth Robustness Analysis of Vision-Language-Action Models](https://arxiv.org/abs/2510.13626)
+- GitHub: [sylvestf/LIBERO-plus](https://github.com/sylvestf/LIBERO-plus)
+- Dataset: [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
+
+![An overview of the LIBERO-plus benchmark perturbation dimensions](https://github.com/sylvestf/LIBERO-plus/raw/main/static/images/libero-plus.jpg)
+
+## Perturbation dimensions
+
+LIBERO-plus creates ~10 000 task variants by perturbing each original LIBERO task along these axes:
+
+| Dimension             | What changes                                          |
+| --------------------- | ----------------------------------------------------- |
+| Objects layout        | Target position, presence of confounding objects      |
+| Camera viewpoints     | Camera position, orientation, field-of-view           |
+| Robot initial states  | Manipulator start pose                                |
+| Language instructions | LLM-rewritten task description (paraphrase / synonym) |
+| Light conditions      | Intensity, direction, color, shadow                   |
+| Background textures   | Scene surface and object appearance                   |
+| Sensor noise          | Photometric distortions and image degradation         |
+
+## Available task suites
+
+LIBERO-plus covers the same five suites as LIBERO:
+
+| Suite          | CLI name         | Tasks | Max steps | Description                                        |
+| -------------- | ---------------- | ----- | --------- | -------------------------------------------------- |
+| LIBERO-Spatial | `libero_spatial` | 10    | 280       | Tasks requiring reasoning about spatial relations  |
+| LIBERO-Object  | `libero_object`  | 10    | 280       | Tasks centered on manipulating different objects   |
+| LIBERO-Goal    | `libero_goal`    | 10    | 300       | Goal-conditioned tasks with changing targets       |
+| LIBERO-90      | `libero_90`      | 90    | 400       | Short-horizon tasks from the LIBERO-100 collection |
+| LIBERO-Long    | `libero_10`      | 10    | 520       | Long-horizon tasks from the LIBERO-100 collection  |
+
+<Tip warning={true}>
+  Installing LIBERO-plus **replaces** vanilla LIBERO — it uninstalls `hf-libero`
+  so that `import libero` resolves to the LIBERO-plus fork. You cannot have both
+  installed at the same time. To switch back to vanilla LIBERO, uninstall the
+  fork and reinstall with `pip install -e ".[libero]"`.
+</Tip>
+
+## Installation
+
+### System dependencies (Linux only)
+
+```bash
+sudo apt install libexpat1 libfontconfig1-dev libmagickwand-dev
+```
+
+### Python package
+
+```bash
+pip install -e ".[libero]" "robosuite==1.4.1" bddl easydict mujoco wand scikit-image gym
+git clone https://github.com/sylvestf/LIBERO-plus.git
+cd LIBERO-plus && pip install --no-deps -e .
+pip uninstall -y hf-libero  # so `import libero` resolves to the fork
+```
+
+LIBERO-plus is installed from its GitHub fork rather than a pyproject extra — the fork ships as a namespace package that pip can't handle, so it must be cloned and added to `PYTHONPATH`. See `docker/Dockerfile.benchmark.libero_plus` for the canonical install. MuJoCo is required, so only Linux is supported.
+
+<Tip>
+Set the MuJoCo rendering backend before running evaluation:
+
+```bash
+export MUJOCO_GL=egl   # headless / HPC / cloud
+```
+
+</Tip>
+
+### Download LIBERO-plus assets
+
+LIBERO-plus ships its extended asset pack separately. Download `assets.zip` from the [Hugging Face dataset](https://huggingface.co/datasets/Sylvest/LIBERO-plus/tree/main) and extract it into the LIBERO-plus package directory:
+
+```bash
+# After installing the package, find where it was installed:
+python -c "import libero; print(libero.__file__)"
+# Then extract assets.zip into <package_root>/libero/assets/
+```
+
+## Evaluation
+
+### Default evaluation (recommended)
+
+Evaluate across the four standard suites (10 episodes per task):
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=libero_plus \
+  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --env.max_parallel_tasks=1
+```
+
+### Single-suite evaluation
+
+Evaluate on one LIBERO-plus suite:
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=libero_plus \
+  --env.task=libero_spatial \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10
+```
+
+- `--env.task` picks the suite (`libero_spatial`, `libero_object`, etc.).
+- `--env.task_ids` restricts to specific task indices (`[0]`, `[1,2,3]`, etc.). Omit to run all tasks in the suite.
+- `--eval.batch_size` controls how many environments run in parallel.
+- `--eval.n_episodes` sets how many episodes to run per task.
+
+### Multi-suite evaluation
+
+Benchmark a policy across multiple suites at once by passing a comma-separated list:
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=libero_plus \
+  --env.task=libero_spatial,libero_object \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10
+```
+
+### Control mode
+
+LIBERO-plus supports two control modes — `relative` (default) and `absolute`. Different VLA checkpoints are trained with different action parameterizations, so make sure the mode matches your policy:
+
+```bash
+--env.control_mode=relative   # or "absolute"
+```
+
+### Policy inputs and outputs
+
+**Observations:**
+
+- `observation.state` — 8-dim proprioceptive features (eef position, axis-angle orientation, gripper qpos)
+- `observation.images.image` — main camera view (`agentview_image`), HWC uint8
+- `observation.images.image2` — wrist camera view (`robot0_eye_in_hand_image`), HWC uint8
+
+**Actions:**
+
+- Continuous control in `Box(-1, 1, shape=(7,))` — 6D end-effector delta + 1D gripper
+
+### Recommended evaluation episodes
+
+For reproducible benchmarking, use **10 episodes per task** across all four standard suites (Spatial, Object, Goal, Long). This gives 400 total episodes and matches the protocol used for published results.
+
+## Training
+
+### Dataset
+
+A LeRobot-format training dataset for LIBERO-plus is available at:
+
+- [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
+
+### Example training command
+
+```bash
+lerobot-train \
+    --policy.type=smolvla \
+    --policy.repo_id=${HF_USER}/smolvla_libero_plus \
+    --policy.load_vlm_weights=true \
+    --dataset.repo_id=lerobot/libero_plus \
+    --env.type=libero_plus \
+    --env.task=libero_spatial \
+    --output_dir=./outputs/ \
+    --steps=100000 \
+    --batch_size=4 \
+    --eval.batch_size=1 \
+    --eval.n_episodes=1 \
+    --eval_freq=1000
+```
+
+## Relationship to LIBERO
+
+LIBERO-plus is a drop-in extension of LIBERO:
+
+- Same Python gym interface (`LiberoEnv`, `LiberoProcessorStep`)
+- Same camera names and observation/action format
+- Same task suite names
+- Installs under the same `libero` Python package name (different GitHub repo)
+
+To use the original LIBERO benchmark, see [LIBERO](./libero) and use `--env.type=libero`.

docs/source/robocerebra.mdx

@@ -0,0 +1,99 @@
+# RoboCerebra
+
+[RoboCerebra](https://robocerebra-project.github.io/) is a long-horizon manipulation benchmark that evaluates **high-level reasoning, planning, and memory** in VLAs. Episodes chain multiple sub-goals with language-grounded intermediate instructions, built on top of LIBERO's simulator stack (MuJoCo + robosuite, Franka Panda 7-DOF).
+
+- Paper: [RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation](https://arxiv.org/abs/2506.06677)
+- Project website: [robocerebra-project.github.io](https://robocerebra-project.github.io/)
+- Dataset: [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) — LeRobot v3.0, 6,660 episodes / 571,116 frames at 20 fps, 1,728 language-grounded sub-tasks.
+- Pretrained policy: [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra)
+
+## Available tasks
+
+RoboCerebra reuses LIBERO's simulator, so evaluation runs against the LIBERO `libero_10` long-horizon suite:
+
+| Suite     | CLI name    | Tasks | Description                                                   |
+| --------- | ----------- | ----- | ------------------------------------------------------------- |
+| LIBERO-10 | `libero_10` | 10    | Long-horizon kitchen/living room tasks chaining 3–6 sub-goals |
+
+Each RoboCerebra episode in the dataset is segmented into multiple sub-tasks with natural-language instructions, which the unified dataset exposes as independent supervision signals.
+
+## Installation
+
+RoboCerebra piggybacks on LIBERO, so the `libero` extra is all you need:
+
+```bash
+pip install -e ".[libero]"
+```
+
+<Tip>
+RoboCerebra requires Linux (MuJoCo / robosuite). Set the rendering backend before training or evaluation:
+
+```bash
+export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
+```
+
+</Tip>
+
+## Evaluation
+
+RoboCerebra eval runs against LIBERO's `libero_10` suite with RoboCerebra's camera naming (`image` + `wrist_image`) and an extra empty-camera slot so a three-view-trained policy receives the expected input layout:
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/smolvla_robocerebra \
+  --env.type=libero \
+  --env.task=libero_10 \
+  --env.fps=20 \
+  --env.obs_type=pixels_agent_pos \
+  --env.observation_height=256 \
+  --env.observation_width=256 \
+  '--env.camera_name_mapping={"agentview_image": "image", "robot0_eye_in_hand_image": "wrist_image"}' \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.wrist_image": "observation.images.camera2"}' \
+  --policy.empty_cameras=1
+```
+
+### Recommended evaluation episodes
+
+**10 episodes per task** across the `libero_10` suite (100 total) for reproducible benchmarking. Matches the protocol used in the RoboCerebra paper.
+
+## Policy inputs and outputs
+
+**Observations:**
+
+- `observation.state` — 8-dim proprioceptive state (7 joint positions + gripper)
+- `observation.images.image` — third-person view, 256×256 HWC uint8
+- `observation.images.wrist_image` — wrist-mounted camera view, 256×256 HWC uint8
+
+**Actions:**
+
+- Continuous control in `Box(-1, 1, shape=(7,))` — end-effector delta (6D) + gripper (1D)
+
+## Training
+
+The unified dataset at [`lerobot/robocerebra_unified`](https://huggingface.co/datasets/lerobot/robocerebra_unified) exposes two RGB streams and language-grounded sub-task annotations:
+
+| Feature                          | Shape         | Description          |
+| -------------------------------- | ------------- | -------------------- |
+| `observation.images.image`       | (256, 256, 3) | Third-person view    |
+| `observation.images.wrist_image` | (256, 256, 3) | Wrist-mounted camera |
+| `observation.state`              | (8,)          | Joint pos + gripper  |
+| `action`                         | (7,)          | EEF delta + gripper  |
+
+Fine-tune a SmolVLA base on it:
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/smolvla_base \
+  --dataset.repo_id=lerobot/robocerebra_unified \
+  --env.type=libero \
+  --env.task=libero_10 \
+  --output_dir=outputs/smolvla_robocerebra
+```
+
+## Reproducing published results
+
+The released checkpoint [`lerobot/smolvla_robocerebra`](https://huggingface.co/lerobot/smolvla_robocerebra) was trained on `lerobot/robocerebra_unified` and evaluated with the command in the [Evaluation](#evaluation) section. CI runs the same command with `--eval.n_episodes=1` as a smoke test on every PR touching the benchmark.

docs/source/robomme.mdx

@@ -0,0 +1,130 @@
+# RoboMME
+
+[RoboMME](https://robomme.github.io) is a memory-augmented manipulation benchmark built on ManiSkill (SAPIEN). It evaluates a robot's ability to retain and use information across an episode — counting, object permanence, reference, and imitation.
+
+- **16 tasks** across 4 memory-skill suites
+- **1,600 training demos** (100 per task, 50 val, 50 test)
+- **Dataset**: [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) — LeRobot v3.0, 768K frames at 10 fps
+- **Simulator**: ManiSkill / SAPIEN, Panda arm, Linux only
+
+![RoboMME benchmark tasks overview](https://cdn-thumbnails.huggingface.co/social-thumbnails/papers/2603.04639/gradient.png)
+
+## Tasks
+
+| Suite                             | Tasks                                                         |
+| --------------------------------- | ------------------------------------------------------------- |
+| **Counting** (temporal memory)    | BinFill, PickXtimes, SwingXtimes, StopCube                    |
+| **Permanence** (spatial memory)   | VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap  |
+| **Reference** (object memory)     | PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder |
+| **Imitation** (procedural memory) | MoveCube, InsertPeg, PatternLock, RouteStick                  |
+
+## Installation
+
+> RoboMME requires **Linux** (ManiSkill/SAPIEN uses Vulkan rendering). Docker is recommended to isolate dependency conflicts.
+
+### Native (Linux)
+
+```bash
+pip install --override <(printf 'gymnasium==0.29.1\nnumpy==1.26.4\n') \
+  -e '.[smolvla,av-dep]' \
+  'robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main'
+```
+
+> **Dependency note**: `mani-skill` (pulled by `robomme`) pins `gymnasium==0.29.1` and `numpy<2.0.0`, which conflict with lerobot's base `numpy>=2.0.0`. That's why `robomme` is not a pyproject extra — use the override install above, or the Docker approach below to avoid conflicts entirely.
+
+### Docker (recommended)
+
+```bash
+# Build base image first (from repo root)
+docker build -f docker/Dockerfile.eval-base -t lerobot-eval-base .
+
+# Build RoboMME eval image (applies gymnasium + numpy pin overrides)
+docker build -f docker/Dockerfile.benchmark.robomme -t lerobot-robomme .
+```
+
+The `docker/Dockerfile.benchmark.robomme` image overrides `gymnasium==0.29.1` and `numpy==1.26.4` after lerobot's install. Both versions are runtime-safe for lerobot's actual API usage.
+
+## Running Evaluation
+
+### Default (single task, single episode)
+
+```bash
+lerobot-eval \
+    --policy.path=<your_policy_repo> \
+    --env.type=robomme \
+    --env.task=PickXtimes \
+    --env.dataset_split=test \
+    --env.task_ids=[0] \
+    --eval.batch_size=1 \
+    --eval.n_episodes=1
+```
+
+### Multi-task evaluation
+
+Evaluate multiple tasks in one run by comma-separating task names. Use `task_ids` to control which episodes are evaluated per task. Recommended: 50 episodes per task for the test split.
+
+```bash
+lerobot-eval \
+    --policy.path=<your_policy_repo> \
+    --env.type=robomme \
+    --env.task=PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
+    --env.dataset_split=test \
+    --env.task_ids=[0,1,2,3,4,5,6,7,8,9] \
+    --eval.batch_size=1 \
+    --eval.n_episodes=50
+```
+
+### Key CLI options for `env.type=robomme`
+
+| Option               | Default       | Description                                        |
+| -------------------- | ------------- | -------------------------------------------------- |
+| `env.task`           | `PickXtimes`  | Any of the 16 task names above (comma-separated)   |
+| `env.dataset_split`  | `test`        | `train`, `val`, or `test`                          |
+| `env.action_space`   | `joint_angle` | `joint_angle` (8-D) or `ee_pose` (7-D)             |
+| `env.episode_length` | `300`         | Max steps per episode                              |
+| `env.task_ids`       | `null`        | List of episode indices to evaluate (null = `[0]`) |
+
+## Dataset
+
+The dataset [`lerobot/robomme`](https://huggingface.co/datasets/lerobot/robomme) is in **LeRobot v3.0 format** and can be loaded directly:
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+dataset = LeRobotDataset("lerobot/robomme")
+```
+
+### Dataset features
+
+| Feature            | Shape         | Description                     |
+| ------------------ | ------------- | ------------------------------- |
+| `image`            | (256, 256, 3) | Front camera RGB                |
+| `wrist_image`      | (256, 256, 3) | Wrist camera RGB                |
+| `actions`          | (8,)          | Joint angles + gripper          |
+| `state`            | (8,)          | Joint positions + gripper state |
+| `simple_subgoal`   | str           | High-level language annotation  |
+| `grounded_subgoal` | str           | Grounded language annotation    |
+| `episode_index`    | int           | Episode ID                      |
+| `frame_index`      | int           | Frame within episode            |
+
+### Feature key alignment (training)
+
+The env wrapper exposes `pixels/image` and `pixels/wrist_image` as observation keys. The `features_map` in `RoboMMEEnv` maps these to `observation.images.image` and `observation.images.wrist_image` for the policy. State is exposed as `agent_pos` and maps to `observation.state`.
+
+The dataset's `image` and `wrist_image` columns already align with the policy input keys, so no renaming is needed when fine-tuning.
+
+## Action Spaces
+
+| Type          | Dim | Description                                               |
+| ------------- | --- | --------------------------------------------------------- |
+| `joint_angle` | 8   | 7 joint angles + 1 gripper (−1 closed, +1 open, absolute) |
+| `ee_pose`     | 7   | xyz + roll/pitch/yaw + gripper                            |
+
+Set via `--env.action_space=joint_angle` (default) or `--env.action_space=ee_pose`.
+
+## Platform Notes
+
+- **Linux only**: ManiSkill requires SAPIEN/Vulkan. macOS and Windows are not supported.
+- **GPU recommended**: Rendering is CPU-capable but slow; CUDA + Vulkan gives full speed.
+- **gymnasium / numpy conflict**: See installation note above. Docker image handles this automatically.
+- **ManiSkill fork**: `robomme` depends on a specific ManiSkill fork (`YinpeiDai/ManiSkill`), pulled in automatically via the `robomme` package.

docs/source/robotwin.mdx

@@ -0,0 +1,223 @@
+# RoboTwin 2.0
+
+RoboTwin 2.0 is a **large-scale dual-arm manipulation benchmark** built on the SAPIEN physics engine. It provides a standardized evaluation protocol for bimanual robotic policies across 50 tasks (as of upstream `main`) with strong domain randomization (clutter, lighting, background, tabletop height, and language instructions).
+
+- Paper: [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://arxiv.org/abs/2506.18088)
+- GitHub: [RoboTwin-Platform/RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin)
+- Leaderboard: [robotwin-platform.github.io/leaderboard](https://robotwin-platform.github.io/leaderboard)
+- Dataset: [lerobot/robotwin_unified](https://huggingface.co/datasets/lerobot/robotwin_unified)
+
+![RoboTwin 2.0 benchmark overview](https://www.aitntnews.com/pictures/2025/7/8/9a7f79cb-5ba9-11f0-8581-fa163e47d677.png)
+
+## Overview
+
+| Property      | Value                                                    |
+| ------------- | -------------------------------------------------------- |
+| Tasks         | 50 dual-arm manipulation tasks                           |
+| Robot         | Aloha-AgileX bimanual (14 DOF, 7 per arm)                |
+| Action space  | 14-dim joint-space, continuous in `[-1, 1]`              |
+| Cameras       | `head_camera`, `left_camera`, `right_camera`             |
+| Simulator     | SAPIEN (not MuJoCo)                                      |
+| Eval protocol | 100 episodes/task, 50 demo_clean demonstrations          |
+| Eval settings | **Easy** (`demo_clean`) and **Hard** (`demo_randomized`) |
+
+## Available tasks
+
+RoboTwin 2.0 ships 50 dual-arm manipulation tasks in its upstream `envs/` directory. The canonical list is the `ROBOTWIN_TASKS` tuple in `src/lerobot/envs/robotwin.py`, mirrored verbatim from the upstream repo. Example tasks:
+
+| Task                     | CLI name                 | Category          |
+| ------------------------ | ------------------------ | ----------------- |
+| Beat block with hammer   | `beat_block_hammer`      | Tool use          |
+| Click bell / alarm clock | `click_bell`             | Precision press   |
+| Stack blocks (2 / 3)     | `stack_blocks_two/three` | Stacking          |
+| Stack bowls (2 / 3)      | `stack_bowls_two/three`  | Stacking          |
+| Handover block / mic     | `handover_block`         | Bimanual coord.   |
+| Lift pot                 | `lift_pot`               | Bimanual lift     |
+| Shake bottle             | `shake_bottle`           | Continuous motion |
+| Turn switch              | `turn_switch`            | Articulated obj   |
+| Stamp seal               | `stamp_seal`             | Precision place   |
+| Scan object              | `scan_object`            | Mobile manip.     |
+
+Pass a comma-separated list to `--env.task` to run multiple tasks in a single eval sweep.
+
+<Tip warning={true}>
+  `open_laptop` is currently broken upstream (its `check_success()` uses
+  `self.arm_tag`, which is only set inside the scripted-expert `play_once()`
+  path and therefore unavailable during normal policy eval). Avoid it until the
+  upstream bug is fixed, or patch the task to default `self.arm_tag = "left"` in
+  `load_actors()`.
+</Tip>
+
+## Dataset
+
+The RoboTwin 2.0 dataset is available in **LeRobot v3.0 format** on the Hugging Face Hub:
+
+```
+lerobot/robotwin_unified
+```
+
+It contains over 100,000 pre-collected trajectories across all 50 tasks (79.6 GB, Apache 2.0 license). No format conversion is needed — it is already in the correct LeRobot v3.0 schema with video observations and action labels.
+
+You can load it directly with the HF Datasets library:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("lerobot/robotwin_unified", split="train")
+```
+
+## Installation
+
+RoboTwin 2.0 requires **Linux** with an NVIDIA GPU (CUDA 12.1 recommended). Installation takes approximately 20 minutes.
+
+### 1. Create a conda environment
+
+```bash
+conda create -n robotwin python=3.10 -y
+conda activate robotwin
+```
+
+### 2. Install LeRobot
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e "."
+```
+
+### 3. Install RoboTwin 2.0
+
+```bash
+git clone https://github.com/RoboTwin-Platform/RoboTwin.git
+cd RoboTwin
+bash script/_install.sh
+bash script/_download_assets.sh
+```
+
+The install script handles all Python dependencies including SAPIEN, CuRobo, mplib, and pytorch3d.
+
+<Tip warning={true}>
+If the automated install fails, install manually:
+
+```bash
+pip install -r requirements.txt
+pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
+cd envs && git clone https://github.com/NVlabs/curobo.git && cd curobo
+pip install -e . --no-build-isolation
+```
+
+Then apply the required mplib fix: in `mplib/planner.py` line 807, remove `or collide` from the conditional.
+
+</Tip>
+
+### 4. Add RoboTwin to PYTHONPATH
+
+The RoboTwin task modules must be importable by LeRobot. From within the `RoboTwin/` directory:
+
+```bash
+export PYTHONPATH="${PYTHONPATH}:$(pwd)"
+```
+
+Add this to your shell profile to make it permanent.
+
+## Evaluation
+
+### Standard evaluation (recommended)
+
+Evaluate a policy on a single task with the official protocol (100 episodes):
+
+```bash
+lerobot-eval \
+  --policy.path="your-hf-policy-id" \
+  --env.type=robotwin \
+  --env.task=beat_block_hammer \
+  --eval.batch_size=1 \
+  --eval.n_episodes=100
+```
+
+### Single-task quick check
+
+```bash
+lerobot-eval \
+  --policy.path="your-hf-policy-id" \
+  --env.type=robotwin \
+  --env.task=beat_block_hammer \
+  --eval.batch_size=1 \
+  --eval.n_episodes=5
+```
+
+### Multi-task sweep
+
+Evaluate on several tasks in one run:
+
+```bash
+lerobot-eval \
+  --policy.path="your-hf-policy-id" \
+  --env.type=robotwin \
+  --env.task=beat_block_hammer,click_bell,handover_block,stack_blocks_two \
+  --eval.batch_size=1 \
+  --eval.n_episodes=100
+```
+
+### Full benchmark (all 50 tasks)
+
+```bash
+lerobot-eval \
+  --policy.path="your-hf-policy-id" \
+  --env.type=robotwin \
+  --env.task=adjust_bottle,beat_block_hammer,blocks_ranking_rgb,blocks_ranking_size,click_alarmclock,click_bell,dump_bin_bigbin,grab_roller,handover_block,handover_mic,hanging_mug,lift_pot,move_can_pot,move_pillbottle_pad,move_playingcard_away,move_stapler_pad,open_microwave,pick_diverse_bottles,pick_dual_bottles,place_a2b_left,place_a2b_right,place_bread_basket,place_bread_skillet,place_burger_fries,place_can_basket,place_cans_plasticbox,place_container_plate,place_dual_shoes,place_empty_cup,place_fan,place_mouse_pad,place_object_basket,place_object_scale,place_object_stand,place_phone_stand,place_shoe,press_stapler,put_bottles_dustbin,put_object_cabinet,rotate_qrcode,scan_object,shake_bottle,shake_bottle_horizontally,stack_blocks_three,stack_blocks_two,stack_bowls_three,stack_bowls_two,stamp_seal,turn_switch \
+  --eval.batch_size=1 \
+  --eval.n_episodes=100
+```
+
+<Tip>
+  `open_laptop` is intentionally omitted above because of the upstream
+  `self.arm_tag` bug (see the **Available tasks** section). Re-add it once the
+  upstream fix lands.
+</Tip>
+
+## Camera configuration
+
+By default, all three cameras are included:
+
+| Camera key     | Description                    |
+| -------------- | ------------------------------ |
+| `head_camera`  | Torso-mounted overhead view    |
+| `left_camera`  | Left arm wrist-mounted camera  |
+| `right_camera` | Right arm wrist-mounted camera |
+
+To use a subset of cameras, override `--env.camera_names`:
+
+```bash
+lerobot-eval \
+  --policy.path="your-hf-policy-id" \
+  --env.type=robotwin \
+  --env.task=beat_block_hammer \
+  --env.camera_names="head_camera,left_camera" \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10
+```
+
+## Environment config reference
+
+Key parameters for `RoboTwinEnvConfig`:
+
+| Parameter            | Default                                  | Description                        |
+| -------------------- | ---------------------------------------- | ---------------------------------- |
+| `task`               | `"beat_block_hammer"`                    | Comma-separated task name(s)       |
+| `fps`                | `25`                                     | Simulation FPS                     |
+| `episode_length`     | `300`                                    | Max steps per episode              |
+| `obs_type`           | `"pixels_agent_pos"`                     | `"pixels"` or `"pixels_agent_pos"` |
+| `camera_names`       | `"head_camera,left_camera,right_camera"` | Comma-separated active cameras     |
+| `observation_height` | `240`                                    | Camera pixel height                |
+| `observation_width`  | `320`                                    | Camera pixel width                 |
+
+## Leaderboard submission
+
+Results can be submitted to the [RoboTwin 2.0 leaderboard](https://robotwin-platform.github.io/leaderboard). The official protocol requires:
+
+- Training on 50 `demo_clean` demonstrations per task
+- Evaluating 100 episodes per task
+- Reporting success rate separately for **Easy** (`demo_clean`) and **Hard** (`demo_randomized`) settings
+
+For submission instructions, refer to the [RoboTwin 2.0 documentation](https://robotwin-platform.github.io/doc/).

docs/source/vlabench.mdx

@@ -0,0 +1,176 @@
+# VLABench
+
+[VLABench](https://github.com/OpenMOSS/VLABench) is a large-scale benchmark for **language-conditioned robotic manipulation with long-horizon reasoning**. The upstream suite covers 100 task categories across 2,000+ objects and evaluates six dimensions of robot intelligence: mesh & texture understanding, spatial reasoning, world-knowledge transfer, semantic instruction comprehension, physical-law understanding, and long-horizon planning. Built on MuJoCo / dm_control with a Franka Panda 7-DOF arm. LeRobot exposes **43 of these tasks** through `--env.task` (21 primitives + 22 composites, see [Available tasks](#available-tasks) below).
+
+- Paper: [VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning](https://arxiv.org/abs/2412.18194)
+- GitHub: [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench)
+- Project website: [vlabench.github.io](https://vlabench.github.io)
+- Pretrained policy: [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench)
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/vlabench.png"
+  alt="VLABench benchmark overview"
+  width="85%"
+/>
+
+## Available tasks
+
+VLABench ships two task suites covering **43 task categories** in LeRobot's `--env.task` surface:
+
+| Suite     | CLI name    | Tasks | Description                                                      |
+| --------- | ----------- | ----- | ---------------------------------------------------------------- |
+| Primitive | `primitive` | 21    | Single / few-skill combinations (select, insert, physics QA)     |
+| Composite | `composite` | 22    | Multi-step reasoning and long-horizon planning (cook, rearrange) |
+
+**Primitive tasks:** `select_fruit`, `select_toy`, `select_chemistry_tube`, `add_condiment`, `select_book`, `select_painting`, `select_drink`, `insert_flower`, `select_billiards`, `select_ingredient`, `select_mahjong`, `select_poker`, and physical-reasoning tasks (`density_qa`, `friction_qa`, `magnetism_qa`, `reflection_qa`, `simple_cuestick_usage`, `simple_seesaw_usage`, `sound_speed_qa`, `thermal_expansion_qa`, `weight_qa`).
+
+**Composite tasks:** `cluster_billiards`, `cluster_book`, `cluster_drink`, `cluster_toy`, `cook_dishes`, `cool_drink`, `find_unseen_object`, `get_coffee`, `hammer_nail`, `heat_food`, `make_juice`, `play_mahjong`, `play_math_game`, `play_poker`, `play_snooker`, `rearrange_book`, `rearrange_chemistry_tube`, `set_dining_table`, `set_study_table`, `store_food`, `take_chemistry_experiment`, `use_seesaw_complex`.
+
+`--env.task` accepts three forms:
+
+- a single task name (`select_fruit`)
+- a comma-separated list (`select_fruit,heat_food`)
+- a suite shortcut (`primitive`, `composite`, or `primitive,composite`)
+
+## Installation
+
+VLABench is **not on PyPI** — its only distribution is the [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench) GitHub repo — so LeRobot does not expose a `vlabench` extra. Install it manually as an editable clone, alongside the MuJoCo / dm_control pins VLABench needs, then fetch the mesh assets:
+
+```bash
+# After following the standard LeRobot installation instructions.
+
+git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench
+git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms
+pip install -e ~/VLABench -e ~/rrt-algorithms
+pip install "mujoco==3.2.2" "dm-control==1.0.22" \
+            open3d colorlog scikit-learn openai gdown
+
+python ~/VLABench/scripts/download_assets.py
+```
+
+<Tip>
+VLABench requires Linux (`sys_platform == 'linux'`) and Python 3.10+. Set the MuJoCo rendering backend before running:
+
+```bash
+export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
+```
+
+</Tip>
+
+## Evaluation
+
+All eval snippets below mirror the command CI runs (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps VLABench's `image` / `second_image` / `wrist_image` camera keys onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_vlabench` policy was trained on.
+
+### Single-task evaluation (recommended for quick iteration)
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/smolvla_vlabench \
+  --env.type=vlabench \
+  --env.task=select_fruit \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
+```
+
+### Multi-task evaluation
+
+Pass a comma-separated list of tasks:
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/smolvla_vlabench \
+  --env.type=vlabench \
+  --env.task=select_fruit,select_toy,add_condiment,heat_food \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
+```
+
+### Suite-wide evaluation
+
+Run an entire suite (all 21 primitives or all 22 composites):
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/smolvla_vlabench \
+  --env.type=vlabench \
+  --env.task=primitive \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  --env.max_parallel_tasks=1 \
+  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
+```
+
+Or both suites:
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/smolvla_vlabench \
+  --env.type=vlabench \
+  --env.task=primitive,composite \
+  --eval.batch_size=1 \
+  --eval.n_episodes=10 \
+  --eval.use_async_envs=false \
+  --policy.device=cuda \
+  --env.max_parallel_tasks=1 \
+  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
+```
+
+### Recommended evaluation episodes
+
+**10 episodes per task** for reproducible benchmarking (210 total for the full primitive suite, 220 for composite). Matches the protocol in the VLABench paper.
+
+## Policy inputs and outputs
+
+**Observations:**
+
+- `observation.state` — 7-dim end-effector state (position xyz + Euler xyz + gripper)
+- `observation.images.image` — front camera, 480×480 HWC uint8
+- `observation.images.second_image` — second camera, 480×480 HWC uint8
+- `observation.images.wrist_image` — wrist camera, 480×480 HWC uint8
+
+**Actions:**
+
+- Continuous control in `Box(-1, 1, shape=(7,))` — 3D position + 3D Euler orientation + 1D gripper.
+
+## Training
+
+### Datasets
+
+Pre-collected VLABench datasets in LeRobot format on the Hub:
+
+- [`VLABench/vlabench_primitive_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_primitive_ft_lerobot_video) — 5,000 episodes, 128 tasks, 480×480 images.
+- [`VLABench/vlabench_composite_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_composite_ft_lerobot_video) — 5,977 episodes, 167 tasks, 224×224 images.
+
+### Example training command
+
+Fine-tune a SmolVLA base on the primitive suite:
+
+```bash
+lerobot-train \
+  --policy.type=smolvla \
+  --policy.repo_id=${HF_USER}/smolvla_vlabench_primitive \
+  --policy.load_vlm_weights=true \
+  --policy.push_to_hub=true \
+  --dataset.repo_id=VLABench/vlabench_primitive_ft_lerobot_video \
+  --env.type=vlabench \
+  --env.task=select_fruit \
+  --output_dir=./outputs/smolvla_vlabench_primitive \
+  --steps=100000 \
+  --batch_size=4 \
+  --eval_freq=5000 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=1 \
+  --save_freq=10000
+```
+
+## Reproducing published results
+
+The released checkpoint [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench) was trained on the primitive-suite dataset above and is evaluated with the [Single-task](#single-task-evaluation-recommended-for-quick-iteration) / [Suite-wide](#suite-wide-evaluation) commands. CI runs a 10-primitive-task smoke eval (one episode each) on every PR touching the benchmark.

docs/source/xvla.mdx

@@ -220,7 +220,7 @@ REAL_DIM = 12
 # Postprocessing: Trim 20D predictions to 12D for deployment
 ```
 
-See the [action_hub.py](/home/jade_choghari/robot/lerobot/src/lerobot/policies/xvla/action_hub.py) implementation for details.
+See the [action_hub.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py) implementation for details.
 
 #### Auto Action Mode (Recommended)
 
@@ -519,9 +519,9 @@ If you use X-VLA in your research, please cite:
 
 - [X-VLA Paper](https://arxiv.org/pdf/2510.10274)
 - [LeRobot Documentation](https://github.com/huggingface/lerobot)
-- [Action Registry Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/action_hub.py)
-- [Processor Implementation](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/processor_xvla.py)
-- [Model Configuration](https://github.com/huggingface/lerobot/src/lerobot/policies/xvla/configuration_xvla.py)
+- [Action Registry Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/action_hub.py)
+- [Processor Implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/processor_xvla.py)
+- [Model Configuration](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/xvla/configuration_xvla.py)
 
 ## Contributing
 

pyproject.toml

@@ -212,6 +212,15 @@ aloha = ["lerobot[dataset]", "gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
 pusht = ["lerobot[dataset]", "gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
 libero = ["lerobot[dataset]", "lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
 metaworld = ["lerobot[dataset]", "metaworld==3.0.0", "lerobot[scipy-dep]"]
+# NOTE: vlabench is NOT exposed as a `lerobot` extra. Its only distribution
+# is the OpenMOSS/VLABench GitHub repo (package name `VLABench`, no PyPI
+# release), so any `vlabench>=X` pip spec is unresolvable. Install it
+# manually alongside MuJoCo / dm-control — see docs/source/vlabench.mdx
+# for the recipe.
+# NOTE: robomme is NOT a pyproject extra — mani-skill hard-pins numpy<2
+# which conflicts with lerobot's numpy>=2 base pin, so the two trees can't
+# resolve into a single env. Install it only in the RoboMME Docker image
+# via `uv pip install --override` (see docker/Dockerfile.benchmark.robomme).
 # NOTE: robocasa is NOT exposed as a `lerobot` extra. Its setup.py pins
 # `lerobot==0.3.3` in install_requires, which cyclically shadows our own
 # workspace `lerobot` and makes the graph unsolvable under any resolver

scripts/ci/extract_task_descriptions.py

@@ -31,9 +31,23 @@ from __future__ import annotations
 
 import argparse
 import json
+import re
 import sys
 from pathlib import Path
 
+# LIBERO-plus derives task.language by space-joining the perturbation-variant
+# filename (grab_language_from_filename in libero/libero/benchmark/__init__.py),
+# so non-_language_ variants inherit a trailing metadata blob like
+# "view 0 0 100 0 0 initstate 0 noise 45" or "add 16". Strip those tokens so
+# the description matches the base instruction used in the training dataset.
+_LIBERO_PERTURBATION_TAIL_RE = re.compile(
+    r"(?:\s(?:view|initstate|noise|add|tb|table|light|level)(?:\s\d+)+)+$"
+)
+
+
+def _strip_libero_perturbation_tail(instruction: str) -> str:
+    return _LIBERO_PERTURBATION_TAIL_RE.sub("", instruction).strip()
+
 
 def _libero_descriptions(task_suite: str) -> dict[str, str]:
     from libero.libero import benchmark  # type: ignore[import-untyped]
@@ -47,7 +61,10 @@ def _libero_descriptions(task_suite: str) -> dict[str, str]:
         )
         return {}
     suite = suite_dict[task_suite]()
-    return {f"{task_suite}_{i}": suite.get_task(i).language for i in range(suite.n_tasks)}
+    return {
+        f"{task_suite}_{i}": _strip_libero_perturbation_tail(suite.get_task(i).language)
+        for i in range(suite.n_tasks)
+    }
 
 
 def _metaworld_descriptions(task_name: str) -> dict[str, str]:
@@ -57,6 +74,24 @@ def _metaworld_descriptions(task_name: str) -> dict[str, str]:
     return {f"{task_name}_0": label}
 
 
+def _robotwin_descriptions(task_names: str) -> dict[str, str]:
+    """Return descriptions for each requested RoboTwin task. Reads
+    `description/task_instruction/<task>.json` from the RoboTwin clone
+    (cwd is /opt/robotwin in CI). Falls back to the task name if missing."""
+    out: dict[str, str] = {}
+    root = Path("description/task_instruction")
+    for name in (t.strip() for t in task_names.split(",") if t.strip()):
+        desc_file = root / f"{name}.json"
+        desc = name.replace("_", " ")
+        if desc_file.is_file():
+            data = json.loads(desc_file.read_text())
+            full = data.get("full_description") or desc
+            # Strip the schema placeholders ({A}, {a}) — keep the sentence readable.
+            desc = full.replace("<", "").replace(">", "")
+        out[f"{name}_0"] = desc
+    return out
+
+
 def _robocasa_descriptions(task_spec: str) -> dict[str, str]:
     """For each task in the comma-separated list, emit a cleaned-name label.
 
@@ -74,21 +109,85 @@ def _robocasa_descriptions(task_spec: str) -> dict[str, str]:
     return out
 
 
+_ROBOMME_DESCRIPTIONS = {
+    "BinFill": "Fill the target bin with the correct number of cubes",
+    "PickXtimes": "Pick the indicated cube the specified number of times",
+    "SwingXtimes": "Swing the object the specified number of times",
+    "StopCube": "Grasp and stop the moving cube",
+    "VideoUnmask": "Pick the cube shown in the reference video",
+    "VideoUnmaskSwap": "Pick the cube matching the reference video after a swap",
+    "ButtonUnmask": "Press the button indicated by the reference",
+    "ButtonUnmaskSwap": "Press the correct button after objects are swapped",
+    "PickHighlight": "Pick the highlighted cube",
+    "VideoRepick": "Repick the cube shown in the reference video",
+    "VideoPlaceButton": "Place the cube on the button shown in the video",
+    "VideoPlaceOrder": "Place cubes in the order shown in the video",
+    "MoveCube": "Move the cube to the target location",
+    "InsertPeg": "Insert the peg into the target hole",
+    "PatternLock": "Unlock the pattern by pressing buttons in sequence",
+    "RouteStick": "Route the stick through the required waypoints",
+}
+
+
+def _robomme_descriptions(task_names: str, task_ids: list[int] | None = None) -> dict[str, str]:
+    """Return descriptions for each requested RoboMME task. Keys match the
+    video filename pattern `<task>_<task_id>` used by the eval script."""
+    if task_ids is None:
+        task_ids = [0]
+    out: dict[str, str] = {}
+    for name in (t.strip() for t in task_names.split(",") if t.strip()):
+        desc = _ROBOMME_DESCRIPTIONS.get(name, name)
+        for tid in task_ids:
+            out[f"{name}_{tid}"] = desc
+    return out
+
+
+def _vlabench_descriptions(task_spec: str) -> dict[str, str]:
+    """For each task in the comma-separated list, emit a cleaned-name label.
+
+    VLABench tasks carry language instructions on their dm_control task
+    object, but pulling them requires loading the full env per task
+    (~seconds each). The CI smoke-eval already captures the instruction
+    inside its episode info; this mapping is just enough to key
+    `metrics.json` by `<task>_0`.
+    """
+    out: dict[str, str] = {}
+    for task in (t.strip() for t in task_spec.split(",") if t.strip()):
+        out[f"{task}_0"] = task.replace("_", " ").strip()
+    return out
+
+
 def main() -> int:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--env", required=True, help="Environment family (libero, metaworld, ...)")
     parser.add_argument("--task", required=True, help="Task/suite name (e.g. libero_spatial)")
+    parser.add_argument(
+        "--task-ids",
+        type=str,
+        default=None,
+        help="Comma-separated task IDs (e.g. '0,1,2'). Default: [0]",
+    )
     parser.add_argument("--output", required=True, help="Path to write task_descriptions.json")
     args = parser.parse_args()
 
+    task_ids: list[int] | None = None
+    if args.task_ids:
+        task_ids = [int(x.strip()) for x in args.task_ids.split(",")]
+
     descriptions: dict[str, str] = {}
     try:
-        if args.env == "libero":
+        if args.env == ("libero", "libero_plus"):
             descriptions = _libero_descriptions(args.task)
         elif args.env == "metaworld":
             descriptions = _metaworld_descriptions(args.task)
+        elif args.env == "robotwin":
+            descriptions = _robotwin_descriptions(args.task)
         elif args.env == "robocasa":
             descriptions = _robocasa_descriptions(args.task)
+        elif args.env == "robomme":
+            descriptions = _robomme_descriptions(args.task, task_ids=task_ids)
+        elif args.env == "vlabench":
+            descriptions = _vlabench_descriptions(args.task)
         else:
             print(
                 f"[extract_task_descriptions] No description extractor for env '{args.env}'.",

src/lerobot/cameras/realsense/camera_realsense.py

@@ -17,6 +17,7 @@ Provides the RealSenseCamera class for capturing frames from Intel RealSense cam
 """
 
 import logging
+import sys
 import time
 from threading import Event, Lock, Thread
 from typing import TYPE_CHECKING, Any
@@ -41,6 +42,7 @@ from ..utils import get_cv2_rotation
 from .configuration_realsense import RealSenseCameraConfig
 
 logger = logging.getLogger(__name__)
+pkg_name = "pyrealsense2-macosx" if sys.platform == "darwin" else "pyrealsense2"
 
 
 class RealSenseCamera(Camera):
@@ -114,7 +116,7 @@ class RealSenseCamera(Camera):
         Args:
             config: The configuration settings for the camera.
         """
-        require_package("pyrealsense2", extra="intelrealsense")
+        require_package(pkg_name, extra="intelrealsense", import_name="pyrealsense2")
         super().__init__(config)
 
         self.config = config

src/lerobot/envs/configs.py

@@ -331,6 +331,7 @@ class LiberoEnv(EnvConfig):
     camera_name_mapping: dict[str, str] | None = None
     observation_height: int = 360
     observation_width: int = 360
+    is_libero_plus: bool = False
     features: dict[str, PolicyFeature] = field(
         default_factory=lambda: {
             ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
@@ -432,6 +433,7 @@ class LiberoEnv(EnvConfig):
             control_mode=self.control_mode,
             episode_length=self.episode_length,
             camera_name_mapping=self.camera_name_mapping,
+            is_libero_plus=self.is_libero_plus,
         )
 
     def get_env_processors(self):
@@ -571,6 +573,71 @@ class RoboCasaEnv(EnvConfig):
         )
 
 
+@EnvConfig.register_subclass("vlabench")
+@dataclass
+class VLABenchEnv(EnvConfig):
+    task: str = "select_fruit"
+    fps: int = 10
+    episode_length: int = 500
+    obs_type: str = "pixels_agent_pos"
+    render_mode: str = "rgb_array"
+    render_resolution: tuple[int, int] = (480, 480)
+    robot: str = "franka"
+    action_mode: str = "eef"
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            ACTION: ACTION,
+            "agent_pos": OBS_STATE,
+            "pixels/image": f"{OBS_IMAGES}.image",
+            "pixels/second_image": f"{OBS_IMAGES}.second_image",
+            "pixels/wrist_image": f"{OBS_IMAGES}.wrist_image",
+        }
+    )
+
+    def __post_init__(self):
+        h, w = self.render_resolution
+        if self.obs_type == "pixels":
+            self.features["pixels/image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+            self.features["pixels/second_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+            self.features["pixels/wrist_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+        elif self.obs_type == "pixels_agent_pos":
+            self.features["pixels/image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+            self.features["pixels/second_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+            self.features["pixels/wrist_image"] = PolicyFeature(type=FeatureType.VISUAL, shape=(h, w, 3))
+            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(7,))
+        else:
+            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {
+            "obs_type": self.obs_type,
+            "render_mode": self.render_mode,
+            "render_resolution": self.render_resolution,
+            "robot": self.robot,
+            "max_episode_steps": self.episode_length,
+            "action_mode": self.action_mode,
+        }
+
+    def create_envs(self, n_envs: int, use_async_envs: bool = False):
+        from .vlabench import create_vlabench_envs
+
+        if self.task is None:
+            raise ValueError("VLABenchEnv requires a task to be specified")
+        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
+        return create_vlabench_envs(
+            task=self.task,
+            n_envs=n_envs,
+            gym_kwargs=self.gym_kwargs,
+            env_cls=env_cls,
+        )
+
+
 @EnvConfig.register_subclass("isaaclab_arena")
 @dataclass
 class IsaaclabArenaEnv(HubEnvConfig):
@@ -649,3 +716,171 @@ class IsaaclabArenaEnv(HubEnvConfig):
             ),
             PolicyProcessorPipeline(steps=[]),
         )
+
+
+@EnvConfig.register_subclass("libero_plus")
+@dataclass
+class LiberoPlusEnv(LiberoEnv):
+    """Config for LIBERO-plus robustness benchmark evaluation.
+
+    LIBERO-plus extends LIBERO with 7 perturbation dimensions (camera viewpoints,
+    object layouts, robot initial states, language instructions, lighting, background
+    textures, sensor noise) producing ~10k task variants.
+
+    The gym interface is identical to LIBERO so this class reuses ``LiberoEnv``
+    entirely — only the registered name and default task suite differ.
+
+    Install: see docker/Dockerfile.benchmark.libero_plus — LIBERO-plus ships
+    as a namespace package from a git fork and must be cloned + PYTHONPATH'd
+    rather than installed as a pyproject extra.
+
+    See Also:
+        https://github.com/sylvestf/LIBERO-plus
+    """
+
+    task: str = "libero_spatial"
+    is_libero_plus: bool = True
+
+
+@EnvConfig.register_subclass("robotwin")
+@dataclass
+class RoboTwinEnvConfig(EnvConfig):
+    """Configuration for RoboTwin 2.0 benchmark environments.
+
+    RoboTwin 2.0 is a dual-arm manipulation benchmark with 50 tasks built on the
+    SAPIEN simulator. The robot is an Aloha-AgileX bimanual platform with 14 DOF
+    (7 per arm). All three cameras are enabled by default.
+
+    See: https://robotwin-platform.github.io
+    Dataset: https://huggingface.co/datasets/lerobot/robotwin_unified
+    """
+
+    task: str = "beat_block_hammer"  # single task or comma-separated list
+    fps: int = 25
+    episode_length: int = 300
+    obs_type: str = "pixels_agent_pos"
+    render_mode: str = "rgb_array"
+    # Available cameras from RoboTwin's aloha-agilex embodiment: head_camera
+    # (torso-mounted) + left_camera / right_camera (wrists).
+    camera_names: str = "head_camera,left_camera,right_camera"
+    # Match the D435 dims in task_config/demo_clean.yml (_camera_config.yml).
+    # Gym's vector-env concatenate pre-allocates buffers of this shape, so it
+    # must equal what SAPIEN actually renders.
+    observation_height: int = 240
+    observation_width: int = 320
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(14,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            ACTION: ACTION,
+            "pixels/head_camera": f"{OBS_IMAGES}.head_camera",
+            "pixels/left_camera": f"{OBS_IMAGES}.left_camera",
+            "pixels/right_camera": f"{OBS_IMAGES}.right_camera",
+            "agent_pos": OBS_STATE,
+        }
+    )
+
+    def __post_init__(self):
+        cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
+        for cam in cam_list:
+            self.features[f"pixels/{cam}"] = PolicyFeature(
+                type=FeatureType.VISUAL,
+                shape=(self.observation_height, self.observation_width, 3),
+            )
+            # Keep features_map entry if already set (default_factory); add if missing.
+            key = f"pixels/{cam}"
+            if key not in self.features_map:
+                self.features_map[key] = f"{OBS_IMAGES}.{cam}"
+
+        if self.obs_type == "pixels_agent_pos":
+            self.features["agent_pos"] = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(14,),  # 14 DOF: 7 per arm
+            )
+        elif self.obs_type != "pixels":
+            raise ValueError(
+                f"Unsupported obs_type '{self.obs_type}'. "
+                "RoboTwinEnvConfig supports 'pixels' and 'pixels_agent_pos'."
+            )
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {}
+
+    def create_envs(self, n_envs: int, use_async_envs: bool = True):
+        from lerobot.envs.robotwin import create_robotwin_envs
+
+        if not self.task:
+            raise ValueError("RoboTwinEnvConfig requires `task` to be specified.")
+
+        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
+        cam_list = [c.strip() for c in self.camera_names.split(",") if c.strip()]
+        return create_robotwin_envs(
+            task=self.task,
+            n_envs=n_envs,
+            env_cls=env_cls,
+            camera_names=cam_list,
+            observation_height=self.observation_height,
+            observation_width=self.observation_width,
+            episode_length=self.episode_length,
+        )
+
+
+@EnvConfig.register_subclass("robomme")
+@dataclass
+class RoboMMEEnv(EnvConfig):
+    """RoboMME memory-augmented manipulation benchmark (ManiSkill/SAPIEN).
+
+    16 tasks across 4 suites: Counting, Permanence, Reference, Imitation.
+    Dataset: lerobot/robomme (LeRobot v3.0, 1,600 episodes).
+    Benchmark: https://github.com/RoboMME/robomme_benchmark
+
+    Requires the `robomme` git package installed separately (Linux only);
+    see docker/Dockerfile.benchmark.robomme for the canonical install.
+    """
+
+    task: str = "PickXtimes"
+    fps: int = 10
+    episode_length: int = 300
+    action_space: str = "joint_angle"  # or "ee_pose" (7-D)
+    dataset_split: str = "test"  # "train" | "val" | "test"
+    task_ids: list[int] | None = None
+    features: dict[str, PolicyFeature] = field(default_factory=dict)
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            ACTION: ACTION,
+            "pixels/image": f"{OBS_IMAGES}.image",
+            "pixels/wrist_image": f"{OBS_IMAGES}.wrist_image",
+            "agent_pos": OBS_STATE,
+        }
+    )
+
+    def __post_init__(self):
+        action_dim = 8 if self.action_space == "joint_angle" else 7
+        self.features = {
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,)),
+            "pixels/image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
+            "pixels/wrist_image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
+            "agent_pos": PolicyFeature(type=FeatureType.STATE, shape=(8,)),
+        }
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {}
+
+    def create_envs(self, n_envs: int, use_async_envs: bool = True):
+        from lerobot.envs.robomme import create_robomme_envs
+
+        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
+        return create_robomme_envs(
+            task=self.task,
+            n_envs=n_envs,
+            action_space_type=self.action_space,
+            dataset=self.dataset_split,
+            episode_length=self.episode_length,
+            task_ids=self.task_ids,
+            env_cls=env_cls,
+        )

src/lerobot/envs/libero.py

@@ -16,6 +16,7 @@
 from __future__ import annotations
 
 import os
+import re
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
@@ -56,14 +57,34 @@ def _select_task_ids(total_tasks: int, task_ids: Iterable[int] | None) -> list[i
     return ids
 
 
-def get_task_init_states(task_suite: Any, i: int) -> np.ndarray:
-    init_states_path = (
-        Path(get_libero_path("init_states"))
-        / task_suite.tasks[i].problem_folder
-        / task_suite.tasks[i].init_states_file
-    )
-    init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
-    return init_states
+# LIBERO-plus perturbation variants encode the perturbation in the filename
+# but on disk only the base `.pruned_init` exists — strip the suffix to match
+# LIBERO-plus's own suite.get_task_init_states() (we reimplement it here so we
+# can pass weights_only=False for PyTorch 2.6+ numpy pickles).
+_LIBERO_PERTURBATION_SUFFIX_RE = re.compile(r"_(?:language|view|light)_[^.]*|_(?:table|tb)_\d+")
+
+
+def get_task_init_states(task_suite: Any, i: int, is_libero_plus: bool = False) -> np.ndarray:
+    task = task_suite.tasks[i]
+    filename = Path(task.init_states_file)
+    root = Path(get_libero_path("init_states"))
+
+    if not is_libero_plus:
+        init_states_path = root / task.problem_folder / filename.name
+        return torch.load(init_states_path, weights_only=False)  # nosec B614
+
+    # LIBERO-plus: `_add_` / `_level` variants store extra-object layouts under
+    # libero_newobj/ as a flat array that must be reshaped to (1, -1).
+    if "_add_" in filename.name or "_level" in filename.name:
+        init_states_path = root / "libero_newobj" / task.problem_folder / filename.name
+        init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
+        return init_states.reshape(1, -1)
+
+    # LIBERO-plus perturbation variants encode the perturbation in the filename
+    # but on disk only the base `.pruned_init` exists — strip the suffix to match.
+    stripped = _LIBERO_PERTURBATION_SUFFIX_RE.sub("", filename.stem) + filename.suffix
+    init_states_path = root / task.problem_folder / stripped
+    return torch.load(init_states_path, weights_only=False)  # nosec B614
 
 
 def get_libero_dummy_action():
@@ -105,9 +126,11 @@ class LiberoEnv(gym.Env):
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
         control_mode: str = "relative",
+        is_libero_plus: bool = False,
     ):
         super().__init__()
         self.task_id = task_id
+        self.is_libero_plus = is_libero_plus
         self.obs_type = obs_type
         self.render_mode = render_mode
         self.observation_width = observation_width
@@ -134,7 +157,11 @@ class LiberoEnv(gym.Env):
         self.episode_index = episode_index
         self.episode_length = episode_length
         # Load once and keep
-        self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
+        self._init_states = (
+            get_task_init_states(task_suite, self.task_id, is_libero_plus=self.is_libero_plus)
+            if self.init_states
+            else None
+        )
         self._reset_stride = n_envs  # when performing a reset, append `_reset_stride` to `init_state_id`.
 
         self.init_state_id = self.episode_index  # tie each sub-env to a fixed init state
@@ -367,6 +394,7 @@ def _make_env_fns(
     gym_kwargs: Mapping[str, Any],
     control_mode: str,
     camera_name_mapping: dict[str, str] | None = None,
+    is_libero_plus: bool = False,
 ) -> list[Callable[[], LiberoEnv]]:
     """Build n_envs factory callables for a single (suite, task_id)."""
 
@@ -383,6 +411,7 @@ def _make_env_fns(
             n_envs=n_envs,
             control_mode=control_mode,
             camera_name_mapping=camera_name_mapping,
+            is_libero_plus=is_libero_plus,
             **local_kwargs,
         )
 
@@ -405,6 +434,7 @@ def create_libero_envs(
     control_mode: str = "relative",
     episode_length: int | None = None,
     camera_name_mapping: dict[str, str] | None = None,
+    is_libero_plus: bool = False,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -463,6 +493,7 @@ def create_libero_envs(
                 gym_kwargs=gym_kwargs,
                 control_mode=control_mode,
                 camera_name_mapping=camera_name_mapping,
+                is_libero_plus=is_libero_plus,
             )
             if is_async:
                 lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)

src/lerobot/envs/robomme.py

@@ -0,0 +1,245 @@
+"""RoboMME environment wrapper for LeRobot evaluation.
+
+Wraps the RoboMME ``BenchmarkEnvBuilder`` into a Gymnasium-compatible
+``VectorEnv`` suitable for ``lerobot_eval``.
+
+RoboMME tasks:
+  Counting:    BinFill, PickXtimes, SwingXtimes, StopCube
+  Permanence:  VideoUnmask, VideoUnmaskSwap, ButtonUnmask, ButtonUnmaskSwap
+  Reference:   PickHighlight, VideoRepick, VideoPlaceButton, VideoPlaceOrder
+  Imitation:   MoveCube, InsertPeg, PatternLock, RouteStick
+
+Dataset: lerobot/robomme (LeRobot v3.0, 1,600 episodes)
+Install: see docker/Dockerfile.benchmark.robomme  (Linux only — mani-skill vs numpy pin conflict)
+Benchmark: https://github.com/RoboMME/robomme_benchmark
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Sequence
+from functools import partial
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+from gymnasium import spaces
+
+from .utils import _LazyAsyncVectorEnv
+
+ROBOMME_TASKS = [
+    "BinFill",
+    "PickXtimes",
+    "SwingXtimes",
+    "StopCube",
+    "VideoUnmask",
+    "VideoUnmaskSwap",
+    "ButtonUnmask",
+    "ButtonUnmaskSwap",
+    "PickHighlight",
+    "VideoRepick",
+    "VideoPlaceButton",
+    "VideoPlaceOrder",
+    "MoveCube",
+    "InsertPeg",
+    "PatternLock",
+    "RouteStick",
+]
+
+
+class RoboMMEGymEnv(gym.Env):
+    """Thin Gymnasium wrapper around a single RoboMME episode env."""
+
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 10}
+
+    def __init__(
+        self,
+        task: str = "PickXtimes",
+        action_space_type: str = "joint_angle",
+        dataset: str = "test",
+        episode_idx: int = 0,
+        max_steps: int = 300,
+    ):
+        super().__init__()
+        from robomme.env_record_wrapper import BenchmarkEnvBuilder
+
+        self._task = task
+        self._action_space_type = action_space_type
+        self._dataset = dataset
+        self._episode_idx = episode_idx
+        self._max_steps = max_steps
+        self._max_episode_steps = max_steps
+
+        self._builder = BenchmarkEnvBuilder(
+            env_id=task,
+            dataset=dataset,
+            action_space=action_space_type,
+            gui_render=False,
+            max_steps=max_steps,
+        )
+        self._env = None
+        self._last_raw_obs: dict | None = None
+
+        action_dim = 8 if action_space_type == "joint_angle" else 7
+        self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(action_dim,), dtype=np.float32)
+        # `pixels` must be a nested Dict so `preprocess_observation()` in
+        # envs/utils.py picks it up and maps each camera to
+        # `observation.images.<cam>`. A flat layout (`pixels/image`,
+        # `pixels/wrist_image`) silently drops every image from the batch.
+        self.observation_space = spaces.Dict(
+            {
+                "pixels": spaces.Dict(
+                    {
+                        "image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
+                        "wrist_image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
+                    }
+                ),
+                "agent_pos": spaces.Box(-np.inf, np.inf, shape=(8,), dtype=np.float32),
+            }
+        )
+
+    def reset(self, *, seed=None, options=None):
+        super().reset(seed=seed)
+        self._env = self._builder.make_env_for_episode(
+            episode_idx=self._episode_idx,
+            max_steps=self._max_steps,
+        )
+        obs, info = self._env.reset()
+        self._last_raw_obs = obs
+        return self._convert_obs(obs), self._convert_info(info)
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = self._env.step(action)
+        self._last_raw_obs = obs
+
+        terminated_bool = bool(terminated.item()) if hasattr(terminated, "item") else bool(terminated)
+        truncated_bool = bool(truncated.item()) if hasattr(truncated, "item") else bool(truncated)
+
+        status = info.get("status", "ongoing")
+        is_success = status == "success"
+        conv_info = self._convert_info(info)
+        conv_info["is_success"] = is_success
+
+        return self._convert_obs(obs), float(reward), terminated_bool, truncated_bool, conv_info
+
+    def render(self) -> np.ndarray | None:
+        """Return the front camera image from the last observation for video recording."""
+        if self._last_raw_obs is None:
+            return np.zeros((256, 256, 3), dtype=np.uint8)
+        front = self._last_raw_obs.get("front_rgb_list")
+        if front is None:
+            return np.zeros((256, 256, 3), dtype=np.uint8)
+        frame = front[-1] if isinstance(front, list) else front
+        return np.asarray(frame, dtype=np.uint8)
+
+    def _convert_obs(self, obs: dict) -> dict:
+        front_rgb = (
+            obs["front_rgb_list"][-1] if isinstance(obs["front_rgb_list"], list) else obs["front_rgb_list"]
+        )
+        wrist_rgb = (
+            obs["wrist_rgb_list"][-1] if isinstance(obs["wrist_rgb_list"], list) else obs["wrist_rgb_list"]
+        )
+        joint_state = (
+            obs["joint_state_list"][-1]
+            if isinstance(obs["joint_state_list"], list)
+            else obs["joint_state_list"]
+        )
+        gripper_state = (
+            obs["gripper_state_list"][-1]
+            if isinstance(obs["gripper_state_list"], list)
+            else obs["gripper_state_list"]
+        )
+
+        front_rgb = np.asarray(front_rgb, dtype=np.uint8)
+        wrist_rgb = np.asarray(wrist_rgb, dtype=np.uint8)
+        joint = np.asarray(joint_state, dtype=np.float32).flatten()[:7]
+        gripper = np.asarray(gripper_state, dtype=np.float32).flatten()[:1]
+        state = np.concatenate([joint, gripper])
+
+        return {
+            "pixels": {"image": front_rgb, "wrist_image": wrist_rgb},
+            "agent_pos": state,
+        }
+
+    def _convert_info(self, info: dict) -> dict:
+        return {
+            "status": info.get("status", "ongoing"),
+            "task_goal": info.get("task_goal", ""),
+        }
+
+
+def _make_env_fns(
+    *,
+    task: str,
+    n_envs: int,
+    action_space_type: str,
+    dataset: str,
+    episode_length: int,
+    task_id: int,
+) -> list[Callable[[], RoboMMEGymEnv]]:
+    """Build n_envs factory callables for one RoboMME task id."""
+
+    def _make_one(episode_index: int) -> RoboMMEGymEnv:
+        return RoboMMEGymEnv(
+            task=task,
+            action_space_type=action_space_type,
+            dataset=dataset,
+            episode_idx=episode_index,
+            max_steps=episode_length,
+        )
+
+    return [partial(_make_one, task_id + i) for i in range(n_envs)]
+
+
+def create_robomme_envs(
+    task: str,
+    n_envs: int = 1,
+    action_space_type: str = "joint_angle",
+    dataset: str = "test",
+    episode_length: int = 300,
+    task_ids: list[int] | None = None,
+    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
+) -> dict[str, dict[int, gym.vector.VectorEnv]]:
+    """Create vectorized RoboMME environments for evaluation.
+
+    `task` may be a single RoboMME task name (e.g. "PickXtimes") or a
+    comma-separated list (e.g. "PickXtimes,BinFill,StopCube"). Each task
+    becomes its own suite in the returned mapping.
+
+    Returns {suite_name: {task_id: VectorEnv}} matching lerobot's expected format.
+    """
+    if env_cls is None or not callable(env_cls):
+        raise ValueError("env_cls must be a callable that wraps a list of env factory callables.")
+    if not isinstance(n_envs, int) or n_envs <= 0:
+        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
+
+    if task_ids is None:
+        task_ids = [0]
+
+    task_names = [t.strip() for t in task.split(",") if t.strip()]
+    is_async = env_cls is gym.vector.AsyncVectorEnv
+    cached_obs_space: spaces.Space | None = None
+    cached_act_space: spaces.Space | None = None
+    cached_metadata: dict[str, Any] | None = None
+    out: dict[str, dict[int, gym.vector.VectorEnv]] = {}
+    for task_name in task_names:
+        envs_by_task: dict[int, gym.vector.VectorEnv] = {}
+        for task_id in task_ids:
+            fns = _make_env_fns(
+                task=task_name,
+                n_envs=n_envs,
+                action_space_type=action_space_type,
+                dataset=dataset,
+                episode_length=episode_length,
+                task_id=task_id,
+            )
+            if is_async:
+                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
+                if cached_obs_space is None:
+                    cached_obs_space = lazy.observation_space
+                    cached_act_space = lazy.action_space
+                    cached_metadata = lazy.metadata
+                envs_by_task[task_id] = lazy
+            else:
+                envs_by_task[task_id] = env_cls(fns)
+        out[task_name] = envs_by_task
+    return out

src/lerobot/envs/robotwin.py

@@ -0,0 +1,488 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import importlib
+import logging
+from collections import defaultdict
+from collections.abc import Callable, Sequence
+from functools import partial
+from typing import Any
+
+import gymnasium as gym
+import numpy as np
+import torch
+from gymnasium import spaces
+
+from lerobot.types import RobotObservation
+
+from .utils import _LazyAsyncVectorEnv
+
+logger = logging.getLogger(__name__)
+
+# Camera names as used by RoboTwin 2.0. The wrapper appends "_rgb" when looking
+# up keys in get_obs() output (e.g. "head_camera" → "head_camera_rgb").
+ROBOTWIN_CAMERA_NAMES: tuple[str, ...] = (
+    "head_camera",
+    "left_camera",
+    "right_camera",
+)
+
+ACTION_DIM = 14  # 7 DOF × 2 arms
+ACTION_LOW = -1.0
+ACTION_HIGH = 1.0
+DEFAULT_EPISODE_LENGTH = 300
+# D435 dims from task_config/_camera_config.yml (what demo_clean.yml selects).
+DEFAULT_CAMERA_H = 240
+DEFAULT_CAMERA_W = 320
+
+# Task list from RoboTwin 2.0's `envs/` directory — mirrors upstream exactly
+# (50 tasks as of main; earlier revisions had 60 with a different split).
+# Keep this in sync with:
+#   gh api /repos/RoboTwin-Platform/RoboTwin/contents/envs --paginate \
+#     | jq -r '.[].name' | grep -E '\.py$' | grep -v '^_' | sed 's/\.py$//'
+ROBOTWIN_TASKS: tuple[str, ...] = (
+    "adjust_bottle",
+    "beat_block_hammer",
+    "blocks_ranking_rgb",
+    "blocks_ranking_size",
+    "click_alarmclock",
+    "click_bell",
+    "dump_bin_bigbin",
+    "grab_roller",
+    "handover_block",
+    "handover_mic",
+    "hanging_mug",
+    "lift_pot",
+    "move_can_pot",
+    "move_pillbottle_pad",
+    "move_playingcard_away",
+    "move_stapler_pad",
+    "open_laptop",
+    "open_microwave",
+    "pick_diverse_bottles",
+    "pick_dual_bottles",
+    "place_a2b_left",
+    "place_a2b_right",
+    "place_bread_basket",
+    "place_bread_skillet",
+    "place_burger_fries",
+    "place_can_basket",
+    "place_cans_plasticbox",
+    "place_container_plate",
+    "place_dual_shoes",
+    "place_empty_cup",
+    "place_fan",
+    "place_mouse_pad",
+    "place_object_basket",
+    "place_object_scale",
+    "place_object_stand",
+    "place_phone_stand",
+    "place_shoe",
+    "press_stapler",
+    "put_bottles_dustbin",
+    "put_object_cabinet",
+    "rotate_qrcode",
+    "scan_object",
+    "shake_bottle",
+    "shake_bottle_horizontally",
+    "stack_blocks_three",
+    "stack_blocks_two",
+    "stack_bowls_three",
+    "stack_bowls_two",
+    "stamp_seal",
+    "turn_switch",
+)
+
+
+_ROBOTWIN_SETUP_CACHE: dict[str, dict[str, Any]] = {}
+
+
+def _load_robotwin_setup_kwargs(task_name: str) -> dict[str, Any]:
+    """Build the kwargs dict RoboTwin's setup_demo expects.
+
+    Mirrors the config loading done by RoboTwin's ``script/eval_policy.py``:
+    reads ``task_config/demo_clean.yml``, resolves the embodiment file from
+    ``_embodiment_config.yml``, loads the robot's own ``config.yml``, and
+    reads camera dimensions from ``_camera_config.yml``.
+
+    Uses ``aloha-agilex`` single-robot dual-arm by default (the only embodiment
+    used by beat_block_hammer and most smoke-test tasks).
+    """
+    if task_name in _ROBOTWIN_SETUP_CACHE:
+        return dict(_ROBOTWIN_SETUP_CACHE[task_name])
+
+    import os
+
+    import yaml  # type: ignore[import-untyped]
+    from envs import CONFIGS_PATH  # type: ignore[import-not-found]
+
+    task_config = "demo_clean"
+    with open(os.path.join(CONFIGS_PATH, f"{task_config}.yml"), encoding="utf-8") as f:
+        args = yaml.safe_load(f)
+
+    # Resolve embodiment — demo_clean.yml uses [aloha-agilex] (dual-arm single robot)
+    with open(os.path.join(CONFIGS_PATH, "_embodiment_config.yml"), encoding="utf-8") as f:
+        embodiment_types = yaml.safe_load(f)
+    embodiment = args.get("embodiment", ["aloha-agilex"])
+    if len(embodiment) == 1:
+        robot_file = embodiment_types[embodiment[0]]["file_path"]
+        args["left_robot_file"] = robot_file
+        args["right_robot_file"] = robot_file
+        args["dual_arm_embodied"] = True
+    elif len(embodiment) == 3:
+        args["left_robot_file"] = embodiment_types[embodiment[0]]["file_path"]
+        args["right_robot_file"] = embodiment_types[embodiment[1]]["file_path"]
+        args["embodiment_dis"] = embodiment[2]
+        args["dual_arm_embodied"] = False
+    else:
+        raise ValueError(f"embodiment must have 1 or 3 items, got {len(embodiment)}")
+
+    with open(os.path.join(args["left_robot_file"], "config.yml"), encoding="utf-8") as f:
+        args["left_embodiment_config"] = yaml.safe_load(f)
+    with open(os.path.join(args["right_robot_file"], "config.yml"), encoding="utf-8") as f:
+        args["right_embodiment_config"] = yaml.safe_load(f)
+
+    # Camera dimensions
+    with open(os.path.join(CONFIGS_PATH, "_camera_config.yml"), encoding="utf-8") as f:
+        camera_config = yaml.safe_load(f)
+    head_cam = args["camera"]["head_camera_type"]
+    args["head_camera_h"] = camera_config[head_cam]["h"]
+    args["head_camera_w"] = camera_config[head_cam]["w"]
+
+    # Headless overrides
+    args["render_freq"] = 0
+    args["task_name"] = task_name
+    args["task_config"] = task_config
+
+    _ROBOTWIN_SETUP_CACHE[task_name] = args
+    return dict(args)
+
+
+def _load_robotwin_task(task_name: str) -> type:
+    """Dynamically import and return a RoboTwin 2.0 task class.
+
+    RoboTwin tasks live in ``envs/<task_name>.py`` relative to the repository
+    root and are expected to be on ``sys.path`` after installation.
+    """
+    try:
+        module = importlib.import_module(f"envs.{task_name}")
+    except ModuleNotFoundError as e:
+        raise ModuleNotFoundError(
+            f"Could not import RoboTwin task '{task_name}'. "
+            "Ensure RoboTwin 2.0 is installed and its 'envs/' directory is on PYTHONPATH. "
+            "See the RoboTwin installation guide: https://robotwin-platform.github.io/doc/usage/robotwin-install.html"
+        ) from e
+    task_cls = getattr(module, task_name, None)
+    if task_cls is None:
+        raise AttributeError(f"Task class '{task_name}' not found in envs/{task_name}.py")
+    return task_cls
+
+
+class RoboTwinEnv(gym.Env):
+    """Gymnasium wrapper around a single RoboTwin 2.0 task.
+
+    RoboTwin uses a custom SAPIEN-based API (``setup_demo`` / ``get_obs`` /
+    ``take_action`` / ``check_success``) rather than the standard gym interface.
+    This class bridges that API to Gymnasium so that ``lerobot-eval`` can drive
+    RoboTwin exactly like LIBERO or Meta-World.
+
+    The underlying SAPIEN environment is created lazily on the first ``reset()``
+    call *inside the worker process*.  This is required for
+    ``gym.vector.AsyncVectorEnv`` compatibility: SAPIEN allocates EGL/GPU
+    contexts that must not be forked from the parent process.
+
+    Observations
+    ------------
+    The ``pixels`` dict uses the raw RoboTwin camera names as keys (e.g.
+    ``"head_camera"``, ``"left_camera"``). ``preprocess_observation`` in
+    ``envs/utils.py`` then converts these to ``observation.images.<cam>``.
+
+    Actions
+    -------
+    14-dim float32 array in ``[-1, 1]`` (joint-space, 7 DOF per arm).
+
+    Autograd
+    --------
+    ``setup_demo`` and ``take_action`` drive CuRobo's Newton trajectory
+    optimizer, which calls ``cost.backward()`` internally. lerobot_eval wraps
+    the rollout in ``torch.no_grad()``, so both call sites re-enable grad.
+    """
+
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 25}
+
+    def __init__(
+        self,
+        task_name: str,
+        episode_index: int = 0,
+        n_envs: int = 1,
+        camera_names: Sequence[str] = ROBOTWIN_CAMERA_NAMES,
+        observation_height: int | None = None,
+        observation_width: int | None = None,
+        episode_length: int = DEFAULT_EPISODE_LENGTH,
+        render_mode: str = "rgb_array",
+    ):
+        super().__init__()
+        self.task_name = task_name
+        self.task = task_name  # used by add_envs_task() in utils.py
+        self.task_description = task_name.replace("_", " ")
+        self.episode_index = episode_index
+        self._reset_stride = n_envs
+        self.camera_names = list(camera_names)
+        # Default to D435 dims (the camera type baked into task_config/demo_clean.yml).
+        # The YAML-driven lookup is deferred to reset() so construction doesn't
+        # import RoboTwin's `envs` module — fast-tests run without RoboTwin installed.
+        self.observation_height = observation_height or DEFAULT_CAMERA_H
+        self.observation_width = observation_width or DEFAULT_CAMERA_W
+        self.episode_length = episode_length
+        self._max_episode_steps = episode_length  # lerobot_eval.rollout reads this
+        self.render_mode = render_mode
+
+        self._env: Any | None = None  # deferred — created on first reset() inside worker
+        self._step_count: int = 0
+        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)
+
+        image_spaces = {
+            cam: spaces.Box(
+                low=0,
+                high=255,
+                shape=(self.observation_height, self.observation_width, 3),
+                dtype=np.uint8,
+            )
+            for cam in self.camera_names
+        }
+        self.observation_space = spaces.Dict(
+            {
+                "pixels": spaces.Dict(image_spaces),
+                "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(ACTION_DIM,), dtype=np.float32),
+            }
+        )
+        self.action_space = spaces.Box(
+            low=ACTION_LOW, high=ACTION_HIGH, shape=(ACTION_DIM,), dtype=np.float32
+        )
+
+    def _ensure_env(self) -> None:
+        """Create the SAPIEN environment on first use.
+
+        Called inside the worker subprocess after fork(), so each worker gets
+        its own EGL/GPU context rather than inheriting a stale one from the
+        parent process (which causes crashes with AsyncVectorEnv).
+        """
+        if self._env is not None:
+            return
+        task_cls = _load_robotwin_task(self.task_name)
+        self._env = task_cls()
+
+    def _get_obs(self) -> RobotObservation:
+        assert self._env is not None, "_get_obs called before _ensure_env()"
+        raw = self._env.get_obs()
+        cameras_raw = raw.get("observation", {})
+
+        images: dict[str, np.ndarray] = {}
+        for cam in self.camera_names:
+            cam_data = cameras_raw.get(cam)
+            img = cam_data.get("rgb") if cam_data else None
+            if img is None:
+                images[cam] = self._black_frame
+                continue
+            img = np.asarray(img, dtype=np.uint8)
+            if img.ndim == 2:
+                img = np.stack([img, img, img], axis=-1)
+            elif img.shape[-1] != 3:
+                img = img[..., :3]
+            images[cam] = img
+
+        ja = raw.get("joint_action") or {}
+        vec = ja.get("vector")
+        if vec is not None:
+            arr = np.asarray(vec, dtype=np.float32).ravel()
+            joint_state = (
+                arr[:ACTION_DIM] if arr.size >= ACTION_DIM else np.zeros(ACTION_DIM, dtype=np.float32)
+            )
+        else:
+            joint_state = np.zeros(ACTION_DIM, dtype=np.float32)
+
+        return {"pixels": images, "agent_pos": joint_state}
+
+    def reset(self, seed: int | None = None, **kwargs) -> tuple[RobotObservation, dict]:
+        self._ensure_env()
+        super().reset(seed=seed)
+        assert self._env is not None  # set by _ensure_env() above
+
+        actual_seed = self.episode_index if seed is None else seed
+        setup_kwargs = _load_robotwin_setup_kwargs(self.task_name)
+        setup_kwargs.update(seed=actual_seed, is_test=True)
+        with torch.enable_grad():
+            self._env.setup_demo(**setup_kwargs)
+        self.episode_index += self._reset_stride
+        self._step_count = 0
+
+        obs = self._get_obs()
+        return obs, {"is_success": False, "task": self.task_name}
+
+    def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
+        assert self._env is not None, "step() called before reset()"
+        if action.ndim != 1 or action.shape[0] != ACTION_DIM:
+            raise ValueError(f"Expected 1-D action of shape ({ACTION_DIM},), got {action.shape}")
+
+        with torch.enable_grad():
+            if hasattr(self._env, "take_action"):
+                self._env.take_action(action)
+            else:
+                self._env.step(action)
+
+        self._step_count += 1
+
+        is_success = bool(getattr(self._env, "eval_success", False))
+        if not is_success and hasattr(self._env, "check_success"):
+            is_success = bool(self._env.check_success())
+
+        obs = self._get_obs()
+        reward = float(is_success)
+        terminated = is_success
+        truncated = self._step_count >= self.episode_length
+
+        info: dict[str, Any] = {
+            "task": self.task_name,
+            "is_success": is_success,
+            "step": self._step_count,
+        }
+        if terminated or truncated:
+            info["final_info"] = {
+                "task": self.task_name,
+                "is_success": is_success,
+            }
+            self.reset()
+
+        return obs, reward, terminated, truncated, info
+
+    def render(self) -> np.ndarray:
+        self._ensure_env()
+        obs = self._get_obs()
+        # Prefer head camera for rendering; fall back to first available.
+        if "head_camera" in obs["pixels"]:
+            return obs["pixels"]["head_camera"]
+        return next(iter(obs["pixels"].values()))
+
+    def close(self) -> None:
+        if self._env is not None:
+            if hasattr(self._env, "close_env"):
+                import contextlib
+
+                with contextlib.suppress(TypeError):
+                    self._env.close_env()
+            self._env = None
+
+
+# ---- Multi-task factory --------------------------------------------------------
+
+
+def _make_env_fns(
+    *,
+    task_name: str,
+    n_envs: int,
+    camera_names: list[str],
+    observation_height: int,
+    observation_width: int,
+    episode_length: int,
+) -> list[Callable[[], RoboTwinEnv]]:
+    """Return n_envs factory callables for a single task."""
+
+    def _make_one(episode_index: int) -> RoboTwinEnv:
+        return RoboTwinEnv(
+            task_name=task_name,
+            episode_index=episode_index,
+            n_envs=n_envs,
+            camera_names=camera_names,
+            observation_height=observation_height,
+            observation_width=observation_width,
+            episode_length=episode_length,
+        )
+
+    return [partial(_make_one, i) for i in range(n_envs)]
+
+
+def create_robotwin_envs(
+    task: str,
+    n_envs: int,
+    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
+    camera_names: Sequence[str] = ROBOTWIN_CAMERA_NAMES,
+    observation_height: int = DEFAULT_CAMERA_H,
+    observation_width: int = DEFAULT_CAMERA_W,
+    episode_length: int = DEFAULT_EPISODE_LENGTH,
+) -> dict[str, dict[int, Any]]:
+    """Create vectorized RoboTwin 2.0 environments.
+
+    Returns:
+        ``dict[task_name][0] -> VectorEnv`` — one entry per task, each wrapping
+        ``n_envs`` parallel rollouts.
+
+    Args:
+        task: Comma-separated list of task names (e.g. ``"beat_block_hammer"``
+            or ``"beat_block_hammer,click_bell"``).
+        n_envs: Number of parallel rollouts per task.
+        env_cls: Vector env constructor (e.g. ``gym.vector.AsyncVectorEnv``).
+        camera_names: Cameras to include in observations.
+        observation_height: Pixel height for all cameras.
+        observation_width: Pixel width for all cameras.
+        episode_length: Max steps before truncation.
+    """
+    if env_cls is None or not callable(env_cls):
+        raise ValueError("env_cls must be callable (e.g. gym.vector.AsyncVectorEnv).")
+    if not isinstance(n_envs, int) or n_envs <= 0:
+        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
+
+    task_names = [t.strip() for t in str(task).split(",") if t.strip()]
+    if not task_names:
+        raise ValueError("`task` must contain at least one RoboTwin task name.")
+
+    unknown = [t for t in task_names if t not in ROBOTWIN_TASKS]
+    if unknown:
+        raise ValueError(f"Unknown RoboTwin tasks: {unknown}. Available tasks: {sorted(ROBOTWIN_TASKS)}")
+
+    logger.info(
+        "Creating RoboTwin envs | tasks=%s | n_envs(per task)=%d",
+        task_names,
+        n_envs,
+    )
+
+    is_async = env_cls is gym.vector.AsyncVectorEnv
+    cached_obs_space: spaces.Space | None = None
+    cached_act_space: spaces.Space | None = None
+    cached_metadata: dict[str, Any] | None = None
+
+    out: dict[str, dict[int, Any]] = defaultdict(dict)
+    for task_name in task_names:
+        fns = _make_env_fns(
+            task_name=task_name,
+            n_envs=n_envs,
+            camera_names=list(camera_names),
+            observation_height=observation_height,
+            observation_width=observation_width,
+            episode_length=episode_length,
+        )
+        if is_async:
+            lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
+            if cached_obs_space is None:
+                cached_obs_space = lazy.observation_space
+                cached_act_space = lazy.action_space
+                cached_metadata = lazy.metadata
+            out[task_name][0] = lazy
+        else:
+            out[task_name][0] = env_cls(fns)
+        logger.info("Built vec env | task=%s | n_envs=%d", task_name, n_envs)
+
+    return {k: dict(v) for k, v in out.items()}

src/lerobot/envs/vlabench.py

@@ -0,0 +1,589 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""VLABench environment wrapper for LeRobot.
+
+VLABench is a large-scale benchmark for language-conditioned robotic manipulation
+with long-horizon reasoning, built on MuJoCo/dm_control.
+
+- Paper: https://arxiv.org/abs/2412.18194
+- GitHub: https://github.com/OpenMOSS/VLABench
+- Website: https://vlabench.github.io
+"""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+from collections import defaultdict
+from collections.abc import Callable, Sequence
+from typing import Any
+
+import cv2
+import gymnasium as gym
+import numpy as np
+from gymnasium import spaces
+from scipy.spatial.transform import Rotation
+
+from lerobot.types import RobotObservation
+
+from .utils import _LazyAsyncVectorEnv
+
+logger = logging.getLogger(__name__)
+
+ACTION_DIM = 7  # pos(3) + euler(3) + gripper(1)
+ACTION_LOW = np.array([-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0], dtype=np.float32)
+ACTION_HIGH = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.float32)
+
+# Default max episode steps per task type
+DEFAULT_MAX_EPISODE_STEPS = 500
+
+# VLABench task suites
+PRIMITIVE_TASKS = [
+    "select_fruit",
+    "select_toy",
+    "select_chemistry_tube",
+    "add_condiment",
+    "select_book",
+    "select_painting",
+    "select_drink",
+    "insert_flower",
+    "select_billiards",
+    "select_ingredient",
+    "select_mahjong",
+    "select_poker",
+    # Physical series
+    "density_qa",
+    "friction_qa",
+    "magnetism_qa",
+    "reflection_qa",
+    "simple_cuestick_usage",
+    "simple_seesaw_usage",
+    "sound_speed_qa",
+    "thermal_expansion_qa",
+    "weight_qa",
+]
+
+COMPOSITE_TASKS = [
+    "cluster_billiards",
+    "cluster_book",
+    "cluster_drink",
+    "cluster_toy",
+    "cook_dishes",
+    "cool_drink",
+    "find_unseen_object",
+    "get_coffee",
+    "hammer_nail",
+    "heat_food",
+    "make_juice",
+    "play_mahjong",
+    "play_math_game",
+    "play_poker",
+    "play_snooker",
+    "rearrange_book",
+    "rearrange_chemistry_tube",
+    "set_dining_table",
+    "set_study_table",
+    "store_food",
+    "take_chemistry_experiment",
+    "use_seesaw_complex",
+]
+
+SUITE_TASKS: dict[str, list[str]] = {
+    "primitive": PRIMITIVE_TASKS,
+    "composite": COMPOSITE_TASKS,
+}
+
+
+class VLABenchEnv(gym.Env):
+    """Gymnasium wrapper for VLABench environments.
+
+    Wraps the dm_control-based VLABench simulator behind a standard gym.Env interface.
+    Supports multiple cameras (front, second, wrist) and end-effector control.
+    """
+
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 10}
+
+    def __init__(
+        self,
+        task: str = "select_fruit",
+        obs_type: str = "pixels_agent_pos",
+        render_mode: str = "rgb_array",
+        render_resolution: tuple[int, int] = (480, 480),
+        robot: str = "franka",
+        max_episode_steps: int = DEFAULT_MAX_EPISODE_STEPS,
+        action_mode: str = "eef",
+    ):
+        super().__init__()
+        self.task = task
+        self.obs_type = obs_type
+        self.render_mode = render_mode
+        self.render_resolution = render_resolution
+        self.robot = robot
+        self._max_episode_steps = max_episode_steps
+        self.action_mode = action_mode
+
+        # Deferred — created on first reset() inside worker subprocess to avoid
+        # inheriting stale GPU/EGL contexts when AsyncVectorEnv spawns workers.
+        # We never cache `env.physics`: dm_control exposes it as a weakref
+        # proxy that goes stale across resets (rebuilds the sim), so we always
+        # refetch it via `self._env.physics` at the call site.
+        self._env = None
+        self.task_description = ""  # populated on first reset
+        # Cached world-frame XYZ of the robot base link. The VLABench datasets
+        # log both `observation.state` positions and `actions` positions in
+        # robot-base frame (see VLABench/scripts/convert_to_lerobot.py which
+        # subtracts `robot_frame_pos` from ee_pos). The robot is attached at a
+        # fixed offset per task so this is safe to cache once per env build.
+        self._robot_base_xyz: np.ndarray | None = None
+
+        h, w = self.render_resolution
+
+        if self.obs_type == "state":
+            raise NotImplementedError(
+                "The 'state' observation type is not supported in VLABenchEnv. "
+                "Please use 'pixels' or 'pixels_agent_pos'."
+            )
+        elif self.obs_type == "pixels":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(
+                        {
+                            "image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                            "second_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                            "wrist_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                        }
+                    ),
+                }
+            )
+        elif self.obs_type == "pixels_agent_pos":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(
+                        {
+                            "image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                            "second_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                            "wrist_image": spaces.Box(low=0, high=255, shape=(h, w, 3), dtype=np.uint8),
+                        }
+                    ),
+                    "agent_pos": spaces.Box(low=-np.inf, high=np.inf, shape=(7,), dtype=np.float64),
+                }
+            )
+        else:
+            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
+
+        self.action_space = spaces.Box(low=ACTION_LOW, high=ACTION_HIGH, dtype=np.float32)
+
+    # Max attempts to rebuild the underlying env when MuJoCo throws
+    # `PhysicsError` (e.g. mjWARN_BADQACC) during VLABench's 20-step
+    # reset warm-up. Some random task/layout samples land in unstable
+    # initial configurations; re-sampling the layout almost always
+    # gives a stable one. A handful of upstream tasks (notably
+    # `select_mahjong`) have layout samplers that diverge often enough
+    # to need >>5 retries, so we pick a generous ceiling.
+    _ENSURE_ENV_MAX_ATTEMPTS = 20
+
+    def _ensure_env(self) -> None:
+        """Create the underlying VLABench env on first use.
+
+        Called inside the worker subprocess after fork(), so each worker gets
+        its own clean rendering context rather than inheriting a stale one from
+        the parent process (which causes crashes with AsyncVectorEnv).
+
+        Retries on `PhysicsError`: VLABench's `LM4ManipDMEnv.reset()` runs 20
+        warm-up `step()` calls while toggling gravity/fluids to let the scene
+        settle; for some random layouts MuJoCo's integrator diverges and
+        raises `mjWARN_BADQACC`. Re-sampling the layout almost always yields
+        a stable one, so we retry a number of times before giving up. Between
+        attempts we reseed NumPy's global RNG from OS entropy so the upstream
+        task sampler explores fresh initial states — without this, retries
+        can replay the same diverging configuration when the sampler is
+        deterministic given the current RNG state.
+        """
+        if self._env is not None:
+            return
+
+        import VLABench.robots  # noqa: F401  # type: ignore[import-untyped]
+        import VLABench.tasks  # noqa: F401  # type: ignore[import-untyped]
+        from dm_control.rl.control import PhysicsError  # type: ignore[import-untyped]
+        from VLABench.envs import load_env  # type: ignore[import-untyped]
+
+        h, w = self.render_resolution
+        last_exc: PhysicsError | None = None
+        for attempt in range(1, self._ENSURE_ENV_MAX_ATTEMPTS + 1):
+            try:
+                env = load_env(task=self.task, robot=self.robot, render_resolution=(h, w))
+                self._env = env
+                break
+            except PhysicsError as exc:
+                last_exc = exc
+                logger.warning(
+                    "PhysicsError on attempt %d/%d while building task '%s': %s. Retrying with fresh layout…",
+                    attempt,
+                    self._ENSURE_ENV_MAX_ATTEMPTS,
+                    self.task,
+                    exc,
+                )
+                np.random.seed(None)
+        if self._env is None:
+            assert last_exc is not None
+            raise RuntimeError(
+                f"VLABench task '{self.task}' failed to produce a stable "
+                f"initial layout after {self._ENSURE_ENV_MAX_ATTEMPTS} "
+                f"attempts. This task's upstream sampler diverges too "
+                f"often for the configured robot; consider removing it "
+                f"from the eval set. Last physics error: {last_exc}"
+            ) from last_exc
+
+        # Extract task description from the dm_control task
+        task_obj = self._env.task
+        if hasattr(task_obj, "task_description"):
+            self.task_description = task_obj.task_description
+        elif hasattr(task_obj, "language_instruction"):
+            self.task_description = task_obj.language_instruction
+        else:
+            self.task_description = self.task
+
+        # Cache robot base world position so `_build_ctrl_from_action` and
+        # `_get_obs` can translate between robot-frame (dataset) and
+        # world-frame (dm_control) without hitting physics every call.
+        try:
+            self._robot_base_xyz = np.asarray(self._env.get_robot_frame_position(), dtype=np.float64).reshape(
+                3
+            )
+        except Exception:
+            # Fallback to VLABench's default Franka base position.
+            self._robot_base_xyz = np.array([0.0, -0.4, 0.78], dtype=np.float64)
+
+    def _get_obs(self) -> dict:
+        """Get current observation from the environment."""
+        assert self._env is not None
+
+        obs = self._env.get_observation()
+        h, w = self.render_resolution
+
+        def _to_hwc3(arr: np.ndarray) -> np.ndarray:
+            """Coerce any camera array to the declared (h, w, 3) uint8 shape."""
+            a = np.asarray(arr)
+            # Drop a leading singleton batch dim if present.
+            while a.ndim > 3 and a.shape[0] == 1:
+                a = a[0]
+            if a.ndim == 3 and a.shape[0] in (1, 3, 4) and a.shape[-1] not in (1, 3, 4):
+                # CHW → HWC
+                a = np.transpose(a, (1, 2, 0))
+            if a.ndim == 2:
+                a = np.stack([a] * 3, axis=-1)
+            if a.ndim != 3:
+                return np.zeros((h, w, 3), dtype=np.uint8)
+            # Force 3 channels.
+            if a.shape[-1] == 1:
+                a = np.repeat(a, 3, axis=-1)
+            elif a.shape[-1] == 4:
+                a = a[..., :3]
+            elif a.shape[-1] != 3:
+                return np.zeros((h, w, 3), dtype=np.uint8)
+            if a.shape[:2] != (h, w):
+                a = cv2.resize(a, (w, h), interpolation=cv2.INTER_AREA)
+            return a.astype(np.uint8)
+
+        # Extract camera images — VLABench returns (n_cameras, C, H, W) or individual arrays
+        raw_frames: list[np.ndarray] = []
+        if "rgb" in obs:
+            rgb = obs["rgb"]
+            if isinstance(rgb, np.ndarray):
+                if rgb.ndim == 4:
+                    raw_frames = [rgb[i] for i in range(rgb.shape[0])]
+                elif rgb.ndim == 3:
+                    raw_frames = [rgb]
+
+        image_keys = ["image", "second_image", "wrist_image"]
+        images: dict[str, np.ndarray] = {}
+        for i, key in enumerate(image_keys):
+            if i < len(raw_frames):
+                images[key] = _to_hwc3(raw_frames[i])
+            else:
+                images[key] = np.zeros((h, w, 3), dtype=np.uint8)
+
+        # Convert VLABench's raw ee_state `[pos_world(3), quat_wxyz(4), open(1)]`
+        # to the dataset's observation.state layout `[pos_robot(3), euler_xyz(3),
+        # gripper(1)]`. See VLABench/scripts/convert_to_lerobot.py — positions
+        # are stored in robot-base frame and orientations as scipy extrinsic
+        # 'xyz' euler angles.
+        raw = np.asarray(obs.get("ee_state", np.zeros(8)), dtype=np.float64).ravel()
+        pos_world = raw[:3] if raw.size >= 3 else np.zeros(3, dtype=np.float64)
+        quat_wxyz = raw[3:7] if raw.size >= 7 else np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float64)
+        gripper = float(raw[7]) if raw.size >= 8 else 0.0
+
+        base = self._robot_base_xyz if self._robot_base_xyz is not None else np.zeros(3, dtype=np.float64)
+        pos_robot = pos_world - base
+        euler_xyz = Rotation.from_quat([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]]).as_euler(
+            "xyz", degrees=False
+        )
+
+        ee_state = np.concatenate([pos_robot, euler_xyz, [gripper]]).astype(np.float64)
+
+        if self.obs_type == "pixels":
+            return {"pixels": images}
+        elif self.obs_type == "pixels_agent_pos":
+            return {
+                "pixels": images,
+                "agent_pos": ee_state.astype(np.float64),
+            }
+        else:
+            raise ValueError(f"Unknown obs_type: {self.obs_type}")
+
+    # ---- Action adaptation (EEF → joint ctrl) --------------------------------
+    #
+    # The HF vlabench datasets log 7D actions
+    # `[x, y, z (robot frame), rx, ry, rz (scipy extrinsic xyz), gripper]`,
+    # exactly matching VLABench's own eval pipeline (evaluator.base):
+    #   pos, euler, g = policy(...)
+    #   quat = euler_to_quaternion(*euler)      # extrinsic xyz -> wxyz
+    #   _, qpos = robot.get_qpos_from_ee_pos(physics, pos=pos + base, quat=quat)
+    #   env.step(np.concatenate([qpos, [g, g]]))
+    #
+    # VLABench's dm_control task writes `data.ctrl[:] = action` directly — for
+    # Franka that's 9 entries (7 arm joints + 2 gripper fingers). We mirror the
+    # above conversion so the policy's EEF commands actually drive the robot.
+
+    _FRANKA_FINGER_OPEN = 0.04  # qpos when gripper fully open
+
+    def _build_ctrl_from_action(self, action: np.ndarray, ctrl_dim: int) -> np.ndarray:
+        """Convert a 7D EEF action into the `ctrl_dim`-sized joint command vector.
+
+        For the Franka default (ctrl_dim=9): 7 arm joint qposes (via IK) +
+        2 gripper finger qposes (open/closed based on the gripper scalar).
+        If the action is already joint-space (shape matches ctrl_dim), pass
+        through.
+        """
+        if action.shape[0] == ctrl_dim:
+            return action.astype(np.float64, copy=False)
+
+        if action.shape[0] != 7:
+            # Unknown layout — fall back to zero-pad so the sim doesn't crash.
+            padded = np.zeros(ctrl_dim, dtype=np.float64)
+            padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
+            return padded
+
+        from dm_control.utils.inverse_kinematics import qpos_from_site_pose
+
+        # Action position is in robot-base frame (see convert_to_lerobot.py);
+        # dm_control's IK expects a world-frame target.
+        base = self._robot_base_xyz if self._robot_base_xyz is not None else np.zeros(3, dtype=np.float64)
+        pos_world = np.asarray(action[:3], dtype=np.float64) + base
+        rx, ry, rz = float(action[3]), float(action[4]), float(action[5])
+        gripper = float(np.clip(action[6], 0.0, 1.0))
+
+        # Dataset euler is scipy extrinsic 'xyz' (same as VLABench's
+        # `euler_to_quaternion`). scipy emits `[x, y, z, w]`; dm_control's IK
+        # and MuJoCo use `[w, x, y, z]`, so reorder.
+        qxyzw = Rotation.from_euler("xyz", [rx, ry, rz], degrees=False).as_quat()
+        quat = np.array([qxyzw[3], qxyzw[0], qxyzw[1], qxyzw[2]], dtype=np.float64)
+
+        assert self._env is not None
+        robot = self._env.task.robot
+        site_name = robot.end_effector_site.full_identifier
+
+        # inplace=False so IK doesn't mutate physics state mid-step — we only
+        # want the solved qpos. Fetch a fresh physics handle — caching it can
+        # yield a stale weakref after a reset.
+        ik_result = qpos_from_site_pose(
+            self._env.physics,
+            site_name=site_name,
+            target_pos=pos_world,
+            target_quat=quat,
+            inplace=False,
+            max_steps=100,
+        )
+        n_dof = robot.n_dof  # 7 for Franka
+        arm_qpos = ik_result.qpos[:n_dof]
+
+        # Dataset gripper convention: 1 = open (finger qpos = 0.04),
+        # 0 = closed (finger qpos = 0.0). See VLABench/scripts/convert_to_lerobot.py
+        # where `trajectory[i][-1] > 0.03` is encoded as `1`.
+        finger_qpos = gripper * self._FRANKA_FINGER_OPEN
+
+        ctrl = np.zeros(ctrl_dim, dtype=np.float64)
+        ctrl[:n_dof] = arm_qpos
+        # Remaining entries are gripper fingers (usually 2 for Franka).
+        ctrl[n_dof:] = finger_qpos
+        return ctrl
+
+    def reset(self, seed=None, **kwargs) -> tuple[RobotObservation, dict[str, Any]]:
+        self._ensure_env()
+        assert self._env is not None
+        super().reset(seed=seed)
+
+        if seed is not None:
+            self._seed_inner_env(int(self.np_random.integers(0, 2**31 - 1)))
+
+        self._env.reset()
+
+        observation = self._get_obs()
+        info = {"is_success": False}
+        return observation, info
+
+    def _seed_inner_env(self, seed: int) -> None:
+        """Propagate `seed` to the inner dm_control env. `Environment.reset()`
+        doesn't accept a seed, so we re-seed the task and environment
+        `RandomState`s directly. Best-effort: silently skipped when the
+        expected attributes are absent on a given VLABench version.
+        """
+        for owner_attr, rng_attr in (("task", "random"), (None, "_random_state")):
+            owner = getattr(self._env, owner_attr) if owner_attr else self._env
+            rng = getattr(owner, rng_attr, None)
+            rng_seed = getattr(rng, "seed", None)
+            if callable(rng_seed):
+                rng_seed(seed)
+
+    def step(self, action: np.ndarray) -> tuple[RobotObservation, float, bool, bool, dict[str, Any]]:
+        from dm_control.rl.control import PhysicsError  # type: ignore[import-untyped]
+
+        self._ensure_env()
+        assert self._env is not None
+
+        if action.ndim != 1:
+            raise ValueError(
+                f"Expected action to be 1-D (shape (action_dim,)), "
+                f"but got shape {action.shape} with ndim={action.ndim}"
+            )
+
+        if self.action_mode not in ("eef", "joint", "delta_eef"):
+            raise ValueError(f"Unknown action_mode: {self.action_mode}")
+
+        # Always refetch physics — dm_control returns a weakref proxy that can
+        # go stale across resets.
+        physics = self._env.physics
+        ctrl_dim = int(physics.data.ctrl.shape[0])
+        ctrl = self._build_ctrl_from_action(action, ctrl_dim)
+        try:
+            timestep = self._env.step(ctrl)
+        except PhysicsError as exc:
+            # Physics integrator diverged (e.g. mjWARN_BADQACC). Treat it as
+            # a graceful failed termination rather than a hard crash — the
+            # rest of the multi-task eval should still run.
+            logger.warning(
+                "PhysicsError during step on task '%s': %s. Terminating episode.",
+                self.task,
+                exc,
+            )
+            observation = self._get_obs()
+            info = {"task": self.task, "is_success": False, "physics_error": True}
+            # Drop the stale env so the next reset() rebuilds it cleanly.
+            with contextlib.suppress(Exception):
+                self._env.close()
+            self._env = None
+            return observation, 0.0, True, False, info
+
+        # Extract reward from dm_control timestep
+        reward = float(timestep.reward) if timestep.reward is not None else 0.0
+
+        # Check success via the task's termination condition
+        is_success = False
+        if hasattr(self._env, "task") and hasattr(self._env.task, "should_terminate_episode"):
+            is_success = bool(self._env.task.should_terminate_episode(self._env.physics))
+
+        terminated = is_success
+        truncated = False
+        info = {
+            "task": self.task,
+            "is_success": is_success,
+        }
+
+        observation = self._get_obs()
+
+        if terminated:
+            self.reset()
+
+        return observation, reward, terminated, truncated, info
+
+    def render(self) -> np.ndarray:
+        self._ensure_env()
+        obs = self._get_obs()
+        return obs["pixels"]["image"]
+
+    def close(self):
+        if self._env is not None:
+            self._env.close()
+            self._env = None
+
+
+# ---- Main API ----------------------------------------------------------------
+
+
+def create_vlabench_envs(
+    task: str,
+    n_envs: int,
+    gym_kwargs: dict[str, Any] | None = None,
+    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
+) -> dict[str, dict[int, Any]]:
+    """
+    Create vectorized VLABench environments with a consistent return shape.
+
+    Returns:
+        dict[suite_name][task_id] -> vec_env (env_cls([...]) with exactly n_envs factories)
+
+    Notes:
+        - n_envs is the number of rollouts *per task*.
+        - `task` can be a suite name ("primitive", "composite"), a comma-separated list of
+          suite names, or individual task names (e.g. "select_fruit,heat_food").
+    """
+    if env_cls is None or not callable(env_cls):
+        raise ValueError("env_cls must be a callable that wraps a list of environment factory callables.")
+    if not isinstance(n_envs, int) or n_envs <= 0:
+        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
+
+    gym_kwargs = dict(gym_kwargs or {})
+    task_groups = [t.strip() for t in task.split(",") if t.strip()]
+    if not task_groups:
+        raise ValueError("`task` must contain at least one VLABench task or suite name.")
+
+    logger.info(
+        "Creating VLABench envs | task_groups=%s | n_envs(per task)=%d",
+        task_groups,
+        n_envs,
+    )
+
+    is_async = env_cls is gym.vector.AsyncVectorEnv
+    cached_obs_space = None
+    cached_act_space = None
+    cached_metadata = None
+    out: dict[str, dict[int, Any]] = defaultdict(dict)
+
+    for group in task_groups:
+        # Check if it's a suite name, otherwise treat as individual task
+        tasks = SUITE_TASKS.get(group, [group])
+
+        for tid, task_name in enumerate(tasks):
+            logger.info(
+                "Building vec env | group=%s | task_id=%d | task=%s",
+                group,
+                tid,
+                task_name,
+            )
+
+            fns = [(lambda tn=task_name: VLABenchEnv(task=tn, **gym_kwargs)) for _ in range(n_envs)]
+
+            if is_async:
+                lazy = _LazyAsyncVectorEnv(fns, cached_obs_space, cached_act_space, cached_metadata)
+                if cached_obs_space is None:
+                    cached_obs_space = lazy.observation_space
+                    cached_act_space = lazy.action_space
+                    cached_metadata = lazy.metadata
+                out[group][tid] = lazy
+            else:
+                out[group][tid] = env_cls(fns)
+
+    return {group: dict(task_map) for group, task_map in out.items()}

src/lerobot/policies/act/modeling_act.py

@@ -142,9 +142,10 @@ class ACTPolicy(PreTrainedPolicy):
 
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
-        l1_loss = (
-            F.l1_loss(batch[ACTION], actions_hat, reduction="none") * ~batch["action_is_pad"].unsqueeze(-1)
-        ).mean()
+        abs_err = F.l1_loss(batch[ACTION], actions_hat, reduction="none")
+        valid_mask = ~batch["action_is_pad"].unsqueeze(-1)
+        num_valid = valid_mask.sum() * abs_err.shape[-1]
+        l1_loss = (abs_err * valid_mask).sum() / num_valid.clamp_min(1)
 
         loss_dict = {"l1_loss": l1_loss.item()}
         if self.config.use_vae:

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -380,7 +380,9 @@ class DiffusionModel(nn.Module):
                     f"{self.config.do_mask_loss_for_padding=}."
                 )
             in_episode_bound = ~batch["action_is_pad"]
-            loss = loss * in_episode_bound.unsqueeze(-1)
+            mask = in_episode_bound.unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 

src/lerobot/policies/groot/groot_n1.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -174,17 +173,14 @@ N_COLOR_CHANNELS = 3
 
 
 # config
-@dataclass
 class GR00TN15Config(PretrainedConfig):
     model_type = "gr00t_n1_5"
-    backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
 
-    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
-
-    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
-
-    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
-    compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
+    backbone_cfg: dict
+    action_head_cfg: dict
+    action_horizon: int
+    action_dim: int
+    compute_dtype: str = "float32"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)

src/lerobot/policies/multi_task_dit/modeling_multi_task_dit.py

@@ -688,8 +688,9 @@ class DiffusionObjective(nn.Module):
         loss = F.mse_loss(predicted, target, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            valid_actions = ~batch["action_is_pad"]
-            loss = loss * valid_actions.unsqueeze(-1)
+            mask = ~batch["action_is_pad"].unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 
@@ -752,8 +753,9 @@ class FlowMatchingObjective(nn.Module):
         loss = F.mse_loss(predicted_velocity, target_velocity, reduction="none")
 
         if self.do_mask_loss_for_padding and "action_is_pad" in batch:
-            valid_mask = ~batch["action_is_pad"]
-            loss = loss * valid_mask.unsqueeze(-1)
+            mask = ~batch["action_is_pad"].unsqueeze(-1)
+            num_valid = mask.sum() * loss.shape[-1]
+            return (loss * mask).sum() / num_valid.clamp_min(1)
 
         return loss.mean()
 

src/lerobot/policies/sarm/processor_sarm.py

@@ -455,7 +455,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
             inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
             # Get image embeddings
-            embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
+            # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
+            output = self.clip_model.get_image_features(**inputs)
+            if not isinstance(output, torch.Tensor):
+                output = output.pooler_output
+                if output is None:
+                    raise ValueError("pooler_output should not be None for CLIP models.")
+            embeddings = output.detach().cpu()
 
             # Handle single frame case
             if embeddings.dim() == 1:
@@ -482,7 +488,13 @@ class SARMEncodingProcessorStep(ProcessorStep):
         inputs = self.clip_processor.tokenizer([text], return_tensors="pt", padding=True, truncation=True)
         inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
-        text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
+        # transformers 5.x returns BaseModelOutputWithPooling instead of a plain tensor
+        output = self.clip_model.get_text_features(**inputs)
+        if not isinstance(output, torch.Tensor):
+            output = output.pooler_output
+            if output is None:
+                raise ValueError("pooler_output should not be None for CLIP models.")
+        text_embedding = output.detach().cpu()
         text_embedding = text_embedding.expand(batch_size, -1)
 
         return text_embedding

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -394,13 +394,21 @@ class SmolVLAPolicy(PreTrainedPolicy):
         loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
 
         if reduction == "none":
-            # Return per-sample losses (B,) by averaging over time and action dims
-            per_sample_loss = losses.mean(dim=(1, 2))
+            # Return per-sample losses (B,) by averaging over valid (time, action) entries
+            if actions_is_pad is None:
+                per_sample_loss = losses.mean(dim=(1, 2))
+            else:
+                num_valid = ((~actions_is_pad).sum(dim=1) * losses.shape[-1]).clamp_min(1)
+                per_sample_loss = losses.sum(dim=(1, 2)) / num_valid
             loss_dict["loss"] = per_sample_loss.mean().item()
             return per_sample_loss, loss_dict
         else:
-            # Default: return scalar mean loss
-            loss = losses.mean()
+            # Default: return scalar mean loss over valid (time, action) entries
+            if actions_is_pad is None:
+                loss = losses.mean()
+            else:
+                num_valid = ((~actions_is_pad).sum() * losses.shape[-1]).clamp_min(1)
+                loss = losses.sum() / num_valid
             loss_dict["loss"] = loss.item()
             return loss, loss_dict
 

src/lerobot/processor/hil_processor.py

@@ -321,6 +321,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
     This step normalizes the `transition` object by:
     1. Copying `teleop_action` from `info` to `complementary_data`.
     2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
+    3. Copying `discrete_penalty` from `info` to `complementary_data`.
     """
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
@@ -330,6 +331,9 @@ class GymHILAdapterProcessorStep(ProcessorStep):
         if TELEOP_ACTION_KEY in info:
             complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
 
+        if DISCRETE_PENALTY_KEY in info:
+            complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
+
         if "is_intervention" in info:
             info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
 
@@ -348,18 +352,24 @@ class GymHILAdapterProcessorStep(ProcessorStep):
 @ProcessorStepRegistry.register("gripper_penalty_processor")
 class GripperPenaltyProcessorStep(ProcessorStep):
     """
-    Applies a penalty for inefficient gripper usage.
+    Applies a small per-transition cost on the discrete gripper action.
 
-    This step penalizes actions that attempt to close an already closed gripper or
-    open an already open one, based on position thresholds.
+    Fires only when the commanded action would actually transition the gripper
+    from one extreme to the other (close-while-open or open-while-closed).
+    This discourages gripper oscillation while leaving "stay" and saturating-further
+    commands unpenalized.
 
     Attributes:
         penalty: The negative reward value to apply.
         max_gripper_pos: The maximum position value for the gripper, used for normalization.
+        open_threshold: Normalized state below which the gripper is considered "open".
+        closed_threshold: Normalized state above which the gripper is considered "closed".
     """
 
-    penalty: float = -0.01
+    penalty: float = -0.02
     max_gripper_pos: float = 30.0
+    open_threshold: float = 0.1
+    closed_threshold: float = 0.9
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         """
@@ -391,9 +401,13 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
 
         # Calculate penalty boolean as in original
-        gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
-            gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
-        )
+        #   - currently open  AND target is closed  -> close transition
+        #   - currently closed AND target is open   -> open transition
+        is_open = gripper_state_normalized < self.open_threshold
+        is_closed = gripper_state_normalized > self.closed_threshold
+        cmd_close = gripper_action_normalized > self.closed_threshold
+        cmd_open = gripper_action_normalized < self.open_threshold
+        gripper_penalty_bool = (is_open and cmd_close) or (is_closed and cmd_open)
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
@@ -409,11 +423,14 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         Returns the configuration of the step for serialization.
 
         Returns:
-            A dictionary containing the penalty value and max gripper position.
+            A dictionary containing the penalty value, max gripper position,
+            and the open/closed thresholds.
         """
         return {
             "penalty": self.penalty,
             "max_gripper_pos": self.max_gripper_pos,
+            "open_threshold": self.open_threshold,
+            "closed_threshold": self.closed_threshold,
         }
 
     def reset(self) -> None:

src/lerobot/processor/normalize_processor.py

@@ -134,6 +134,15 @@ class _NormalizationMixin:
         if self.dtype is None:
             self.dtype = torch.float32
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
+
+    def _reshape_visual_stats(self) -> None:
+        """Reshape visual stats from ``[C]`` to ``[C, 1, 1]`` for image broadcasting."""
+        for key, feature in self.features.items():
+            if feature.type == FeatureType.VISUAL and key in self._tensor_stats:
+                for stat_name, stat_tensor in self._tensor_stats[key].items():
+                    if isinstance(stat_tensor, Tensor) and stat_tensor.ndim == 1:
+                        self._tensor_stats[key][stat_name] = stat_tensor.reshape(-1, 1, 1)
 
     def to(
         self, device: torch.device | str | None = None, dtype: torch.dtype | None = None
@@ -152,6 +161,7 @@ class _NormalizationMixin:
         if dtype is not None:
             self.dtype = dtype
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
         return self
 
     def state_dict(self) -> dict[str, Tensor]:
@@ -201,6 +211,7 @@ class _NormalizationMixin:
             # Don't load from state_dict, keep the explicitly provided stats
             # But ensure _tensor_stats is properly initialized
             self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)  # type: ignore[assignment]
+            self._reshape_visual_stats()
             return
 
         # Normal behavior: load stats from state_dict
@@ -211,6 +222,7 @@ class _NormalizationMixin:
             self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
                 dtype=torch.float32, device=self.device
             )
+        self._reshape_visual_stats()
 
         # Reconstruct the original stats dict from tensor stats for compatibility with to() method
         # and other functions that rely on self.stats

src/lerobot/rl/actor.py

@@ -60,7 +60,7 @@ from torch.multiprocessing import Event, Queue
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
-from lerobot.policies import make_policy
+from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -89,9 +89,9 @@ from lerobot.utils.utils import (
 )
 
 from .gym_manipulator import (
-    create_transition,
     make_processors,
     make_robot_env,
+    reset_and_build_transition,
     step_env_and_process_transition,
 )
 from .process import ProcessSignalHandler
@@ -261,13 +261,12 @@ def act_with_policy(
     policy = policy.eval()
     assert isinstance(policy, nn.Module)
 
-    obs, info = online_env.reset()
-    env_processor.reset()
-    action_processor.reset()
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
 
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info)
-    transition = env_processor(transition)
+    transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
     # NOTE: For the moment we will solely handle the case of a single environment
     sum_reward_episode = 0
@@ -291,8 +290,21 @@ def act_with_policy(
 
         # Time policy inference and check if it meets FPS requirement
         with policy_timer:
-            # Extract observation from transition for policy
-            action = policy.select_action(batch=observation)
+            normalized_observation = preprocessor.process_observation(observation)
+            action = policy.select_action(batch=normalized_observation)
+            # Unnormalize only the continuous part. When `num_discrete_actions` is set,
+            # `select_action` concatenates an argmax index in env space at the last dim;
+            # action stats cover the continuous dims only, so feeding the full vector to
+            # the unnormalizer would shape-mismatch and would also corrupt the discrete
+            # index by treating it as a normalized value.
+            if cfg.policy.num_discrete_actions is not None:
+                continuous_action = postprocessor.process_action(action[..., :-1])
+                discrete_action = action[..., -1:].to(
+                    device=continuous_action.device, dtype=continuous_action.dtype
+                )
+                action = torch.cat([continuous_action, discrete_action], dim=-1)
+            else:
+                action = postprocessor.process_action(action)
         policy_fps = policy_timer.fps_last
 
         log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
@@ -326,7 +338,8 @@ def act_with_policy(
 
         # Check for intervention from transition info
         intervention_info = new_transition[TransitionKey.INFO]
-        if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
+        is_intervention = bool(intervention_info.get(TeleopEvents.IS_INTERVENTION, False))
+        if is_intervention:
             episode_intervention = True
             episode_intervention_steps += 1
 
@@ -334,6 +347,10 @@ def act_with_policy(
             "discrete_penalty": torch.tensor(
                 [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
             ),
+            # Forward the intervention flag so the learner can route this transition
+            # into the offline replay buffer (see `process_transitions` in learner.py).
+            # Use the plain string key so the payload survives torch.load(weights_only=True).
+            TeleopEvents.IS_INTERVENTION.value: is_intervention,
         }
         # Create transition for learner (convert to old format)
         list_transition_to_send_to_learner.append(
@@ -390,14 +407,7 @@ def act_with_policy(
             episode_intervention_steps = 0
             episode_total_steps = 0
 
-            # Reset environment and processors
-            obs, info = online_env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            # Process initial observation
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+            transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time

src/lerobot/rl/gym_manipulator.py

@@ -383,10 +383,21 @@ def make_processors(
             GymHILAdapterProcessorStep(),
             Numpy2TorchActionProcessorStep(),
             VanillaObservationProcessorStep(),
-            AddBatchDimensionProcessorStep(),
-            DeviceProcessorStep(device=device),
         ]
 
+        # Add time limit processor if reset config exists
+        if cfg.processor.reset is not None:
+            env_pipeline_steps.append(
+                TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps))
+            )
+
+        env_pipeline_steps.extend(
+            [
+                AddBatchDimensionProcessorStep(),
+                DeviceProcessorStep(device=device),
+            ]
+        )
+
         return DataProcessorPipeline(
             steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition
         ), DataProcessorPipeline(
@@ -551,8 +562,19 @@ def step_env_and_process_transition(
     terminated = terminated or processed_action_transition[TransitionKey.DONE]
     truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED]
     complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy()
+
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+
+    # Merge env and action-processor info: env wins for str keys, action-processor
+    # wins for `TeleopEvents` enum keys
+    action_info = processed_action_transition[TransitionKey.INFO]
     new_info = info.copy()
-    new_info.update(processed_action_transition[TransitionKey.INFO])
+    for key, value in action_info.items():
+        if isinstance(key, TeleopEvents):
+            new_info[key] = value
 
     new_transition = create_transition(
         observation=obs,
@@ -568,6 +590,24 @@ def step_env_and_process_transition(
     return new_transition
 
 
+def reset_and_build_transition(
+    env: gym.Env,
+    env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+    action_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+) -> EnvTransition:
+    """Reset env + processors and return the first env-processed transition."""
+    obs, info = env.reset()
+    env_processor.reset()
+    action_processor.reset()
+    complementary_data: dict[str, Any] = {}
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
+    return env_processor(data=transition)
+
+
 def control_loop(
     env: gym.Env,
     env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
@@ -593,17 +633,7 @@ def control_loop(
     print("- When not intervening, robot will stay still")
     print("- Press Ctrl+C to exit")
 
-    # Reset environment and processors
-    obs, info = env.reset()
-    complementary_data = (
-        {"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {}
-    )
-    env_processor.reset()
-    action_processor.reset()
-
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
-    transition = env_processor(data=transition)
+    transition = reset_and_build_transition(env, env_processor, action_processor)
 
     # Determine if gripper is used
     use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True
@@ -665,7 +695,7 @@ def control_loop(
         # Create a neutral action (no movement)
         neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
         if use_gripper:
-            neutral_action = torch.cat([neutral_action, torch.tensor([0.0])])  # Gripper stay
+            neutral_action = torch.cat([neutral_action, torch.tensor([1.0])])  # Gripper stay
 
         # Use the new step function
         transition = step_env_and_process_transition(
@@ -723,12 +753,7 @@ def control_loop(
                     dataset.save_episode()
 
             # Reset for new episode
-            obs, info = env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+            transition = reset_and_build_transition(env, env_processor, action_processor)
 
         # Maintain fps timing
         precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))

src/lerobot/rl/learner.py

@@ -70,7 +70,7 @@ from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets import LeRobotDataset, make_dataset
-from lerobot.policies import make_policy
+from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.policies.sac.modeling_sac import SACPolicy
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -317,6 +317,11 @@ def add_actor_information_and_train(
 
     policy.train()
 
+    preprocessor, _postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
+
     push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
 
     last_time_policy_pushed = time.time()
@@ -405,8 +410,8 @@ def add_actor_information_and_train(
 
             actions = batch[ACTION]
             rewards = batch["reward"]
-            observations = batch["state"]
-            next_observations = batch["next_state"]
+            observations = preprocessor.process_observation(batch["state"])
+            next_observations = preprocessor.process_observation(batch["next_state"])
             done = batch["done"]
             check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
 
@@ -463,8 +468,8 @@ def add_actor_information_and_train(
 
         actions = batch[ACTION]
         rewards = batch["reward"]
-        observations = batch["state"]
-        next_observations = batch["next_state"]
+        observations = preprocessor.process_observation(batch["state"])
+        next_observations = preprocessor.process_observation(batch["next_state"])
         done = batch["done"]
 
         check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
@@ -1163,7 +1168,7 @@ def process_transitions(
 
             # Add to offline buffer if it's an intervention
             if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
-                TeleopEvents.IS_INTERVENTION
+                TeleopEvents.IS_INTERVENTION.value
             ):
                 offline_replay_buffer.add(**transition)
 

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -353,7 +353,8 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
         speed_factor: A scaling factor to convert the normalized velocity command to a position change.
         clip_min: The minimum allowed gripper joint position.
         clip_max: The maximum allowed gripper joint position.
-        discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay).
+        discrete_gripper: If True, interpret the input as a discrete class index
+            {0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
     """
 
     speed_factor: float = 20.0
@@ -377,10 +378,10 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
             raise ValueError("Joints observation is require for computing robot kinematics")
 
         if self.discrete_gripper:
-            # Discrete gripper actions are in [0, 1, 2]
-            # 0: open, 1: close, 2: stay
-            # We need to shift them to [-1, 0, 1] and then scale them to clip_max
-            gripper_vel = (gripper_vel - 1) * self.clip_max
+            # Map discrete command {0=close, 1=stay, 2=open} -> signed velocity.
+            # Negation accounts for SO100 sign (joint position increases on close).
+            #   0 -> +clip_max (close), 1 -> 0 (stay), 2 -> -clip_max (open)
+            gripper_vel = -(gripper_vel - 1) * self.clip_max
 
         # Compute desired gripper position
         delta = gripper_vel * float(self.speed_factor)

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -150,11 +150,24 @@ Show dataset information without feature details:
         --operation.type info \
         --operation.show_features false
 
-Recompute dataset statistics:
+Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default):
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type recompute_stats
 
+Recompute stats and save to a specific new repo_id:
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_new_stats \
+        --operation.type recompute_stats
+
+Recompute stats in-place (overwrites original dataset stats):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht \
+        --operation.type recompute_stats \
+        --operation.overwrite true
+
 Recompute stats for relative actions and push to hub:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht \
@@ -256,6 +269,7 @@ class RecomputeStatsConfig(OperationConfig):
     relative_exclude_joints: list[str] | None = None
     chunk_size: int = 50
     num_workers: int = 0
+    overwrite: bool = False
 
 
 @OperationConfig.register_subclass("info")
@@ -280,16 +294,30 @@ class EditDatasetConfig:
     push_to_hub: bool = False
 
 
+def _resolve_io_paths(
+    repo_id: str,
+    new_repo_id: str | None,
+    root: Path | str | None,
+    new_root: Path | str | None,
+    default_new_repo_id: str | None = None,
+) -> tuple[str, Path, Path]:
+    """Resolve input/output paths and repo_id for dataset operations.
+
+    Returns (output_repo_id, input_path, output_path) with resolved (symlink-safe) paths.
+    """
+    input_path = (Path(root) if root else HF_LEROBOT_HOME / repo_id).resolve()
+    output_repo_id = new_repo_id or default_new_repo_id or repo_id
+    output_path = (Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id).resolve()
+    return output_repo_id, input_path, output_path
+
+
 def get_output_path(
     repo_id: str,
     new_repo_id: str | None,
     root: Path | str | None,
     new_root: Path | str | None,
 ) -> tuple[str, Path]:
-    input_path = Path(root) if root else HF_LEROBOT_HOME / repo_id
-
-    output_repo_id = new_repo_id if new_repo_id else repo_id
-    output_path = Path(new_root) if new_root else HF_LEROBOT_HOME / output_repo_id
+    output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
 
     # In case of in-place modification, create a backup of the original dataset (if it exists)
     if output_path == input_path:
@@ -557,7 +585,39 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     if not isinstance(cfg.operation, RecomputeStatsConfig):
         raise ValueError("Operation config must be RecomputeStatsConfig")
 
-    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    # Determine whether this is an in-place operation
+    output_repo_id, input_root, output_root = _resolve_io_paths(
+        cfg.repo_id,
+        cfg.new_repo_id,
+        cfg.root,
+        cfg.new_root,
+        default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
+    )
+    in_place = output_root == input_root
+
+    if in_place and not cfg.operation.overwrite:
+        raise ValueError(
+            f"recompute_stats would overwrite the dataset in-place at {input_root}. "
+            "Pass --operation.overwrite true to allow in-place modification, "
+            "or use --new_repo_id / --new_root to write to a different location. "
+            f"Default output repo_id when neither is set: '{cfg.repo_id}_recomputed_stats'."
+        )
+
+    if in_place:
+        logging.warning(
+            f"Overwriting dataset stats in-place at {input_root}. The original stats will be lost."
+        )
+        dataset = LeRobotDataset(cfg.repo_id, root=input_root)
+    else:
+        logging.info(f"Copying dataset from {input_root} to {output_root}")
+        if output_root.exists():
+            backup_path = output_root.with_name(output_root.name + "_old")
+            logging.warning(f"Output directory {output_root} already exists. Moving to {backup_path}")
+            if backup_path.exists():
+                shutil.rmtree(backup_path)
+            shutil.move(output_root, backup_path)
+        shutil.copytree(input_root, output_root)
+        dataset = LeRobotDataset(output_repo_id, root=output_root)
 
     logging.info(f"Recomputing stats for {cfg.repo_id}")
     if cfg.operation.relative_action:
@@ -578,7 +638,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
     logging.info(f"Stats written to {dataset.root}")
 
     if cfg.push_to_hub:
-        logging.info(f"Pushing to hub as {dataset.meta.repo_id}...")
+        logging.info(f"Pushing to hub as {dataset.repo_id}...")
         dataset.push_to_hub()
 
 

src/lerobot/utils/import_utils.py

@@ -115,7 +115,9 @@ _feetech_sdk_available = is_package_available("feetech-servo-sdk", import_name="
 _reachy2_sdk_available = is_package_available("reachy2_sdk")
 _can_available = is_package_available("python-can", "can")
 _unitree_sdk_available = is_package_available("unitree-sdk2py", "unitree_sdk2py")
-_pyrealsense2_available = is_package_available("pyrealsense2")
+_pyrealsense2_available = is_package_available("pyrealsense2") or is_package_available(
+    "pyrealsense2-macosx", import_name="pyrealsense2"
+)
 _zmq_available = is_package_available("pyzmq", import_name="zmq")
 _hebi_available = is_package_available("hebi-py", import_name="hebi")
 _teleop_available = is_package_available("teleop")

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c2b8f8532c7a0b776de5e536b8b54e30b1a0c2e3d5cc25a2d86fe43e40ae5e8c
+oid sha256:8a31653c11eccdd4d80fd3f6a351cd54c49b8a48db1f7e9faf38fddd7900a09f
 size 515400

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:224b5fa4828aa88171b68c036e8919c1eae563e2113f03b6461eadf5bf8525a6
+oid sha256:75bf051698b37dcd7517ec8025a896ab5a0551a6dde5f89d0a3d5d50966e83e6
 size 31672

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/output_dict.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:016d2fa8fe5f58017dfd46f4632fdc19dfd751e32a2c7cde2077c6f95546d6bd
+oid sha256:88e10930a10041d50f2cf369e6813ac14618d13dad1c21bdde1ac7798611c6ba
 size 68

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eca0d87a699620e4fec7e68539b0be91e4cc933f6bf12032da52c182ab6f38cf
+oid sha256:89833a5ccdb7d85c83f717ff8ec68b8e822005cb8803899acaae88c578e2e3ae
 size 31672

tests/envs/test_robotwin.py

@@ -0,0 +1,282 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Unit tests for the RoboTwin 2.0 Gymnasium wrapper.
+
+These tests mock out the SAPIEN-based RoboTwin runtime (task modules +
+YAML config loader) so they run without the full RoboTwin installation
+(SAPIEN, CuRobo, mplib, asset downloads, etc.).
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from unittest.mock import MagicMock, patch
+
+import gymnasium as gym
+import numpy as np
+import pytest
+
+from lerobot.envs.robotwin import (
+    ACTION_DIM,
+    ROBOTWIN_CAMERA_NAMES,
+    ROBOTWIN_TASKS,
+    RoboTwinEnv,
+    create_robotwin_envs,
+)
+
+# ---------------------------------------------------------------------------
+# Fixtures / helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_mock_task_env(
+    height: int = 240,
+    width: int = 320,
+    cameras: tuple[str, ...] = ROBOTWIN_CAMERA_NAMES,
+) -> MagicMock:
+    """Return a mock that mimics the RoboTwin task class API.
+
+    RoboTwin's real get_obs returns
+        {"observation": {cam: {"rgb": img}}, "joint_action": {"vector": np.ndarray}, ...}
+    so the mock follows the same nested shape.
+    """
+    obs_dict = {
+        "observation": {cam: {"rgb": np.zeros((height, width, 3), dtype=np.uint8)} for cam in cameras},
+        "joint_action": {"vector": np.zeros(ACTION_DIM, dtype=np.float32)},
+        "endpose": {},
+    }
+
+    mock = MagicMock()
+    mock.get_obs.return_value = obs_dict
+    mock.setup_demo.return_value = None
+    mock.take_action.return_value = None
+    mock.eval_success = False
+    mock.check_success.return_value = False
+    mock.close_env.return_value = None
+    return mock
+
+
+@contextmanager
+def _patch_runtime(mock_task_instance: MagicMock):
+    """Patch both the task-class loader and the YAML config loader so the
+    env can construct + reset without a real RoboTwin install."""
+    task_cls = MagicMock(return_value=mock_task_instance)
+    fake_setup = {
+        "head_camera_h": 240,
+        "head_camera_w": 320,
+        "left_embodiment_config": {},
+        "right_embodiment_config": {},
+        "left_robot_file": "",
+        "right_robot_file": "",
+        "dual_arm_embodied": True,
+        "render_freq": 0,
+        "task_name": "beat_block_hammer",
+        "task_config": "demo_clean",
+    }
+    with (
+        patch("lerobot.envs.robotwin._load_robotwin_task", return_value=task_cls),
+        patch("lerobot.envs.robotwin._load_robotwin_setup_kwargs", return_value=fake_setup),
+    ):
+        yield
+
+
+# ---------------------------------------------------------------------------
+# RoboTwinEnv unit tests
+# ---------------------------------------------------------------------------
+
+
+class TestRoboTwinEnv:
+    def test_observation_space_shape(self):
+        """observation_space should have the configured h×w×3 for every camera."""
+        h, w = 240, 320
+        env = RoboTwinEnv(
+            task_name="beat_block_hammer",
+            observation_height=h,
+            observation_width=w,
+            camera_names=["head_camera", "left_camera"],
+        )
+        pixels_space = env.observation_space["pixels"]
+        assert pixels_space["head_camera"].shape == (h, w, 3)
+        assert pixels_space["left_camera"].shape == (h, w, 3)
+        assert "right_camera" not in pixels_space
+
+    def test_action_space(self):
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        assert env.action_space.shape == (ACTION_DIM,)
+        assert env.action_space.dtype == np.float32
+
+    def test_reset_returns_correct_obs_keys(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        with _patch_runtime(mock_task):
+            obs, info = env.reset()
+
+        assert "pixels" in obs
+        for cam in ROBOTWIN_CAMERA_NAMES:
+            assert cam in obs["pixels"], f"Missing camera '{cam}' in obs"
+        assert "agent_pos" in obs
+        assert obs["agent_pos"].shape == (ACTION_DIM,)
+        assert info["is_success"] is False
+
+    def test_reset_calls_setup_demo(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        with _patch_runtime(mock_task):
+            env.reset(seed=42)
+        # setup_demo receives the full YAML-derived kwargs plus seed + is_test;
+        # we only assert the caller-provided bits.
+        assert mock_task.setup_demo.call_count == 1
+        call_kwargs = mock_task.setup_demo.call_args.kwargs
+        assert call_kwargs["seed"] == 42
+        assert call_kwargs["is_test"] is True
+
+    def test_step_returns_correct_types(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        action = np.zeros(ACTION_DIM, dtype=np.float32)
+        with _patch_runtime(mock_task):
+            env.reset()
+            obs, reward, terminated, truncated, info = env.step(action)
+
+        assert isinstance(obs, dict)
+        assert isinstance(reward, float)
+        assert isinstance(terminated, bool)
+        assert isinstance(truncated, bool)
+        assert isinstance(info, dict)
+
+    def test_step_wrong_action_shape_raises(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        bad_action = np.zeros(7, dtype=np.float32)  # wrong dim
+        with _patch_runtime(mock_task):
+            env.reset()
+            with pytest.raises(ValueError, match="Expected 1-D action"):
+                env.step(bad_action)
+
+    def test_success_terminates_episode(self):
+        mock_task = _make_mock_task_env()
+        mock_task.check_success.return_value = True
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        action = np.zeros(ACTION_DIM, dtype=np.float32)
+        with _patch_runtime(mock_task):
+            env.reset()
+            _, _, terminated, _, info = env.step(action)
+        assert terminated is True
+        assert info["is_success"] is True
+
+    def test_truncation_after_episode_length(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer", episode_length=2)
+        action = np.zeros(ACTION_DIM, dtype=np.float32)
+        with _patch_runtime(mock_task):
+            env.reset()
+            env.step(action)  # step 1
+            _, _, _, truncated, _ = env.step(action)  # step 2 → truncated
+        assert truncated is True
+
+    def test_close_calls_close_env(self):
+        mock_task = _make_mock_task_env()
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        with _patch_runtime(mock_task):
+            env.reset()
+            env.close()
+        mock_task.close_env.assert_called_once()
+
+    def test_black_frame_for_missing_camera(self):
+        """If a camera key is absent from get_obs(), a black frame is returned."""
+        # Mock exposes only head_camera; we ask for both head_camera + left_camera.
+        mock_task = _make_mock_task_env(height=10, width=10, cameras=("head_camera",))
+        env = RoboTwinEnv(
+            task_name="beat_block_hammer",
+            camera_names=["head_camera", "left_camera"],
+            observation_height=10,
+            observation_width=10,
+        )
+        with _patch_runtime(mock_task):
+            obs, _ = env.reset()
+        assert obs["pixels"]["left_camera"].shape == (10, 10, 3)
+        assert obs["pixels"]["left_camera"].sum() == 0
+
+    def test_task_and_task_description_attributes(self):
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        assert env.task == "beat_block_hammer"
+        assert isinstance(env.task_description, str)
+
+    def test_deferred_init_env_is_none_before_reset(self):
+        env = RoboTwinEnv(task_name="beat_block_hammer")
+        assert env._env is None  # noqa: SLF001  (testing internal state)
+
+
+# ---------------------------------------------------------------------------
+# create_robotwin_envs tests
+# ---------------------------------------------------------------------------
+
+
+class TestCreateRoboTwinEnvs:
+    def test_returns_correct_structure(self):
+        mock_task = _make_mock_task_env()
+        with _patch_runtime(mock_task):
+            envs = create_robotwin_envs(
+                task="beat_block_hammer",
+                n_envs=1,
+                env_cls=gym.vector.SyncVectorEnv,
+            )
+        assert "beat_block_hammer" in envs
+        assert 0 in envs["beat_block_hammer"]
+        assert isinstance(envs["beat_block_hammer"][0], gym.vector.SyncVectorEnv)
+
+    def test_multi_task(self):
+        mock_task = _make_mock_task_env()
+        with _patch_runtime(mock_task):
+            envs = create_robotwin_envs(
+                task="beat_block_hammer,click_bell",
+                n_envs=1,
+                env_cls=gym.vector.SyncVectorEnv,
+            )
+        assert set(envs.keys()) == {"beat_block_hammer", "click_bell"}
+
+    def test_unknown_task_raises(self):
+        with pytest.raises(ValueError, match="Unknown RoboTwin tasks"):
+            create_robotwin_envs(
+                task="not_a_real_task",
+                n_envs=1,
+                env_cls=gym.vector.SyncVectorEnv,
+            )
+
+    def test_invalid_n_envs_raises(self):
+        with pytest.raises(ValueError, match="n_envs must be a positive int"):
+            create_robotwin_envs(
+                task="beat_block_hammer",
+                n_envs=0,
+                env_cls=gym.vector.SyncVectorEnv,
+            )
+
+
+# ---------------------------------------------------------------------------
+# ROBOTWIN_TASKS list
+# ---------------------------------------------------------------------------
+
+
+def test_task_list_not_empty():
+    assert len(ROBOTWIN_TASKS) >= 50
+
+
+def test_all_tasks_are_strings():
+    assert all(isinstance(t, str) and t for t in ROBOTWIN_TASKS)
+
+
+def test_no_duplicate_tasks():
+    assert len(ROBOTWIN_TASKS) == len(set(ROBOTWIN_TASKS))

tests/test_robomme_env.py

@@ -0,0 +1,232 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Unit tests for the RoboMME env wrapper and config.
+
+RoboMME requires Linux + ManiSkill (Vulkan/SAPIEN), so tests that touch the
+env wrapper mock the ``robomme`` package. Tests that only exercise the
+dataclass config run without any mocking.
+"""
+
+from __future__ import annotations
+
+import sys
+from types import ModuleType
+from unittest.mock import MagicMock
+
+import numpy as np
+
+
+def _install_robomme_stub():
+    """Register a minimal stub for the ``robomme`` package on sys.modules."""
+    stub = ModuleType("robomme")
+    wrapper_stub = ModuleType("robomme.env_record_wrapper")
+
+    class FakeBuilder:
+        def __init__(self, **kwargs):
+            pass
+
+        def make_env_for_episode(self, episode_idx: int, max_steps: int):
+            env = MagicMock()
+            obs = {
+                "front_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
+                "wrist_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
+                "joint_state_list": [np.zeros(7, dtype=np.float32)],
+                "gripper_state_list": [np.zeros(2, dtype=np.float32)],
+            }
+            env.reset.return_value = (obs, {"status": "ongoing", "task_goal": "pick the cube"})
+            env.step.return_value = (obs, 0.0, False, False, {"status": "ongoing", "task_goal": ""})
+            return env
+
+    wrapper_stub.BenchmarkEnvBuilder = FakeBuilder
+    stub.env_record_wrapper = wrapper_stub
+    sys.modules["robomme"] = stub
+    sys.modules["robomme.env_record_wrapper"] = wrapper_stub
+
+
+def _uninstall_robomme_stub():
+    sys.modules.pop("robomme", None)
+    sys.modules.pop("robomme.env_record_wrapper", None)
+
+
+# ---------------------------------------------------------------------------
+# Config tests (no sim required)
+# ---------------------------------------------------------------------------
+
+
+def test_robomme_env_config_defaults():
+    from lerobot.envs.configs import RoboMMEEnv
+
+    cfg = RoboMMEEnv()
+    assert cfg.task == "PickXtimes"
+    assert cfg.fps == 10
+    assert cfg.episode_length == 300
+    assert cfg.action_space == "joint_angle"
+    assert cfg.dataset_split == "test"
+    assert cfg.task_ids is None
+
+
+def test_robomme_env_config_type():
+    from lerobot.envs.configs import RoboMMEEnv
+
+    cfg = RoboMMEEnv()
+    assert cfg.type == "robomme"
+
+
+def test_robomme_features_map():
+    from lerobot.envs.configs import RoboMMEEnv
+    from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
+
+    cfg = RoboMMEEnv()
+    assert cfg.features_map[ACTION] == ACTION
+    assert cfg.features_map["pixels/image"] == f"{OBS_IMAGES}.image"
+    assert cfg.features_map["pixels/wrist_image"] == f"{OBS_IMAGES}.wrist_image"
+    assert cfg.features_map["agent_pos"] == OBS_STATE
+
+
+def test_robomme_features_action_dim_joint_angle():
+    from lerobot.envs.configs import RoboMMEEnv
+    from lerobot.utils.constants import ACTION
+
+    cfg = RoboMMEEnv(action_space="joint_angle")
+    assert cfg.features[ACTION].shape == (8,)
+
+
+def test_robomme_features_action_dim_ee_pose():
+    """`ee_pose` uses a 7-D action; __post_init__ sets the correct shape."""
+    from lerobot.envs.configs import RoboMMEEnv
+    from lerobot.utils.constants import ACTION
+
+    cfg = RoboMMEEnv(action_space="ee_pose")
+    assert cfg.features[ACTION].shape == (7,)
+
+
+# ---------------------------------------------------------------------------
+# Obs conversion (pure Python, no sim)
+# ---------------------------------------------------------------------------
+
+
+def test_convert_obs_list_format():
+    """_convert_obs takes the last element from list-format obs fields and
+    emits a nested ``pixels`` dict (image, wrist_image) plus ``agent_pos``.
+
+    The nested layout is required so ``preprocess_observation()`` in
+    ``envs/utils.py`` maps each camera to ``observation.images.<cam>``.
+    """
+    _install_robomme_stub()
+    try:
+        from lerobot.envs.robomme import RoboMMEGymEnv
+
+        env = RoboMMEGymEnv.__new__(RoboMMEGymEnv)
+
+        front = np.full((256, 256, 3), 42, dtype=np.uint8)
+        wrist = np.full((256, 256, 3), 7, dtype=np.uint8)
+        joints = np.arange(7, dtype=np.float32)
+        gripper = np.array([0.5, 0.5], dtype=np.float32)
+
+        obs_raw = {
+            "front_rgb_list": [np.zeros_like(front), front],
+            "wrist_rgb_list": [np.zeros_like(wrist), wrist],
+            "joint_state_list": [np.zeros(7, dtype=np.float32), joints],
+            "gripper_state_list": [np.zeros(2, dtype=np.float32), gripper],
+        }
+
+        result = env._convert_obs(obs_raw)
+        np.testing.assert_array_equal(result["pixels"]["image"], front)
+        np.testing.assert_array_equal(result["pixels"]["wrist_image"], wrist)
+        assert result["agent_pos"].shape == (8,)
+        np.testing.assert_array_almost_equal(result["agent_pos"][:7], joints)
+        assert result["agent_pos"][7] == gripper[0]
+    finally:
+        _uninstall_robomme_stub()
+
+
+def test_convert_obs_array_format():
+    """_convert_obs also handles non-list (direct array) obs."""
+    _install_robomme_stub()
+    try:
+        from lerobot.envs.robomme import RoboMMEGymEnv
+
+        env = RoboMMEGymEnv.__new__(RoboMMEGymEnv)
+
+        front = np.zeros((256, 256, 3), dtype=np.uint8)
+        obs_raw = {
+            "front_rgb_list": front,
+            "wrist_rgb_list": front,
+            "joint_state_list": np.zeros(7, dtype=np.float32),
+            "gripper_state_list": np.zeros(2, dtype=np.float32),
+        }
+        result = env._convert_obs(obs_raw)
+        assert result["pixels"]["image"].shape == (256, 256, 3)
+        assert result["pixels"]["wrist_image"].shape == (256, 256, 3)
+        assert result["agent_pos"].shape == (8,)
+    finally:
+        _uninstall_robomme_stub()
+
+
+# ---------------------------------------------------------------------------
+# create_robomme_envs (mocked sim)
+# ---------------------------------------------------------------------------
+
+
+def test_create_robomme_envs_returns_correct_structure():
+    """Single task -> {task_name: {task_id: VectorEnv}} with one entry per task_id."""
+    _install_robomme_stub()
+    try:
+        from lerobot.envs.robomme import create_robomme_envs
+
+        env_cls = MagicMock(return_value=MagicMock())
+        result = create_robomme_envs(
+            task="PickXtimes",
+            n_envs=1,
+            task_ids=[0, 1],
+            env_cls=env_cls,
+        )
+
+        assert "PickXtimes" in result
+        assert 0 in result["PickXtimes"]
+        assert 1 in result["PickXtimes"]
+        assert env_cls.call_count == 2
+    finally:
+        _uninstall_robomme_stub()
+
+
+def test_create_robomme_envs_multi_task():
+    """Comma-separated task list produces one suite per task."""
+    _install_robomme_stub()
+    try:
+        from lerobot.envs.robomme import create_robomme_envs
+
+        env_cls = MagicMock(return_value=MagicMock())
+        result = create_robomme_envs(
+            task="PickXtimes,BinFill,StopCube",
+            n_envs=1,
+            env_cls=env_cls,
+        )
+
+        assert set(result.keys()) == {"PickXtimes", "BinFill", "StopCube"}
+    finally:
+        _uninstall_robomme_stub()
+
+
+def test_create_robomme_envs_raises_on_invalid_env_cls():
+    _install_robomme_stub()
+    try:
+        import pytest
+
+        from lerobot.envs.robomme import create_robomme_envs
+
+        with pytest.raises(ValueError, match="env_cls must be a callable"):
+            create_robomme_envs(task="PickXtimes", n_envs=1, env_cls=None)
+    finally:
+        _uninstall_robomme_stub()

uv.lock

@@ -2,30 +2,39 @@ version = 1
 revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.15' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "python_full_version == '3.14.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
+    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
+    "(python_full_version >= '3.15' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'armv7l' and sys_platform == 'linux')",
+    "(python_full_version == '3.14.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.14.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
     "(python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
     "(python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "(python_full_version >= '3.15' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "(python_full_version == '3.14.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
     "(python_full_version == '3.13.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
     "(python_full_version < '3.13' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
     "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "(python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "(python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.15' and platform_machine != 's390x' and sys_platform == 'win32')",
+    "python_full_version >= '3.15' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "(python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and platform_machine != 's390x' and sys_platform == 'win32')",
     "(python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32')",
+    "python_full_version == '3.14.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
     "(python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32')",
     "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
@@ -1110,8 +1119,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
     { name = "dm-env" },
-    { name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "dm-tree" },
     { name = "glfw" },
     { name = "labmaze" },
     { name = "lxml" },
@@ -1136,8 +1144,7 @@ version = "1.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "absl-py" },
-    { name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "dm-tree" },
     { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/62/c9/93e8d6239d5806508a2ee4b370e67c6069943ca149f59f533923737a99b7/dm-env-1.6.tar.gz", hash = "sha256:a436eb1c654c39e0c986a516cee218bea7140b510fceff63f97eb4fcff3d93de", size = 20187, upload-time = "2022-12-21T00:25:29.306Z" }
@@ -1149,22 +1156,11 @@ wheels = [
 name = "dm-tree"
 version = "0.1.9"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version >= '3.14' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "(python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
-]
 dependencies = [
-    { name = "absl-py", marker = "python_full_version >= '3.14'" },
-    { name = "attrs", marker = "python_full_version >= '3.14'" },
-    { name = "numpy", marker = "python_full_version >= '3.14'" },
-    { name = "wrapt", marker = "python_full_version >= '3.14'" },
+    { name = "absl-py" },
+    { name = "attrs" },
+    { name = "numpy" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1181,58 +1177,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c5/37/15603079854394f16e3833a7b50696c1f3cbf30a2243a119f64f18a16f36/dm_tree-0.1.9-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1f5d1e96b3a7de22b25b13a5eb30f41f8cf9c02dd4479a24920de99e780903c", size = 153052, upload-time = "2025-01-30T20:45:35.907Z" },
 ]
 
-[[package]]
-name = "dm-tree"
-version = "0.1.10"
-source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l' and platform_machine != 's390x' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "(python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'arm64' and sys_platform == 'linux') or (python_full_version < '3.13' and platform_machine == 'armv7l' and sys_platform == 'linux')",
-    "(python_full_version == '3.13.*' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "(python_full_version < '3.13' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
-    "(python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "(python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32')",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
-]
-dependencies = [
-    { name = "absl-py", marker = "python_full_version < '3.14'" },
-    { name = "attrs", marker = "python_full_version < '3.14'" },
-    { name = "numpy", marker = "python_full_version < '3.14'" },
-    { name = "wrapt", marker = "python_full_version < '3.14'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/34/a1/17e0d68eec978c483db4712b14d083ee01484381b29ea85edb2b20210bd0/dm_tree-0.1.10-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:94af18e4fd22ce69eccae89eeed8ed498b6b4cc4957f4ed10b4160e59f620e1d", size = 315976, upload-time = "2026-03-31T17:35:15.21Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/6f/ed603715fbc29c887a8985252e2cfe0d449497aea96bac51010159771617/dm_tree-0.1.10-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b442a0c1e9d0960e0314a2e4af81fd328a87921b6d6db6dc41bfa420536884d6", size = 184053, upload-time = "2026-03-31T17:35:16.512Z" },
-    { url = "https://files.pythonhosted.org/packages/83/eb/1d55c679cee9a54e552480d308535753c72e2250cf720d7aa777bff2a4fe/dm_tree-0.1.10-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:012c2b376e88d3685c73a4b5c23be41fe933e14e380dcd90172971690b0e02d2", size = 186506, upload-time = "2026-03-31T17:35:17.593Z" },
-    { url = "https://files.pythonhosted.org/packages/89/2d/adef6924f8dc7f1665eea4ce066387820c14a629d0e1005568892d56ea6a/dm_tree-0.1.10-cp312-cp312-win_amd64.whl", hash = "sha256:da8d5b8995bea1b6bb93f457e0dad5d16e6e2344a6488ced55320e7f3fd50f56", size = 112708, upload-time = "2026-03-31T17:35:18.699Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/29/f39e8412c16740f4c914c6674a04a66ace344ce5cb99b537c2270ef4f204/dm_tree-0.1.10-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:4a782f0382be16d66c9ed003e6992e56674504a1d9636f44d2807123f5df6343", size = 316108, upload-time = "2026-03-31T17:35:20.139Z" },
-    { url = "https://files.pythonhosted.org/packages/02/83/1b94d45351bd75a83976a88c9fcf109da6ce336f38a3b443703bb6b18e5d/dm_tree-0.1.10-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0e8f8f1354f178112732b30d2293bc53d212ea64a9556db80a926af3d4647a6b", size = 183834, upload-time = "2026-03-31T17:35:21.463Z" },
-    { url = "https://files.pythonhosted.org/packages/2f/23/bd3e75cbff06a464339d32667d740acf49812b027142a013b54d2c4d830a/dm_tree-0.1.10-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6d7134c0805294c640b94d85cc725084f0c5087bcda5a7fb38eeb7f479ecc37c", size = 186187, upload-time = "2026-03-31T17:35:23.495Z" },
-    { url = "https://files.pythonhosted.org/packages/aa/75/4b460253b9af862388940404b5df6a22b399800c850aab4724c95f8635f9/dm_tree-0.1.10-cp313-cp313-win_amd64.whl", hash = "sha256:b42e04482880b017d931511d7b5997be372fff26a1ee9b9be55eef03ef1c2918", size = 112768, upload-time = "2026-03-31T17:35:24.622Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/ca/3b40a8a50f9c3492b795b157d769180edb5f2605e3c61ae826208f917baa/dm_tree-0.1.10-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:bde02efacca66514524922538b8a0c5dc15d482565d1c796edc34a726b376830", size = 324138, upload-time = "2026-03-31T17:35:25.627Z" },
-    { url = "https://files.pythonhosted.org/packages/83/e4/33c9218aa607f610e2b0334fc824c2abd5a6bc232bf0726cf275f88e639d/dm_tree-0.1.10-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:033f9a063e1e19b6c65fb5c76079bd923044f5a6095357ad2637845513d47938", size = 185110, upload-time = "2026-03-31T17:35:26.784Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/da/f8811666d61b6829ba1c2716c4119039428dd86078eddd120354aaf26a3b/dm_tree-0.1.10-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6d4237da7b072fff1e93db109ab545f00d2b978ead35e85e7a84908e15197826", size = 187013, upload-time = "2026-03-31T17:35:27.969Z" },
-    { url = "https://files.pythonhosted.org/packages/94/8d/135ddeea875fd1a2768e7aee6c224f92c9b7643ead1ec8b68bdbee52c60a/dm_tree-0.1.10-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:f395390d6acfb5d39c564c8bbcaf35352a81eb2f0d34d449739039b2ef786e14", size = 316599, upload-time = "2026-03-31T17:35:29.339Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/50/1eda610e9ca8ac59950ae028080e7c5320d7abc5567d6723d0cb3623e838/dm_tree-0.1.10-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c0f54547fbd4b82e88c71694b3836c90059b97102d3e36209f5d2fa66950964", size = 184263, upload-time = "2026-03-31T17:35:30.534Z" },
-    { url = "https://files.pythonhosted.org/packages/c7/59/07461ceb563702ba3943725bdf0e04be4de0ed7ef093837cdd2d67141d2a/dm_tree-0.1.10-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf6706ac425272c9b7e05f05a23a1ff3e670fb59a787f6089a638eea2d06f1d0", size = 186328, upload-time = "2026-03-31T17:35:31.894Z" },
-    { url = "https://files.pythonhosted.org/packages/88/af/d9c84787fefe9f7c35f474a945217c38396f2ca5ab06432fb566e32a7d1a/dm_tree-0.1.10-cp314-cp314-win_amd64.whl", hash = "sha256:a132047e846e769ddacefe77c42ae79bf3d0e9fce2a6adb638a0ea4cbadb8cdb", size = 114799, upload-time = "2026-03-31T17:35:33.361Z" },
-    { url = "https://files.pythonhosted.org/packages/fd/2c/2aaa63a510db520cd9e0c51e053a608486169bb9710f51f4ecf5699cebb4/dm_tree-0.1.10-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:23682221f63ad011dbd762ce5314740d7900b0426a2681614ea2472369b0c49c", size = 324205, upload-time = "2026-03-31T17:35:34.679Z" },
-    { url = "https://files.pythonhosted.org/packages/b0/89/a5a302bcf9c345e6bd0498627ee2aa12f0a1c3538d08a2f5884d3c6783ba/dm_tree-0.1.10-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8baeb3db1e92587d686022fb67a52f6c584a7d32bd98444ed3aafb399ad9ce67", size = 185113, upload-time = "2026-03-31T17:35:36.179Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/e8/2d4fbc54bb68905588945cfb47c05445c66cab2d822b05827f1c62e23a70/dm_tree-0.1.10-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2236c9a4cf64ed0b04004a94902f39341be652b70dce322b33f08ada9b146baa", size = 187009, upload-time = "2026-03-31T17:35:37.584Z" },
-]
-
 [[package]]
 name = "docopt"
 version = "0.6.2"
@@ -1418,11 +1362,11 @@ sdist = { url = "https://files.pythonhosted.org/packages/5f/8e/c53d6f9a8bf3a86a6
 
 [[package]]
 name = "filelock"
-version = "3.28.0"
+version = "3.29.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d6/17/6e8890271880903e3538660a21d63a6c1fea969ac71d0d6b608b78727fa9/filelock-3.28.0.tar.gz", hash = "sha256:4ed1010aae813c4ee8d9c660e4792475ee60c4a0ba76073ceaf862bd317e3ca6", size = 56474, upload-time = "2026-04-14T22:54:33.625Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571, upload-time = "2026-04-19T15:39:10.068Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3b/21/2f728888c45033d34a417bfcd248ea2564c9e08ab1bfd301377cf05d5586/filelock-3.28.0-py3-none-any.whl", hash = "sha256:de9af6712788e7171df1b28b15eba2446c69721433fa427a9bee07b17820a9db", size = 39189, upload-time = "2026-04-14T22:54:32.037Z" },
+    { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" },
 ]
 
 [[package]]
@@ -1619,14 +1563,14 @@ wheels = [
 
 [[package]]
 name = "gitpython"
-version = "3.1.46"
+version = "3.1.47"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "gitdb" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/df/b5/59d16470a1f0dfe8c793f9ef56fd3826093fc52b3bd96d6b9d6c26c7e27b/gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f", size = 215371, upload-time = "2026-01-01T15:37:32.073Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c1/bd/50db468e9b1310529a19fce651b3b0e753b5c07954d486cba31bbee9a5d5/gitpython-3.1.47.tar.gz", hash = "sha256:dba27f922bd2b42cb54c87a8ab3cb6beb6bf07f3d564e21ac848913a05a8a3cd", size = 216978, upload-time = "2026-04-22T02:44:44.059Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6a/09/e21df6aef1e1ffc0c816f0522ddc3f6dcded766c3261813131c78a704470/gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058", size = 208620, upload-time = "2026-01-01T15:37:30.574Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/c5/a1bc0996af85757903cf2bf444a7824e68e0035ce63fb41d6f76f9def68b/gitpython-3.1.47-py3-none-any.whl", hash = "sha256:489f590edfd6d20571b2c0e72c6a6ac6915ee8b8cd04572330e3842207a78905", size = 209547, upload-time = "2026-04-22T02:44:41.271Z" },
 ]
 
 [[package]]
@@ -2029,11 +1973,11 @@ wheels = [
 
 [[package]]
 name = "idna"
-version = "3.11"
+version = "3.12"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582, upload-time = "2025-10-12T14:55:20.501Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/22/12/2948fbe5513d062169bd91f7d7b1cd97bc8894f32946b71fa39f6e63ca0c/idna-3.12.tar.gz", hash = "sha256:724e9952cc9e2bd7550ea784adb098d837ab5267ef67a1ab9cf7846bdbdd8254", size = 194350, upload-time = "2026-04-21T13:32:48.916Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" },
+    { url = "https://files.pythonhosted.org/packages/53/b2/acc33950394b3becb2b664741a0c0889c7ef9f9ffbfa8d47eddb53a50abd/idna-3.12-py3-none-any.whl", hash = "sha256:60ffaa1858fac94c9c124728c24fcde8160f3fb4a7f79aa8cdd33a9d1af60a67", size = 68634, upload-time = "2026-04-21T13:32:47.403Z" },
 ]
 
 [[package]]
@@ -2345,7 +2289,7 @@ wheels = [
 
 [[package]]
 name = "jupyter-events"
-version = "0.12.0"
+version = "0.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonschema", extra = ["format-nongpl"] },
@@ -2357,9 +2301,9 @@ dependencies = [
     { name = "rfc3986-validator" },
     { name = "traitlets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9d/c3/306d090461e4cf3cd91eceaff84bede12a8e52cd821c2d20c9a4fd728385/jupyter_events-0.12.0.tar.gz", hash = "sha256:fc3fce98865f6784c9cd0a56a20644fc6098f21c8c33834a8d9fe383c17e554b", size = 62196, upload-time = "2025-02-03T17:23:41.485Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/f8/475c4241b2b75af0deaae453ed003c6c851766dbc44d332d8baf245dc931/jupyter_events-0.12.1.tar.gz", hash = "sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3", size = 62854, upload-time = "2026-04-20T23:17:50.66Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl", hash = "sha256:6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb", size = 19430, upload-time = "2025-02-03T17:23:38.643Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/6c/6fcde0c8f616ed360ffd3587f7db9e225a7e62b583a04494d2f069cf64ea/jupyter_events-0.12.1-py3-none-any.whl", hash = "sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf", size = 19512, upload-time = "2026-04-20T23:17:48.927Z" },
 ]
 
 [[package]]
@@ -2793,8 +2737,7 @@ gamepad = [
 groot = [
     { name = "decord", marker = "platform_machine == 'AMD64' or platform_machine == 'x86_64'" },
     { name = "diffusers" },
-    { name = "dm-tree", version = "0.1.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-    { name = "dm-tree", version = "0.1.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
+    { name = "dm-tree" },
     { name = "flash-attn", marker = "sys_platform != 'darwin'" },
     { name = "ninja" },
     { name = "peft" },
@@ -3248,82 +3191,82 @@ wheels = [
 
 [[package]]
 name = "lxml"
-version = "6.0.4"
+version = "6.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ce/08/1217ca4043f55c3c92993b283a7dbfa456a2058d8b57bbb416cc96b6efff/lxml-6.0.4.tar.gz", hash = "sha256:4137516be2a90775f99d8ef80ec0283f8d78b5d8bd4630ff20163b72e7e9abf2", size = 4237780, upload-time = "2026-04-12T16:28:24.182Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/28/30/9abc9e34c657c33834eaf6cd02124c61bdf5944d802aa48e69be8da3585d/lxml-6.1.0.tar.gz", hash = "sha256:bfd57d8008c4965709a919c3e9a98f76c2c7cb319086b3d26858250620023b13", size = 4197006, upload-time = "2026-04-18T04:32:51.613Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3d/18/4732abab49bbb041b1ded9dd913ca89735a0dcca038eacec64c44ba02163/lxml-6.0.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:af0b8459c4e21a8417db967b2e453d1855022dac79c79b61fb8214f3da50f17e", size = 8570033, upload-time = "2026-04-12T16:24:10.728Z" },
-    { url = "https://files.pythonhosted.org/packages/72/7e/38523ec7178ca35376551911455d1b2766bc9d98bcc18f606a167fa9ecbb/lxml-6.0.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:e0cdcea2affa53fa17dc4bf5cefc0edf72583eac987d669493a019998a623fa3", size = 4623270, upload-time = "2026-04-12T16:24:13.2Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/cf/f9b6c9bf9d8c63d923ef893915141767cea4cea71774f20c36d0c14e1585/lxml-6.0.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:8da4d4840c1bc07da6fcd647784f7fbaf538eeb7a57ce6b2487acc54c5e33330", size = 4929471, upload-time = "2026-04-12T16:24:15.453Z" },
-    { url = "https://files.pythonhosted.org/packages/e5/53/3117f988c9e20be4156d2b8e1bda82ae06878d11aeb820dea111a7cfa4e3/lxml-6.0.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fb04a997588c3980894ded9172c10c5a3e45d3f1c5410472733626d268683806", size = 5092355, upload-time = "2026-04-12T16:24:17.876Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/ca/05c6ac773a2bd3edb48fa8a5c5101e927ce044c4a8aed1a85ff00fab20a5/lxml-6.0.4-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ca449642a08a6ceddf6e6775b874b6aee1b6242ed80aea84124497aba28e5384", size = 5004520, upload-time = "2026-04-12T16:24:20.184Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/db/d8aa5aa3a51d0aa6706ef85f85027f7c972cd840fe69ba058ecaf32d093d/lxml-6.0.4-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:35b3ccdd137e62033662787dd4d2b8be900c686325d6b91e3b1ff6213d05ba11", size = 5629961, upload-time = "2026-04-12T16:24:22.242Z" },
-    { url = "https://files.pythonhosted.org/packages/9d/75/8fff4444e0493aeb15ab0f4a55c767b5baed9074cf67a1835dc1161f3a1f/lxml-6.0.4-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:45dc690c54b1341fec01743caed02e5f1ea49d7cfb81e3ba48903e5e844ed68a", size = 5237561, upload-time = "2026-04-12T16:24:24.572Z" },
-    { url = "https://files.pythonhosted.org/packages/2a/9f/6d6cd73014f2dbf47a8aa7accd9712726f46ef4891e1c126bc285cfb94e4/lxml-6.0.4-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:15ae922e8f74b05798a0e88cee46c0244aaec6a66b5e00be7d18648fed8c432e", size = 5349197, upload-time = "2026-04-12T16:24:26.805Z" },
-    { url = "https://files.pythonhosted.org/packages/2d/43/e3e9a126e166234d1659d1dd9004dc1dd50cdc3c68575b071b0a1524b4de/lxml-6.0.4-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:ebd816653707fbf10c65e3dee3bc24dac6b691654c21533b1ae49287433f4db0", size = 4693123, upload-time = "2026-04-12T16:24:28.812Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/98/b146dd123a4a7b69b571ff23ea8e8c68de8d8c1b03e23d01c6374d4fd835/lxml-6.0.4-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:21284cf36b95dd8be774eb06c304b440cf49ee811800a30080ce6d93700f0383", size = 5242967, upload-time = "2026-04-12T16:24:30.811Z" },
-    { url = "https://files.pythonhosted.org/packages/7e/60/8c275584452b55a902c883e8ab63d755c5ef35d7ad1f06f9e6559095521d/lxml-6.0.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0c08a2a9d0c4028ef5fc5a513b2e1e51af069a83c5b4206139edd08b3b8c2926", size = 5046810, upload-time = "2026-04-12T16:24:33.289Z" },
-    { url = "https://files.pythonhosted.org/packages/19/aa/19ec216147e1105e5403fe73657c693a6e91bde855a13242dd6031e829e5/lxml-6.0.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:1bc2f0f417112cf1a428599dd58125ab74d8e1c66893efd9b907cbb4a5db6e44", size = 4776383, upload-time = "2026-04-12T16:24:36.008Z" },
-    { url = "https://files.pythonhosted.org/packages/41/c8/90afdb838705a736268fcffd2698c05e9a129144ce215d5e14db3bdfc295/lxml-6.0.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:c0d86e328405529bc93913add9ff377e8b8ea9be878e611f19dbac7766a84483", size = 5643497, upload-time = "2026-04-12T16:24:38.276Z" },
-    { url = "https://files.pythonhosted.org/packages/32/ec/1135261ec9822dafb90be0ff6fb0ec79cee0b7fe878833dfe5f2b8c393bd/lxml-6.0.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:3cce9420fe8f91eae5d457582599d282195c958cb670aa4bea313a79103ba33f", size = 5232185, upload-time = "2026-04-12T16:24:40.516Z" },
-    { url = "https://files.pythonhosted.org/packages/13/f2/7380b11cae6943720f525e5a28ad9dbead96ac710417e556b7c03f3a8af3/lxml-6.0.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:96214985ec194ce97b9028414e179cfb21230cba4e2413aee7e249461bb84f4d", size = 5259968, upload-time = "2026-04-12T16:24:42.917Z" },
-    { url = "https://files.pythonhosted.org/packages/65/8f/141734f2c456f2253fed4237d8d4b241e3d701129cf6f0b135ccf241a75a/lxml-6.0.4-cp312-cp312-win32.whl", hash = "sha256:b2209b310e7ed1d4cd1c00d405ec9c49722fce731c7036abc1d876bf8df78139", size = 3594958, upload-time = "2026-04-12T16:24:45.039Z" },
-    { url = "https://files.pythonhosted.org/packages/b7/a9/c6d3531c6d8814af0919fbdb9bda43c9e8b5deffcb70c8534017db233512/lxml-6.0.4-cp312-cp312-win_amd64.whl", hash = "sha256:03affcacfba4671ebc305813b02bfaf34d80b6a7c5b23eafc5d6da14a1a6e623", size = 3995897, upload-time = "2026-04-12T16:24:46.98Z" },
-    { url = "https://files.pythonhosted.org/packages/03/5d/1dabeddf762e5a315a31775b2bca39811d7e7a15fc3e677d044b9da973fe/lxml-6.0.4-cp312-cp312-win_arm64.whl", hash = "sha256:af9678e3a2a047465515d95a61690109af7a4c9486f708249119adcef7861049", size = 3658607, upload-time = "2026-04-12T16:24:49.19Z" },
-    { url = "https://files.pythonhosted.org/packages/78/f6/550a1ed9afde66e24bfcf9892446ea9779152df336062c6df0f7733151a2/lxml-6.0.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ecc3d55ed756ee6c3447748862a97e1f5392d2c5d7f474bace9382345e4fc274", size = 8559522, upload-time = "2026-04-12T16:24:51.563Z" },
-    { url = "https://files.pythonhosted.org/packages/11/93/3f687c14d2b4d24b60fe13fd5482c8853f82a10bb87f2b577123e342ed1a/lxml-6.0.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a7d5a627a368a0e861350ccc567a70ec675d2bc4d8b3b54f48995ae78d8d530e", size = 4617380, upload-time = "2026-04-12T16:24:54.042Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/ed/91e443366063d3fb7640ae2badd5d7b65be4095ac6d849788e39c043baae/lxml-6.0.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d385141b186cc39ebe4863c1e41936282c65df19b2d06a701dedc2a898877d6a", size = 4922791, upload-time = "2026-04-12T16:24:56.381Z" },
-    { url = "https://files.pythonhosted.org/packages/30/4b/2243260b70974aca9ba0cc71bd668c0c3a79644d80ddcabbfbdb4b131848/lxml-6.0.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0132bb040e9bb5a199302e12bf942741defbc52922a2a06ce9ff7be0d0046483", size = 5080972, upload-time = "2026-04-12T16:24:58.823Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/c3/54c53c4f772341bc12331557f8b0882a426f53133926306cbe6d7f0ee7e4/lxml-6.0.4-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:26aee5321e4aa1f07c9090a35f6ab8b703903fb415c6c823cfdb20ee0d779855", size = 4992236, upload-time = "2026-04-12T16:25:01.099Z" },
-    { url = "https://files.pythonhosted.org/packages/be/0f/416de42e22f287585abee610eb0d1c2638c9fe24cee7e15136e0b5e138f8/lxml-6.0.4-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b5652455de198ff76e02cfa57d5efc5f834fa45521aaf3fcc13d6b5a88bde23d", size = 5612398, upload-time = "2026-04-12T16:25:03.517Z" },
-    { url = "https://files.pythonhosted.org/packages/7d/63/29a3fa79b8a182f5bd5b5bdcb6f625f49f08f41d60a26ca25482820a1b99/lxml-6.0.4-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:75842801fb48aea73f4c281b923a010dfb39bad75edf8ceb2198ec30c27f01cc", size = 5227480, upload-time = "2026-04-12T16:25:06.119Z" },
-    { url = "https://files.pythonhosted.org/packages/7c/4a/44d1843de599b1c6dbe578e4248c2f15e7fac90c5c86eb26775eaeac0fe0/lxml-6.0.4-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:94a1f74607a5a049ff6ff8de429fec922e643e32b5b08ec7a4fe49e8de76e17c", size = 5341001, upload-time = "2026-04-12T16:25:08.563Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/52/c8aebde49f169e4e3452e7756be35be1cb2903e30d961cb57aa65a27055f/lxml-6.0.4-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:173cc246d3d3b6d3b6491f0b3aaf22ebdf2eed616879482acad8bd84d73eb231", size = 4699105, upload-time = "2026-04-12T16:25:10.757Z" },
-    { url = "https://files.pythonhosted.org/packages/78/60/76fc3735c31c28b70220d99452fb72052e84b618693ca2524da96f0131d8/lxml-6.0.4-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f0f2ee1be1b72e9890da87e4e422f2f703ff4638fd5ec5383055db431e8e30e9", size = 5231095, upload-time = "2026-04-12T16:25:13.305Z" },
-    { url = "https://files.pythonhosted.org/packages/e5/60/448f01c52110102f23df5f07b3f4fde57c8e13e497e182a743d125324c0b/lxml-6.0.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c51a274b7e8b9ce394c3f8b471eb0b23c1914eec64fdccf674e082daf72abf11", size = 5042411, upload-time = "2026-04-12T16:25:15.541Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/2a/90612a001fa4fa0ff0443ebb0256a542670fe35473734c559720293e7aff/lxml-6.0.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:210ea934cba1a1ec42f88c4190c4d5c67b2d14321a8faed9b39e8378198ff99d", size = 4768431, upload-time = "2026-04-12T16:25:17.581Z" },
-    { url = "https://files.pythonhosted.org/packages/84/d8/572845a7d741c8a8ffeaf928185263e14d97fbd355de164677340951d7a5/lxml-6.0.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:14fe654a59eebe16368c51778caeb0c8fda6f897adcd9afe828d87d13b5d5e51", size = 5634972, upload-time = "2026-04-12T16:25:20.111Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/1d/392b8c9f8cf1d502bbec50dee137c7af3dd5def5e5cd84572fbf0ba0541c/lxml-6.0.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:ec160a2b7e2b3cb71ec35010b19a1adea05785d19ba5c9c5f986b64b78fef564", size = 5222909, upload-time = "2026-04-12T16:25:22.243Z" },
-    { url = "https://files.pythonhosted.org/packages/21/ab/949fc96f825cf083612aee65d5a02eacc5eaeb2815561220e33e1e160677/lxml-6.0.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d305b86ef10b23cf3a6d62a2ad23fa296f76495183ee623f64d2600f65ffe09c", size = 5249096, upload-time = "2026-04-12T16:25:24.781Z" },
-    { url = "https://files.pythonhosted.org/packages/56/e8/fbe44df79ede5ff760401cc3c49c4204f49f0f529cc6b27d0af7b63f5472/lxml-6.0.4-cp313-cp313-win32.whl", hash = "sha256:a2f31380aa9a9b52591e79f1c1d3ac907688fbeb9d883ba28be70f2eb5db2277", size = 3595808, upload-time = "2026-04-12T16:25:26.747Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/df/e873abb881092256520edf0d67d686e36f3c86b3cf289f01b6458272dede/lxml-6.0.4-cp313-cp313-win_amd64.whl", hash = "sha256:b8efa9f681f15043e497293d58a4a63199564b253ed2291887d92bb3f74f59ab", size = 3994635, upload-time = "2026-04-12T16:25:28.828Z" },
-    { url = "https://files.pythonhosted.org/packages/23/a8/9c56c8914b9b18d89face5a7472445002baf309167f7af65d988842129fd/lxml-6.0.4-cp313-cp313-win_arm64.whl", hash = "sha256:905abe6a5888129be18f85f2aea51f0c9863fa0722fb8530dfbb687d2841d221", size = 3657374, upload-time = "2026-04-12T16:25:30.901Z" },
-    { url = "https://files.pythonhosted.org/packages/10/18/36e28a809c509a67496202771f545219ac5a2f1cd61aae325991fcf5ab91/lxml-6.0.4-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:569d3b18340863f603582d2124e742a68e85755eff5e47c26a55e298521e3a01", size = 8575045, upload-time = "2026-04-12T16:25:33.57Z" },
-    { url = "https://files.pythonhosted.org/packages/11/38/a168c820e3b08d3b4fa0f4e6b53b3930086b36cc11e428106d38c36778cd/lxml-6.0.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:3b6245ee5241342d45e1a54a4a8bc52ef322333ada74f24aa335c4ab36f20161", size = 4622963, upload-time = "2026-04-12T16:25:36.818Z" },
-    { url = "https://files.pythonhosted.org/packages/53/e0/2c9d6abdd82358cea3c0d8d6ca272a6af0f38156abce7827efb6d5b62d17/lxml-6.0.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:79a1173ba3213a3693889a435417d4e9f3c07d96e30dc7cc3a712ed7361015fe", size = 4948832, upload-time = "2026-04-12T16:25:39.104Z" },
-    { url = "https://files.pythonhosted.org/packages/96/d7/f2202852e91d7baf3a317f4523a9c14834145301e5b0f2e80c01c4bfbd49/lxml-6.0.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:dc18bb975666b443ba23aedd2fcf57e9d0d97546b52a1de97a447c4061ba4110", size = 5085865, upload-time = "2026-04-12T16:25:41.226Z" },
-    { url = "https://files.pythonhosted.org/packages/09/57/abee549324496e92708f71391c6060a164d3c95369656a1a15e9f20d8162/lxml-6.0.4-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2079f5dc83291ac190a52f8354b78648f221ecac19fb2972a2d056b555824de7", size = 5030001, upload-time = "2026-04-12T16:25:43.695Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/f8/432da7178c5917a16468af6c5da68fef7cf3357d4bd0e6f50272ec9a59b5/lxml-6.0.4-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3eda02da4ca16e9ca22bbe5654470c17fa1abcd967a52e4c2e50ff278221e351", size = 5646303, upload-time = "2026-04-12T16:25:46.577Z" },
-    { url = "https://files.pythonhosted.org/packages/82/f9/e1c04ef667a6bf9c9dbd3bf04c50fa51d7ee25b258485bb748b27eb9a1c7/lxml-6.0.4-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c3787cdc3832b70e21ac2efafea2a82a8ccb5e85bec110dc68b26023e9d3caae", size = 5237940, upload-time = "2026-04-12T16:25:49.157Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/f0/cdea60d92df731725fc3c4f33e387b100f210acd45c92969e42d2ba993fa/lxml-6.0.4-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:3f276d49c23103565d39440b9b3f4fc08fa22f5a96395ea4b4d4fea4458b1505", size = 5350050, upload-time = "2026-04-12T16:25:52.027Z" },
-    { url = "https://files.pythonhosted.org/packages/2e/15/bf52c7a70b6081bb9e00d37cc90fcf60aa84468d9d173ad2fade38ec34c5/lxml-6.0.4-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:fdfdad73736402375b11b3a137e48cd09634177516baf5fc0bd80d1ca85f3cda", size = 4696409, upload-time = "2026-04-12T16:25:55.141Z" },
-    { url = "https://files.pythonhosted.org/packages/c5/69/9bade267332cc06f9a9aa773b5a11bdfb249af485df9e142993009ea1fc4/lxml-6.0.4-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:75912421456946931daba0ec3cedfa824c756585d05bde97813a17992bfbd013", size = 5249072, upload-time = "2026-04-12T16:25:57.362Z" },
-    { url = "https://files.pythonhosted.org/packages/14/ca/043bcacb096d6ed291cbbc58724e9625a453069d6edeb840b0bf18038d05/lxml-6.0.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:48cd5a88da67233fd82f2920db344503c2818255217cd6ea462c9bb8254ba7cb", size = 5083779, upload-time = "2026-04-12T16:26:00.018Z" },
-    { url = "https://files.pythonhosted.org/packages/04/89/f5fb18d76985969e84af13682e489acabee399bb54738a363925ea6e7390/lxml-6.0.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:87af86a8fa55b9ff1e6ee4233d762296f2ce641ba948af783fb995c5a8a3371b", size = 4736953, upload-time = "2026-04-12T16:26:02.289Z" },
-    { url = "https://files.pythonhosted.org/packages/84/ba/d1d7284bb4ba951f188c3fc0455943c1fcbd1c33d1324d6d57b7d4a45be6/lxml-6.0.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:a743714cd656ba7ccb29d199783906064c7b5ba3c0e2a79f0244ea0badc6a98c", size = 5669605, upload-time = "2026-04-12T16:26:04.694Z" },
-    { url = "https://files.pythonhosted.org/packages/72/05/1463e55f2de27bb60feddc894dd7c0833bd501f8861392ed416291b38db5/lxml-6.0.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e31c76bd066fb4f81d9a32e5843bffdf939ab27afb1ffc1c924e749bfbdb00e3", size = 5236886, upload-time = "2026-04-12T16:26:07.659Z" },
-    { url = "https://files.pythonhosted.org/packages/fe/fb/0b6ee9194ce3ac49db4cadaa8a9158f04779fc768b6c27c4e2945d71a99d/lxml-6.0.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f185fd6e7d550e9917d7103dccf51be589aba953e15994fb04646c1730019685", size = 5263382, upload-time = "2026-04-12T16:26:10.067Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/93/ec18a08e98dd82cac39f1d2511ee2bed5affb94d228356d8ef165a4ec3b9/lxml-6.0.4-cp314-cp314-win32.whl", hash = "sha256:774660028f8722a598400430d2746fb0075949f84a9a5cd9767d9152e3baaac5", size = 3656164, upload-time = "2026-04-12T16:26:59.568Z" },
-    { url = "https://files.pythonhosted.org/packages/15/86/52507316abfc7150bf6bb191e39a12e301ee80334610a493884ae2f9d20d/lxml-6.0.4-cp314-cp314-win_amd64.whl", hash = "sha256:fbd7d14349413f5609c0b537b1a48117d6ccef1af37986af6b03766ad05bf43e", size = 4062512, upload-time = "2026-04-12T16:27:02.212Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/d5/09c593a2ef2234b8cd6cf059e2dc212e0654bf05c503f0ef2daf05adb680/lxml-6.0.4-cp314-cp314-win_arm64.whl", hash = "sha256:a61a01ec3fbfd5b73a69a7bf513271051fd6c5795d82fc5daa0255934cd8db3d", size = 3740745, upload-time = "2026-04-12T16:27:04.444Z" },
-    { url = "https://files.pythonhosted.org/packages/4a/3c/42a98bf6693938bf7b285ec7f70ba2ae9d785d0e5b2cdb85d2ee29e287eb/lxml-6.0.4-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:504edb62df33cea502ea6e73847c647ba228623ca3f80a228be5723a70984dd5", size = 8826437, upload-time = "2026-04-12T16:26:12.911Z" },
-    { url = "https://files.pythonhosted.org/packages/c2/c2/ad13f39b2db8709788aa2dcb6e90b81da76db3b5b2e7d35e0946cf984960/lxml-6.0.4-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:f01b7b0316d4c0926d49a7f003b2d30539f392b140a3374bb788bad180bc8478", size = 4734892, upload-time = "2026-04-12T16:26:15.871Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/6d/c559d7b5922c5b0380fc2cb5ac134b6a3f9d79d368347a624ee5d68b0816/lxml-6.0.4-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ab999933e662501efe4b16e6cfb7c9f9deca7d072cd1788b99c8defde78c0dfb", size = 4969173, upload-time = "2026-04-12T16:26:18.335Z" },
-    { url = "https://files.pythonhosted.org/packages/c7/78/ca521e36157f38e3e1a29276855cdf48d213138fc0c8365693ff5c876ca7/lxml-6.0.4-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:67c3f084389fe75932c39b6869a377f6c8e21e818f31ae8a30c71dd2e59360e2", size = 5103134, upload-time = "2026-04-12T16:26:20.612Z" },
-    { url = "https://files.pythonhosted.org/packages/28/a7/7d62d023bacaa0aaf60af8c0a77c6c05f84327396d755f3aa64b788678a9/lxml-6.0.4-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:377ea1d654f76ed6205c87d14920f829c9f4d31df83374d3cbcbdaae804d37b2", size = 5027205, upload-time = "2026-04-12T16:26:22.981Z" },
-    { url = "https://files.pythonhosted.org/packages/34/be/51b194b81684f2e85e5d992771c45d70cb22ac6f7291ac6bc7b255830afe/lxml-6.0.4-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e60cd0bcacbfd1a96d63516b622183fb2e3f202300df9eb5533391a8a939dbfa", size = 5594461, upload-time = "2026-04-12T16:26:25.316Z" },
-    { url = "https://files.pythonhosted.org/packages/39/24/8850f38fbf89dd072ff31ba22f9e40347aeada7cadf710ecb04b8d9f32d4/lxml-6.0.4-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6e9e30fd63d41dd0bbdb020af5cdfffd5d9b554d907cb210f18e8fcdc8eac013", size = 5223378, upload-time = "2026-04-12T16:26:28.68Z" },
-    { url = "https://files.pythonhosted.org/packages/2a/9b/595239ba8c719b0fdc7bc9ebdb7564459c9a6b24b8b363df4a02674aeece/lxml-6.0.4-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:1fb4a1606bb68c533002e7ed50d7e55e58f0ef1696330670281cb79d5ab2050d", size = 5311415, upload-time = "2026-04-12T16:26:31.513Z" },
-    { url = "https://files.pythonhosted.org/packages/be/cb/aa27ac8d041acf34691577838494ad08df78e83fdfdb66948d2903e9291e/lxml-6.0.4-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:695c7708438e449d57f404db8cc1b769e77ad5b50655f32f8175686ba752f293", size = 4637953, upload-time = "2026-04-12T16:26:33.806Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/f2/f19114fd86825c2d1ce41cd99daad218d30cfdd2093d4de9273986fb4d68/lxml-6.0.4-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d49c35ae1e35ee9b569892cf8f8f88db9524f28d66e9daee547a5ef9f3c5f468", size = 5231532, upload-time = "2026-04-12T16:26:36.518Z" },
-    { url = "https://files.pythonhosted.org/packages/9a/0e/c3fa354039ec0b6b09f40fbe1129efc572ac6239faa4906de42d5ce87c0a/lxml-6.0.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5801072f8967625e6249d162065d0d6011ef8ce3d0efb8754496b5246b81a74b", size = 5083767, upload-time = "2026-04-12T16:26:39.332Z" },
-    { url = "https://files.pythonhosted.org/packages/b3/4b/1a0dbb6d6ffae16e54a8a3796ded0ad2f9c3bc1ff3728bde33456f4e1d63/lxml-6.0.4-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:cbf768541526eba5ef1a49f991122e41b39781eafd0445a5a110fc09947a20b5", size = 4758079, upload-time = "2026-04-12T16:26:42.138Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/01/a246cf5f80f96766051de4b305d6552f80bdaefb37f04e019e42af0aba69/lxml-6.0.4-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:eecce87cc09233786fc31c230268183bf6375126cfec1c8b3673fcdc8767b560", size = 5618686, upload-time = "2026-04-12T16:26:44.507Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/1f/b072a92369039ebef11b0a654be5134fcf3ed04c0f437faf9435ac9ba845/lxml-6.0.4-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:07dce892881179e11053066faca2da17b0eeb0bb7298f11bcf842a86db207dbd", size = 5227259, upload-time = "2026-04-12T16:26:47.083Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/a0/dc97034f9d4c0c4d30875147d81fd2c0c7f3d261b109db36ed746bf8ab1d/lxml-6.0.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e4f97aee337b947e6699e5574c90d087d3e2ce517016241c07e7e98a28dca885", size = 5246190, upload-time = "2026-04-12T16:26:49.468Z" },
-    { url = "https://files.pythonhosted.org/packages/f2/ef/85cb69835113583c2516fee07d0ffb4d824b557424b06ba5872c20ba6078/lxml-6.0.4-cp314-cp314t-win32.whl", hash = "sha256:064477c0d4c695aa1ea4b9c1c4ee9043ab740d12135b74c458cc658350adcd86", size = 3896005, upload-time = "2026-04-12T16:26:52.163Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/5e/2231f34cc54b8422b793593138d86d3fa4588fb2297d4ea0472390f25627/lxml-6.0.4-cp314-cp314t-win_amd64.whl", hash = "sha256:25bad2d8438f4ef5a7ad4a8d8bcaadde20c0daced8bdb56d46236b0a7d1cbdd0", size = 4391037, upload-time = "2026-04-12T16:26:54.398Z" },
-    { url = "https://files.pythonhosted.org/packages/39/53/8ba3cd5984f8363635450c93f63e541a0721b362bb32ae0d8237d9674aee/lxml-6.0.4-cp314-cp314t-win_arm64.whl", hash = "sha256:1dcd9e6cb9b7df808ea33daebd1801f37a8f50e8c075013ed2a2343246727838", size = 3816184, upload-time = "2026-04-12T16:26:57.011Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/d4/9326838b59dc36dfae42eec9656b97520f9997eee1de47b8316aaeed169c/lxml-6.1.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d2f17a16cd8751e8eb233a7e41aecdf8e511712e00088bf9be455f604cd0d28d", size = 8570663, upload-time = "2026-04-18T04:27:48.253Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/a4/053745ce1f8303ccbb788b86c0db3a91b973675cefc42566a188637b7c40/lxml-6.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f0cea5b1d3e6e77d71bd2b9972eb2446221a69dc52bb0b9c3c6f6e5700592d93", size = 4624024, upload-time = "2026-04-18T04:27:52.594Z" },
+    { url = "https://files.pythonhosted.org/packages/90/97/a517944b20f8fd0932ad2109482bee4e29fe721416387a363306667941f6/lxml-6.1.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc46da94826188ed45cb53bd8e3fc076ae22675aea2087843d4735627f867c6d", size = 4930895, upload-time = "2026-04-18T04:32:56.29Z" },
+    { url = "https://files.pythonhosted.org/packages/94/7c/e08a970727d556caa040a44773c7b7e3ad0f0d73dedc863543e9a8b931f2/lxml-6.1.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9147d8e386ec3b82c3b15d88927f734f565b0aaadef7def562b853adca45784a", size = 5093820, upload-time = "2026-04-18T04:32:58.94Z" },
+    { url = "https://files.pythonhosted.org/packages/88/ee/2a5c2aa2c32016a226ca25d3e1056a8102ea6e1fe308bf50213586635400/lxml-6.1.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5715e0e28736a070f3f34a7ccc09e2fdcba0e3060abbcf61a1a5718ff6d6b105", size = 5005790, upload-time = "2026-04-18T04:33:01.272Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/38/a0db9be8f38ad6043ab9429487c128dd1d30f07956ef43040402f8da49e8/lxml-6.1.0-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4937460dc5df0cdd2f06a86c285c28afda06aefa3af949f9477d3e8df430c485", size = 5630827, upload-time = "2026-04-18T04:33:04.036Z" },
+    { url = "https://files.pythonhosted.org/packages/31/ba/3c13d3fc24b7cacf675f808a3a1baabf43a30d0cd24c98f94548e9aa58eb/lxml-6.1.0-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bc783ee3147e60a25aa0445ea82b3e8aabb83b240f2b95d32cb75587ff781814", size = 5240445, upload-time = "2026-04-18T04:33:06.87Z" },
+    { url = "https://files.pythonhosted.org/packages/55/ba/eeef4ccba09b2212fe239f46c1692a98db1878e0872ae320756488878a94/lxml-6.1.0-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:40d9189f80075f2e1f88db21ef815a2b17b28adf8e50aaf5c789bfe737027f32", size = 5350121, upload-time = "2026-04-18T04:33:09.365Z" },
+    { url = "https://files.pythonhosted.org/packages/7e/01/1da87c7b587c38d0cbe77a01aae3b9c1c49ed47d76918ef3db8fc151b1ca/lxml-6.1.0-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:05b9b8787e35bec69e68daf4952b2e6dfcfb0db7ecf1a06f8cdfbbac4eb71aad", size = 4694949, upload-time = "2026-04-18T04:33:11.628Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/88/7db0fe66d5aaf128443ee1623dec3db1576f3e4c17751ec0ef5866468590/lxml-6.1.0-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0f0f08beb0182e3e9a86fae124b3c47a7b41b7b69b225e1377db983802404e54", size = 5243901, upload-time = "2026-04-18T04:33:13.95Z" },
+    { url = "https://files.pythonhosted.org/packages/00/a8/1346726af7d1f6fca1f11223ba34001462b0a3660416986d37641708d57c/lxml-6.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:73becf6d8c81d4c76b1014dbd3584cb26d904492dcf73ca85dc8bff08dcd6d2d", size = 5048054, upload-time = "2026-04-18T04:33:16.965Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/b7/85057012f035d1a0c87e02f8c723ca3c3e6e0728bcf4cb62080b21b1c1e3/lxml-6.1.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:1ae225f66e5938f4fa29d37e009a3bb3b13032ac57eb4eb42afa44f6e4054e69", size = 4777324, upload-time = "2026-04-18T04:33:19.832Z" },
+    { url = "https://files.pythonhosted.org/packages/75/6c/ad2f94a91073ef570f33718040e8e160d5fb93331cf1ab3ca1323f939e2d/lxml-6.1.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:690022c7fae793b0489aa68a658822cea83e0d5933781811cabbf5ea3bcfe73d", size = 5645702, upload-time = "2026-04-18T04:33:22.436Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/89/0bb6c0bd549c19004c60eea9dc554dd78fd647b72314ef25d460e0d208c6/lxml-6.1.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:63aeafc26aac0be8aff14af7871249e87ea1319be92090bfd632ec68e03b16a5", size = 5232901, upload-time = "2026-04-18T04:33:26.21Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/d9/d609a11fb567da9399f525193e2b49847b5a409cdebe737f06a8b7126bdc/lxml-6.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:264c605ab9c0e4aa1a679636f4582c4d3313700009fac3ec9c3412ed0d8f3e1d", size = 5261333, upload-time = "2026-04-18T04:33:28.984Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/3a/ac3f99ec8ac93089e7dd556f279e0d14c24de0a74a507e143a2e4b496e7c/lxml-6.1.0-cp312-cp312-win32.whl", hash = "sha256:56971379bc5ee8037c5a0f09fa88f66cdb7d37c3e38af3e45cf539f41131ac1f", size = 3596289, upload-time = "2026-04-18T04:27:42.819Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/a7/0a915557538593cb1bbeedcd40e13c7a261822c26fecbbdb71dad0c2f540/lxml-6.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:bba078de0031c219e5dd06cf3e6bf8fb8e6e64a77819b358f53bb132e3e03366", size = 3997059, upload-time = "2026-04-18T04:27:46.764Z" },
+    { url = "https://files.pythonhosted.org/packages/92/96/a5dc078cf0126fbfbc35611d77ecd5da80054b5893e28fb213a5613b9e1d/lxml-6.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:c3592631e652afa34999a088f98ba7dfc7d6aff0d535c410bea77a71743f3819", size = 3659552, upload-time = "2026-04-18T04:27:51.133Z" },
+    { url = "https://files.pythonhosted.org/packages/08/03/69347590f1cf4a6d5a4944bb6099e6d37f334784f16062234e1f892fdb1d/lxml-6.1.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a0092f2b107b69601adf562a57c956fbb596e05e3e6651cabd3054113b007e45", size = 8559689, upload-time = "2026-04-18T04:31:57.785Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/58/25e00bb40b185c974cfe156c110474d9a8a8390d5f7c92a4e328189bb60e/lxml-6.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:fc7140d7a7386e6b545d41b7358f4d02b656d4053f5fa6859f92f4b9c2572c4d", size = 4617892, upload-time = "2026-04-18T04:32:01.78Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/54/92ad98a94ac318dc4f97aaac22ff8d1b94212b2ae8af5b6e9b354bf825f7/lxml-6.1.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:419c58fc92cc3a2c3fa5f78c63dbf5da70c1fa9c1b25f25727ecee89a96c7de2", size = 4923489, upload-time = "2026-04-18T04:33:31.401Z" },
+    { url = "https://files.pythonhosted.org/packages/15/3b/a20aecfab42bdf4f9b390590d345857ad3ffd7c51988d1c89c53a0c73faf/lxml-6.1.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:37fabd1452852636cf38ecdcc9dd5ca4bba7a35d6c53fa09725deeb894a87491", size = 5082162, upload-time = "2026-04-18T04:33:34.262Z" },
+    { url = "https://files.pythonhosted.org/packages/45/26/2cdb3d281ac1bd175603e290cbe4bad6eff127c0f8de90bafd6f8548f0fd/lxml-6.1.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a2853c8b2170cc6cd54a6b4d50d2c1a8a7aeca201f23804b4898525c7a152cfc", size = 4993247, upload-time = "2026-04-18T04:33:36.674Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/05/d735aef963740022a08185c84821f689fc903acb3d50326e6b1e9886cc22/lxml-6.1.0-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8e369cbd690e788c8d15e56222d91a09c6a417f49cbc543040cba0fe2e25a79e", size = 5613042, upload-time = "2026-04-18T04:33:39.205Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/b8/ead7c10efff731738c72e59ed6eb5791854879fbed7ae98781a12006263a/lxml-6.1.0-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e69aa6805905807186eb00e66c6d97a935c928275182eb02ee40ba00da9623b2", size = 5228304, upload-time = "2026-04-18T04:33:41.647Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/10/e9842d2ec322ea65f0a7270aa0315a53abed06058b88ef1b027f620e7a5f/lxml-6.1.0-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:4bd1bdb8a9e0e2dd229de19b5f8aebac80e916921b4b2c6ef8a52bc131d0c1f9", size = 5341578, upload-time = "2026-04-18T04:33:44.596Z" },
+    { url = "https://files.pythonhosted.org/packages/89/54/40d9403d7c2775fa7301d3ddd3464689bfe9ba71acc17dfff777071b4fdc/lxml-6.1.0-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:cbd7b79cdcb4986ad78a2662625882747f09db5e4cd7b2ae178a88c9c51b3dfe", size = 4700209, upload-time = "2026-04-18T04:33:47.552Z" },
+    { url = "https://files.pythonhosted.org/packages/85/b2/bbdcc2cf45dfc7dfffef4fd97e5c47b15919b6a365247d95d6f684ef5e82/lxml-6.1.0-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:43e4d297f11080ec9d64a4b1ad7ac02b4484c9f0e2179d9c4ef78e886e747b88", size = 5232365, upload-time = "2026-04-18T04:33:50.249Z" },
+    { url = "https://files.pythonhosted.org/packages/48/5a/b06875665e53aaba7127611a7bed3b7b9658e20b22bc2dd217a0b7ab0091/lxml-6.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cc16682cc987a3da00aa56a3aa3075b08edb10d9b1e476938cfdbee8f3b67181", size = 5043654, upload-time = "2026-04-18T04:33:52.71Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/9c/e71a069d09641c1a7abeb30e693f828c7c90a41cbe3d650b2d734d876f85/lxml-6.1.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:d6d8efe71429635f0559579092bb5e60560d7b9115ee38c4adbea35632e7fa24", size = 4769326, upload-time = "2026-04-18T04:33:55.244Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/06/7a9cd84b3d4ed79adf35f874750abb697dec0b4a81a836037b36e47c091a/lxml-6.1.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e39ab3a28af7784e206d8606ec0e4bcad0190f63a492bca95e94e5a4aef7f6e", size = 5635879, upload-time = "2026-04-18T04:33:58.509Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/f0/9d57916befc1e54c451712c7ee48e9e74e80ae4d03bdce49914e0aee42cd/lxml-6.1.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:9eb667bf50856c4a58145f8ca2d5e5be160191e79eb9e30855a476191b3c3495", size = 5224048, upload-time = "2026-04-18T04:34:00.943Z" },
+    { url = "https://files.pythonhosted.org/packages/99/75/90c4eefda0c08c92221fe0753db2d6699a4c628f76ff4465ec20dea84cc1/lxml-6.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7f4a77d6f7edf9230cee3e1f7f6764722a41604ee5681844f18db9a81ea0ec33", size = 5250241, upload-time = "2026-04-18T04:34:03.365Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/73/16596f7e4e38fa33084b9ccbccc22a15f82a290a055126f2c1541236d2ff/lxml-6.1.0-cp313-cp313-win32.whl", hash = "sha256:28902146ffbe5222df411c5d19e5352490122e14447e98cd118907ee3fd6ee62", size = 3596938, upload-time = "2026-04-18T04:31:56.206Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/63/981401c5680c1eb30893f00a19641ac80db5d1e7086c62cb4b13ed813038/lxml-6.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:4a1503c56e4e2b38dc76f2f2da7bae69670c0f1933e27cfa34b2fa5876410b16", size = 3995728, upload-time = "2026-04-18T04:31:58.763Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/e8/c358a38ac3e541d16a1b527e4e9cb78c0419b0506a070ace11777e5e8404/lxml-6.1.0-cp313-cp313-win_arm64.whl", hash = "sha256:e0af85773850417d994d019741239b901b22c6680206f46a34766926e466141d", size = 3658372, upload-time = "2026-04-18T04:32:03.629Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/45/cee4cf203ef0bab5c52afc118da61d6b460c928f2893d40023cfa27e0b80/lxml-6.1.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:ab863fd37458fed6456525f297d21239d987800c46e67da5ef04fc6b3dd93ac8", size = 8576713, upload-time = "2026-04-18T04:32:06.831Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/a7/eda05babeb7e046839204eaf254cd4d7c9130ce2bbf0d9e90ea41af5654d/lxml-6.1.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6fd8b1df8254ff4fd93fd31da1fc15770bde23ac045be9bb1f87425702f61cc9", size = 4623874, upload-time = "2026-04-18T04:32:10.755Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/e9/db5846de9b436b91890a62f29d80cd849ea17948a49bf532d5278ee69a9e/lxml-6.1.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:47024feaae386a92a146af0d2aeed65229bf6fff738e6a11dda6b0015fb8fd03", size = 4949535, upload-time = "2026-04-18T04:34:06.657Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/ba/0d3593373dcae1d68f40dc3c41a5a92f2544e68115eb2f62319a4c2a6500/lxml-6.1.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3f00972f84450204cd5d93a5395965e348956aaceaadec693a22ec743f8ae3eb", size = 5086881, upload-time = "2026-04-18T04:34:09.556Z" },
+    { url = "https://files.pythonhosted.org/packages/43/76/759a7484539ad1af0d125a9afe9c3fb5f82a8779fd1f5f56319d9e4ea2fd/lxml-6.1.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97faa0860e13b05b15a51fb4986421ef7a30f0b3334061c416e0981e9450ca4c", size = 5031305, upload-time = "2026-04-18T04:34:12.336Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/b9/c1f0daf981a11e47636126901fd4ab82429e18c57aeb0fc3ad2940b42d8b/lxml-6.1.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:972a6451204798675407beaad97b868d0c733d9a74dafefc63120b81b8c2de28", size = 5647522, upload-time = "2026-04-18T04:34:14.89Z" },
+    { url = "https://files.pythonhosted.org/packages/31/e6/1f533dcd205275363d9ba3511bcec52fa2df86abf8abe6a5f2c599f0dc31/lxml-6.1.0-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fe022f20bc4569ec66b63b3fb275a3d628d9d32da6326b2982584104db6d3086", size = 5239310, upload-time = "2026-04-18T04:34:17.652Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/8c/4175fb709c78a6e315ed814ed33be3defd8b8721067e70419a6cf6f971da/lxml-6.1.0-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:75c4c7c619a744f972f4451bf5adf6d0fb00992a1ffc9fd78e13b0bc817cc99f", size = 5350799, upload-time = "2026-04-18T04:34:20.529Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/77/6ffdebc5994975f0dde4acb59761902bd9d9bb84422b9a0bd239a7da9ca8/lxml-6.1.0-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:3648f20d25102a22b6061c688beb3a805099ea4beb0a01ce62975d926944d292", size = 4697693, upload-time = "2026-04-18T04:34:23.541Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/f1/565f36bd5c73294602d48e04d23f81ff4c8736be6ba5e1d1ec670ac9be80/lxml-6.1.0-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:77b9f99b17cbf14026d1e618035077060fc7195dd940d025149f3e2e830fbfcb", size = 5250708, upload-time = "2026-04-18T04:34:26.001Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/11/a68ab9dd18c5c499404deb4005f4bc4e0e88e5b72cd755ad96efec81d18d/lxml-6.1.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:32662519149fd7a9db354175aa5e417d83485a8039b8aaa62f873ceee7ea4cad", size = 5084737, upload-time = "2026-04-18T04:34:28.32Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/78/e8f41e2c74f4af564e6a0348aea69fb6daaefa64bc071ef469823d22cc18/lxml-6.1.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:73d658216fc173cf2c939e90e07b941c5e12736b0bf6a99e7af95459cfe8eabb", size = 4737817, upload-time = "2026-04-18T04:34:30.784Z" },
+    { url = "https://files.pythonhosted.org/packages/06/2d/aa4e117aa2ce2f3b35d9ff246be74a2f8e853baba5d2a92c64744474603a/lxml-6.1.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ac4db068889f8772a4a698c5980ec302771bb545e10c4b095d4c8be26749616f", size = 5670753, upload-time = "2026-04-18T04:34:33.675Z" },
+    { url = "https://files.pythonhosted.org/packages/08/f5/dd745d50c0409031dbfcc4881740542a01e54d6f0110bd420fa7782110b8/lxml-6.1.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:45e9dfbd1b661eb64ba0d4dbe762bd210c42d86dd1e5bd2bdf89d634231beb43", size = 5238071, upload-time = "2026-04-18T04:34:36.12Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/74/ad424f36d0340a904665867dab310a3f1f4c96ff4039698de83b77f44c1f/lxml-6.1.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:89e8d73d09ac696a5ba42ec69787913d53284f12092f651506779314f10ba585", size = 5264319, upload-time = "2026-04-18T04:34:39.035Z" },
+    { url = "https://files.pythonhosted.org/packages/53/36/a15d8b3514ec889bfd6aa3609107fcb6c9189f8dc347f1c0b81eded8d87c/lxml-6.1.0-cp314-cp314-win32.whl", hash = "sha256:ebe33f4ec1b2de38ceb225a1749a2965855bffeef435ba93cd2d5d540783bf2f", size = 3657139, upload-time = "2026-04-18T04:32:20.006Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/a4/263ebb0710851a3c6c937180a9a86df1206fdfe53cc43005aa2237fd7736/lxml-6.1.0-cp314-cp314-win_amd64.whl", hash = "sha256:398443df51c538bd578529aa7e5f7afc6c292644174b47961f3bf87fe5741120", size = 4064195, upload-time = "2026-04-18T04:32:23.876Z" },
+    { url = "https://files.pythonhosted.org/packages/80/68/2000f29d323b6c286de077ad20b429fc52272e44eae6d295467043e56012/lxml-6.1.0-cp314-cp314-win_arm64.whl", hash = "sha256:8c8984e1d8c4b3949e419158fda14d921ff703a9ed8a47236c6eb7a2b6cb4946", size = 3741870, upload-time = "2026-04-18T04:32:27.922Z" },
+    { url = "https://files.pythonhosted.org/packages/30/e9/21383c7c8d43799f0da90224c0d7c921870d476ec9b3e01e1b2c0b8237c5/lxml-6.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1081dd10bc6fa437db2500e13993abf7cc30716d0a2f40e65abb935f02ec559c", size = 8827548, upload-time = "2026-04-18T04:32:15.094Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/01/c6bc11cd587030dd4f719f65c5657960649fe3e19196c844c75bf32cd0d6/lxml-6.1.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:dabecc48db5f42ba348d1f5d5afdc54c6c4cc758e676926c7cd327045749517d", size = 4735866, upload-time = "2026-04-18T04:32:18.924Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/01/757132fff5f4acf25463b5298f1a46099f3a94480b806547b29ce5e385de/lxml-6.1.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e3dd5fe19c9e0ac818a9c7f132a5e43c1339ec1cbbfecb1a938bd3a47875b7c9", size = 4969476, upload-time = "2026-04-18T04:34:41.889Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/fb/1bc8b9d27ed64be7c8903db6c89e74dc8c2cd9ec630a7462e4654316dc5b/lxml-6.1.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9e7b0a4ca6dcc007a4cef00a761bba2dea959de4bd2df98f926b33c92ca5dfb9", size = 5103719, upload-time = "2026-04-18T04:34:44.797Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/e7/5bf82fa28133536a54601aae633b14988e89ed61d4c1eb6b899b023233aa/lxml-6.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d27bbe326c6b539c64b42638b18bc6003a8d88f76213a97ac9ed4f885efeab7", size = 5027890, upload-time = "2026-04-18T04:34:47.634Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/20/e048db5d4b4ea0366648aa595f26bb764b2670903fc585b87436d0a5032c/lxml-6.1.0-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4e425db0c5445ef0ad56b0eec54f89b88b2d884656e536a90b2f52aecb4ca86", size = 5596008, upload-time = "2026-04-18T04:34:51.503Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/c2/d10807bc8da4824b39e5bd01b5d05c077b6fd01bd91584167edf6b269d22/lxml-6.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4b89b098105b8599dc57adac95d1813409ac476d3c948a498775d3d0c6124bfb", size = 5224451, upload-time = "2026-04-18T04:34:54.263Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/15/2ebea45bea427e7f0057e9ce7b2d62c5aba20c6b001cca89ed0aadb3ad41/lxml-6.1.0-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:c4a699432846df86cc3de502ee85f445ebad748a1c6021d445f3e514d2cd4b1c", size = 5312135, upload-time = "2026-04-18T04:34:56.818Z" },
+    { url = "https://files.pythonhosted.org/packages/31/e2/87eeae151b0be2a308d49a7ec444ff3eb192b14251e62addb29d0bf3778f/lxml-6.1.0-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:30e7b2ed63b6c8e97cca8af048589a788ab5c9c905f36d9cf1c2bb549f450d2f", size = 4639126, upload-time = "2026-04-18T04:34:59.704Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/51/8a3f6a20902ad604dd746ec7b4000311b240d389dac5e9d95adefd349e0c/lxml-6.1.0-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:022981127642fe19866d2907d76241bb07ed21749601f727d5d5dd1ce5d1b773", size = 5232579, upload-time = "2026-04-18T04:35:02.658Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/d2/650d619bdbe048d2c3f2c31edb00e35670a5e2d65b4fe3b61bce37b19121/lxml-6.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:23cad0cc86046d4222f7f418910e46b89971c5a45d3c8abfad0f64b7b05e4a9b", size = 5084206, upload-time = "2026-04-18T04:35:05.175Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/8a/672ca1a3cbeabd1f511ca275a916c0514b747f4b85bdaae103b8fa92f307/lxml-6.1.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:21c3302068f50d1e8728c67c87ba92aa87043abee517aa2576cca1855326b405", size = 4758906, upload-time = "2026-04-18T04:35:08.098Z" },
+    { url = "https://files.pythonhosted.org/packages/be/f1/ef4b691da85c916cb2feb1eec7414f678162798ac85e042fa164419ac05c/lxml-6.1.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:be10838781cb3be19251e276910cd508fe127e27c3242e50521521a0f3781690", size = 5620553, upload-time = "2026-04-18T04:35:11.23Z" },
+    { url = "https://files.pythonhosted.org/packages/59/17/94e81def74107809755ac2782fdad4404420f1c92ca83433d117a6d5acf0/lxml-6.1.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2173a7bffe97667bbf0767f8a99e587740a8c56fdf3befac4b09cb29a80276fd", size = 5229458, upload-time = "2026-04-18T04:35:14.254Z" },
+    { url = "https://files.pythonhosted.org/packages/21/55/c4be91b0f830a871fc1b0d730943d56013b683d4671d5198260e2eae722b/lxml-6.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c6854e9cf99c84beb004eecd7d3a3868ef1109bf2b1df92d7bc11e96a36c2180", size = 5247861, upload-time = "2026-04-18T04:35:17.006Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/ca/77123e4d77df3cb1e968ade7b1f808f5d3a5c1c96b18a33895397de292c1/lxml-6.1.0-cp314-cp314t-win32.whl", hash = "sha256:00750d63ef0031a05331b9223463b1c7c02b9004cef2346a5b2877f0f9494dd2", size = 3897377, upload-time = "2026-04-18T04:32:07.656Z" },
+    { url = "https://files.pythonhosted.org/packages/64/ce/3554833989d074267c063209bae8b09815e5656456a2d332b947806b05ff/lxml-6.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:80410c3a7e3c617af04de17caa9f9f20adaa817093293d69eae7d7d0522836f5", size = 4392701, upload-time = "2026-04-18T04:32:12.113Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/a0/9b916c68c0e57752c07f8f64b30138d9d4059dbeb27b90274dedbea128ff/lxml-6.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:26dd9f57ee3bd41e7d35b4c98a2ffd89ed11591649f421f0ec19f67d50ec67ac", size = 3817120, upload-time = "2026-04-18T04:32:15.803Z" },
 ]
 
 [[package]]
@@ -3746,7 +3689,7 @@ wheels = [
 
 [[package]]
 name = "mypy"
-version = "1.20.1"
+version = "1.20.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "librt", marker = "platform_python_implementation != 'PyPy'" },
@@ -3754,37 +3697,37 @@ dependencies = [
     { name = "pathspec" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0b/3d/5b373635b3146264eb7a68d09e5ca11c305bbb058dfffbb47c47daf4f632/mypy-1.20.1.tar.gz", hash = "sha256:6fc3f4ecd52de81648fed1945498bf42fa2993ddfad67c9056df36ae5757f804", size = 3815892, upload-time = "2026-04-13T02:46:51.474Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/04/af/e3d4b3e9ec91a0ff9aabfdb38692952acf49bbb899c2e4c29acb3a6da3ae/mypy-1.20.2.tar.gz", hash = "sha256:e8222c26daaafd9e8626dec58ae36029f82585890589576f769a650dd20fd665", size = 3817349, upload-time = "2026-04-21T17:12:28.473Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/69/1b/75a7c825a02781ca10bc2f2f12fba2af5202f6d6005aad8d2d1f264d8d78/mypy-1.20.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:36ee2b9c6599c230fea89bbd79f401f9f9f8e9fcf0c777827789b19b7da90f51", size = 14494077, upload-time = "2026-04-13T02:45:55.085Z" },
-    { url = "https://files.pythonhosted.org/packages/b0/54/5e5a569ea5c2b4d48b729fb32aa936eeb4246e4fc3e6f5b3d36a2dfbefb9/mypy-1.20.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fba3fb0968a7b48806b0c90f38d39296f10766885a94c83bd21399de1e14eb28", size = 13319495, upload-time = "2026-04-13T02:45:29.674Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/a4/a1945b19f33e91721b59deee3abb484f2fa5922adc33bb166daf5325d76d/mypy-1.20.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef1415a637cd3627d6304dfbeddbadd21079dafc2a8a753c477ce4fc0c2af54f", size = 13696948, upload-time = "2026-04-13T02:46:15.006Z" },
-    { url = "https://files.pythonhosted.org/packages/b2/c6/75e969781c2359b2f9c15b061f28ec6d67c8b61865ceda176e85c8e7f2de/mypy-1.20.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ef3461b1ad5cd446e540016e90b5984657edda39f982f4cc45ca317b628f5a37", size = 14706744, upload-time = "2026-04-13T02:46:00.482Z" },
-    { url = "https://files.pythonhosted.org/packages/a8/6e/b221b1de981fc4262fe3e0bf9ec272d292dfe42394a689c2d49765c144c4/mypy-1.20.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:542dd63c9e1339b6092eb25bd515f3a32a1453aee8c9521d2ddb17dacd840237", size = 14949035, upload-time = "2026-04-13T02:45:06.021Z" },
-    { url = "https://files.pythonhosted.org/packages/ca/4b/298ba2de0aafc0da3ff2288da06884aae7ba6489bc247c933f87847c41b3/mypy-1.20.1-cp312-cp312-win_amd64.whl", hash = "sha256:1d55c7cd8ca22e31f93af2a01160a9e95465b5878de23dba7e48116052f20a8d", size = 10883216, upload-time = "2026-04-13T02:45:47.232Z" },
-    { url = "https://files.pythonhosted.org/packages/c7/f9/5e25b8f0b8cb92f080bfed9c21d3279b2a0b6a601cdca369a039ba84789d/mypy-1.20.1-cp312-cp312-win_arm64.whl", hash = "sha256:f5b84a79070586e0d353ee07b719d9d0a4aa7c8ee90c0ea97747e98cbe193019", size = 9814299, upload-time = "2026-04-13T02:45:21.934Z" },
-    { url = "https://files.pythonhosted.org/packages/21/e8/ef0991aa24c8f225df10b034f3c2681213cb54cf247623c6dec9a5744e70/mypy-1.20.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8f3886c03e40afefd327bd70b3f634b39ea82e87f314edaa4d0cce4b927ddcc1", size = 14500739, upload-time = "2026-04-13T02:46:05.442Z" },
-    { url = "https://files.pythonhosted.org/packages/23/73/416ebec3047636ed89fa871dc8c54bf05e9e20aa9499da59790d7adb312d/mypy-1.20.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e860eb3904f9764e83bafd70c8250bdffdc7dde6b82f486e8156348bf7ceb184", size = 13314735, upload-time = "2026-04-13T02:46:47.154Z" },
-    { url = "https://files.pythonhosted.org/packages/10/1e/1505022d9c9ac2e014a384eb17638fb37bf8e9d0a833ea60605b66f8f7ba/mypy-1.20.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a4b5aac6e785719da51a84f5d09e9e843d473170a9045b1ea7ea1af86225df4b", size = 13704356, upload-time = "2026-04-13T02:45:19.773Z" },
-    { url = "https://files.pythonhosted.org/packages/98/91/275b01f5eba5c467a3318ec214dd865abb66e9c811231c8587287b92876a/mypy-1.20.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f37b6cd0fe2ad3a20f05ace48ca3523fc52ff86940e34937b439613b6854472e", size = 14696420, upload-time = "2026-04-13T02:45:24.205Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/57/b3779e134e1b7250d05f874252780d0a88c068bc054bcff99ca20a3a2986/mypy-1.20.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e4bbb0f6b54ce7cc350ef4a770650d15fa70edd99ad5267e227133eda9c94218", size = 14936093, upload-time = "2026-04-13T02:45:32.087Z" },
-    { url = "https://files.pythonhosted.org/packages/be/33/81b64991b0f3f278c3b55c335888794af190b2d59031a5ad1401bcb69f1e/mypy-1.20.1-cp313-cp313-win_amd64.whl", hash = "sha256:c3dc20f8ec76eecd77148cdd2f1542ed496e51e185713bf488a414f862deb8f2", size = 10889659, upload-time = "2026-04-13T02:46:02.926Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/fd/7adcb8053572edf5ef8f3db59599dfeeee3be9cc4c8c97e2d28f66f42ac5/mypy-1.20.1-cp313-cp313-win_arm64.whl", hash = "sha256:a9d62bbac5d6d46718e2b0330b25e6264463ed832722b8f7d4440ff1be3ca895", size = 9815515, upload-time = "2026-04-13T02:46:32.103Z" },
-    { url = "https://files.pythonhosted.org/packages/40/cd/db831e84c81d57d4886d99feee14e372f64bbec6a9cb1a88a19e243f2ef5/mypy-1.20.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:12927b9c0ed794daedcf1dab055b6c613d9d5659ac511e8d936d96f19c087d12", size = 14483064, upload-time = "2026-04-13T02:45:26.901Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/82/74e62e7097fa67da328ac8ece8de09133448c04d20ddeaeba251a3000f01/mypy-1.20.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:752507dd481e958b2c08fc966d3806c962af5a9433b5bf8f3bdd7175c20e34fe", size = 13335694, upload-time = "2026-04-13T02:46:12.514Z" },
-    { url = "https://files.pythonhosted.org/packages/74/c4/97e9a0abe4f3cdbbf4d079cb87a03b786efeccf5bf2b89fe4f96939ab2e6/mypy-1.20.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c614655b5a065e56274c6cbbe405f7cf7e96c0654db7ba39bc680238837f7b08", size = 13726365, upload-time = "2026-04-13T02:45:17.422Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/aa/a19d884a8d28fcd3c065776323029f204dbc774e70ec9c85eba228b680de/mypy-1.20.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2c3f6221a76f34d5100c6d35b3ef6b947054123c3f8d6938a4ba00b1308aa572", size = 14693472, upload-time = "2026-04-13T02:46:41.253Z" },
-    { url = "https://files.pythonhosted.org/packages/84/44/cc9324bd21cf786592b44bf3b5d224b3923c1230ec9898d508d00241d465/mypy-1.20.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:4bdfc06303ac06500af71ea0cdbe995c502b3c9ba32f3f8313523c137a25d1b6", size = 14919266, upload-time = "2026-04-13T02:46:28.37Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/dc/779abb25a8c63e8f44bf5a336217fa92790fa17e0c40e0c725d10cb01bbd/mypy-1.20.1-cp314-cp314-win_amd64.whl", hash = "sha256:0131edd7eba289973d1ba1003d1a37c426b85cdef76650cd02da6420898a5eb3", size = 11049713, upload-time = "2026-04-13T02:45:57.673Z" },
-    { url = "https://files.pythonhosted.org/packages/28/08/4172be2ad7de9119b5a92ca36abbf641afdc5cb1ef4ae0c3a8182f29674f/mypy-1.20.1-cp314-cp314-win_arm64.whl", hash = "sha256:33f02904feb2c07e1fdf7909026206396c9deeb9e6f34d466b4cfedb0aadbbe4", size = 9999819, upload-time = "2026-04-13T02:46:35.039Z" },
-    { url = "https://files.pythonhosted.org/packages/2d/af/af9e46b0c8eabbce9fc04a477564170f47a1c22b308822282a59b7ff315f/mypy-1.20.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:168472149dd8cc505c98cefd21ad77e4257ed6022cd5ed2fe2999bed56977a5a", size = 15547508, upload-time = "2026-04-13T02:46:25.588Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/cd/39c9e4ad6ba33e069e5837d772a9e6c304b4a5452a14a975d52b36444650/mypy-1.20.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:eb674600309a8f22790cca883a97c90299f948183ebb210fbef6bcee07cb1986", size = 14399557, upload-time = "2026-04-13T02:46:10.021Z" },
-    { url = "https://files.pythonhosted.org/packages/83/c1/3fd71bdc118ffc502bf57559c909927bb7e011f327f7bb8e0488e98a5870/mypy-1.20.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef2b2e4cc464ba9795459f2586923abd58a0055487cbe558cb538ea6e6bc142a", size = 15045789, upload-time = "2026-04-13T02:45:10.81Z" },
-    { url = "https://files.pythonhosted.org/packages/8e/73/6f07ff8b57a7d7b3e6e5bf34685d17632382395c8bb53364ec331661f83e/mypy-1.20.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dee461d396dd46b3f0ed5a098dbc9b8860c81c46ad44fa071afcfbc149f167c9", size = 15850795, upload-time = "2026-04-13T02:45:03.349Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/e2/f7dffec1c7767078f9e9adf0c786d1fe0ff30964a77eb213c09b8b58cb76/mypy-1.20.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e364926308b3e66f1361f81a566fc1b2f8cd47fc8525e8136d4058a65a4b4f02", size = 16088539, upload-time = "2026-04-13T02:46:17.841Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/76/e0dee71035316e75a69d73aec2f03c39c21c967b97e277fd0ef8fd6aec66/mypy-1.20.1-cp314-cp314t-win_amd64.whl", hash = "sha256:a0c17fbd746d38c70cbc42647cfd884f845a9708a4b160a8b4f7e70d41f4d7fa", size = 12575567, upload-time = "2026-04-13T02:45:34.795Z" },
-    { url = "https://files.pythonhosted.org/packages/22/a8/7ed43c9d9c3d1468f86605e323a5d97e411a448790a00f07e779f3211a46/mypy-1.20.1-cp314-cp314t-win_arm64.whl", hash = "sha256:db2cb89654626a912efda69c0d5c1d22d948265e2069010d3dde3abf751c7d08", size = 10378823, upload-time = "2026-04-13T02:45:13.35Z" },
-    { url = "https://files.pythonhosted.org/packages/d8/28/926bd972388e65a39ee98e188ccf67e81beb3aacfd5d6b310051772d974b/mypy-1.20.1-py3-none-any.whl", hash = "sha256:1aae28507f253fe82d883790d1c0a0d35798a810117c88184097fe8881052f06", size = 2636553, upload-time = "2026-04-13T02:46:30.45Z" },
+    { url = "https://files.pythonhosted.org/packages/71/4e/7560e4528db9e9b147e4c0f22660466bf30a0a1fe3d63d1b9d3b0fd354ee/mypy-1.20.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4dbfcf869f6b0517f70cf0030ba6ea1d6645e132337a7d5204a18d8d5636c02b", size = 14539393, upload-time = "2026-04-21T17:07:12.52Z" },
+    { url = "https://files.pythonhosted.org/packages/32/d9/34a5efed8124f5a9234f55ac6a4ced4201e2c5b81e1109c49ad23190ec8c/mypy-1.20.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4b6481b228d072315b053210b01ac320e1be243dc17f9e5887ef167f23f5fae4", size = 13361642, upload-time = "2026-04-21T17:06:53.742Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/14/eb377acf78c03c92d566a1510cda8137348215b5335085ef662ab82ecd3a/mypy-1.20.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:34397cdced6b90b836e38182076049fdb41424322e0b0728c946b0939ebdf9f6", size = 13740347, upload-time = "2026-04-21T17:12:04.73Z" },
+    { url = "https://files.pythonhosted.org/packages/b9/94/7e4634a32b641aa1c112422eed1bbece61ee16205f674190e8b536f884de/mypy-1.20.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a5da6976f20cae27059ea8d0c86e7cef3de720e04c4bb9ee18e3690fdb792066", size = 14734042, upload-time = "2026-04-21T17:07:43.16Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/f3/f7e62395cb7f434541b4491a01149a4439e28ace4c0c632bbf5431e92d1f/mypy-1.20.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:56908d7e08318d39f85b1f0c6cfd47b0cac1a130da677630dac0de3e0623e102", size = 14964958, upload-time = "2026-04-21T17:11:00.665Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/0d/47e3c3a0ec2a876e35aeac365df3cac7776c36bbd4ed18cc521e1b9d255b/mypy-1.20.2-cp312-cp312-win_amd64.whl", hash = "sha256:d52ad8d78522da1d308789df651ee5379088e77c76cb1994858d40a426b343b9", size = 10911340, upload-time = "2026-04-21T17:10:49.179Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/b2/6c852d72e0ea8b01f49da817fb52539993cde327e7d010e0103dc12d0dac/mypy-1.20.2-cp312-cp312-win_arm64.whl", hash = "sha256:785b08db19c9f214dc37d65f7c165d19a30fcecb48abfa30f31b01b5acaabb58", size = 9833947, upload-time = "2026-04-21T17:09:05.267Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/c4/b93812d3a192c9bcf5df405bd2f30277cd0e48106a14d1023c7f6ed6e39b/mypy-1.20.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:edfbfca868cdd6bd8d974a60f8a3682f5565d3f5c99b327640cedd24c4264026", size = 14524670, upload-time = "2026-04-21T17:10:30.737Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/47/42c122501bff18eaf1e8f457f5c017933452d8acdc52918a9f59f6812955/mypy-1.20.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e2877a02380adfcdbc69071a0f74d6e9dbbf593c0dc9d174e1f223ffd5281943", size = 13336218, upload-time = "2026-04-21T17:08:44.069Z" },
+    { url = "https://files.pythonhosted.org/packages/92/8f/75bbc92f41725fbd585fb17b440b1119b576105df1013622983e18640a93/mypy-1.20.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7488448de6007cd5177c6cea0517ac33b4c0f5ee9b5e9f2be51ce75511a85517", size = 13724906, upload-time = "2026-04-21T17:08:01.02Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/32/4c49da27a606167391ff0c39aa955707a00edc500572e562f7c36c08a71f/mypy-1.20.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bb9c2fa06887e21d6a3a868762acb82aec34e2c6fd0174064f27c93ede68ad15", size = 14726046, upload-time = "2026-04-21T17:11:22.354Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/fc/4e354a1bd70216359deb0c9c54847ee6b32ef78dfb09f5131ff99b494078/mypy-1.20.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9d56a78b646f2e3daa865bc70cd5ec5a46c50045801ca8ff17a0c43abc97e3ee", size = 14955587, upload-time = "2026-04-21T17:12:16.033Z" },
+    { url = "https://files.pythonhosted.org/packages/62/b2/c0f2056e9eb8f08c62cafd9715e4584b89132bdc832fcf85d27d07b5f3e5/mypy-1.20.2-cp313-cp313-win_amd64.whl", hash = "sha256:2a4102b03bb7481d9a91a6da8d174740c9c8c4401024684b9ca3b7cc5e49852f", size = 10922681, upload-time = "2026-04-21T17:06:35.842Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/14/065e333721f05de8ef683d0aa804c23026bcc287446b61cac657b902ccac/mypy-1.20.2-cp313-cp313-win_arm64.whl", hash = "sha256:a95a9248b0c6fd933a442c03c3b113c3b61320086b88e2c444676d3fd1ca3330", size = 9830560, upload-time = "2026-04-21T17:07:51.023Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/d1/b4ec96b0ecc620a4443570c6e95c867903428cfcde4206518eafdd5880c3/mypy-1.20.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:419413398fe250aae057fd2fe50166b61077083c9b82754c341cf4fd73038f30", size = 14524561, upload-time = "2026-04-21T17:06:27.325Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/63/d2c2ff4fa66bc49477d32dfa26e8a167ba803ea6a69c5efb416036909d30/mypy-1.20.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:e73c07f23009962885c197ccb9b41356a30cc0e5a1d0c2ea8fd8fb1362d7f924", size = 13363883, upload-time = "2026-04-21T17:11:11.239Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/56/983916806bf4eddeaaa2c9230903c3669c6718552a921154e1c5182c701f/mypy-1.20.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c64e5973df366b747646fc98da921f9d6eba9716d57d1db94a83c026a08e0fb", size = 13742945, upload-time = "2026-04-21T17:08:34.181Z" },
+    { url = "https://files.pythonhosted.org/packages/19/65/0cd9285ab010ee8214c83d67c6b49417c40d86ce46f1aa109457b5a9b8d7/mypy-1.20.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a65aa591af023864fd08a97da9974e919452cfe19cb146c8a5dc692626445dc", size = 14706163, upload-time = "2026-04-21T17:05:15.51Z" },
+    { url = "https://files.pythonhosted.org/packages/94/97/48ff3b297cafcc94d185243a9190836fb1b01c1b0918fff64e941e973cc9/mypy-1.20.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:4fef51b01e638974a6e69885687e9bd40c8d1e09a6cd291cca0619625cf1f558", size = 14938677, upload-time = "2026-04-21T17:05:39.562Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/a1/1b4233d255bdd0b38a1f284feeb1c143ca508c19184964e22f8d837ec851/mypy-1.20.2-cp314-cp314-win_amd64.whl", hash = "sha256:913485a03f1bcf5d279409a9d2b9ed565c151f61c09f29991e5faa14033da4c8", size = 11089322, upload-time = "2026-04-21T17:06:44.29Z" },
+    { url = "https://files.pythonhosted.org/packages/78/c2/ce7ee2ba36aeb954ba50f18fa25d9c1188578654b97d02a66a15b6f09531/mypy-1.20.2-cp314-cp314-win_arm64.whl", hash = "sha256:c3bae4f855d965b5453784300c12ffc63a548304ac7f99e55d4dc7c898673aa3", size = 10017775, upload-time = "2026-04-21T17:07:20.732Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/a1/9d93a7d0b5859af0ead82b4888b46df6c8797e1bc5e1e262a08518c6d48e/mypy-1.20.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:2de3dcea53babc1c3237a19002bc3d228ce1833278f093b8d619e06e7cc79609", size = 15549002, upload-time = "2026-04-21T17:08:23.107Z" },
+    { url = "https://files.pythonhosted.org/packages/00/d2/09a6a10ee1bf0008f6c144d9676f2ca6a12512151b4e0ad0ff6c4fac5337/mypy-1.20.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:52b176444e2e5054dfcbcb8c75b0b719865c96247b37407184bbfca5c353f2c2", size = 14401942, upload-time = "2026-04-21T17:07:31.837Z" },
+    { url = "https://files.pythonhosted.org/packages/57/da/9594b75c3c019e805250bed3583bdf4443ff9e6ef08f97e39ae308cb06f2/mypy-1.20.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:688c3312e5dadb573a2c69c82af3a298d43ecf9e6d264e0f95df960b5f6ac19c", size = 15041649, upload-time = "2026-04-21T17:09:34.653Z" },
+    { url = "https://files.pythonhosted.org/packages/97/77/f75a65c278e6e8eba2071f7f5a90481891053ecc39878cc444634d892abe/mypy-1.20.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:29752dbbf8cc53f89f6ac096d363314333045c257c9c75cbd189ca2de0455744", size = 15864588, upload-time = "2026-04-21T17:11:44.936Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/46/1a4e1c66e96c1a3246ddf5403d122ac9b0a8d2b7e65730b9d6533ba7a6d3/mypy-1.20.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:803203d2b6ea644982c644895c2f78b28d0e208bba7b27d9b921e0ec5eb207c6", size = 16093956, upload-time = "2026-04-21T17:10:17.683Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/2c/78a8851264dec38cd736ca5b8bc9380674df0dd0be7792f538916157716c/mypy-1.20.2-cp314-cp314t-win_amd64.whl", hash = "sha256:9bcb8aa397ff0093c824182fd76a935a9ba7ad097fcbef80ae89bf6c1731d8ec", size = 12568661, upload-time = "2026-04-21T17:11:54.473Z" },
+    { url = "https://files.pythonhosted.org/packages/83/01/cd7318aa03493322ce275a0e14f4f52b8896335e4e79d4fb8153a7ad2b77/mypy-1.20.2-cp314-cp314t-win_arm64.whl", hash = "sha256:e061b58443f1736f8a37c48978d7ab581636d6ab03e3d4f99e3fa90463bb9382", size = 10389240, upload-time = "2026-04-21T17:09:42.719Z" },
+    { url = "https://files.pythonhosted.org/packages/28/9a/f23c163e25b11074188251b0b5a0342625fc1cdb6af604757174fa9acc9b/mypy-1.20.2-py3-none-any.whl", hash = "sha256:a94c5a76ab46c5e6257c7972b6c8cff0574201ca7dc05647e33e795d78680563", size = 2637314, upload-time = "2026-04-21T17:05:54.5Z" },
 ]
 
 [[package]]
@@ -4508,7 +4451,7 @@ wheels = [
 
 [[package]]
 name = "pre-commit"
-version = "4.5.1"
+version = "4.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cfgv" },
@@ -4517,9 +4460,9 @@ dependencies = [
     { name = "pyyaml" },
     { name = "virtualenv" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/40/f1/6d86a29246dfd2e9b6237f0b5823717f60cad94d47ddc26afa916d21f525/pre_commit-4.5.1.tar.gz", hash = "sha256:eb545fcff725875197837263e977ea257a402056661f09dae08e4b149b030a61", size = 198232, upload-time = "2025-12-16T21:14:33.552Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/22/2de9408ac81acbb8a7d05d4cc064a152ccf33b3d480ebe0cd292153db239/pre_commit-4.6.0.tar.gz", hash = "sha256:718d2208cef53fdc38206e40524a6d4d9576d103eb16f0fec11c875e7716e9d9", size = 198525, upload-time = "2026-04-21T20:31:41.613Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5d/19/fd3ef348460c80af7bb4669ea7926651d1f95c23ff2df18b9d24bab4f3fa/pre_commit-4.5.1-py2.py3-none-any.whl", hash = "sha256:3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77", size = 226437, upload-time = "2025-12-16T21:14:32.409Z" },
+    { url = "https://files.pythonhosted.org/packages/80/6e/4b28b62ecb6aae56769c34a8ff1d661473ec1e9519e2d5f8b2c150086b26/pre_commit-4.6.0-py2.py3-none-any.whl", hash = "sha256:e2cf246f7299edcabcf15f9b0571fdce06058527f0a06535068a86d38089f29b", size = 226472, upload-time = "2026-04-21T20:31:40.092Z" },
 ]
 
 [[package]]
@@ -4689,45 +4632,45 @@ wheels = [
 
 [[package]]
 name = "pyarrow"
-version = "23.0.1"
+version = "24.0.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/88/22/134986a4cc224d593c1afde5494d18ff629393d74cc2eddb176669f234a4/pyarrow-23.0.1.tar.gz", hash = "sha256:b8c5873e33440b2bc2f4a79d2b47017a89c5a24116c055625e6f2ee50523f019", size = 1167336, upload-time = "2026-02-16T10:14:12.39Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/13/13e1069b351bdc3881266e11147ffccf687505dbb0ea74036237f5d454a5/pyarrow-24.0.0.tar.gz", hash = "sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83", size = 1180261, upload-time = "2026-04-21T10:51:25.837Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/9a/4b/4166bb5abbfe6f750fc60ad337c43ecf61340fa52ab386da6e8dbf9e63c4/pyarrow-23.0.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f4b0dbfa124c0bb161f8b5ebb40f1a680b70279aa0c9901d44a2b5a20806039f", size = 34214575, upload-time = "2026-02-16T10:09:56.225Z" },
-    { url = "https://files.pythonhosted.org/packages/e1/da/3f941e3734ac8088ea588b53e860baeddac8323ea40ce22e3d0baa865cc9/pyarrow-23.0.1-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:7707d2b6673f7de054e2e83d59f9e805939038eebe1763fe811ee8fa5c0cd1a7", size = 35832540, upload-time = "2026-02-16T10:10:03.428Z" },
-    { url = "https://files.pythonhosted.org/packages/88/7c/3d841c366620e906d54430817531b877ba646310296df42ef697308c2705/pyarrow-23.0.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:86ff03fb9f1a320266e0de855dee4b17da6794c595d207f89bba40d16b5c78b9", size = 44470940, upload-time = "2026-02-16T10:10:10.704Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/a5/da83046273d990f256cb79796a190bbf7ec999269705ddc609403f8c6b06/pyarrow-23.0.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:813d99f31275919c383aab17f0f455a04f5a429c261cc411b1e9a8f5e4aaaa05", size = 47586063, upload-time = "2026-02-16T10:10:17.95Z" },
-    { url = "https://files.pythonhosted.org/packages/5b/3c/b7d2ebcff47a514f47f9da1e74b7949138c58cfeb108cdd4ee62f43f0cf3/pyarrow-23.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:bf5842f960cddd2ef757d486041d57c96483efc295a8c4a0e20e704cbbf39c67", size = 48173045, upload-time = "2026-02-16T10:10:25.363Z" },
-    { url = "https://files.pythonhosted.org/packages/43/b2/b40961262213beaba6acfc88698eb773dfce32ecdf34d19291db94c2bd73/pyarrow-23.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:564baf97c858ecc03ec01a41062e8f4698abc3e6e2acd79c01c2e97880a19730", size = 50621741, upload-time = "2026-02-16T10:10:33.477Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/70/1fdda42d65b28b078e93d75d371b2185a61da89dda4def8ba6ba41ebdeb4/pyarrow-23.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:07deae7783782ac7250989a7b2ecde9b3c343a643f82e8a4df03d93b633006f0", size = 27620678, upload-time = "2026-02-16T10:10:39.31Z" },
-    { url = "https://files.pythonhosted.org/packages/47/10/2cbe4c6f0fb83d2de37249567373d64327a5e4d8db72f486db42875b08f6/pyarrow-23.0.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:6b8fda694640b00e8af3c824f99f789e836720aa8c9379fb435d4c4953a756b8", size = 34210066, upload-time = "2026-02-16T10:10:45.487Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/4f/679fa7e84dadbaca7a65f7cdba8d6c83febbd93ca12fa4adf40ba3b6362b/pyarrow-23.0.1-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:8ff51b1addc469b9444b7c6f3548e19dc931b172ab234e995a60aea9f6e6025f", size = 35825526, upload-time = "2026-02-16T10:10:52.266Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/63/d2747d930882c9d661e9398eefc54f15696547b8983aaaf11d4a2e8b5426/pyarrow-23.0.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:71c5be5cbf1e1cb6169d2a0980850bccb558ddc9b747b6206435313c47c37677", size = 44473279, upload-time = "2026-02-16T10:11:01.557Z" },
-    { url = "https://files.pythonhosted.org/packages/b3/93/10a48b5e238de6d562a411af6467e71e7aedbc9b87f8d3a35f1560ae30fb/pyarrow-23.0.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:9b6f4f17b43bc39d56fec96e53fe89d94bac3eb134137964371b45352d40d0c2", size = 47585798, upload-time = "2026-02-16T10:11:09.401Z" },
-    { url = "https://files.pythonhosted.org/packages/5c/20/476943001c54ef078dbf9542280e22741219a184a0632862bca4feccd666/pyarrow-23.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:9fc13fc6c403d1337acab46a2c4346ca6c9dec5780c3c697cf8abfd5e19b6b37", size = 48179446, upload-time = "2026-02-16T10:11:17.781Z" },
-    { url = "https://files.pythonhosted.org/packages/4b/b6/5dd0c47b335fcd8edba9bfab78ad961bd0fd55ebe53468cc393f45e0be60/pyarrow-23.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5c16ed4f53247fa3ffb12a14d236de4213a4415d127fe9cebed33d51671113e2", size = 50623972, upload-time = "2026-02-16T10:11:26.185Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/09/a532297c9591a727d67760e2e756b83905dd89adb365a7f6e9c72578bcc1/pyarrow-23.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:cecfb12ef629cf6be0b1887f9f86463b0dd3dc3195ae6224e74006be4736035a", size = 27540749, upload-time = "2026-02-16T10:12:23.297Z" },
-    { url = "https://files.pythonhosted.org/packages/a5/8e/38749c4b1303e6ae76b3c80618f84861ae0c55dd3c2273842ea6f8258233/pyarrow-23.0.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:29f7f7419a0e30264ea261fdc0e5fe63ce5a6095003db2945d7cd78df391a7e1", size = 34471544, upload-time = "2026-02-16T10:11:32.535Z" },
-    { url = "https://files.pythonhosted.org/packages/a3/73/f237b2bc8c669212f842bcfd842b04fc8d936bfc9d471630569132dc920d/pyarrow-23.0.1-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:33d648dc25b51fd8055c19e4261e813dfc4d2427f068bcecc8b53d01b81b0500", size = 35949911, upload-time = "2026-02-16T10:11:39.813Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/86/b912195eee0903b5611bf596833def7d146ab2d301afeb4b722c57ffc966/pyarrow-23.0.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:cd395abf8f91c673dd3589cadc8cc1ee4e8674fa61b2e923c8dd215d9c7d1f41", size = 44520337, upload-time = "2026-02-16T10:11:47.764Z" },
-    { url = "https://files.pythonhosted.org/packages/69/c2/f2a717fb824f62d0be952ea724b4f6f9372a17eed6f704b5c9526f12f2f1/pyarrow-23.0.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:00be9576d970c31defb5c32eb72ef585bf600ef6d0a82d5eccaae96639cf9d07", size = 47548944, upload-time = "2026-02-16T10:11:56.607Z" },
-    { url = "https://files.pythonhosted.org/packages/84/a7/90007d476b9f0dc308e3bc57b832d004f848fd6c0da601375d20d92d1519/pyarrow-23.0.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c2139549494445609f35a5cda4eb94e2c9e4d704ce60a095b342f82460c73a83", size = 48236269, upload-time = "2026-02-16T10:12:04.47Z" },
-    { url = "https://files.pythonhosted.org/packages/b0/3f/b16fab3e77709856eb6ac328ce35f57a6d4a18462c7ca5186ef31b45e0e0/pyarrow-23.0.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:7044b442f184d84e2351e5084600f0d7343d6117aabcbc1ac78eb1ae11eb4125", size = 50604794, upload-time = "2026-02-16T10:12:11.797Z" },
-    { url = "https://files.pythonhosted.org/packages/e9/a1/22df0620a9fac31d68397a75465c344e83c3dfe521f7612aea33e27ab6c0/pyarrow-23.0.1-cp313-cp313t-win_amd64.whl", hash = "sha256:a35581e856a2fafa12f3f54fce4331862b1cfb0bef5758347a858a4aa9d6bae8", size = 27660642, upload-time = "2026-02-16T10:12:17.746Z" },
-    { url = "https://files.pythonhosted.org/packages/8d/1b/6da9a89583ce7b23ac611f183ae4843cd3a6cf54f079549b0e8c14031e73/pyarrow-23.0.1-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:5df1161da23636a70838099d4aaa65142777185cc0cdba4037a18cee7d8db9ca", size = 34238755, upload-time = "2026-02-16T10:12:32.819Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/b5/d58a241fbe324dbaeb8df07be6af8752c846192d78d2272e551098f74e88/pyarrow-23.0.1-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:fa8e51cb04b9f8c9c5ace6bab63af9a1f88d35c0d6cbf53e8c17c098552285e1", size = 35847826, upload-time = "2026-02-16T10:12:38.949Z" },
-    { url = "https://files.pythonhosted.org/packages/54/a5/8cbc83f04aba433ca7b331b38f39e000efd9f0c7ce47128670e737542996/pyarrow-23.0.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:0b95a3994f015be13c63148fef8832e8a23938128c185ee951c98908a696e0eb", size = 44536859, upload-time = "2026-02-16T10:12:45.467Z" },
-    { url = "https://files.pythonhosted.org/packages/36/2e/c0f017c405fcdc252dbccafbe05e36b0d0eb1ea9a958f081e01c6972927f/pyarrow-23.0.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:4982d71350b1a6e5cfe1af742c53dfb759b11ce14141870d05d9e540d13bc5d1", size = 47614443, upload-time = "2026-02-16T10:12:55.525Z" },
-    { url = "https://files.pythonhosted.org/packages/af/6b/2314a78057912f5627afa13ba43809d9d653e6630859618b0fd81a4e0759/pyarrow-23.0.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c250248f1fe266db627921c89b47b7c06fee0489ad95b04d50353537d74d6886", size = 48232991, upload-time = "2026-02-16T10:13:04.729Z" },
-    { url = "https://files.pythonhosted.org/packages/40/f2/1bcb1d3be3460832ef3370d621142216e15a2c7c62602a4ea19ec240dd64/pyarrow-23.0.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5f4763b83c11c16e5f4c15601ba6dfa849e20723b46aa2617cb4bffe8768479f", size = 50645077, upload-time = "2026-02-16T10:13:14.147Z" },
-    { url = "https://files.pythonhosted.org/packages/eb/3f/b1da7b61cd66566a4d4c8383d376c606d1c34a906c3f1cb35c479f59d1aa/pyarrow-23.0.1-cp314-cp314-win_amd64.whl", hash = "sha256:3a4c85ef66c134161987c17b147d6bffdca4566f9a4c1d81a0a01cdf08414ea5", size = 28234271, upload-time = "2026-02-16T10:14:09.397Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/78/07f67434e910a0f7323269be7bfbf58699bd0c1d080b18a1ab49ba943fe8/pyarrow-23.0.1-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:17cd28e906c18af486a499422740298c52d7c6795344ea5002a7720b4eadf16d", size = 34488692, upload-time = "2026-02-16T10:13:21.541Z" },
-    { url = "https://files.pythonhosted.org/packages/50/76/34cf7ae93ece1f740a04910d9f7e80ba166b9b4ab9596a953e9e62b90fe1/pyarrow-23.0.1-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:76e823d0e86b4fb5e1cf4a58d293036e678b5a4b03539be933d3b31f9406859f", size = 35964383, upload-time = "2026-02-16T10:13:28.63Z" },
-    { url = "https://files.pythonhosted.org/packages/46/90/459b827238936d4244214be7c684e1b366a63f8c78c380807ae25ed92199/pyarrow-23.0.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:a62e1899e3078bf65943078b3ad2a6ddcacf2373bc06379aac61b1e548a75814", size = 44538119, upload-time = "2026-02-16T10:13:35.506Z" },
-    { url = "https://files.pythonhosted.org/packages/28/a1/93a71ae5881e99d1f9de1d4554a87be37da11cd6b152239fb5bd924fdc64/pyarrow-23.0.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:df088e8f640c9fae3b1f495b3c64755c4e719091caf250f3a74d095ddf3c836d", size = 47571199, upload-time = "2026-02-16T10:13:42.504Z" },
-    { url = "https://files.pythonhosted.org/packages/88/a3/d2c462d4ef313521eaf2eff04d204ac60775263f1fb08c374b543f79f610/pyarrow-23.0.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:46718a220d64677c93bc243af1d44b55998255427588e400677d7192671845c7", size = 48259435, upload-time = "2026-02-16T10:13:49.226Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/f1/11a544b8c3d38a759eb3fbb022039117fd633e9a7b19e4841cc3da091915/pyarrow-23.0.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a09f3876e87f48bc2f13583ab551f0379e5dfb83210391e68ace404181a20690", size = 50629149, upload-time = "2026-02-16T10:13:57.238Z" },
-    { url = "https://files.pythonhosted.org/packages/50/f2/c0e76a0b451ffdf0cf788932e182758eb7558953f4f27f1aff8e2518b653/pyarrow-23.0.1-cp314-cp314t-win_amd64.whl", hash = "sha256:527e8d899f14bd15b740cd5a54ad56b7f98044955373a17179d5956ddb93d9ce", size = 28365807, upload-time = "2026-02-16T10:14:03.892Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/a9/9686d9f07837f91f775e8932659192e02c74f9d8920524b480b85212cc68/pyarrow-24.0.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:6233c9ed9ab9d1db47de57d9753256d9dcffbf42db341576099f0fd9f6bf4810", size = 34981559, upload-time = "2026-04-21T10:47:22.17Z" },
+    { url = "https://files.pythonhosted.org/packages/80/b6/0ddf0e9b6ead3474ab087ae598c76b031fc45532bf6a63f3a553440fb258/pyarrow-24.0.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:f7616236ec1bc2b15bfdec22a71ab38851c86f8f05ff64f379e1278cf20c634a", size = 36663654, upload-time = "2026-04-21T10:47:28.315Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/3b/926382efe8ce27ba729071d3566ade6dfb86bdf112f366000196b2f5780a/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:1617043b99bd33e5318ae18eb2919af09c71322ef1ca46566cdafc6e6712fb66", size = 45679394, upload-time = "2026-04-21T10:47:34.821Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/7a/829f7d9dfd37c207206081d6dad474d81dde29952401f07f2ba507814818/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6165461f55ef6314f026de6638d661188e3455d3ec49834556a0ebbdbace18bb", size = 48863122, upload-time = "2026-04-21T10:47:42.056Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/e8/f88ce625fe8babaae64e8db2d417c7653adb3019b08aae85c5ed787dc816/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3b13dedfe76a0ad2d1d859b0811b53827a4e9d93a0bcb05cf59333ab4980cc7e", size = 49376032, upload-time = "2026-04-21T10:47:48.967Z" },
+    { url = "https://files.pythonhosted.org/packages/36/7a/82c363caa145fff88fb475da50d3bf52bb024f61917be5424c3392eaf878/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:25ea65d868eb04015cd18e6df2fbe98f07e5bda2abefabcb88fce39a947716f6", size = 51929490, upload-time = "2026-04-21T10:47:55.981Z" },
+    { url = "https://files.pythonhosted.org/packages/66/1c/e3e72c8014ad2743ca64a701652c733cc5cbcee15c0463a32a8c55518d9e/pyarrow-24.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:295f0a7f2e242dabd513737cf076007dc5b2d59237e3eca37b05c0c6446f3826", size = 27355660, upload-time = "2026-04-21T10:48:01.718Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/d3/a1abf004482026ddc17f4503db227787fa3cfe41ec5091ff20e4fea55e57/pyarrow-24.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba", size = 34976759, upload-time = "2026-04-21T10:48:07.258Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/4a/34f0a36d28a2dd32225301b79daad44e243dc1a2bb77d43b60749be255c4/pyarrow-24.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68", size = 36658471, upload-time = "2026-04-21T10:48:13.347Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/78/543b94712ae8bb1a6023bcc1acf1a740fbff8286747c289cd9468fced2a5/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2", size = 45675981, upload-time = "2026-04-21T10:48:20.201Z" },
+    { url = "https://files.pythonhosted.org/packages/84/9f/8fb7c222b100d314137fa40ec050de56cd8c6d957d1cfff685ce72f15b17/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0", size = 48859172, upload-time = "2026-04-21T10:48:27.541Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/d3/1ea72538e6c8b3b475ed78d1049a2c518e655761ea50fe1171fc855fcab7/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495", size = 49385733, upload-time = "2026-04-21T10:48:34.7Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/be/c3d8b06a1ba35f2260f8e1f771abbee7d5e345c0937aab90675706b1690a/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f", size = 51934335, upload-time = "2026-04-21T10:48:42.099Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/62/89e07a1e7329d2cde3e3c6994ba0839a24977a2beda8be6005ea3d860b99/pyarrow-24.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91", size = 27271748, upload-time = "2026-04-21T10:49:42.532Z" },
+    { url = "https://files.pythonhosted.org/packages/17/1a/cff3a59f80b5b1658549d46611b67163f65e0664431c076ad728bf9d5af4/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275", size = 35238554, upload-time = "2026-04-21T10:48:48.526Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/99/cce0f42a327bfef2c420fb6078a3eb834826e5d6697bf3009fe11d2ad051/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b", size = 36782301, upload-time = "2026-04-21T10:48:55.181Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/66/8e560d5ff6793ca29aca213c53eec0dd482dd46cb93b2819e5aab52e4252/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42", size = 45721929, upload-time = "2026-04-21T10:49:03.676Z" },
+    { url = "https://files.pythonhosted.org/packages/27/0c/a26e25505d030716e078d9f16eb74973cbf0b33b672884e9f9da1c83b871/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b", size = 48825365, upload-time = "2026-04-21T10:49:11.714Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/eb/771f9ecb0c65e73fe9dccdd1717901b9594f08c4515d000c7c62df573811/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37", size = 49451819, upload-time = "2026-04-21T10:49:21.474Z" },
+    { url = "https://files.pythonhosted.org/packages/48/da/61ae89a88732f5a785646f3ec6125dbb640fa98a540eb2b9889caa561403/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca", size = 51909252, upload-time = "2026-04-21T10:49:31.164Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/1a/8dd5cafab7b66573fa91c03d06d213356ad4edd71813aa75e08ce2b3a844/pyarrow-24.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d", size = 27388127, upload-time = "2026-04-21T10:49:37.334Z" },
+    { url = "https://files.pythonhosted.org/packages/ad/80/d022a34ff05d2cbedd8ccf841fc1f532ecfa9eb5ed1711b56d0e0ea71fc9/pyarrow-24.0.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838", size = 35007997, upload-time = "2026-04-21T10:49:48.796Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/ff/f01485fda6f4e5d441afb8dd5e7681e4db18826c1e271852f5d3957d6a80/pyarrow-24.0.0-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b", size = 36678720, upload-time = "2026-04-21T10:49:55.858Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/c2/2d2d5fea814237923f71b36495211f20b43a1576f9a4d6da7e751a64ec6f/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795", size = 45741852, upload-time = "2026-04-21T10:50:04.624Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/3a/28ba9c1c1ebdbb5f1b94dfebb46f207e52e6a554b7fe4132540fde29a3a0/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26", size = 48889852, upload-time = "2026-04-21T10:50:12.293Z" },
+    { url = "https://files.pythonhosted.org/packages/df/51/4a389acfd31dca009f8fb82d7f510bb4130f2b3a8e18cf00194d0687d8ac/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde", size = 49445207, upload-time = "2026-04-21T10:50:20.677Z" },
+    { url = "https://files.pythonhosted.org/packages/19/4b/0bab2b23d2ae901b1b9a03c0efd4b2d070256f8ce3fc43f6e58c167b2081/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76", size = 51954117, upload-time = "2026-04-21T10:50:29.14Z" },
+    { url = "https://files.pythonhosted.org/packages/29/88/f4e9145da0417b3d2c12035a8492b35ff4a3dbc653e614fcfb51d9dedb38/pyarrow-24.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e", size = 28001155, upload-time = "2026-04-21T10:51:22.337Z" },
+    { url = "https://files.pythonhosted.org/packages/79/4f/46a49a63f43526da895b1a45bbb51d5baf8e4d77159f8528fc3e5490007f/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05", size = 35250387, upload-time = "2026-04-21T10:50:35.552Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/da/d5e0cd5ef00796922404806d5f00325cdadc3441ce2c13fe7115f2df9a64/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a", size = 36797102, upload-time = "2026-04-21T10:50:42.417Z" },
+    { url = "https://files.pythonhosted.org/packages/34/c7/5904145b0a593a05236c882933d439b5720f0a145381179063722fbfc123/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072", size = 45745118, upload-time = "2026-04-21T10:50:49.324Z" },
+    { url = "https://files.pythonhosted.org/packages/13/d3/cca42fe166d1c6e4d5b80e530b7949104d10e17508a90ae202dac205ce2a/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931", size = 48844765, upload-time = "2026-04-21T10:50:55.579Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/49/942c3b79878ba928324d1e17c274ed84581db8c0a749b24bcf4cbdf15bd3/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699", size = 49471890, upload-time = "2026-04-21T10:51:02.439Z" },
+    { url = "https://files.pythonhosted.org/packages/76/97/ff71431000a75d84135a1ace5ca4ba11726a231a8007bbb320a4c54075d5/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136", size = 51932250, upload-time = "2026-04-21T10:51:10.576Z" },
+    { url = "https://files.pythonhosted.org/packages/51/be/6f79d55816d5c22557cf27533543d5d70dfe692adfbee4b99f2760674f38/pyarrow-24.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19", size = 28131282, upload-time = "2026-04-21T10:51:16.815Z" },
 ]
 
 [[package]]
@@ -4741,7 +4684,7 @@ wheels = [
 
 [[package]]
 name = "pydantic"
-version = "2.13.2"
+version = "2.13.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-types" },
@@ -4749,84 +4692,84 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/09/e5/06d23afac9973109d1e3c8ad38e1547a12e860610e327c05ee686827dc37/pydantic-2.13.2.tar.gz", hash = "sha256:b418196607e61081c3226dcd4f0672f2a194828abb9109e9cfb84026564df2d1", size = 843836, upload-time = "2026-04-17T09:31:59.636Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d9/e4/40d09941a2cebcb20609b86a559817d5b9291c49dd6f8c87e5feffbe703a/pydantic-2.13.3.tar.gz", hash = "sha256:af09e9d1d09f4e7fe37145c1f577e1d61ceb9a41924bf0094a36506285d0a84d", size = 844068, upload-time = "2026-04-20T14:46:43.632Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/77/ca/b45c378e6e8d0b90577288b533e04e95b7afd61bb1d51b6c263176435489/pydantic-2.13.2-py3-none-any.whl", hash = "sha256:a525087f4c03d7e7456a3de89b64cd693d2229933bb1068b9af6befd5563694e", size = 471947, upload-time = "2026-04-17T09:31:57.541Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/0a/fd7d723f8f8153418fb40cf9c940e82004fce7e987026b08a68a36dd3fe7/pydantic-2.13.3-py3-none-any.whl", hash = "sha256:6db14ac8dfc9a1e57f87ea2c0de670c251240f43cb0c30a5130e9720dc612927", size = 471981, upload-time = "2026-04-20T14:46:41.402Z" },
 ]
 
 [[package]]
 name = "pydantic-core"
-version = "2.46.2"
+version = "2.46.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/43/bb/4742f05b739b2478459bb16fa8470549518c802e06ddcf3f106c5081315e/pydantic_core-2.46.2.tar.gz", hash = "sha256:37bb079f9ee3f1a519392b73fda2a96379b31f2013c6b467fe693e7f2987f596", size = 471269, upload-time = "2026-04-17T09:10:07.017Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/ef/f7abb56c49382a246fd2ce9c799691e3c3e7175ec74b14d99e798bcddb1a/pydantic_core-2.46.3.tar.gz", hash = "sha256:41c178f65b8c29807239d47e6050262eb6bf84eb695e41101e62e38df4a5bc2c", size = 471412, upload-time = "2026-04-20T14:40:56.672Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/97/ec/2fafa4c86f5d2a69372c7cddef30925fd0e370b1efaf556609c1a0196d8a/pydantic_core-2.46.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:ea1ad8c89da31512fe2d249cf0638fb666925bda341901541bc5f3311c6fcc9e", size = 2101729, upload-time = "2026-04-17T09:12:30.042Z" },
-    { url = "https://files.pythonhosted.org/packages/cf/55/be5386c2c4b49af346e8a26b748194ff25757bbb6cf544130854e997af7a/pydantic_core-2.46.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b308da17b92481e0587244631c5529e5d91d04cb2b08194825627b1eca28e21e", size = 1951546, upload-time = "2026-04-17T09:10:10.585Z" },
-    { url = "https://files.pythonhosted.org/packages/29/92/89e273a055ce440e6636c756379af35ad86da9d336a560049c3ba5e41c80/pydantic_core-2.46.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d333a50bdd814a917d8d6a7ee35ba2395d53ddaa882613bc24e54a9d8b129095", size = 1976178, upload-time = "2026-04-17T09:11:49.619Z" },
-    { url = "https://files.pythonhosted.org/packages/91/b3/e4664469cf70c0cb0f7b2f5719d64e5968bb6f38217042c2afa3d3c4ba17/pydantic_core-2.46.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1d00b99590c5bd1fabbc5d28b170923e32c1b1071b1f1de1851a4d14d89eb192", size = 2051697, upload-time = "2026-04-17T09:12:04.917Z" },
-    { url = "https://files.pythonhosted.org/packages/98/58/dbf68213ee06ce51cdd6d8c95f97980e646858c45bd96bd2dfb40433be73/pydantic_core-2.46.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9f0e686960ffe9e65066395af856ac2d52c159043144433602c50c221d81c1ba", size = 2233160, upload-time = "2026-04-17T09:12:00.956Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/d3/68092aa0ee6c60ff4de4740eb82db3d4ce338ec89b3cecb978c532472f12/pydantic_core-2.46.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2d1128da41c9cb474e0a4701f9c363ec645c9d1a02229904c76bf4e0a194fde2", size = 2298398, upload-time = "2026-04-17T09:10:29.694Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/51/5d6155eb737db55b0ad354ca5f333ef009f75feb67df2d79a84bace45af6/pydantic_core-2.46.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:48649cf2d8c358d79586e9fb2f8235902fcaa2d969ec1c5301f2d1873b2f8321", size = 2094058, upload-time = "2026-04-17T09:12:10.995Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/f3/eb4a986197d71319430464ff181226c95adc8f06d932189b158bae5a82f5/pydantic_core-2.46.2-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:b902f0fc7c2cf503865a05718b68147c6cd5d0a3867af38c527be574a9fa6e9d", size = 2130388, upload-time = "2026-04-17T09:12:41.159Z" },
-    { url = "https://files.pythonhosted.org/packages/56/00/44a9c4fe6d0f64b5786d6a8c649d6f0e34ba6c89b3663add1066e54451a2/pydantic_core-2.46.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e80011f808b03d1d87a8f1e76ae3da19a18eb706c823e17981dcf1fae43744fc", size = 2184245, upload-time = "2026-04-17T09:12:36.532Z" },
-    { url = "https://files.pythonhosted.org/packages/78/6b/685b98a834d5e3d1c34a1bde1627525559dd223b75075bc7490cdb24eb33/pydantic_core-2.46.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b839d5c802e31348b949b6473f8190cddbf7d47475856d8ac995a373ee16ec59", size = 2186842, upload-time = "2026-04-17T09:13:04.054Z" },
-    { url = "https://files.pythonhosted.org/packages/22/64/caa2f5a2ac8b6113adaa410ccdf31ba7f54897a6e54cd0d726fc7e780c88/pydantic_core-2.46.2-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:c6b1064f3f9cf9072e1d59dd2936f9f3b668bec1c37039708c9222db703c0d5b", size = 2336066, upload-time = "2026-04-17T09:12:13.006Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/f9/7d2701bf82945b5b9e7df8347be97ef6a36da2846bfe5b4afec299ffe27b/pydantic_core-2.46.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:37a68e6f2ac95578ce3c0564802404b27b24988649616e556c07e77111ed3f1d", size = 2363691, upload-time = "2026-04-17T09:13:42.972Z" },
-    { url = "https://files.pythonhosted.org/packages/3b/65/0dab11574101522941055109419db3cc09db871643dc3fc74e2413215e5b/pydantic_core-2.46.2-cp312-cp312-win32.whl", hash = "sha256:d9ffa75a7ef4b97d6e5e205fabd4304ef01fec09e6f1bdde04b9ad1b07d20289", size = 1958801, upload-time = "2026-04-17T09:11:31.981Z" },
-    { url = "https://files.pythonhosted.org/packages/13/2b/df84baa609c676f6450b8ecad44ea59146c805e3371b7b52443c0899f989/pydantic_core-2.46.2-cp312-cp312-win_amd64.whl", hash = "sha256:0551f2d2ddb68af5a00e26497f8025c538f73ef3cb698f8e5a487042cd2792a8", size = 2072634, upload-time = "2026-04-17T09:11:02.407Z" },
-    { url = "https://files.pythonhosted.org/packages/d1/4e/e1ce8029fc438086a946739bf9d596f70ff470aad4a8345555920618cabe/pydantic_core-2.46.2-cp312-cp312-win_arm64.whl", hash = "sha256:83aef30f106edcc21a6a4cc44b82d3169a1dbe255508db788e778f3c804d3583", size = 2026188, upload-time = "2026-04-17T09:13:11.083Z" },
-    { url = "https://files.pythonhosted.org/packages/07/2b/662e48254479a2d3450ba24b1e25061108b64339794232f503990c519144/pydantic_core-2.46.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:d26e9eea3715008a09a74585fe9becd0c67fbb145dc4df9756d597d7230a652c", size = 2101762, upload-time = "2026-04-17T09:10:13.87Z" },
-    { url = "https://files.pythonhosted.org/packages/73/ab/bafd7c7503757ccc8ec4d1911e106fe474c629443648c51a88f08b0fe91a/pydantic_core-2.46.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:48b36e3235140510dc7861f0cd58b714b1cdd3d48f75e10ce52e69866b746f10", size = 1951814, upload-time = "2026-04-17T09:12:25.934Z" },
-    { url = "https://files.pythonhosted.org/packages/92/cc/7549c2d57ba2e9a42caa5861a2d398dbe31c02c6aca783253ace59ce84f8/pydantic_core-2.46.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36b1f99dc451f1a3981f236151465bcf995bbe712d0727c9f7b236fe228a8133", size = 1977329, upload-time = "2026-04-17T09:13:37.605Z" },
-    { url = "https://files.pythonhosted.org/packages/18/50/7ed4a8a0d478a4dca8f0134a5efa7193f03cc8520dd4c9509339fb2e5002/pydantic_core-2.46.2-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8641c8d535c2d95b45c2e19b646ecd23ebba35d461e0ae48a3498277006250ab", size = 2051832, upload-time = "2026-04-17T09:12:49.771Z" },
-    { url = "https://files.pythonhosted.org/packages/dc/16/bb35b193741c0298ddc5f5e4234269efdc0c65e2bcd198aa0de9b68845e4/pydantic_core-2.46.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:20fb194788a0a50993e87013e693494ba183a2af5b44e99cf060bbae10912b11", size = 2233127, upload-time = "2026-04-17T09:11:04.449Z" },
-    { url = "https://files.pythonhosted.org/packages/91/a5/98f4b637149185addea19e1785ea20c373cca31b202f589111d8209d9873/pydantic_core-2.46.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9262d11d0cd11ee3303a95156939402bed6cedfe5ed0e331b95a283a4da6eb8b", size = 2297418, upload-time = "2026-04-17T09:11:25.929Z" },
-    { url = "https://files.pythonhosted.org/packages/36/90/93a5d21990b152da7b7507b7fddb0b935f6a0984d57ac3ec45a6e17777a2/pydantic_core-2.46.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac204542736aa295fa25f713b7fad6fc50b46ab7764d16087575c85f085174f3", size = 2093735, upload-time = "2026-04-17T09:12:06.908Z" },
-    { url = "https://files.pythonhosted.org/packages/14/22/b8b1ffdddf08b4e84380bcb67f41dbbf4c171377c1d36fc6290794bb2094/pydantic_core-2.46.2-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:9a7c43a0584742dface3ca0daf6f719d46c1ac2f87cf080050f9ae052c75e1b2", size = 2127570, upload-time = "2026-04-17T09:11:53.906Z" },
-    { url = "https://files.pythonhosted.org/packages/c6/26/e60d72b4e2d0ce1fa811044a974412ac1c567fe067d97b3e6b290530786e/pydantic_core-2.46.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fd05e1edb6a90ad446fa268ab09e59202766b837597b714b2492db11ee87fab9", size = 2183524, upload-time = "2026-04-17T09:11:30.092Z" },
-    { url = "https://files.pythonhosted.org/packages/35/32/36bec7584a1eefb17dec4dfa1c946d3fe4440f466c5705b8adfda69c9a9f/pydantic_core-2.46.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:91155b110788b5501abc7ea954f1d08606219e4e28e3c73a94124307c06efb80", size = 2185408, upload-time = "2026-04-17T09:10:57.228Z" },
-    { url = "https://files.pythonhosted.org/packages/fc/d6/1a5689d873620efd67d6b163db0c444c056adb0849b5bc33e2b9f09665a6/pydantic_core-2.46.2-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:e4e2c72a529fa03ff228be1d2b76944013f428220b764e03cc50ada67e17a42c", size = 2335171, upload-time = "2026-04-17T09:11:43.369Z" },
-    { url = "https://files.pythonhosted.org/packages/3e/8e/675104802abe8ef502b072050ee5f2e915251aa1a3af87e1015ce31ec42d/pydantic_core-2.46.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:56291ec1a11c3499890c99a8fd9053b47e60fe837a77ec72c0671b1b8b3dce24", size = 2362743, upload-time = "2026-04-17T09:10:18.333Z" },
-    { url = "https://files.pythonhosted.org/packages/8d/bc/86c5dde4fa6e24467680eef5047da3c1a19be0a527d0d8e14aa76b39307c/pydantic_core-2.46.2-cp313-cp313-win32.whl", hash = "sha256:b50f9c5f826ddca1246f055148df939f5f3f2d0d96db73de28e2233f22210d4c", size = 1958074, upload-time = "2026-04-17T09:12:38.622Z" },
-    { url = "https://files.pythonhosted.org/packages/2a/97/2537e8c1282b2c4eb062580c0d7a4339e10b072b803d1ee0b7f1f0a5c22c/pydantic_core-2.46.2-cp313-cp313-win_amd64.whl", hash = "sha256:251a57788823230ca8cbc99e6245d1a2ed6e180ec4864f251c94182c580c7f2e", size = 2071741, upload-time = "2026-04-17T09:13:32.405Z" },
-    { url = "https://files.pythonhosted.org/packages/da/aa/2ee75798706f9dbc4e76dbe59e41a396c5c311e3d6223b9cf6a5fa7780be/pydantic_core-2.46.2-cp313-cp313-win_arm64.whl", hash = "sha256:315d32d1a71494d6b4e1e14a9fa7a4329597b4c4340088ad7e1a9dafbeed92a9", size = 2025955, upload-time = "2026-04-17T09:10:15.567Z" },
-    { url = "https://files.pythonhosted.org/packages/d0/96/a50ccb6b539ae780f73cea74905468777680e30c6c3bdf714b9d4c116ea0/pydantic_core-2.46.2-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:4f59b45f3ef8650c0c736a57f59031d47ed9df4c0a64e83796849d7d14863a2d", size = 2097111, upload-time = "2026-04-17T09:10:49.617Z" },
-    { url = "https://files.pythonhosted.org/packages/34/5f/fdead7b3afa822ab6e5a18ee0ecffd54937de1877c01ed13a342e0fb3f07/pydantic_core-2.46.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3a075a29ebef752784a91532a1a85be6b234ccffec0a9d7978a92696387c3da6", size = 1951904, upload-time = "2026-04-17T09:12:32.062Z" },
-    { url = "https://files.pythonhosted.org/packages/95/e0/1c5d547e550cdab1bec737492aa08865337af6fe7fc9b96f7f45f17d9519/pydantic_core-2.46.2-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d12d786e30c04a9d307c5d7080bf720d9bac7f1668191d8e37633a9562749e2", size = 1978667, upload-time = "2026-04-17T09:11:35.589Z" },
-    { url = "https://files.pythonhosted.org/packages/0e/cb/665ce629e218c8228302cb94beff4f6531082a2c87d3ecc3d5e63a26f392/pydantic_core-2.46.2-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0d5e6d6343b0b5dcacb3503b5de90022968da8ed0ab9ab39d3eda71c20cbf84e", size = 2046721, upload-time = "2026-04-17T09:11:47.725Z" },
-    { url = "https://files.pythonhosted.org/packages/77/e9/6cb2cf60f54c1472bbdfce19d957553b43dbba79d1d7b2930a195c594785/pydantic_core-2.46.2-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:233eebac0999b6b9ba76eb56f3ec8fce13164aa16b6d2225a36a79e0f95b5973", size = 2228483, upload-time = "2026-04-17T09:12:08.837Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/2a/93e018dd5571f781ebaeda8c0cf65398489d5bee9b1f484df0b6149b43b9/pydantic_core-2.46.2-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9cc0eee720dd2f14f3b7c349469402b99ad81a174ab49d3533974529e9d93992", size = 2294663, upload-time = "2026-04-17T09:12:52.053Z" },
-    { url = "https://files.pythonhosted.org/packages/5e/4f/49e57ca55c770c93d9bb046666a54949b42e3c9099a0c5fe94557873fe30/pydantic_core-2.46.2-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83ee76bf2c9910513dbc19e7d82367131fa7508dedd6186a462393071cc11059", size = 2098742, upload-time = "2026-04-17T09:13:45.472Z" },
-    { url = "https://files.pythonhosted.org/packages/c6/b0/6e46b5cd3332af665f794b8cdeea206618a8630bd9e7bcc36864518fce81/pydantic_core-2.46.2-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:d61db38eb4ee5192f0c261b7f2d38e420b554df8912245e3546aee5c45e2fd78", size = 2125922, upload-time = "2026-04-17T09:12:54.304Z" },
-    { url = "https://files.pythonhosted.org/packages/06/d1/40850c81585be443a2abfdf7f795f8fae831baf8e2f9b2133c8246ac671c/pydantic_core-2.46.2-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8f09a713d17bcd55da8ab02ebd9110c5246a49c44182af213b5212800af8bc83", size = 2183000, upload-time = "2026-04-17T09:10:59.027Z" },
-    { url = "https://files.pythonhosted.org/packages/04/af/8493d7dfa03ebb7866909e577c6aa65ea0de7377b86023cc51d0c8e11db3/pydantic_core-2.46.2-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:30cacc5fb696e64b8ef6fd31d9549d394dd7d52760db072eecb98e37e3af1677", size = 2180335, upload-time = "2026-04-17T09:12:57.01Z" },
-    { url = "https://files.pythonhosted.org/packages/72/5b/1f6a344c4ffdf284da41c6067b82d5ebcbd11ce1b515ae4b662d4adb6f61/pydantic_core-2.46.2-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:7ccfb105fcfe91a22bbb5563ad3dc124bc1aa75bfd2e53a780ab05f78cdf6108", size = 2330002, upload-time = "2026-04-17T09:12:02.958Z" },
-    { url = "https://files.pythonhosted.org/packages/25/ff/9a694126c12d6d2f48a0cafa6f8eef88ef0d8825600e18d03ff2e896c3b2/pydantic_core-2.46.2-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:13ffef637dc8370c249e5b26bd18e9a80a4fca3d809618c44e18ec834a7ca7a8", size = 2359920, upload-time = "2026-04-17T09:10:27.764Z" },
-    { url = "https://files.pythonhosted.org/packages/51/c8/3a35c763d68a9cb2675eb10ef242cf66c5d4701b28ae12e688d67d2c180e/pydantic_core-2.46.2-cp314-cp314-win32.whl", hash = "sha256:1b0ab6d756ca2704a938e6c31b53f290c2f9c10d3914235410302a149de1a83e", size = 1953701, upload-time = "2026-04-17T09:13:30.021Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/6a/f2726a780365f7dfd89d62036f984f7acb99978c60c5e1fa7c0cb898ed11/pydantic_core-2.46.2-cp314-cp314-win_amd64.whl", hash = "sha256:99ebade8c9ada4df975372d8dd25883daa0e379a05f1cd0c99aa0c04368d01a6", size = 2071867, upload-time = "2026-04-17T09:10:39.205Z" },
-    { url = "https://files.pythonhosted.org/packages/e1/79/76baacb9feba3d7c399b245ca1a29c74ea0db04ea693811374827eec2290/pydantic_core-2.46.2-cp314-cp314-win_arm64.whl", hash = "sha256:de87422197cf7f83db91d89c86a21660d749b3cd76cd8a45d115b8e675670f02", size = 2017252, upload-time = "2026-04-17T09:10:26.175Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/3b/77c26938f817668d9ad9bab1a905cb23f11d9a3d4bf724d429b3e55a8eaf/pydantic_core-2.46.2-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:236f22b4a206b5b61db955396b7cf9e2e1ff77f372efe9570128ccfcd6a525eb", size = 2094545, upload-time = "2026-04-17T09:12:19.339Z" },
-    { url = "https://files.pythonhosted.org/packages/fe/de/42c13f590e3c260966aa49bcdb1674774f975467c49abd51191e502bea28/pydantic_core-2.46.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c2012f64d2cd7cca50f49f22445aa5a88691ac2b4498ee0a9a977f8ca4f7289f", size = 1933953, upload-time = "2026-04-17T09:09:55.889Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/84/ebe3ebb3e2d8db656937cfa6f97f544cb7132f2307a4a7dfdcd0ea102a12/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d07d6c63106d3a9c9a333e2636f9c82c703b1a9e3b079299e58747964e4fdb72", size = 1974435, upload-time = "2026-04-17T09:10:12.371Z" },
-    { url = "https://files.pythonhosted.org/packages/b9/15/0bf51ca6709477cd4ef86148b6d7844f3308f029eac361dd0383f1e17b1a/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c326a2b4b85e959d9a1fc3a11f32f84611b6ec07c053e1828a860edf8d068208", size = 2031113, upload-time = "2026-04-17T09:10:00.752Z" },
-    { url = "https://files.pythonhosted.org/packages/02/ae/b7b5af9b79db036d9e61a44c481c17a213dc8fc4b8b71fe6875a72fc778b/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ac8a65e798f2462552c00d2e013d532c94d646729dda98458beaf51f9ec7b120", size = 2236325, upload-time = "2026-04-17T09:10:33.227Z" },
-    { url = "https://files.pythonhosted.org/packages/a6/ae/ecef7477b5a03d4a499708f7e75d2836452ebb70b776c2d64612b334f57a/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a3c2bc1cc8164bedbc160b7bb1e8cc1e8b9c27f69ae4f9ae2b976cdae02b2dd", size = 2278135, upload-time = "2026-04-17T09:10:23.287Z" },
-    { url = "https://files.pythonhosted.org/packages/db/e4/2f9d82faa47af6c39fc3f120145fd915971e1e0cb6b55b494fad9fdf8275/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e69aa5e10b7e8b1bb4a6888650fd12fcbf11d396ca11d4a44de1450875702830", size = 2109071, upload-time = "2026-04-17T09:11:06.149Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/9c/677cf10873fbd0b116575ab7b97c90482b21564f8a8040beb18edef7a577/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:4e6df5c3301e65fb42bc5338bf9a1027a02b0a31dc7f54c33775229af474daf0", size = 2106028, upload-time = "2026-04-17T09:10:51.525Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/53/6a06183544daba51c059123a2064a99039df25f115a06bdb26f2ea177038/pydantic_core-2.46.2-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2c2f6e32548ac8d559b47944effcf8ae4d81c161f6b6c885edc53bc08b8f192d", size = 2164816, upload-time = "2026-04-17T09:11:56.187Z" },
-    { url = "https://files.pythonhosted.org/packages/57/6f/10fcdd9e3eca66fc828eef0f6f5850f2dd3bca2c59e6e041fb8bc3da39be/pydantic_core-2.46.2-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:b089a81c58e6ea0485562bbbbbca4f65c0549521606d5ef27fba217aac9b665a", size = 2166130, upload-time = "2026-04-17T09:10:03.804Z" },
-    { url = "https://files.pythonhosted.org/packages/29/83/92d3fd0e0156cad2e3cb5c26de73794af78ac9fa0c22ab666e566dd67061/pydantic_core-2.46.2-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:7f700a6d6f64112ae9193709b84303bbab84424ad4b47d0253301aabce9dfc70", size = 2316605, upload-time = "2026-04-17T09:12:45.249Z" },
-    { url = "https://files.pythonhosted.org/packages/97/f1/facffdb970981068219582e499b8d0871ed163ffcc6b347de5c412669e4c/pydantic_core-2.46.2-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:67db6814beaa5fefe91101ec7eb9efda613795767be96f7cf58b1ca8c9ca9972", size = 2358385, upload-time = "2026-04-17T09:09:54.657Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/a1/b8160b2f22b2199467bc68581a4ed380643c16b348a27d6165c6c242d694/pydantic_core-2.46.2-cp314-cp314t-win32.whl", hash = "sha256:32fbc7447be8e3be99bf7869f7066308f16be55b61f9882c2cefc7931f5c7664", size = 1942373, upload-time = "2026-04-17T09:12:59.594Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/90/db89acabe5b150e11d1b59fe3d947dda2ef6abbfef5c82f056ff63802f5d/pydantic_core-2.46.2-cp314-cp314t-win_amd64.whl", hash = "sha256:b317a2b97019c0b95ce99f4f901ae383f40132da6706cdf1731066a73394c25c", size = 2052078, upload-time = "2026-04-17T09:10:19.96Z" },
-    { url = "https://files.pythonhosted.org/packages/97/32/e19b83ceb07a3f1bb21798407790bbc9a31740158fd132b94139cb84e16c/pydantic_core-2.46.2-cp314-cp314t-win_arm64.whl", hash = "sha256:7dcb9d40930dfad7ab6b20bcc6ca9d2b030b0f347a0cd9909b54bd53ead521b1", size = 2016941, upload-time = "2026-04-17T09:12:34.447Z" },
-    { url = "https://files.pythonhosted.org/packages/f3/d2/66c146f421178641bda880b0267c0d57dd84f5fec9ecc8e46be17b480742/pydantic_core-2.46.2-graalpy312-graalpy250_312_native-macosx_10_12_x86_64.whl", hash = "sha256:e9fcabd1857492b5bf16f90258babde50f618f55d046b1309972da2396321ff9", size = 2091621, upload-time = "2026-04-17T09:12:47.501Z" },
-    { url = "https://files.pythonhosted.org/packages/ee/b2/c28419aa9fc8055f4ac8e801d1d11c6357351bfa4321ed9bafab3eb98087/pydantic_core-2.46.2-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:fb3ec2c7f54c07b30d89983ce78dc32c37dd06a972448b8716d609493802d628", size = 1937059, upload-time = "2026-04-17T09:10:53.554Z" },
-    { url = "https://files.pythonhosted.org/packages/30/ce/cd0824a2db213dc17113291b7a09b9b0ccd9fbf97daa4b81548703341baf/pydantic_core-2.46.2-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:130a6c837d819ef33e8c2bf702ed2c3429237ea69807f1140943d6f4bdaf52fa", size = 1997278, upload-time = "2026-04-17T09:12:23.784Z" },
-    { url = "https://files.pythonhosted.org/packages/c9/69/47283fe3c0c967d3e9e9cd6c42b70907610c8a6f8d6e8381f1bb55f8006c/pydantic_core-2.46.2-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c2e25417cec5cd9bddb151e33cb08c50160f317479ecc02b22a95ec18f8fe004", size = 2147096, upload-time = "2026-04-17T09:12:43.124Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/cb/5b47425556ecc1f3fe18ed2a0083188aa46e1dd812b06e406475b3a5d536/pydantic_core-2.46.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:b11b59b3eee90a80a36701ddb4576d9ae31f93f05cb9e277ceaa09e6bf074a67", size = 2101946, upload-time = "2026-04-20T14:40:52.581Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/4f/2fb62c2267cae99b815bbf4a7b9283812c88ca3153ef29f7707200f1d4e5/pydantic_core-2.46.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:af8653713055ea18a3abc1537fe2ebc42f5b0bbb768d1eb79fd74eb47c0ac089", size = 1951612, upload-time = "2026-04-20T14:42:42.996Z" },
+    { url = "https://files.pythonhosted.org/packages/50/6e/b7348fd30d6556d132cddd5bd79f37f96f2601fe0608afac4f5fb01ec0b3/pydantic_core-2.46.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:75a519dab6d63c514f3a81053e5266c549679e4aa88f6ec57f2b7b854aceb1b0", size = 1977027, upload-time = "2026-04-20T14:42:02.001Z" },
+    { url = "https://files.pythonhosted.org/packages/82/11/31d60ee2b45540d3fb0b29302a393dbc01cd771c473f5b5147bcd353e593/pydantic_core-2.46.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:a6cd87cb1575b1ad05ba98894c5b5c96411ef678fa2f6ed2576607095b8d9789", size = 2063008, upload-time = "2026-04-20T14:44:17.952Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/db/3a9d1957181b59258f44a2300ab0f0be9d1e12d662a4f57bb31250455c52/pydantic_core-2.46.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f80a55484b8d843c8ada81ebf70a682f3f00a3d40e378c06cf17ecb44d280d7d", size = 2233082, upload-time = "2026-04-20T14:40:57.934Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/e1/3277c38792aeb5cfb18c2f0c5785a221d9ff4e149abbe1184d53d5f72273/pydantic_core-2.46.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3861f1731b90c50a3266316b9044f5c9b405eecb8e299b0a7120596334e4fe9c", size = 2304615, upload-time = "2026-04-20T14:42:12.584Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/d5/e3d9717c9eba10855325650afd2a9cba8e607321697f18953af9d562da2f/pydantic_core-2.46.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb528e295ed31570ac3dcc9bfdd6e0150bc11ce6168ac87a8082055cf1a67395", size = 2094380, upload-time = "2026-04-20T14:43:05.522Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/20/abac35dedcbfd66c6f0b03e4e3564511771d6c9b7ede10a362d03e110d9b/pydantic_core-2.46.3-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:367508faa4973b992b271ba1494acaab36eb7e8739d1e47be5035fb1ea225396", size = 2135429, upload-time = "2026-04-20T14:41:55.549Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/a5/41bfd1df69afad71b5cf0535055bccc73022715ad362edbc124bc1e021d7/pydantic_core-2.46.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5ad3c826fe523e4becf4fe39baa44286cff85ef137c729a2c5e269afbfd0905d", size = 2174582, upload-time = "2026-04-20T14:41:45.96Z" },
+    { url = "https://files.pythonhosted.org/packages/79/65/38d86ea056b29b2b10734eb23329b7a7672ca604df4f2b6e9c02d4ee22fe/pydantic_core-2.46.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ec638c5d194ef8af27db69f16c954a09797c0dc25015ad6123eb2c73a4d271ca", size = 2187533, upload-time = "2026-04-20T14:40:55.367Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/55/a1129141678a2026badc539ad1dee0a71d06f54c2f06a4bd68c030ac781b/pydantic_core-2.46.3-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:28ed528c45446062ee66edb1d33df5d88828ae167de76e773a3c7f64bd14e976", size = 2332985, upload-time = "2026-04-20T14:44:13.05Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/60/cb26f4077719f709e54819f4e8e1d43f4091f94e285eb6bd21e1190a7b7c/pydantic_core-2.46.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:aed19d0c783886d5bd86d80ae5030006b45e28464218747dcf83dabfdd092c7b", size = 2373670, upload-time = "2026-04-20T14:41:53.421Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/7e/c3f21882bdf1d8d086876f81b5e296206c69c6082551d776895de7801fa0/pydantic_core-2.46.3-cp312-cp312-win32.whl", hash = "sha256:06d5d8820cbbdb4147578c1fe7ffcd5b83f34508cb9f9ab76e807be7db6ff0a4", size = 1966722, upload-time = "2026-04-20T14:44:30.588Z" },
+    { url = "https://files.pythonhosted.org/packages/57/be/6b5e757b859013ebfbd7adba02f23b428f37c86dcbf78b5bb0b4ffd36e99/pydantic_core-2.46.3-cp312-cp312-win_amd64.whl", hash = "sha256:c3212fda0ee959c1dd04c60b601ec31097aaa893573a3a1abd0a47bcac2968c1", size = 2072970, upload-time = "2026-04-20T14:42:54.248Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/f8/a989b21cc75e9a32d24192ef700eea606521221a89faa40c919ce884f2b1/pydantic_core-2.46.3-cp312-cp312-win_arm64.whl", hash = "sha256:f1f8338dd7a7f31761f1f1a3c47503a9a3b34eea3c8b01fa6ee96408affb5e72", size = 2035963, upload-time = "2026-04-20T14:44:20.4Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/3c/9b5e8eb9821936d065439c3b0fb1490ffa64163bfe7e1595985a47896073/pydantic_core-2.46.3-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:12bc98de041458b80c86c56b24df1d23832f3e166cbaff011f25d187f5c62c37", size = 2102109, upload-time = "2026-04-20T14:41:24.219Z" },
+    { url = "https://files.pythonhosted.org/packages/91/97/1c41d1f5a19f241d8069f1e249853bcce378cdb76eec8ab636d7bc426280/pydantic_core-2.46.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:85348b8f89d2c3508b65b16c3c33a4da22b8215138d8b996912bb1532868885f", size = 1951820, upload-time = "2026-04-20T14:42:14.236Z" },
+    { url = "https://files.pythonhosted.org/packages/30/b4/d03a7ae14571bc2b6b3c7b122441154720619afe9a336fa3a95434df5e2f/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1105677a6df914b1fb71a81b96c8cce7726857e1717d86001f29be06a25ee6f8", size = 1977785, upload-time = "2026-04-20T14:42:31.648Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/0c/4086f808834b59e3c8f1aa26df8f4b6d998cdcf354a143d18ef41529d1fe/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:87082cd65669a33adeba5470769e9704c7cf026cc30afb9cc77fd865578ebaad", size = 2062761, upload-time = "2026-04-20T14:40:37.093Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/71/a649be5a5064c2df0db06e0a512c2281134ed2fcc981f52a657936a7527c/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:60e5f66e12c4f5212d08522963380eaaeac5ebd795826cfd19b2dfb0c7a52b9c", size = 2232989, upload-time = "2026-04-20T14:42:59.254Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/84/7756e75763e810b3a710f4724441d1ecc5883b94aacb07ca71c5fb5cfb69/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b6cdf19bf84128d5e7c37e8a73a0c5c10d51103a650ac585d42dd6ae233f2b7f", size = 2303975, upload-time = "2026-04-20T14:41:32.287Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/35/68a762e0c1e31f35fa0dac733cbd9f5b118042853698de9509c8e5bf128b/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:031bb17f4885a43773c8c763089499f242aee2ea85cf17154168775dccdecf35", size = 2095325, upload-time = "2026-04-20T14:42:47.685Z" },
+    { url = "https://files.pythonhosted.org/packages/77/bf/1bf8c9a8e91836c926eae5e3e51dce009bf495a60ca56060689d3df3f340/pydantic_core-2.46.3-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:bcf2a8b2982a6673693eae7348ef3d8cf3979c1d63b54fca7c397a635cc68687", size = 2133368, upload-time = "2026-04-20T14:41:22.766Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/50/87d818d6bab915984995157ceb2380f5aac4e563dddbed6b56f0ed057aba/pydantic_core-2.46.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:28e8cf2f52d72ced402a137145923a762cbb5081e48b34312f7a0c8f55928ec3", size = 2173908, upload-time = "2026-04-20T14:42:52.044Z" },
+    { url = "https://files.pythonhosted.org/packages/91/88/a311fb306d0bd6185db41fa14ae888fb81d0baf648a761ae760d30819d33/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:17eaface65d9fc5abb940003020309c1bf7a211f5f608d7870297c367e6f9022", size = 2186422, upload-time = "2026-04-20T14:43:29.55Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/79/28fd0d81508525ab2054fef7c77a638c8b5b0afcbbaeee493cf7c3fef7e1/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:93fd339f23408a07e98950a89644f92c54d8729719a40b30c0a30bb9ebc55d23", size = 2332709, upload-time = "2026-04-20T14:42:16.134Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/21/795bf5fe5c0f379308b8ef19c50dedab2e7711dbc8d0c2acf08f1c7daa05/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:23cbdb3aaa74dfe0837975dbf69b469753bbde8eacace524519ffdb6b6e89eb7", size = 2372428, upload-time = "2026-04-20T14:41:10.974Z" },
+    { url = "https://files.pythonhosted.org/packages/45/b3/ed14c659cbe7605e3ef063077680a64680aec81eb1a04763a05190d49b7f/pydantic_core-2.46.3-cp313-cp313-win32.whl", hash = "sha256:610eda2e3838f401105e6326ca304f5da1e15393ae25dacae5c5c63f2c275b13", size = 1965601, upload-time = "2026-04-20T14:41:42.128Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/bb/adb70d9a762ddd002d723fbf1bd492244d37da41e3af7b74ad212609027e/pydantic_core-2.46.3-cp313-cp313-win_amd64.whl", hash = "sha256:68cc7866ed863db34351294187f9b729964c371ba33e31c26f478471c52e1ed0", size = 2071517, upload-time = "2026-04-20T14:43:36.096Z" },
+    { url = "https://files.pythonhosted.org/packages/52/eb/66faefabebfe68bd7788339c9c9127231e680b11906368c67ce112fdb47f/pydantic_core-2.46.3-cp313-cp313-win_arm64.whl", hash = "sha256:f64b5537ac62b231572879cd08ec05600308636a5d63bcbdb15063a466977bec", size = 2035802, upload-time = "2026-04-20T14:43:38.507Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/db/a7bcb4940183fda36022cd18ba8dd12f2dff40740ec7b58ce7457befa416/pydantic_core-2.46.3-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:afa3aa644f74e290cdede48a7b0bee37d1c35e71b05105f6b340d484af536d9b", size = 2097614, upload-time = "2026-04-20T14:44:38.374Z" },
+    { url = "https://files.pythonhosted.org/packages/24/35/e4066358a22e3e99519db370494c7528f5a2aa1367370e80e27e20283543/pydantic_core-2.46.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ced3310e51aa425f7f77da8bbbb5212616655bedbe82c70944320bc1dbe5e018", size = 1951896, upload-time = "2026-04-20T14:40:53.996Z" },
+    { url = "https://files.pythonhosted.org/packages/87/92/37cf4049d1636996e4b888c05a501f40a43ff218983a551d57f9d5e14f0d/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e29908922ce9da1a30b4da490bd1d3d82c01dcfdf864d2a74aacee674d0bfa34", size = 1979314, upload-time = "2026-04-20T14:41:49.446Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/36/9ff4d676dfbdfb2d591cf43f3d90ded01e15b1404fd101180ed2d62a2fd3/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0c9ff69140423eea8ed2d5477df3ba037f671f5e897d206d921bc9fdc39613e7", size = 2056133, upload-time = "2026-04-20T14:42:23.574Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/f0/405b442a4d7ba855b06eec8b2bf9c617d43b8432d099dfdc7bf999293495/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b675ab0a0d5b1c8fdb81195dc5bcefea3f3c240871cdd7ff9a2de8aa50772eb2", size = 2228726, upload-time = "2026-04-20T14:44:22.816Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/f8/65cd92dd5a0bd89ba277a98ecbfaf6fc36bbd3300973c7a4b826d6ab1391/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0087084960f209a9a4af50ecd1fb063d9ad3658c07bb81a7a53f452dacbfb2ba", size = 2301214, upload-time = "2026-04-20T14:44:48.792Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/86/ef96a4c6e79e7a2d0410826a68fbc0eccc0fd44aa733be199d5fcac3bb87/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed42e6cc8e1b0e2b9b96e2276bad70ae625d10d6d524aed0c93de974ae029f9f", size = 2099927, upload-time = "2026-04-20T14:41:40.196Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/53/269caf30e0096e0a8a8f929d1982a27b3879872cca2d917d17c2f9fdf4fe/pydantic_core-2.46.3-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:f1771ce258afb3e4201e67d154edbbae712a76a6081079fe247c2f53c6322c22", size = 2128789, upload-time = "2026-04-20T14:41:15.868Z" },
+    { url = "https://files.pythonhosted.org/packages/00/b0/1a6d9b6a587e118482910c244a1c5acf4d192604174132efd12bf0ac486f/pydantic_core-2.46.3-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a7610b6a5242a6c736d8ad47fd5fff87fcfe8f833b281b1c409c3d6835d9227f", size = 2173815, upload-time = "2026-04-20T14:44:25.152Z" },
+    { url = "https://files.pythonhosted.org/packages/87/56/e7e00d4041a7e62b5a40815590114db3b535bf3ca0bf4dca9f16cef25246/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:ff5e7783bcc5476e1db448bf268f11cb257b1c276d3e89f00b5727be86dd0127", size = 2181608, upload-time = "2026-04-20T14:41:28.933Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/22/4bd23c3d41f7c185d60808a1de83c76cf5aeabf792f6c636a55c3b1ec7f9/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:9d2e32edcc143bc01e95300671915d9ca052d4f745aa0a49c48d4803f8a85f2c", size = 2326968, upload-time = "2026-04-20T14:42:03.962Z" },
+    { url = "https://files.pythonhosted.org/packages/24/ac/66cd45129e3915e5ade3b292cb3bc7fd537f58f8f8dbdaba6170f7cabb74/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:6e42d83d1c6b87fa56b521479cff237e626a292f3b31b6345c15a99121b454c1", size = 2369842, upload-time = "2026-04-20T14:41:35.52Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/51/dd4248abb84113615473aa20d5545b7c4cd73c8644003b5259686f93996c/pydantic_core-2.46.3-cp314-cp314-win32.whl", hash = "sha256:07bc6d2a28c3adb4f7c6ae46aa4f2d2929af127f587ed44057af50bf1ce0f505", size = 1959661, upload-time = "2026-04-20T14:41:00.042Z" },
+    { url = "https://files.pythonhosted.org/packages/20/eb/59980e5f1ae54a3b86372bd9f0fa373ea2d402e8cdcd3459334430f91e91/pydantic_core-2.46.3-cp314-cp314-win_amd64.whl", hash = "sha256:8940562319bc621da30714617e6a7eaa6b98c84e8c685bcdc02d7ed5e7c7c44e", size = 2071686, upload-time = "2026-04-20T14:43:16.471Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/db/1cf77e5247047dfee34bc01fa9bca134854f528c8eb053e144298893d370/pydantic_core-2.46.3-cp314-cp314-win_arm64.whl", hash = "sha256:5dcbbcf4d22210ced8f837c96db941bdb078f419543472aca5d9a0bb7cddc7df", size = 2026907, upload-time = "2026-04-20T14:43:31.732Z" },
+    { url = "https://files.pythonhosted.org/packages/57/c0/b3df9f6a543276eadba0a48487b082ca1f201745329d97dbfa287034a230/pydantic_core-2.46.3-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:d0fe3dce1e836e418f912c1ad91c73357d03e556a4d286f441bf34fed2dbeecf", size = 2095047, upload-time = "2026-04-20T14:42:37.982Z" },
+    { url = "https://files.pythonhosted.org/packages/66/57/886a938073b97556c168fd99e1a7305bb363cd30a6d2c76086bf0587b32a/pydantic_core-2.46.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9ce92e58abc722dac1bf835a6798a60b294e48eb0e625ec9fd994b932ac5feee", size = 1934329, upload-time = "2026-04-20T14:43:49.655Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/7c/b42eaa5c34b13b07ecb51da21761297a9b8eb43044c864a035999998f328/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a03e6467f0f5ab796a486146d1b887b2dc5e5f9b3288898c1b1c3ad974e53e4a", size = 1974847, upload-time = "2026-04-20T14:42:10.737Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/9b/92b42db6543e7de4f99ae977101a2967b63122d4b6cf7773812da2d7d5b5/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2798b6ba041b9d70acfb9071a2ea13c8456dd1e6a5555798e41ba7b0790e329c", size = 2041742, upload-time = "2026-04-20T14:40:44.262Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/19/46fbe1efabb5aa2834b43b9454e70f9a83ad9c338c1291e48bdc4fecf167/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9be3e221bdc6d69abf294dcf7aff6af19c31a5cdcc8f0aa3b14be29df4bd03b1", size = 2236235, upload-time = "2026-04-20T14:41:27.307Z" },
+    { url = "https://files.pythonhosted.org/packages/77/da/b3f95bc009ad60ec53120f5d16c6faa8cabdbe8a20d83849a1f2b8728148/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f13936129ce841f2a5ddf6f126fea3c43cd128807b5a59588c37cf10178c2e64", size = 2282633, upload-time = "2026-04-20T14:44:33.271Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/6e/401336117722e28f32fb8220df676769d28ebdf08f2f4469646d404c43a3/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28b5f2ef03416facccb1c6ef744c69793175fd27e44ef15669201601cf423acb", size = 2109679, upload-time = "2026-04-20T14:44:41.065Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/53/b289f9bc8756a32fe718c46f55afaeaf8d489ee18d1a1e7be1db73f42cc4/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:830d1247d77ad23852314f069e9d7ddafeec5f684baf9d7e7065ed46a049c4e6", size = 2108342, upload-time = "2026-04-20T14:42:50.144Z" },
+    { url = "https://files.pythonhosted.org/packages/10/5b/8292fc7c1f9111f1b2b7c1b0dcf1179edcd014fc3ea4517499f50b829d71/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d0793c90c1a3c74966e7975eaef3ed30ebdff3260a0f815a62a22adc17e4c01c", size = 2157208, upload-time = "2026-04-20T14:42:08.133Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/9e/f80044e9ec07580f057a89fc131f78dda7a58751ddf52bbe05eaf31db50f/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:d2d0aead851b66f5245ec0c4fb2612ef457f8bbafefdf65a2bf9d6bac6140f47", size = 2167237, upload-time = "2026-04-20T14:42:25.412Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/84/6781a1b037f3b96be9227edbd1101f6d3946746056231bf4ac48cdff1a8d/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:2f40e4246676beb31c5ce77c38a55ca4e465c6b38d11ea1bd935420568e0b1ab", size = 2312540, upload-time = "2026-04-20T14:40:40.313Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/db/19c0839feeb728e7df03255581f198dfdf1c2aeb1e174a8420b63c5252e5/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:cf489cf8986c543939aeee17a09c04d6ffb43bfef8ca16fcbcc5cfdcbed24dba", size = 2369556, upload-time = "2026-04-20T14:41:09.427Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/15/3228774cb7cd45f5f721ddf1b2242747f4eb834d0c491f0c02d606f09fed/pydantic_core-2.46.3-cp314-cp314t-win32.whl", hash = "sha256:ffe0883b56cfc05798bf994164d2b2ff03efe2d22022a2bb080f3b626176dd56", size = 1949756, upload-time = "2026-04-20T14:41:25.717Z" },
+    { url = "https://files.pythonhosted.org/packages/b8/2a/c79cf53fd91e5a87e30d481809f52f9a60dd221e39de66455cf04deaad37/pydantic_core-2.46.3-cp314-cp314t-win_amd64.whl", hash = "sha256:706d9d0ce9cf4593d07270d8e9f53b161f90c57d315aeec4fb4fd7a8b10240d8", size = 2051305, upload-time = "2026-04-20T14:43:18.627Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/db/d8182a7f1d9343a032265aae186eb063fe26ca4c40f256b21e8da4498e89/pydantic_core-2.46.3-cp314-cp314t-win_arm64.whl", hash = "sha256:77706aeb41df6a76568434701e0917da10692da28cb69d5fb6919ce5fdb07374", size = 2026310, upload-time = "2026-04-20T14:41:01.778Z" },
+    { url = "https://files.pythonhosted.org/packages/34/42/f426db557e8ab2791bc7562052299944a118655496fbff99914e564c0a94/pydantic_core-2.46.3-graalpy312-graalpy250_312_native-macosx_10_12_x86_64.whl", hash = "sha256:b12dd51f1187c2eb489af8e20f880362db98e954b54ab792fa5d92e8bcc6b803", size = 2091877, upload-time = "2026-04-20T14:43:27.091Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/4f/86a832a9d14df58e663bfdf4627dc00d3317c2bd583c4fb23390b0f04b8e/pydantic_core-2.46.3-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:f00a0961b125f1a47af7bcc17f00782e12f4cd056f83416006b30111d941dfa3", size = 1932428, upload-time = "2026-04-20T14:40:45.781Z" },
+    { url = "https://files.pythonhosted.org/packages/11/1a/fe857968954d93fb78e0d4b6df5c988c74c4aaa67181c60be7cfe327c0ca/pydantic_core-2.46.3-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57697d7c056aca4bbb680200f96563e841a6386ac1129370a0102592f4dddff5", size = 1997550, upload-time = "2026-04-20T14:44:02.425Z" },
+    { url = "https://files.pythonhosted.org/packages/17/eb/9d89ad2d9b0ba8cd65393d434471621b98912abb10fbe1df08e480ba57b5/pydantic_core-2.46.3-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd35aa21299def8db7ef4fe5c4ff862941a9a158ca7b63d61e66fe67d30416b4", size = 2137657, upload-time = "2026-04-20T14:42:45.149Z" },
 ]
 
 [[package]]
@@ -5969,7 +5912,7 @@ wheels = [
 
 [[package]]
 name = "teleop"
-version = "0.1.4"
+version = "0.1.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "fastapi" },
@@ -5980,9 +5923,9 @@ dependencies = [
     { name = "uvicorn", extra = ["standard"] },
     { name = "websocket-client" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ec/8c/902ef4c0fa148325e6b19a5af63c3aac5927c67551efabcd5732fc446c6d/teleop-0.1.4.tar.gz", hash = "sha256:b5cedcff336c612a3f7e6f93e379e24979ed42070903b722f5fefe07c8fca3ce", size = 44051, upload-time = "2025-12-08T10:49:45.823Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/87/dc/312c19122c8e64fcff16dc8a74659b84ba8a7bcd3ef7b3c330cfc65a2a29/teleop-0.1.5.tar.gz", hash = "sha256:9f5367b167e0f67abe818f346c467671bd2c1ad653df604bdfb2fa69b2937da9", size = 44173, upload-time = "2026-04-19T21:17:42.795Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b0/9c/217176617df23f634b0388111adbeb17ccb0409072639a97512e6c1c818d/teleop-0.1.4-py3-none-any.whl", hash = "sha256:6b8013947b27b89dbce50f9231a57d29f2e59ea864807b1ce6611ea3ad1694f4", size = 42332, upload-time = "2025-12-08T10:49:44.531Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/d1/45c79fcbf2551f2035c375e81d560c4ac46a5bbdb1622583b559eedcfc4e/teleop-0.1.5-py3-none-any.whl", hash = "sha256:75c3e63bb9eed1ea8ca32b48086cea45fa5ae3eb022dd0dcf0d615cf0b0d58dc", size = 42380, upload-time = "2026-04-19T21:17:41.386Z" },
 ]
 
 [[package]]
@@ -6424,15 +6367,15 @@ wheels = [
 
 [[package]]
 name = "uvicorn"
-version = "0.44.0"
+version = "0.45.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
     { name = "h11" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/5e/da/6eee1ff8b6cbeed47eeb5229749168e81eb4b7b999a1a15a7176e51410c9/uvicorn-0.44.0.tar.gz", hash = "sha256:6c942071b68f07e178264b9152f1f16dfac5da85880c4ce06366a96d70d4f31e", size = 86947, upload-time = "2026-04-06T09:23:22.826Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/2e/62b0d9a2cfc8b4de6771322dae30f2db76c66dae9ec32e94e176a44ad563/uvicorn-0.45.0.tar.gz", hash = "sha256:3fe650df136c5bd2b9b06efc5980636344a2fbb840e9ddd86437d53144fa335d", size = 87818, upload-time = "2026-04-21T10:43:46.815Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/23/a5bbd9600dd607411fa644c06ff4951bec3a4d82c4b852374024359c19c0/uvicorn-0.44.0-py3-none-any.whl", hash = "sha256:ce937c99a2cc70279556967274414c087888e8cec9f9c94644dfca11bd3ced89", size = 69425, upload-time = "2026-04-06T09:23:21.524Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/88/d0f7512465b166a4e931ccf7e77792be60fb88466a43964c7566cbaff752/uvicorn-0.45.0-py3-none-any.whl", hash = "sha256:2db26f588131aeec7439de00f2dd52d5f210710c1f01e407a52c90b880d1fd4f", size = 69838, upload-time = "2026-04-21T10:43:45.029Z" },
 ]
 
 [package.optional-dependencies]