diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..bee8a64b79a99590d5303307144172cfe824fbf7
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__
diff --git a/experiments/logs/EVAL-real_data-openvla-2025_09_21-13_59_52.txt b/experiments/logs/EVAL-real_data-openvla-2025_09_21-13_59_52.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiments/logs/EVAL-real_data-openvla-2025_09_21-14_00_36.txt b/experiments/logs/EVAL-real_data-openvla-2025_09_21-14_00_36.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/libero_utils.py b/libero_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9543a4845428e6617cbe78b1bad7cdb36cdcc6e3
--- /dev/null
+++ b/libero_utils.py
@@ -0,0 +1,104 @@
+"""Utils for evaluating policies in LIBERO simulation environments."""
+
+import math
+import os
+
+import imageio
+import numpy as np
+import tensorflow as tf
+from libero.libero import get_libero_path
+from libero.libero.envs import OffScreenRenderEnv
+
+from robot_utils import (
+    DATE,
+    DATE_TIME,
+)
+
+
+def get_libero_env(task, model_family, resolution=256):
+    """Initializes and returns the LIBERO environment, along with the task description."""
+    task_description = task.language
+    task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file)
+    env_args = {"bddl_file_name": task_bddl_file, "camera_heights": resolution, "camera_widths": resolution}
+    env = OffScreenRenderEnv(**env_args)
+    env.seed(0)  # IMPORTANT: seed seems to affect object positions even when using fixed initial state
+    return env, task_description
+
+
+def get_libero_dummy_action(model_family: str):
+    """Get dummy/no-op action, used to roll out the simulation while the robot does nothing."""
+    return [0, 0, 0, 0, 0, 0, -1]
+
+
+def resize_image(img, resize_size):
+    """
+    Takes numpy array corresponding to a single image and returns resized image as numpy array.
+
+    NOTE (Moo Jin): To make input images in distribution with respect to the inputs seen at training time, we follow
+                    the same resizing scheme used in the Octo dataloader, which OpenVLA uses for training.
+    """
+    assert isinstance(resize_size, tuple)
+    # Resize to image size expected by model
+    img = tf.image.encode_jpeg(img)  # Encode as JPEG, as done in RLDS dataset builder
+    img = tf.io.decode_image(img, expand_animations=False, dtype=tf.uint8)  # Immediately decode back
+    img = tf.image.resize(img, resize_size, method="lanczos3", antialias=True)
+    img = tf.cast(tf.clip_by_value(tf.round(img), 0, 255), tf.uint8)
+    img = img.numpy()
+    return img
+
+
+def get_libero_image(obs, resize_size):
+    """Extracts image from observations and preprocesses it."""
+    assert isinstance(resize_size, int) or isinstance(resize_size, tuple)
+    if isinstance(resize_size, int):
+        resize_size = (resize_size, resize_size)
+    img = obs["agentview_image"]
+    img = img[::-1, ::-1]  # IMPORTANT: rotate 180 degrees to match train preprocessing
+    img = resize_image(img, resize_size)
+    return img
+
+
+def save_rollout_video(rollout_images, idx, success, task_description, log_file=None, saved_dir=None):
+    """Saves an MP4 replay of an episode."""
+    if saved_dir is None:
+        rollout_dir = f"./rollouts/{DATE}"
+    else:
+        rollout_dir = f"./rollouts/{saved_dir}/{DATE}"
+    os.makedirs(rollout_dir, exist_ok=True)
+    processed_task_description = task_description.lower().replace(" ", "_").replace("\n", "_").replace(".", "_")[:50]
+    mp4_path = f"{rollout_dir}/{DATE_TIME}--episode={idx}--success={success}--task={processed_task_description}.mp4"
+    video_writer = imageio.get_writer(mp4_path, fps=30)
+    for img in rollout_images:
+        video_writer.append_data(img)
+    video_writer.close()
+    print(f"Saved rollout MP4 at path {mp4_path}")
+    if log_file is not None:
+        log_file.write(f"Saved rollout MP4 at path {mp4_path}\n")
+    return mp4_path
+
+
+def quat2axisangle(quat):
+    """
+    Copied from robosuite: https://github.com/ARISE-Initiative/robosuite/blob/eafb81f54ffc104f905ee48a16bb15f059176ad3/robosuite/utils/transform_utils.py#L490C1-L512C55
+
+    Converts quaternion to axis-angle format.
+    Returns a unit vector direction scaled by its angle in radians.
+
+    Args:
+        quat (np.array): (x,y,z,w) vec4 float angles
+
+    Returns:
+        np.array: (ax,ay,az) axis-angle exponential coordinates
+    """
+    # clip quaternion
+    if quat[3] > 1.0:
+        quat[3] = 1.0
+    elif quat[3] < -1.0:
+        quat[3] = -1.0
+
+    den = np.sqrt(1.0 - quat[3] * quat[3])
+    if math.isclose(den, 0.0):
+        # This is (close to) a zero degree rotation, immediately return
+        return np.zeros(3)
+
+    return (quat[:3] * 2.0 * math.acos(quat[3])) / den
diff --git a/oc_model/README.md b/oc_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..14740d8af86138df70f05bea03ec90fd8987a765
--- /dev/null
+++ b/oc_model/README.md
@@ -0,0 +1,202 @@
+---
+base_model: output_hf_model_openx
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1
\ No newline at end of file
diff --git a/oc_model/adapter_config.json b/oc_model/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ce49f6af417bf37914d225408b8a85a7ae055931
--- /dev/null
+++ b/oc_model/adapter_config.json
@@ -0,0 +1,258 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "OpenVLAForActionPrediction",
+    "parent_library": "prismatic.extern.hf.modeling_prismatic"
+  },
+  "base_model_name_or_path": "output_hf_model_openx",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "language_model.model.layers.8.mlp.down_proj",
+    "language_model.model.layers.7.self_attn.k_proj",
+    "language_model.model.layers.16.mlp.gate_proj",
+    "language_model.model.layers.25.self_attn.v_proj",
+    "language_model.model.layers.15.self_attn.v_proj",
+    "language_model.model.layers.3.self_attn.o_proj",
+    "language_model.model.layers.17.self_attn.q_proj",
+    "language_model.model.layers.30.mlp.gate_proj",
+    "language_model.model.layers.16.self_attn.k_proj",
+    "language_model.model.layers.5.self_attn.k_proj",
+    "language_model.model.layers.3.self_attn.v_proj",
+    "language_model.model.layers.9.mlp.up_proj",
+    "language_model.model.layers.28.self_attn.v_proj",
+    "language_model.model.layers.10.self_attn.k_proj",
+    "language_model.model.layers.3.mlp.up_proj",
+    "language_model.model.layers.25.mlp.gate_proj",
+    "language_model.model.layers.20.self_attn.v_proj",
+    "language_model.model.layers.14.self_attn.o_proj",
+    "language_model.model.layers.2.mlp.gate_proj",
+    "language_model.model.layers.17.self_attn.v_proj",
+    "language_model.model.layers.30.self_attn.o_proj",
+    "language_model.lm_head",
+    "language_model.model.layers.1.mlp.up_proj",
+    "language_model.model.layers.28.mlp.up_proj",
+    "language_model.model.layers.4.self_attn.o_proj",
+    "language_model.model.layers.29.mlp.down_proj",
+    "language_model.model.layers.21.mlp.up_proj",
+    "language_model.model.layers.10.self_attn.v_proj",
+    "language_model.model.layers.18.self_attn.o_proj",
+    "language_model.model.layers.19.mlp.down_proj",
+    "language_model.model.layers.2.self_attn.o_proj",
+    "language_model.model.layers.22.self_attn.q_proj",
+    "language_model.model.layers.26.mlp.up_proj",
+    "language_model.model.layers.17.mlp.gate_proj",
+    "language_model.model.layers.11.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.q_proj",
+    "language_model.model.layers.30.self_attn.k_proj",
+    "language_model.model.layers.27.mlp.gate_proj",
+    "language_model.model.layers.6.mlp.gate_proj",
+    "language_model.model.layers.10.mlp.gate_proj",
+    "language_model.model.layers.12.mlp.down_proj",
+    "language_model.model.layers.13.mlp.down_proj",
+    "language_model.model.layers.6.self_attn.q_proj",
+    "language_model.model.layers.0.mlp.down_proj",
+    "language_model.model.layers.20.self_attn.o_proj",
+    "language_model.model.layers.23.self_attn.k_proj",
+    "language_model.model.layers.23.mlp.gate_proj",
+    "language_model.model.layers.3.self_attn.q_proj",
+    "language_model.model.layers.18.self_attn.k_proj",
+    "language_model.model.layers.19.mlp.gate_proj",
+    "language_model.model.layers.20.mlp.down_proj",
+    "language_model.model.layers.21.self_attn.v_proj",
+    "language_model.model.layers.30.mlp.down_proj",
+    "language_model.model.layers.18.self_attn.q_proj",
+    "language_model.model.layers.26.self_attn.o_proj",
+    "language_model.model.layers.19.self_attn.o_proj",
+    "language_model.model.layers.14.self_attn.v_proj",
+    "language_model.model.layers.31.mlp.gate_proj",
+    "language_model.model.layers.11.mlp.up_proj",
+    "language_model.model.layers.24.self_attn.k_proj",
+    "language_model.model.layers.9.mlp.down_proj",
+    "language_model.model.layers.16.self_attn.o_proj",
+    "language_model.model.layers.28.mlp.gate_proj",
+    "language_model.model.layers.4.mlp.up_proj",
+    "language_model.model.layers.31.self_attn.o_proj",
+    "language_model.model.layers.9.self_attn.v_proj",
+    "language_model.model.layers.20.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.q_proj",
+    "language_model.model.layers.19.mlp.up_proj",
+    "language_model.model.layers.28.mlp.down_proj",
+    "language_model.model.layers.26.self_attn.v_proj",
+    "language_model.model.layers.16.self_attn.q_proj",
+    "language_model.model.layers.21.mlp.gate_proj",
+    "language_model.model.layers.6.self_attn.v_proj",
+    "language_model.model.layers.15.self_attn.q_proj",
+    "language_model.model.layers.5.self_attn.o_proj",
+    "language_model.model.layers.14.mlp.gate_proj",
+    "language_model.model.layers.11.self_attn.k_proj",
+    "language_model.model.layers.5.mlp.gate_proj",
+    "language_model.model.layers.10.self_attn.o_proj",
+    "language_model.model.layers.11.mlp.down_proj",
+    "language_model.model.layers.8.mlp.gate_proj",
+    "projector.fc1",
+    "language_model.model.layers.9.self_attn.k_proj",
+    "language_model.model.layers.21.self_attn.o_proj",
+    "language_model.model.layers.5.mlp.down_proj",
+    "language_model.model.layers.8.self_attn.k_proj",
+    "language_model.model.layers.22.self_attn.o_proj",
+    "language_model.model.layers.3.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.o_proj",
+    "language_model.model.layers.30.self_attn.q_proj",
+    "language_model.model.layers.0.self_attn.q_proj",
+    "language_model.model.layers.11.self_attn.o_proj",
+    "language_model.model.layers.17.self_attn.k_proj",
+    "language_model.model.layers.14.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.v_proj",
+    "language_model.model.layers.21.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.v_proj",
+    "language_model.model.layers.0.self_attn.v_proj",
+    "language_model.model.layers.0.mlp.up_proj",
+    "language_model.model.layers.20.self_attn.q_proj",
+    "language_model.model.layers.7.self_attn.v_proj",
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+    "language_model.model.layers.10.mlp.up_proj",
+    "language_model.model.layers.17.self_attn.o_proj",
+    "language_model.model.layers.16.mlp.up_proj",
+    "language_model.model.layers.6.mlp.down_proj",
+    "language_model.model.layers.13.mlp.gate_proj",
+    "language_model.model.layers.24.mlp.down_proj",
+    "language_model.model.layers.26.self_attn.k_proj",
+    "language_model.model.layers.29.mlp.up_proj",
+    "language_model.model.layers.25.mlp.up_proj",
+    "language_model.model.layers.29.mlp.gate_proj",
+    "language_model.model.layers.2.self_attn.v_proj",
+    "language_model.model.layers.1.self_attn.o_proj",
+    "language_model.model.layers.27.mlp.down_proj",
+    "language_model.model.layers.21.self_attn.k_proj",
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+    "language_model.model.layers.10.self_attn.q_proj",
+    "language_model.model.layers.22.self_attn.k_proj",
+    "language_model.model.layers.12.self_attn.q_proj",
+    "language_model.model.layers.31.self_attn.k_proj",
+    "language_model.model.layers.22.mlp.up_proj",
+    "language_model.model.layers.14.mlp.up_proj",
+    "language_model.model.layers.17.mlp.down_proj",
+    "language_model.model.layers.23.self_attn.o_proj",
+    "language_model.model.layers.1.self_attn.v_proj",
+    "language_model.model.layers.12.self_attn.k_proj",
+    "language_model.model.layers.7.mlp.gate_proj",
+    "language_model.model.layers.20.mlp.up_proj",
+    "language_model.model.layers.31.self_attn.q_proj",
+    "language_model.model.layers.15.self_attn.k_proj",
+    "language_model.model.layers.26.mlp.down_proj",
+    "language_model.model.layers.19.self_attn.k_proj",
+    "language_model.model.layers.6.self_attn.k_proj",
+    "language_model.model.layers.2.self_attn.k_proj",
+    "language_model.model.layers.12.mlp.gate_proj",
+    "language_model.model.layers.13.self_attn.q_proj",
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+    "language_model.model.layers.23.mlp.up_proj",
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+    "language_model.model.layers.1.mlp.gate_proj",
+    "language_model.model.layers.0.self_attn.k_proj",
+    "language_model.model.layers.1.mlp.down_proj",
+    "language_model.model.layers.12.mlp.up_proj",
+    "language_model.model.layers.28.self_attn.k_proj",
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+      "▁liegt": 11478,
+      "▁Figure": 11479,
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+      "▁konnte": 11589,
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+      "▁macro": 11758,
+      "=[": 11759,
+      "Then": 11760,
+      "▁controls": 11761,
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+      "stmt": 17868,
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+      "▁equally": 18018,
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+      "▁c atalog",
+      "▁cat alog",
+      "▁catal og",
+      "▁ catalog",
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+      "▁fe rm",
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+      "▁patr i",
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+      "▁ FM",
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+      "▁Bra n",
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+      "▁seu l",
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+      "▁A lle",
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+      "▁s unt",
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+      "c ot",
+      "▁G én",
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+      "▁sei s",
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+      "}} $.",
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+      "h nen",
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+      "▁Ge ographic",
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+      "▁на ј",
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+      "▁p m",
+      "▁ pm",
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+      "▁H amm",
+      "▁Ha mm",
+      "▁Ham m",
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+      "▁Eu r",
+      "▁d ogs",
+      "▁do gs",
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+      "▁ azon",
+      "▁em ulator",
+      "▁r icon",
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+      "be eld",
+      "▁н у",
+      "▁ ну",
+      "▁approxim ate",
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+      "▁B ond",
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+      "▁Bon d",
+      "▁en h",
+      "ęd z",
+      "ę dz",
+      "▁s olit",
+      "▁so lit",
+      "▁sol it",
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+      "amento s",
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+      "ib ől",
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+      "▁gro s",
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+      "command s",
+      "Ex port",
+      "Exp ort",
+      "▁A vec",
+      "▁Av ec",
+      "▁sole mn",
+      "▁solem n",
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+      "▁Mo sk",
+      "▁Mos k",
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+      "▁под о",
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+      "▁Dr iver",
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+      "▁O ok",
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+      "▁Ve c",
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+      "▁lun go",
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+      "▁be lle",
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+      "▁ wx",
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+      "▁sug ar",
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+      "▁amer y",
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+      "▁an i",
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+      "▁o bey",
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+      "▁N ell",
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+      "▁Nel l",
+      "▁in ne",
+      "▁inn e",
+      "▁т ро",
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+      "F D",
+      "cc o",
+      "c co",
+      "▁Z ob",
+      "▁Zo b",
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+      "a lette",
+      "▁má jus",
+      "ect ed",
+      "ec ted",
+      "e cted",
+      "▁Tur key",
+      "▁Turk ey",
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+      "▁Whe ther",
+      "q i",
+      "▁ш то",
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+      "aru s",
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+      "o pus",
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+      "▁de stru",
+      "▁dest ru",
+      "▁L ok",
+      "▁Lo k",
+      "▁satisf action",
+      "() \r",
+      "( )\r",
+      "▁Т ер",
+      "▁Те р",
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+      "J ose",
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+      "▁conqu er",
+      "▁E ffect",
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+      "▁ES P",
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+      "▁Ku lt",
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+      "▁tie r",
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+      "▁пи сь",
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+      "▁hear ts",
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+      "alle s",
+      "a lles",
+      "▁Mag ic",
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+      "tra ining",
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+      "ol ine",
+      "oli ne",
+      "olin e",
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+      ">\\< ^",
+      "> \\<^",
+      "ці аль",
+      "ex ports",
+      "export s",
+      "Work book",
+      "▁вере сня",
+      "▁t eles",
+      "▁te les",
+      "▁tele s",
+      "▁tel es",
+      "▁econom y",
+      "▁econ omy",
+      "▁ec onomy",
+      "▁t rap",
+      "▁tr ap",
+      "▁tra p",
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+      "o lan",
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+      "▁nin g",
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+      "▁p én",
+      "▁pé n",
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+      "▁Á ng",
+      "▁A UT",
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+      "▁rout ine",
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+      "▁est ruct",
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+      "▁Ga ng",
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+      "r iers",
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+      "рово й",
+      "р овой",
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+      "adi c",
+      "a dic",
+      "Sp eed",
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+      "String s",
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+      "▁abit anti",
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+      "b rit",
+      "▁N CAA",
+      "▁NC AA",
+      "▁С П",
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+      "▁ag ed",
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+      "▁rev ision",
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+      "o pro",
+      "▁over write",
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+      "embro s",
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+      "▁sorti e",
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+      "▁ott en",
+      "xi v",
+      "x iv",
+      "▁d eli",
+      "▁de li",
+      "▁del i",
+      "▁A sp",
+      "▁As p",
+      "▁b alls",
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+      "▁ball s",
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+      "▁br ave",
+      "▁bra ve",
+      "▁все го",
+      "▁вс его",
+      "eg n",
+      "e gn",
+      "jp eg",
+      "▁O sten",
+      "▁Os ten",
+      "▁Ost en",
+      "Const ants",
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+      "▁N ev",
+      "▁Ne v",
+      "▁я ких",
+      "▁як их",
+      "▁му ниципа",
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+      "c ija",
+      "▁p oem",
+      "▁po em",
+      "▁ne gro",
+      "▁neg ro",
+      "ха р",
+      "х ар",
+      "▁A sk",
+      "▁As k",
+      "▁a vo",
+      "▁av o",
+      "▁ avo",
+      "▁Me yer",
+      "▁Mey er",
+      "▁W esten",
+      "▁We sten",
+      "▁West en",
+      "▁Wes ten",
+      "▁o ko",
+      "▁ok o",
+      "▁ oko",
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+      "agi n",
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+      "▁Süd en",
+      "▁Sü den",
+      "ent ries",
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+      "▁Rep ublik",
+      "▁Repub lik",
+      "Collection View",
+      "-- -----",
+      "---- ---",
+      "--- ----",
+      "------ -",
+      "----- --",
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+      "▁fire fox",
+      "▁alc une",
+      "▁фо то",
+      "▁отри ма",
+      "~~~~ ~~~~",
+      "▁Ра з",
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+      "▁Comp lex",
+      "▁Comple x",
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+      "ioni c",
+      "i onic",
+      "▁h oly",
+      "▁ho ly",
+      "▁hol y",
+      "▁Sebast ian",
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+      "▁{ @",
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+      "▁мож но",
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+      "▁inte ress",
+      "▁C T",
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+      "i ções",
+      "▁ch ant",
+      "▁cha nt",
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+      "▁wsp ół",
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+      "▁ran ked",
+      "▁rank ed",
+      "▁paździer nika",
+      "▁b rut",
+      "▁br ut",
+      "▁bru t",
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+      "▁Fig ht",
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+      "ife n",
+      "i fen",
+      "▁b og",
+      "▁bo g",
+      "▁re gres",
+      "▁reg res",
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+      "▁El li",
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+      "▁pie rw",
+      "▁pier w",
+      "me asure",
+      "ński ej",
+      "ń skiej",
+      "# {",
+      "▁де ся",
+      "▁var maste",
+      "▁Un ix",
+      "I Z",
+      "iti é",
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+      "▁Ter ry",
+      "▁Terr y",
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+      "▁об у",
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+      "^{ +",
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+      "▁mo stra",
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+      "H H",
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+      "▁Bo log",
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+      "▁cor s",
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+      "▁hö ch",
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+      "▁m iser",
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+      "▁mise r",
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+      "▁phen omen",
+      "▁W ing",
+      "▁Win g",
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+      "▁fil ls",
+      "▁fill s",
+      "▁ú nico",
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+      "v és",
+      "▁G am",
+      "▁Ga m",
+      "d w",
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+      "▁K ob",
+      "▁Ko b",
+      "▁j ack",
+      "▁ja ck",
+      "▁ jack",
+      "Hi nt",
+      "H int",
+      "▁A pol",
+      "▁Ap ol",
+      "▁други е",
+      "▁N UM",
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+      "▁o fic",
+      "▁of ic",
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+      "▁were ld",
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+      "▁nar od",
+      "▁это т",
+      "Side note",
+      "S idenote",
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+      "▁от ме",
+      "▁cour ts",
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+      "fi r",
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+      "▁окру гу",
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+      "l ime",
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+      "▁Gi ll",
+      "▁Gil l",
+      "▁S ent",
+      "▁Se nt",
+      "▁Sen t",
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+      "iel la",
+      "i ella",
+      "▁Cra ig",
+      "▁S ele",
+      "▁Se le",
+      "▁Sel e",
+      "▁Indep end",
+      "▁prov incie",
+      "▁provin cie",
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+      "oss en",
+      "▁за пад",
+      "▁запа д",
+      "▁inf ant",
+      "▁pr events",
+      "▁prevent s",
+      "▁prev ents",
+      "▁provin ces",
+      "▁province s",
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+      "be g",
+      "b eg",
+      "▁col ours",
+      "▁colour s",
+      "B F",
+      "ë n",
+      "▁Ме жду",
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+      "Ob server",
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+      "í gen",
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+      "▁поло ви",
+      "▁` &",
+      "▁o re",
+      "▁or e",
+      "▁ ore",
+      "▁supp lies",
+      "▁parent hes",
+      "Found ation",
+      "▁v ou",
+      "▁vo u",
+      "▁T out",
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+      "Don ald",
+      "▁R ET",
+      "▁RE T",
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+      "m ix",
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+      "▁ent ão",
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+      "▁na zw",
+      "▁naz w",
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+      "▁Ra v",
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+      "▁rep ro",
+      "▁repr o",
+      "▁M ason",
+      "▁Ma son",
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+      "▁bla nc",
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+      "▁meet s",
+      "(. *",
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+      "ma ker",
+      "make r",
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+      "▁ele ven",
+      "▁elev en",
+      "▁part idos",
+      "▁partido s",
+      "în e",
+      "î ne",
+      "▁R egin",
+      "▁Re gin",
+      "▁Reg in",
+      "▁é x",
+      "▁Bu lg",
+      "▁Bul g",
+      "▁network ing",
+      "▁net working",
+      "▁se parator",
+      "▁separ ator",
+      "User Name",
+      "▁edific io",
+      "▁M ie",
+      "▁Mi e",
+      "▁id le",
+      "ye d",
+      "y ed",
+      "▁pass engers",
+      "▁passenger s",
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+      "▁nice ly",
+      "▁nic ely",
+      "end encia",
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+      "▁Hal f",
+      "▁fe wer",
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+      "▁pro pi",
+      "▁prop i",
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+      "▁Ha lle",
+      "▁Hall e",
+      "▁Hal le",
+      "▁Maj esty",
+      "▁Sw itch",
+      "▁ Switch",
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+      "▁ос тан",
+      "▁оста н",
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+      "▁ць ого",
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+      "▁T rain",
+      "▁Tr ain",
+      "▁Tra in",
+      "▁á prilis",
+      "▁p uesto",
+      "▁pu esto",
+      "▁pue sto",
+      "▁t oss",
+      "▁to ss",
+      "▁irre levant",
+      "▁d ip",
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+      "▁versch ill",
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+      "▁D oc",
+      "▁Do c",
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+      "▁Olymp edia",
+      "▁Gener ation",
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+      "metro s",
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+      "▁Z ahl",
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+      "▁Bu g",
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+      "▁subs tring",
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+      "▁c x",
+      "▁ cx",
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+      "▁p ending",
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+      "▁Be rl",
+      "▁Ber l",
+      "▁c ler",
+      "▁cl er",
+      "▁cle r",
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+      "▁S oc",
+      "▁So c",
+      "▁r ounded",
+      "▁round ed",
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+      "▁Di plom",
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+      "▁G an",
+      "▁Ga n",
+      "▁Inv estig",
+      "▁index Path",
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+      "▁molt i",
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+      "ge le",
+      "gel e",
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+      "▁M aler",
+      "▁Ma ler",
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+      "▁Male r",
+      "▁proyect o",
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+      "▁Ba th",
+      "▁Bat h",
+      "el lers",
+      "ell ers",
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+      "eller s",
+      "▁G P",
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+      "▁p ři",
+      "▁př i",
+      "▁d ed",
+      "▁de d",
+      "▁ ded",
+      "▁Od kazy",
+      "▁M sg",
+      "▁ Msg",
+      "▁B eing",
+      "▁Be ing",
+      "▁Bei ng",
+      "▁De puis",
+      "▁Dep uis",
+      "▁Pri mary",
+      "▁Prim ary",
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+      "▁deliver y",
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+      "▁им пе",
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+      "▁v esc",
+      "▁ve sc",
+      "▁ves c",
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+      "▁Air l",
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+      "▁Bre ak",
+      "▁St äd",
+      "is miss",
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+      "▁n ou",
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+      "Ital ie",
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+      "▁s vo",
+      "▁sv o",
+      "▁kir ály",
+      "▁Re ferences",
+      "▁Refer ences",
+      "▁Reference s",
+      "si x",
+      "s ix",
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+      "▁Archive s",
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+      "éta t",
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+      "▁fe as",
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+      "▁di vent",
+      "▁div ent",
+      "▁k é",
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+      "p ta",
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+      "▁sj ö",
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+      "▁tedes co",
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+      "▁st al",
+      "▁sta l",
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+      "▁Be ruf",
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+      "▁s vě",
+      "▁sv ě",
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+      "▁flu sh",
+      "▁ flush",
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+      "▁rad ial",
+      "▁radi al",
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+      "▁Per ry",
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+      "Co ll",
+      "C oll",
+      "li qu",
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+      "▁Fr anc",
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+      "▁Gu illaume",
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+      "k now",
+      "▁Un its",
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+      "▁Sal es",
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+      "Bo ard",
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+      "▁Ka y",
+      "ci an",
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+      "c ian",
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+      "stell e",
+      "▁disput e",
+      "▁disp ute",
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+      "Data Set",
+      "no thing",
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+      "n othing",
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+      "▁s hed",
+      "▁sh ed",
+      "▁she d",
+      "▁p aused",
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+      "▁pau sed",
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+      "s an",
+      "▁nun ca",
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+      "Se cret",
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+      "▁share s",
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+      "▁rem arks",
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+      "▁tra verse",
+      "▁travers e",
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+      "▁All ow",
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+      "▁T oy",
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+      "▁dah er",
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+      "▁model o",
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+      "▁Fo rum",
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+      "▁enf er",
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+      "Num bers",
+      "▁B inary",
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+      "▁О фициаль",
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+      "▁équipe s",
+      "▁efficient ly",
+      "▁Per iod",
+      "▁ Period",
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+      "lea f",
+      "▁similar ity",
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+      "▁pel s",
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+      "etr o",
+      "it ie",
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+      "▁Met eor",
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+      "▁pol ygon",
+      "▁poly gon",
+      "▁eg gs",
+      "▁egg s",
+      "At IndexPath",
+      "AtIndex Path",
+      "мен таль",
+      "мент аль",
+      "мента ль",
+      "▁in cred",
+      "▁incre d",
+      "▁inc red",
+      "ch unk",
+      "web driver",
+      "▁с вобо",
+      "▁сво бо",
+      "▁mi ędzy",
+      "Rece ived",
+      "Receive d",
+      "▁M onde",
+      "▁Mon de",
+      "▁Mo nde",
+      "▁Mond e",
+      "▁J Query",
+      "Bu tt",
+      "But t",
+      "B utt",
+      "▁P DO",
+      "▁for ec",
+      "▁fo rec",
+      "▁fore c",
+      "▁discipl ine",
+      "ch ev",
+      "che v",
+      "на т",
+      "н ат",
+      "▁re dis",
+      "▁red is",
+      "▁hun ting",
+      "▁al k",
+      "▁ alk",
+      "▁proof s",
+      "PR I",
+      "P RI",
+      "▁c hip",
+      "▁ch ip",
+      "▁chi p",
+      "és ie",
+      "▁H O",
+      "▁ HO",
+      "▁r ug",
+      "▁ru g",
+      "▁ rug",
+      "zo s",
+      "z os",
+      "▁s orte",
+      "▁sort e",
+      "▁sor te",
+      "▁ze igt",
+      "▁Phys ics",
+      "leg te",
+      "legt e",
+      "▁proport ional",
+      "▁proportion al",
+      "▁tool bar",
+      "ve ment",
+      "v ement",
+      "not in",
+      "▁prv ní",
+      "bl ah",
+      "bla h",
+      "b lah",
+      "▁prés ence",
+      "▁l loc",
+      "▁ll oc",
+      "▁lí der",
+      "▁Ac cept",
+      "▁ Accept",
+      "▁Al ways",
+      "▁\" {",
+      "▁divers i",
+      "▁diver si",
+      "ik or",
+      "iko r",
+      "i kor",
+      "Per iod",
+      "ж ён",
+      "▁Al liance",
+      "▁All iance",
+      "▁re lay",
+      "▁rel ay",
+      "▁rela y",
+      "Br o",
+      "B ro",
+      "jö n",
+      "j ön",
+      "▁B aud",
+      "▁Ba ud",
+      "▁Bau d",
+      "▁B ian",
+      "▁Bi an",
+      "') [",
+      "' )[",
+      "чи в",
+      "▁P oss",
+      "▁Po ss",
+      "▁Pos s",
+      "▁Mitg lieder",
+      "▁Mitglied er",
+      "▁n ev",
+      "▁ne v",
+      "Dan iel",
+      "▁t ends",
+      "▁ten ds",
+      "▁tend s",
+      "▁compag nie",
+      "▁liv res",
+      "▁livre s",
+      "lu b",
+      "l ub",
+      "▁ ▁",
+      "▁▁ ▁▁",
+      "▁▁▁ ▁",
+      "▁ ▁▁▁",
+      "▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁",
+      "▁▁▁▁ ▁",
+      "▁▁▁ ▁▁",
+      "▁ ▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁",
+      "▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁",
+      "▁ ▁▁",
+      "▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/oc_model/tokenizer.model b/oc_model/tokenizer.model
new file mode 100644
index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899
--- /dev/null
+++ b/oc_model/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/oc_model/tokenizer_config.json b/oc_model/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d39defd338dee423ae0a71abfd7ea69a44fb1e52
--- /dev/null
+++ b/oc_model/tokenizer_config.json
@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/openvla_utils.py b/openvla_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2762bbc0295b5bc7ff4d2cc89081f975c83ff25f
--- /dev/null
+++ b/openvla_utils.py
@@ -0,0 +1,244 @@
+"""Utils for evaluating the OpenVLA policy."""
+
+import json
+import os
+import time
+
+import numpy as np
+import tensorflow as tf
+import torch
+from PIL import Image
+from transformers import AutoConfig, AutoImageProcessor, AutoModelForVision2Seq, AutoProcessor
+
+from prismatic.extern.hf.configuration_prismatic import OpenVLAConfig
+from prismatic.extern.hf.modeling_prismatic import OpenVLAForActionPrediction
+from prismatic.extern.hf.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor
+
+# Initialize important constants and pretty-printing mode in NumPy.
+ACTION_DIM = 7
+DATE = time.strftime("%Y_%m_%d")
+DATE_TIME = time.strftime("%Y_%m_%d-%H_%M_%S")
+DEVICE = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+np.set_printoptions(formatter={"float": lambda x: "{0:0.3f}".format(x)})
+
+# Initialize system prompt for OpenVLA v0.1.
+OPENVLA_V01_SYSTEM_PROMPT = (
+    "A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions."
+)
+
+from peft import PeftModel
+from safetensors.torch import load_file
+
+
+def crop_and_resize(image, crop_scale, batch_size):
+    """
+    Center-crops an image to have area `crop_scale` * (original image area), and then resizes back
+    to original size. We use the same logic seen in the `dlimp` RLDS datasets wrapper to avoid
+    distribution shift at test time.
+
+    Args:
+        image: TF Tensor of shape (batch_size, H, W, C) or (H, W, C) and datatype tf.float32 with
+               values between [0,1].
+        crop_scale: The area of the center crop with respect to the original image.
+        batch_size: Batch size.
+    """
+    # Convert from 3D Tensor (H, W, C) to 4D Tensor (batch_size, H, W, C)
+    assert image.shape.ndims == 3 or image.shape.ndims == 4
+    expanded_dims = False
+    if image.shape.ndims == 3:
+        image = tf.expand_dims(image, axis=0)
+        expanded_dims = True
+
+    # Get height and width of crop
+    new_heights = tf.reshape(tf.clip_by_value(tf.sqrt(crop_scale), 0, 1), shape=(batch_size,))
+    new_widths = tf.reshape(tf.clip_by_value(tf.sqrt(crop_scale), 0, 1), shape=(batch_size,))
+
+    # Get bounding box representing crop
+    height_offsets = (1 - new_heights) / 2
+    width_offsets = (1 - new_widths) / 2
+    bounding_boxes = tf.stack(
+        [
+            height_offsets,
+            width_offsets,
+            height_offsets + new_heights,
+            width_offsets + new_widths,
+        ],
+        axis=1,
+    )
+
+    # Crop and then resize back up
+    image = tf.image.crop_and_resize(image, bounding_boxes, tf.range(batch_size), (224, 224))
+
+    # Convert back to 3D Tensor (H, W, C)
+    if expanded_dims:
+        image = image[0]
+
+    return image
+
+
+def get_vla_action(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    image = Image.fromarray(obs["full_image"])
+    image = image.convert("RGB")
+
+    # (If trained with image augmentations) Center crop image and then resize back up to original size.
+    # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+    #            the original height and width by sqrt(0.9) -- not 0.9!
+    if center_crop:
+        batch_size = 1
+        crop_scale = 0.9
+
+        # Convert to TF Tensor and record original data type (should be tf.uint8)
+        image = tf.convert_to_tensor(np.array(image))
+        orig_dtype = image.dtype
+
+        # Convert to data type tf.float32 and values between [0,1]
+        image = tf.image.convert_image_dtype(image, tf.float32)
+
+        # Crop and then resize back to original size
+        image = crop_and_resize(image, crop_scale, batch_size)
+
+        # Convert back to original data type
+        image = tf.clip_by_value(image, 0, 1)
+        image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+        # Convert back to PIL Image
+        image = Image.fromarray(image.numpy())
+        image = image.convert("RGB")
+
+    # Build VLA prompt
+    if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+        prompt = (
+            f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+        )
+    else:  # OpenVLA
+        prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut: _ _ _ _ _ _ _ _</s>"
+
+    # Process inputs.
+    inputs = processor(prompt, image).to(DEVICE, dtype=torch.float16)
+    inputs['input_ids'][0][-9] = 29871
+    # print(inputs['input_ids']); 1/0
+
+    # Get action.
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action
+
+
+def get_vla_action_v2(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    images = [obs["full_image"], obs["wrist_image"]]
+    input_data = []
+    for image in images:
+        image = Image.fromarray(image)
+        image = image.convert("RGB")
+
+        # (If trained with image augmentations) Center crop image and then resize back up to original size.
+        # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+        #            the original height and width by sqrt(0.9) -- not 0.9!
+        if center_crop:
+            batch_size = 1
+            crop_scale = 0.9
+
+            # Convert to TF Tensor and record original data type (should be tf.uint8)
+            image = tf.convert_to_tensor(np.array(image))
+            orig_dtype = image.dtype
+
+            # Convert to data type tf.float32 and values between [0,1]
+            image = tf.image.convert_image_dtype(image, tf.float32)
+
+            # Crop and then resize back to original size
+            image = crop_and_resize(image, crop_scale, batch_size)
+
+            # Convert back to original data type
+            image = tf.clip_by_value(image, 0, 1)
+            image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+            # Convert back to PIL Image
+            image = Image.fromarray(image.numpy())
+            image = image.convert("RGB")
+        
+        # Build VLA prompt
+        if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+            prompt = (
+                f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+            )
+        else:  # OpenVLA
+            prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut: _ _ _ _ _ _ _ _</s>"
+
+        # Process inputs.
+        inputs = processor(prompt, image).to(DEVICE, dtype=torch.float16)
+        inputs['input_ids'][0][-9] = 29871
+        input_data.append(inputs)
+
+    # Get action.
+    inputs = {}
+    inputs['pixel_values'] = []
+    for datum in input_data:
+        inputs['input_ids'] = datum['input_ids']
+        inputs['attention_mask'] = datum['attention_mask']
+        bz, cc, h, w = datum['pixel_values'].shape
+        datum['pixel_values'] = datum['pixel_values'].reshape(bz, 1, cc, h, w)
+        inputs['pixel_values'].append(datum['pixel_values'])
+    inputs['pixel_values'] = torch.stack(inputs['pixel_values'], dim=1)
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action
+
+
+def get_vla_action_v3(vla, processor, base_vla_name, obs, task_label, unnorm_key, center_crop=False):
+    """Generates an action with the VLA policy."""
+    images = [obs["full_image"], obs["wrist_image"], obs["depth_full_image"], obs["depth_wrist_image"]]
+    input_data = []
+    for image in images:
+        image = Image.fromarray(image)
+        image = image.convert("RGB")
+
+        # (If trained with image augmentations) Center crop image and then resize back up to original size.
+        # IMPORTANT: Let's say crop scale == 0.9. To get the new height and width (post-crop), multiply
+        #            the original height and width by sqrt(0.9) -- not 0.9!
+        if center_crop:
+            batch_size = 1
+            crop_scale = 0.9
+
+            # Convert to TF Tensor and record original data type (should be tf.uint8)
+            image = tf.convert_to_tensor(np.array(image))
+            orig_dtype = image.dtype
+
+            # Convert to data type tf.float32 and values between [0,1]
+            image = tf.image.convert_image_dtype(image, tf.float32)
+
+            # Crop and then resize back to original size
+            image = crop_and_resize(image, crop_scale, batch_size)
+
+            # Convert back to original data type
+            image = tf.clip_by_value(image, 0, 1)
+            image = tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
+
+            # Convert back to PIL Image
+            image = Image.fromarray(image.numpy())
+            image = image.convert("RGB")
+        
+        # Build VLA prompt
+        if "openvla-v01" in base_vla_name:  # OpenVLA v0.1
+            prompt = (
+                f"{OPENVLA_V01_SYSTEM_PROMPT} USER: What action should the robot take to {task_label.lower()}? ASSISTANT:"
+            )
+        else:  # OpenVLA
+            prompt = f"In: What action should the robot take to {task_label.lower()}?\nOut:"
+
+        # Process inputs.
+        inputs = processor(prompt, image).to(DEVICE, dtype=torch.bfloat16)
+        input_data.append(inputs)
+
+    # Get action.
+    inputs = {}
+    inputs['pixel_values'] = []
+    for datum in input_data:
+        inputs['input_ids'] = datum['input_ids']
+        inputs['attention_mask'] = datum['attention_mask']
+        bz, cc, h, w = datum['pixel_values'].shape
+        datum['pixel_values'] = datum['pixel_values'].reshape(bz, 1, cc, h, w)
+        inputs['pixel_values'].append(datum['pixel_values'])
+    inputs['pixel_values'] = torch.stack(inputs['pixel_values'], dim=1)
+    action = vla.predict_action(**inputs, unnorm_key=unnorm_key, do_sample=False)
+    return action
diff --git a/orc_model/README.md b/orc_model/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..14740d8af86138df70f05bea03ec90fd8987a765
--- /dev/null
+++ b/orc_model/README.md
@@ -0,0 +1,202 @@
+---
+base_model: output_hf_model_openx
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1
\ No newline at end of file
diff --git a/orc_model/adapter_config.json b/orc_model/adapter_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..aa97957fd64d4e2264e91f56780f0c69d5d069a5
--- /dev/null
+++ b/orc_model/adapter_config.json
@@ -0,0 +1,258 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "OpenVLAForActionPrediction",
+    "parent_library": "prismatic.extern.hf.modeling_prismatic"
+  },
+  "base_model_name_or_path": "output_hf_model_openx",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "language_model.model.layers.22.mlp.up_proj",
+    "language_model.model.layers.11.self_attn.k_proj",
+    "language_model.model.layers.23.self_attn.q_proj",
+    "language_model.model.layers.15.mlp.up_proj",
+    "language_model.model.layers.20.mlp.up_proj",
+    "language_model.model.layers.29.mlp.gate_proj",
+    "language_model.model.layers.18.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.v_proj",
+    "language_model.model.layers.18.mlp.up_proj",
+    "language_model.model.layers.19.self_attn.q_proj",
+    "language_model.model.layers.4.self_attn.o_proj",
+    "language_model.model.layers.20.self_attn.q_proj",
+    "language_model.model.layers.26.self_attn.k_proj",
+    "projector.fc1",
+    "language_model.model.layers.2.self_attn.q_proj",
+    "language_model.model.layers.0.mlp.gate_proj",
+    "language_model.model.layers.17.self_attn.q_proj",
+    "language_model.model.layers.23.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.k_proj",
+    "language_model.model.layers.16.self_attn.q_proj",
+    "language_model.model.layers.11.self_attn.o_proj",
+    "language_model.model.layers.18.self_attn.v_proj",
+    "language_model.model.layers.14.mlp.gate_proj",
+    "language_model.model.layers.4.mlp.down_proj",
+    "language_model.model.layers.12.mlp.gate_proj",
+    "language_model.model.layers.23.self_attn.v_proj",
+    "language_model.model.layers.7.mlp.gate_proj",
+    "language_model.model.layers.17.mlp.down_proj",
+    "language_model.model.layers.21.mlp.down_proj",
+    "language_model.model.layers.22.mlp.down_proj",
+    "language_model.model.layers.13.self_attn.k_proj",
+    "language_model.model.layers.17.mlp.gate_proj",
+    "language_model.model.layers.10.mlp.down_proj",
+    "language_model.model.layers.11.mlp.down_proj",
+    "language_model.model.layers.2.self_attn.k_proj",
+    "language_model.model.layers.2.mlp.gate_proj",
+    "language_model.model.layers.7.mlp.down_proj",
+    "language_model.model.layers.19.mlp.gate_proj",
+    "language_model.lm_head",
+    "language_model.model.layers.1.self_attn.k_proj",
+    "language_model.model.layers.16.mlp.down_proj",
+    "language_model.model.layers.13.mlp.down_proj",
+    "language_model.model.layers.19.self_attn.k_proj",
+    "language_model.model.layers.25.self_attn.v_proj",
+    "language_model.model.layers.4.self_attn.v_proj",
+    "language_model.model.layers.9.mlp.gate_proj",
+    "language_model.model.layers.3.self_attn.q_proj",
+    "language_model.model.layers.7.self_attn.o_proj",
+    "language_model.model.layers.30.self_attn.k_proj",
+    "language_model.model.layers.31.self_attn.k_proj",
+    "language_model.model.layers.0.self_attn.q_proj",
+    "language_model.model.layers.25.mlp.down_proj",
+    "language_model.model.layers.31.mlp.gate_proj",
+    "language_model.model.layers.26.self_attn.v_proj",
+    "language_model.model.layers.27.self_attn.v_proj",
+    "language_model.model.layers.27.mlp.gate_proj",
+    "language_model.model.layers.7.self_attn.k_proj",
+    "language_model.model.layers.27.self_attn.o_proj",
+    "language_model.model.layers.0.self_attn.k_proj",
+    "language_model.model.layers.28.self_attn.v_proj",
+    "language_model.model.layers.25.mlp.gate_proj",
+    "language_model.model.layers.30.mlp.down_proj",
+    "language_model.model.layers.29.self_attn.k_proj",
+    "language_model.model.layers.17.self_attn.k_proj",
+    "language_model.model.layers.11.self_attn.q_proj",
+    "language_model.model.layers.5.mlp.gate_proj",
+    "language_model.model.layers.24.self_attn.v_proj",
+    "language_model.model.layers.31.mlp.up_proj",
+    "language_model.model.layers.1.mlp.gate_proj",
+    "language_model.model.layers.13.mlp.up_proj",
+    "language_model.model.layers.23.mlp.down_proj",
+    "language_model.model.layers.5.self_attn.k_proj",
+    "language_model.model.layers.9.self_attn.q_proj",
+    "language_model.model.layers.14.self_attn.v_proj",
+    "language_model.model.layers.24.mlp.gate_proj",
+    "language_model.model.layers.8.self_attn.o_proj",
+    "language_model.model.layers.31.self_attn.o_proj",
+    "language_model.model.layers.5.self_attn.q_proj",
+    "language_model.model.layers.12.self_attn.q_proj",
+    "language_model.model.layers.26.mlp.down_proj",
+    "language_model.model.layers.16.self_attn.k_proj",
+    "language_model.model.layers.12.self_attn.k_proj",
+    "language_model.model.layers.24.self_attn.k_proj",
+    "language_model.model.layers.26.mlp.gate_proj",
+    "language_model.model.layers.29.mlp.up_proj",
+    "projector.fc2",
+    "language_model.model.layers.5.self_attn.o_proj",
+    "language_model.model.layers.5.mlp.down_proj",
+    "language_model.model.layers.31.mlp.down_proj",
+    "language_model.model.layers.0.mlp.up_proj",
+    "language_model.model.layers.12.mlp.up_proj",
+    "language_model.model.layers.9.mlp.up_proj",
+    "language_model.model.layers.9.self_attn.o_proj",
+    "language_model.model.layers.22.self_attn.q_proj",
+    "language_model.model.layers.20.self_attn.k_proj",
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+    "language_model.model.layers.3.self_attn.k_proj",
+    "language_model.model.layers.30.mlp.gate_proj",
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+    "language_model.model.layers.21.self_attn.o_proj",
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+    "language_model.model.layers.15.mlp.down_proj",
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+      "▁Internacional": 17489,
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+      "hält": 17495,
+      "unas": 17496,
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+      "▁+\\": 17501,
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+      "▁conclude": 17668,
+      "Seg": 17669,
+      "▁акаде": 17670,
+      "}\",": 17671,
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+      "řed": 17673,
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+      "▁resistance": 17711,
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+      "▁subjects": 17800,
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+      "▁detected": 17809,
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+      "Complete": 17813,
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+      "');\r": 17822,
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+      "▁periodo": 17848,
+      "▁Weltkrie": 17849,
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+      "▁regression": 17855,
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+      "▁dabei": 17857,
+      "perm": 17858,
+      "мене": 17859,
+      "▁Abd": 17860,
+      "▁CF": 17861,
+      "arks": 17862,
+      "resolve": 17863,
+      "wedge": 17864,
+      "▁initialization": 17865,
+      "▁Véase": 17866,
+      "▁приня": 17867,
+      "stmt": 17868,
+      "▁income": 17869,
+      "MY": 17870,
+      "▁odkazy": 17871,
+      "▁Siehe": 17872,
+      "▁bodies": 17873,
+      "▁soc": 17874,
+      "Random": 17875,
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+      "▁regarded": 17878,
+      "onCreate": 17879,
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+      "▁Raf": 17881,
+      "▁Buenos": 17882,
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+      ")));": 17884,
+      "capt": 17885,
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+      "▁rôle": 17889,
+      "▁статьи": 17890,
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+      "▁représent": 17899,
+      "▁LA": 17900,
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+      "▁numero": 17910,
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+      "▁Пра": 17913,
+      "onsieur": 17914,
+      "qt": 17915,
+      "abestanden": 17916,
+      "ność": 17917,
+      "Connect": 17918,
+      "▁oracle": 17919,
+      "▁Stockholm": 17920,
+      "sizeof": 17921,
+      "▁gemäß": 17922,
+      "ACT": 17923,
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+      "▁logger": 17927,
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+      "▁Einwohner": 17940,
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+      "▁absor": 17977,
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+      "odge": 17979,
+      "▁WHEN": 17980,
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+      "▁Regiment": 17982,
+      "▁evaluation": 17983,
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+      "prec": 17990,
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+      "▁Fernando": 17993,
+      "▁Bibliographie": 17994,
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+      "▁marks": 17997,
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+      "▁loved": 18012,
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+      "]\"": 18017,
+      "▁equally": 18018,
+      "ihe": 18019,
+      "care": 18020,
+      "писок": 18021,
+      "lijk": 18022,
+      "rinn": 18023,
+      "▁\\[\\": 18024,
+      "▁sons": 18025,
+      "▁tät": 18026,
+      "icamente": 18027,
+      "▁listing": 18028,
+      "iellement": 18029,
+      "▁nyelven": 18030,
+      "▁ds": 18031,
+      "▁agricult": 18032,
+      "▁Hermann": 18033,
+      "▁besides": 18034,
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+      "▁Становништво": 18044,
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+      "▁bott": 18046,
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+      "▁савезној": 18051,
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+      "▁май": 18063,
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+      "ambiguation": 18065,
+      "▁challeng": 18066,
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+      "▁absence": 18070,
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+      "вое": 18072,
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+      "▁Ign": 18076,
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+      "▁Madame": 18080,
+      "▁gruppo": 18081,
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+      "▁externas": 18083,
+      "▁Александр": 18084,
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+      "▁живе": 18086,
+      "Amount": 18087,
+      "▁correlate": 18088,
+      "▁Fant": 18089,
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+      "fp": 18091,
+      "министратив": 18092,
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+      "▁filters": 18094,
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+      "▁zd": 18100,
+      "awn": 18101,
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+      "▁serialize": 28755,
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+      "▁exponent": 28869,
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+      "▁awful": 28893,
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+      "▁Fine": 28896,
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+      "▁Роди": 28900,
+      "▁Düsseldorf": 28901,
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+      "▁instanceof": 28919,
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+      "e book",
+      "s yn",
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+      "▁Ge gen",
+      "▁Geg en",
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+      "▁L ub",
+      "▁Lu b",
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+      "izz ato",
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+      "▁st ub",
+      "▁i e",
+      "▁ ie",
+      "▁' ./",
+      "▁'. /",
+      "co d",
+      "c od",
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+      "▁p ue",
+      "▁pu e",
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+      "▁al berga",
+      "an ych",
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+      "a nych",
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+      "que lle",
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+      "q uelle",
+      "▁Ar k",
+      "▁p ris",
+      "▁pr is",
+      "▁pri s",
+      "▁v oll",
+      "▁vo ll",
+      "▁vol l",
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+      "▁ce ux",
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+      "▁popula te",
+      "▁popul ate",
+      "\" \r",
+      "▁fem mes",
+      "▁femme s",
+      "AN G",
+      "A NG",
+      "▁Desp ite",
+      "вы е",
+      "в ые",
+      "is ke",
+      "isk e",
+      "i ske",
+      "zu g",
+      "z ug",
+      "на ча",
+      "▁h atten",
+      "▁hat ten",
+      "▁hatte n",
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+      "▁mo ments",
+      "▁moment s",
+      "▁mom ents",
+      "▁últ ima",
+      "▁h older",
+      "▁hold er",
+      "▁ho lder",
+      "▁hol der",
+      "▁ holder",
+      "bl ank",
+      "Col lections",
+      "Collection s",
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+      "ath ers",
+      "ather s",
+      "a thers",
+      "▁g rade",
+      "▁gr ade",
+      "▁gra de",
+      "▁grad e",
+      "▁ grade",
+      "▁aff airs",
+      "▁affair s",
+      ".$ $",
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+      "▁d elta",
+      "▁del ta",
+      "▁ delta",
+      "▁Jug end",
+      "▁españ ol",
+      "▁O UT",
+      "▁ OUT",
+      "▁mathemat ical",
+      "▁m ongo",
+      "▁mon go",
+      "▁Ф е",
+      "ul ing",
+      "uli ng",
+      "u ling",
+      "▁re volution",
+      "▁revol ution",
+      "▁c oin",
+      "▁co in",
+      "▁sub class",
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+      "äch e",
+      "ä che",
+      "▁p yg",
+      "▁py g",
+      "ща я",
+      "ill ery",
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+      "iller y",
+      "▁com enz",
+      "dep th",
+      "▁c él",
+      "▁re size",
+      "▁res ize",
+      "▁ resize",
+      "▁S ame",
+      "▁Sam e",
+      "▁Sa me",
+      "▁st rik",
+      "▁str ik",
+      "▁stri k",
+      "▁t ir",
+      "▁ti r",
+      "▁sc arc",
+      "▁scar c",
+      "▁M ember",
+      "▁Mem ber",
+      "▁ Member",
+      "sub scribe",
+      "ó ż",
+      "út bol",
+      "ex cept",
+      "▁dr iving",
+      "▁dri ving",
+      "▁driv ing",
+      "ki e",
+      "k ie",
+      "zo ny",
+      "zon y",
+      "z ony",
+      "ème s",
+      "è mes",
+      "Da vid",
+      "D avid",
+      "iss ant",
+      "issa nt",
+      "▁т ы",
+      "▁ ты",
+      "▁é lect",
+      "▁él ect",
+      "▁re name",
+      "▁r ename",
+      "▁ren ame",
+      "▁R unning",
+      "▁Run ning",
+      "▁ Running",
+      "▁inter faces",
+      "▁interface s",
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+      "▁Wal ker",
+      "▁Walk er",
+      "▁soci été",
+      "▁as ks",
+      "▁ask s",
+      "br id",
+      "b rid",
+      "▁je we",
+      "▁se ines",
+      "▁sein es",
+      "▁seine s",
+      "▁sei nes",
+      "▁ag ents",
+      "▁agent s",
+      "▁M Y",
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+      "ie sen",
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+      "iese n",
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+      "▁людя х",
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+      "r aum",
+      "▁o rb",
+      "▁or b",
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+      "sc al",
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+      "▁Le ar",
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+      "▁we ar",
+      "▁s lave",
+      "▁sl ave",
+      "▁sla ve",
+      "▁re named",
+      "▁ren amed",
+      "▁rename d",
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+      "ma ste",
+      "mas te",
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+      "▁t i",
+      "▁ ti",
+      "▁dem sel",
+      "▁bene ath",
+      "bin ary",
+      "b inary",
+      "▁ed ición",
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+      "▁kilom et",
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+      "▁ent rance",
+      "▁entr ance",
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+      "▁b ag",
+      "▁ba g",
+      "▁ bag",
+      "▁Ar men",
+      "▁Arm en",
+      "ij o",
+      "i jo",
+      "▁L ors",
+      "▁Lo rs",
+      "▁Lor s",
+      "▁demsel ben",
+      "ê m",
+      "▁dis crete",
+      "▁prom inent",
+      "▁J ay",
+      "▁Ja y",
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+      "D L",
+      "▁d í",
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+      "th ey",
+      "the y",
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+      "sch nitt",
+      "▁C ou",
+      "▁Co u",
+      "▁l ex",
+      "▁le x",
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+      "y outube",
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+      "▁mi rror",
+      "▁mir ror",
+      "▁h its",
+      "▁hit s",
+      "▁hi ts",
+      "▁Be ck",
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+      "▁L az",
+      "▁La z",
+      "▁w inner",
+      "▁win ner",
+      "DE X",
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+      "▁I NT",
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+      "z ing",
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+      "▁Bayer n",
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+      "▁bus y",
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+      "▁p t",
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+      "▁com plement",
+      "▁comp lement",
+      "▁comple ment",
+      "▁compl ement",
+      "ár s",
+      "á rs",
+      "▁Amer ika",
+      "рі й",
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+      "▁val ley",
+      "▁vall ey",
+      "▁valle y",
+      "▁ro oms",
+      "▁room s",
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+      "AB C",
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+      "▁K ath",
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+      "▁komm t",
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+      "де на",
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+      "д ена",
+      "ęp u",
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+      "usz t",
+      "▁B ear",
+      "▁Be ar",
+      "Class Name",
+      "▁Par lament",
+      "▁a ix",
+      "▁ai x",
+      "▁inv ited",
+      "▁P ATH",
+      "▁PA TH",
+      "▁ PATH",
+      "xt er",
+      "x ter",
+      "▁R ace",
+      "▁Ra ce",
+      "▁h echo",
+      "▁he cho",
+      "▁T ower",
+      "▁To wer",
+      "▁Tow er",
+      "▁u tf",
+      "▁ut f",
+      "▁ utf",
+      "act ly",
+      "▁бу де",
+      "▁ang les",
+      "▁angle s",
+      "▁ angles",
+      "ня я",
+      "ouv elles",
+      "ouve lles",
+      "ouvel les",
+      "ouvelle s",
+      "▁cl imate",
+      "▁cli mate",
+      "▁clim ate",
+      "▁sing ing",
+      "▁sin ging",
+      "▁navig ate",
+      ">' ;",
+      "> ';",
+      "ad ows",
+      "ado ws",
+      "adow s",
+      "▁l eta",
+      "▁le ta",
+      "▁let a",
+      "▁S itz",
+      "▁Si tz",
+      "▁Sit z",
+      "▁part itions",
+      "▁partition s",
+      "▁d ock",
+      "▁do ck",
+      "▁doc k",
+      "▁ż y",
+      "▁ ży",
+      "▁alloc ate",
+      "▁benef its",
+      "▁benefit s",
+      "▁n ieder",
+      "▁nie der",
+      "▁ni eder",
+      "xp ath",
+      "x path",
+      "me ck",
+      "äl le",
+      "äll e",
+      "ä lle",
+      "▁cou pling",
+      "▁coup ling",
+      "жи л",
+      "ж ил",
+      "For Key",
+      "ar gent",
+      "arg ent",
+      "cl ou",
+      "clo u",
+      "c lou",
+      "▁instru ments",
+      "▁instrument s",
+      "▁ent hus",
+      "▁m ég",
+      "▁mé g",
+      "▁Па в",
+      "▁R ach",
+      "▁Ra ch",
+      "-- ---",
+      "---- -",
+      "--- --",
+      "- ----",
+      "▁API s",
+      "▁AP Is",
+      "▁V ier",
+      "▁Vi er",
+      "▁Vie r",
+      "C md",
+      "it ore",
+      "ito re",
+      "itor e",
+      "▁C uba",
+      "▁Cu ba",
+      "▁Cub a",
+      "▁dátum mal",
+      "▁embed ding",
+      "std io",
+      "▁Gil bert",
+      "▁ge prüft",
+      "▁st ating",
+      "▁stat ing",
+      "▁sta ting",
+      "▁stati ng",
+      "▁trigger s",
+      "▁trig gers",
+      "+ =",
+      "▁spé cial",
+      "▁del iber",
+      "▁deli ber",
+      "ми н",
+      "м ин",
+      "Pro du",
+      "Pr odu",
+      "P rodu",
+      "▁St ati",
+      "▁Stat i",
+      "▁Sta ti",
+      "▁z us",
+      "▁zu s",
+      "kt ionen",
+      "ktion en",
+      "Dispatch er",
+      "id al",
+      "ida l",
+      "i dal",
+      "▁L P",
+      "▁ LP",
+      "op tera",
+      "opt era",
+      "opter a",
+      "▁e star",
+      "▁est ar",
+      "▁es tar",
+      "▁esta r",
+      "▁зна чи",
+      "с мо",
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+      "ouse s",
+      "o uses",
+      "eng ono",
+      "engo no",
+      "▁W PF",
+      "pub lish",
+      "▁t eor",
+      "▁te or",
+      "el if",
+      "eli f",
+      "▁e rg",
+      "▁er g",
+      "▁ erg",
+      "▁separ ation",
+      "Pa n",
+      "P an",
+      "▁Or chestra",
+      "Pe ter",
+      "P eter",
+      "bound s",
+      "b ounds",
+      "▁Shakespe are",
+      "▁cant ante",
+      "▁d emi",
+      "▁de mi",
+      "▁dem i",
+      "▁Pop ular",
+      "ф р",
+      "ar ring",
+      "arr ing",
+      "ци н",
+      "ц ин",
+      "▁И с",
+      "vo n",
+      "v on",
+      "▁subst itution",
+      "▁lí nea",
+      "\\}$ .",
+      "\\} $.",
+      "\\ }$.",
+      "com o",
+      "co mo",
+      "c omo",
+      "▁ва ж",
+      "wa gen",
+      "w agen",
+      "▁rare ly",
+      "▁period s",
+      "▁peri ods",
+      "gl ob",
+      "g lob",
+      "▁F rid",
+      "▁Fr id",
+      "▁Fri d",
+      "▁T err",
+      "▁Te rr",
+      "▁Ter r",
+      "▁Re lease",
+      "▁ Release",
+      "Brain z",
+      "▁гра ф",
+      "▁ граф",
+      "DI S",
+      "D IS",
+      "compat ible",
+      "▁po č",
+      "LI N",
+      "L IN",
+      "▁K ällor",
+      "▁A rizona",
+      "pp y",
+      "p py",
+      "Se q",
+      "S eq",
+      "▁A in",
+      "▁T ourn",
+      "▁To urn",
+      "▁Tour n",
+      "br ow",
+      "bro w",
+      "b row",
+      "▁K ör",
+      "▁Kö r",
+      "▁a sh",
+      "▁as h",
+      "▁ ash",
+      "ogene ous",
+      "▁dia lect",
+      "▁насе ља",
+      "mysql i",
+      "mysq li",
+      "цо в",
+      "ц ов",
+      "▁f lor",
+      "▁fl or",
+      "▁flo r",
+      "▁ф ло",
+      "IA B",
+      "I AB",
+      "▁With in",
+      "▁Wit hin",
+      "^ (",
+      "▁b ois",
+      "▁bo is",
+      "▁t ank",
+      "▁tan k",
+      "▁aff ili",
+      "▁h ijo",
+      "▁hij o",
+      "▁hi jo",
+      "▁K ate",
+      "▁Kat e",
+      "▁Ka te",
+      "▁Ver l",
+      "▁Ve rl",
+      "▁M iami",
+      "▁Mi ami",
+      "▁type script",
+      "▁types cript",
+      "њ у",
+      "▁V ern",
+      "▁Ver n",
+      "▁Ve rn",
+      "▁ви со",
+      "ie mann",
+      "iem ann",
+      "i emann",
+      "▁co verage",
+      "▁cover age",
+      "br ie",
+      "b rie",
+      "▁Start ing",
+      "▁Star ting",
+      "num py",
+      "▁J enkins",
+      "▁Jen kins",
+      "▁k ét",
+      "▁ké t",
+      "▁g rup",
+      "▁gr up",
+      "▁gru p",
+      "▁S cient",
+      "▁Sc ient",
+      "▁Sci ent",
+      "▁inter rupt",
+      "▁b lob",
+      "▁bl ob",
+      "▁blo b",
+      "▁ blob",
+      "ug el",
+      "uge l",
+      "u gel",
+      "▁Or th",
+      "▁Ort h",
+      "ab ama",
+      "aba ma",
+      "▁B apt",
+      "▁Ba pt",
+      "ow nik",
+      "own ik",
+      "▁бы ть",
+      "▁Jul ius",
+      "▁Ju lius",
+      "▁Juli us",
+      "▁П рез",
+      "▁Пре з",
+      "▁subst itute",
+      "support ed",
+      "supp orted",
+      "ch y",
+      "c hy",
+      "egy zetek",
+      "▁Per formance",
+      "▁Perform ance",
+      "less ly",
+      "Con structor",
+      "▁ext ending",
+      "▁extend ing",
+      "▁Mus lim",
+      "Over flow",
+      "▁J enn",
+      "▁Je nn",
+      "▁Jen n",
+      "▁produ z",
+      "▁prod uz",
+      "мі ї",
+      "м ії",
+      "▁país es",
+      "▁e ux",
+      "▁eu x",
+      "▁f ate",
+      "▁fa te",
+      "▁fat e",
+      "ol oge",
+      "olog e",
+      "olo ge",
+      "у к",
+      "▁wo bei",
+      "▁wob ei",
+      "▁S achsen",
+      "▁Sach sen",
+      "▁са йт",
+      "▁сай т",
+      "Mod els",
+      "Model s",
+      "Mode ls",
+      "▁F ast",
+      "▁Fa st",
+      "bes ondere",
+      "▁F R",
+      "▁ FR",
+      "▁a con",
+      "▁ac on",
+      "▁ acon",
+      "▁Den kmal",
+      "▁an ch",
+      "▁anc h",
+      "▁ anch",
+      "▁públic o",
+      "▁T as",
+      "▁Ta s",
+      "▁c and",
+      "▁can d",
+      "▁ca nd",
+      "▁pa ździer",
+      "▁М он",
+      "▁Мо н",
+      "▁vers us",
+      "ru t",
+      "r ut",
+      "G T",
+      "▁insert ing",
+      "▁inser ting",
+      "▁can ad",
+      "▁ca nad",
+      "є м",
+      "▁M etro",
+      "▁Met ro",
+      "▁Herz og",
+      "Ign ore",
+      "▁decre ase",
+      "▁п ун",
+      "▁пу н",
+      "▁F ischer",
+      "▁M all",
+      "▁Ma ll",
+      "▁Mal l",
+      "▁n örd",
+      "io stream",
+      "i ostream",
+      "▁Lux emb",
+      "pay load",
+      "▁Ze itung",
+      "▁Zeit ung",
+      "▁mod ifying",
+      "▁modify ing",
+      "▁C her",
+      "▁Ch er",
+      "▁Che r",
+      "▁Lu ci",
+      "▁Luc i",
+      "n x",
+      "▁lo ose",
+      "▁top ics",
+      "▁topic s",
+      "▁var ied",
+      "▁vari ed",
+      "▁va ried",
+      "▁p g",
+      "▁ pg",
+      "aj es",
+      "aje s",
+      "a jes",
+      "um m",
+      "u mm",
+      "View s",
+      "▁B eau",
+      "▁Be au",
+      "MA P",
+      "M AP",
+      "ip eline",
+      "ipe line",
+      "▁Inter est",
+      "ar ith",
+      "ari th",
+      "▁seg ún",
+      "▁Geme ins",
+      "▁Att ribute",
+      "▁ Attribute",
+      "comm unity",
+      "▁цент р",
+      "▁kil ometer",
+      "▁kilomet er",
+      "▁kilom eter",
+      "▁é conom",
+      "▁éc onom",
+      "lar ation",
+      "▁к ъ",
+      "▁car riage",
+      "▁carri age",
+      "▁L ane",
+      "▁La ne",
+      "▁Lan e",
+      "▁не об",
+      "ku r",
+      "k ur",
+      "▁A F",
+      "▁ AF",
+      "IN TER",
+      "INT ER",
+      ")) $",
+      ") )$",
+      "▁be ide",
+      "▁bei de",
+      "dest ination",
+      "▁font s",
+      "▁fon ts",
+      "▁ fonts",
+      "append Child",
+      "▁M AR",
+      "▁MA R",
+      "▁g ay",
+      "▁ga y",
+      "mi l",
+      "m il",
+      "le sh",
+      "les h",
+      "l esh",
+      "è t",
+      "▁W ang",
+      "▁Wa ng",
+      "▁Y ears",
+      "▁Year s",
+      "▁Ye ars",
+      "▁S ymbol",
+      "▁Sym bol",
+      "▁ Symbol",
+      "Li ve",
+      "L ive",
+      "qu ency",
+      "▁U sers",
+      "▁Use rs",
+      "▁User s",
+      "▁Us ers",
+      "▁ Users",
+      "▁Un icode",
+      "▁S au",
+      "▁Sa u",
+      "▁t ons",
+      "▁to ns",
+      "▁ton s",
+      "▁ tons",
+      "▁Н і",
+      "▁кра ї",
+      "▁ краї",
+      "AX I",
+      "▁P ick",
+      "▁Pi ck",
+      "▁Pic k",
+      "A I",
+      "▁h ath",
+      "▁ha th",
+      "▁hat h",
+      "▁a inda",
+      "▁ain da",
+      "▁p apa",
+      "▁pa pa",
+      "▁pap a",
+      "▁C enso",
+      "▁B ald",
+      "▁Ba ld",
+      "▁Bal d",
+      "▁Насе ље",
+      "▁sim ulations",
+      "▁simulation s",
+      "▁j aren",
+      "▁ja ren",
+      "▁jar en",
+      "▁inher ited",
+      "▁inherit ed",
+      "▁то й",
+      "▁ той",
+      "▁fe els",
+      "▁feel s",
+      "▁fee ls",
+      "ress ion",
+      "r ession",
+      "▁o któber",
+      "bi d",
+      "b id",
+      "ás i",
+      "á si",
+      "▁m uss",
+      "▁mus s",
+      "▁mu ss",
+      "vent ory",
+      "▁me ist",
+      "▁b ore",
+      "▁bo re",
+      "▁bor e",
+      "▁sl ider",
+      "▁slide r",
+      "▁sli der",
+      "▁ slider",
+      "де ли",
+      "\\ ;",
+      "▁extra cted",
+      "▁extract ed",
+      "ку р",
+      "к ур",
+      "Ed ge",
+      "▁per f",
+      "▁pe rf",
+      "▁Brig ade",
+      "▁гра д",
+      "▁ град",
+      "ie nie",
+      "ien ie",
+      "i enie",
+      "▁N orden",
+      "▁Nor den",
+      "▁Nord en",
+      "▁c ancer",
+      "▁can cer",
+      "\" /",
+      "C ur",
+      "▁С ере",
+      "▁Се ре",
+      "▁Сер е",
+      "▁liqu id",
+      "str ucture",
+      "struct ure",
+      "▁cho osing",
+      "▁Per l",
+      "▁Pe rl",
+      "Si de",
+      "S ide",
+      "ü s",
+      "ри тор",
+      "рито р",
+      "рит ор",
+      "▁k ost",
+      "▁ko st",
+      "▁pa ckets",
+      "▁pack ets",
+      "▁packet s",
+      "▁кото рого",
+      "▁Com un",
+      "▁Co mun",
+      "▁f ingers",
+      "▁fin gers",
+      "▁finger s",
+      "ográ fica",
+      "> :",
+      "▁champion nat",
+      "▁bl ieb",
+      "▁S itu",
+      "▁Si tu",
+      "▁Sit u",
+      "▁su ic",
+      "an dis",
+      "and is",
+      "Fr e",
+      "F re",
+      "▁C onc",
+      "▁Con c",
+      "▁Co nc",
+      "▁re public",
+      "▁rep ublic",
+      "▁repub lic",
+      "▁ar med",
+      "▁arm ed",
+      "▁h ell",
+      "▁he ll",
+      "▁hel l",
+      "▁ hell",
+      "▁h ög",
+      "▁hö g",
+      "rag ma",
+      "▁en se",
+      "▁ens e",
+      "▁ ense",
+      "▁ac res",
+      "▁В ід",
+      "▁Ві д",
+      "▁Re form",
+      "▁Ref orm",
+      "Main Activity",
+      "ke eper",
+      "keep er",
+      "kee per",
+      "er b",
+      "e rb",
+      "▁mon aster",
+      "sub subsection",
+      "▁Ди в",
+      "▁cre ature",
+      "▁indic ating",
+      "▁url s",
+      "▁ur ls",
+      "▁ urls",
+      "▁k ein",
+      "▁ke in",
+      "об раз",
+      "обра з",
+      "pi ck",
+      "pic k",
+      "p ick",
+      "▁Ad mir",
+      "▁old est",
+      "▁ol dest",
+      "▁m uz",
+      "▁mu z",
+      "▁contra diction",
+      "▁contrad iction",
+      "▁contradict ion",
+      "▁prob abil",
+      "illi ant",
+      "▁p av",
+      "▁pa v",
+      "▁pa pel",
+      "▁pap el",
+      "ub s",
+      "u bs",
+      "▁ж ена",
+      "▁же на",
+      "▁жен а",
+      "▁ жена",
+      "AM L",
+      "A ML",
+      "▁re cip",
+      "▁rec ip",
+      "▁reci p",
+      "▁C OL",
+      "▁CO L",
+      "▁ COL",
+      "ad ded",
+      "add ed",
+      "▁cl ue",
+      "▁Uk raine",
+      "▁Ukrain e",
+      "▁jel ent",
+      "че нь",
+      "чен ь",
+      "ч ень",
+      "▁mathemat ics",
+      "Ac cept",
+      "▁с от",
+      "▁со т",
+      "▁се вер",
+      "▁isol ated",
+      "▁по я",
+      "w ür",
+      "Ro uter",
+      "Route r",
+      "Rout er",
+      "R outer",
+      "CA T",
+      "C AT",
+      "rg b",
+      "r gb",
+      "▁L ov",
+      "▁Lo v",
+      "mu table",
+      "mut able",
+      "m utable",
+      "▁W es",
+      "▁We s",
+      "▁Ital ien",
+      "Dra g",
+      "Dr ag",
+      "D rag",
+      "en ium",
+      "eni um",
+      "at ting",
+      "att ing",
+      "atti ng",
+      "tc p",
+      "t cp",
+      "▁erfolg te",
+      "▁Be it",
+      "▁Bei t",
+      "га то",
+      "▁System s",
+      "▁Syst ems",
+      "▁re serve",
+      "▁res erve",
+      "er ee",
+      "ere e",
+      "e ree",
+      "▁Па ри",
+      "▁Пар и",
+      "▁з али",
+      "▁за ли",
+      "▁re nt",
+      "▁r ent",
+      "▁ren t",
+      "▁ rent",
+      "▁s unt",
+      "▁su nt",
+      "▁sun t",
+      "▁G irls",
+      "▁Girl s",
+      "▁Gir ls",
+      "▁Er nest",
+      "▁Ern est",
+      "▁f its",
+      "▁fi ts",
+      "▁fit s",
+      "▁op pon",
+      "▁opp on",
+      "▁живе ло",
+      "▁av aient",
+      "▁Flor ence",
+      "▁Flo rence",
+      "▁чи сле",
+      "▁eng ines",
+      "▁engine s",
+      "D ynamic",
+      "▁stycz nia",
+      "▁b ias",
+      "▁bi as",
+      "▁Ex change",
+      "ди й",
+      "▁histor iques",
+      "▁historique s",
+      "▁H ä",
+      "ho d",
+      "h od",
+      "▁w ł",
+      "sch ap",
+      "▁l ac",
+      "▁la c",
+      "▁ lac",
+      "▁F oi",
+      "▁Fo i",
+      "▁d well",
+      "▁dw ell",
+      "▁Unter nehmen",
+      "UR N",
+      "▁kilomet res",
+      "▁Одна ко",
+      "к ли",
+      "▁S ri",
+      "▁Sr i",
+      "Gr oups",
+      "Group s",
+      "min d",
+      "mi nd",
+      "m ind",
+      "os lov",
+      "fer n",
+      "fe rn",
+      "f ern",
+      "eg u",
+      "e gu",
+      "abel ed",
+      "abe led",
+      "F iddle",
+      "▁Cent ury",
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+      "▁J egyzetek",
+      "He n",
+      "H en",
+      "ens emble",
+      "▁G ut",
+      "▁Gu t",
+      "_{ {\\",
+      "_ {{\\",
+      "▁ran king",
+      "▁rank ing",
+      "+ $",
+      "ал а",
+      "а ла",
+      "▁# {",
+      "▁ #{",
+      "im ientos",
+      "imiento s",
+      "ach im",
+      "ac him",
+      "achi m",
+      "ri des",
+      "ride s",
+      "rid es",
+      "r ides",
+      "▁K laus",
+      "▁Kl aus",
+      "▁int end",
+      "▁inte nd",
+      "▁inten d",
+      "▁Kent ucky",
+      "ci pe",
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+      "▁Sc he",
+      "▁vess els",
+      "▁vessel s",
+      "IMA RY",
+      "IM ARY",
+      "▁reason ing",
+      "▁про те",
+      "▁пр оте",
+      "FI LES",
+      "FILE S",
+      "ver k",
+      "v erk",
+      "os os",
+      "oso s",
+      "▁ком му",
+      "ді ї",
+      "д ії",
+      "▁d d",
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+      "▁со ответ",
+      "▁IO Exception",
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+      "sk ých",
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+      "▁C LI",
+      "▁CL I",
+      "▁ CLI",
+      "▁ ње",
+      "C M",
+      "T D",
+      "▁possib ilities",
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+      "▁Com pos",
+      "▁Comp os",
+      "hal f",
+      "h alf",
+      "▁web page",
+      "▁s wing",
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+      "▁e euw",
+      "▁Admin istr",
+      "▁War ren",
+      "▁b s",
+      "▁ bs",
+      "▁p am",
+      "▁pa m",
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+      "ex pl",
+      "exp l",
+      "▁K ant",
+      "▁Kan t",
+      "▁Ka nt",
+      "▁Aust in",
+      "▁c sak",
+      "▁cs ak",
+      "▁the atre",
+      "▁compat ibility",
+      "ма тиче",
+      "мати че",
+      "set State",
+      "б ю",
+      "}{ |",
+      "} {|",
+      "▁D y",
+      "▁Zw ischen",
+      "Al t",
+      "A lt",
+      "CLA RE",
+      "st eps",
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+      "▁L age",
+      "▁La ge",
+      "▁Lag e",
+      "▁M itt",
+      "▁Mit t",
+      "▁Mi tt",
+      "▁Dub lin",
+      "▁рабо ты",
+      "de ep",
+      "▁fl ows",
+      "▁flow s",
+      "▁flo ws",
+      "▁Pa lace",
+      "▁Pal ace",
+      "▁Pala ce",
+      "un ix",
+      "uni x",
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+      "▁b ugs",
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+      "ader s",
+      "ade rs",
+      "a ders",
+      "▁Гер ма",
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+      "▁esta ven",
+      "▁ear liest",
+      "▁res ultado",
+      "▁result ado",
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+      "m un",
+      "▁pl ots",
+      "▁plot s",
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+      "▁pre ference",
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+      "ph r",
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+      "▁po cket",
+      "▁poc ket",
+      "▁par l",
+      "▁pa rl",
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+      "▁la k",
+      "▁ lak",
+      "▁p owie",
+      "▁po wie",
+      "▁pow ie",
+      "▁al tres",
+      "▁alt res",
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+      "pe rp",
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+      "▁ver n",
+      "▁ve rn",
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+      "▁email s",
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+      "▁distance s",
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+      "▁a nth",
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+      "▁moder ne",
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+      "▁clear ed",
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+      "▁div s",
+      "ć i",
+      "▁э той",
+      "▁это й",
+      "▁Ге ор",
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+      "▁a ges",
+      "▁ag es",
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+      "▁To ul",
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+      "j át",
+      "▁med iante",
+      "▁medi ante",
+      "▁median te",
+      "▁em pres",
+      "▁emp res",
+      "▁Emp loyee",
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+      "▁polynomial s",
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+      "▁bo tan",
+      "▁bot an",
+      "▁def end",
+      "▁defe nd",
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+      "v b",
+      "ti ck",
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+      "W D",
+      "min e",
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+      "m ine",
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+      "▁d enn",
+      "▁de nn",
+      "▁den n",
+      "▁al go",
+      "▁alg o",
+      "▁S po",
+      "▁Sp o",
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+      "▁apr esent",
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+      "▁пр од",
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+      "▁p ouv",
+      "▁po uv",
+      "▁pou v",
+      "▁perí odo",
+      "sk og",
+      "sko g",
+      "▁ent ropy",
+      "▁entr opy",
+      "ze d",
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+      "тор и",
+      "▁l ij",
+      "▁li j",
+      "▁ lij",
+      "bo ards",
+      "board s",
+      "▁ста ту",
+      "Bo ol",
+      "B ool",
+      "▁pol ity",
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+      "@\" ,",
+      "@ \",",
+      "▁рі к",
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+      "n ée",
+      "▁Z ug",
+      "▁Zu g",
+      "▁Un iti",
+      "▁Unit i",
+      "ém et",
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+      "at ience",
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+      "d imen",
+      "▁St even",
+      "▁Ste ven",
+      "▁Steve n",
+      "H a",
+      "ACT ION",
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+      "▁w and",
+      "▁wa nd",
+      "▁ wand",
+      "▁Na var",
+      "▁Nav ar",
+      "▁сі чня",
+      "W atch",
+      "▁Stu art",
+      "▁z de",
+      "▁zd e",
+      "▁кон тро",
+      "data set",
+      "dat aset",
+      "datas et",
+      "y ó",
+      "▁B ush",
+      "▁Bu sh",
+      "▁Bus h",
+      "▁се бя",
+      "▁wor thy",
+      "▁worth y",
+      "▁B le",
+      "▁Bl e",
+      "▁pro por",
+      "▁prop or",
+      "▁Vill age",
+      "▁Villa ge",
+      "▁Vil lage",
+      "▁r y",
+      "▁ ry",
+      "▁v oit",
+      "▁vo it",
+      "▁копи я",
+      "▁z p",
+      "▁c ura",
+      "▁cu ra",
+      "▁cur a",
+      "▁H tml",
+      "▁ Html",
+      "▁Die ser",
+      "▁Dies er",
+      "▁Diese r",
+      "▁D ays",
+      "▁Da ys",
+      "▁Day s",
+      "▁ Days",
+      "on nes",
+      "onn es",
+      "onne s",
+      "▁ant igu",
+      "▁anti gu",
+      "▁Sta aten",
+      "▁Staat en",
+      "▁f aint",
+      "▁fa int",
+      "on gs",
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+      "▁ö st",
+      "▁ öst",
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+      "at orial",
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+      "atori al",
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+      "▁bo ther",
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+      "Edit Text",
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+      "▁зав о",
+      "▁pue blo",
+      "▁Mississ ippi",
+      "ja k",
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+      "▁win gs",
+      "▁wing s",
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+      "ien cia",
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+      "entlich t",
+      "▁B TW",
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+      "▁Ко ро",
+      "▁Кор о",
+      "▁од ним",
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+      "▁Pa blo",
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+      "▁z eros",
+      "▁ze ros",
+      "▁zero s",
+      "▁Stra ßen",
+      "▁Straße n",
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+      "üs sen",
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+      "▁Se ason",
+      "▁Sea son",
+      "▁vol ont",
+      "Bean Factory",
+      "▁помо щ",
+      "▁pres sing",
+      "▁press ing",
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+      "▁c att",
+      "▁ca tt",
+      "▁cat t",
+      "ic ity",
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+      "▁lo an",
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+      "▁ass ess",
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+      "clo jure",
+      "▁Sant os",
+      "▁Santo s",
+      "▁inform áció",
+      "PO S",
+      "P OS",
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+      "▁gar e",
+      "▁ga re",
+      "▁k ick",
+      "▁ki ck",
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+      "▁Pe ace",
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+      "ca mp",
+      "cam p",
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+      "гово р",
+      "▁Reg ierung",
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+      "лен ы",
+      "▁ear nest",
+      "▁Par ad",
+      "▁Pa rad",
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+      "▁T ig",
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+      "▁s itt",
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+      "▁Ener gy",
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+      "▁diag n",
+      "▁dia gn",
+      "▁r etro",
+      "▁ret ro",
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+      "▁fig lio",
+      "▁z og",
+      "▁zo g",
+      "▁re y",
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+      "▁es a",
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+      "▁tr ouv",
+      "▁tro uv",
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+      "▁comer cial",
+      "sk ap",
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+      "en ario",
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+      "▁ju ris",
+      "▁jur is",
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+      "▁v ij",
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+      "▁fun kc",
+      "▁t ienen",
+      "▁tiene n",
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+      "▁tie nen",
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+      "▁E rr",
+      "▁Er r",
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+      "▁res ort",
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+      "▁Lib ert",
+      "▁Liber t",
+      "▁Har old",
+      "▁life time",
+      "▁lif etime",
+      "▁T ür",
+      "▁za wod",
+      "▁zaw od",
+      "om ic",
+      "omi c",
+      "o mic",
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+      "leb en",
+      "l eben",
+      "▁St ars",
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+      "▁cond ado",
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+      "▁F ood",
+      "▁Foo d",
+      "▁Fo od",
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+      "l jen",
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+      "▁an ten",
+      "▁ant en",
+      "▁ante n",
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+      "▁в з",
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+      "▁ho s",
+      "▁ hos",
+      "▁s int",
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+      "▁sin t",
+      "▁h iding",
+      "▁hi ding",
+      "▁hid ing",
+      "▁wojew ództ",
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+      "▁l äng",
+      "▁län g",
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+      "▁Gr ey",
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+      "▁mot h",
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+      "▁mon arch",
+      "▁Down load",
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+      "▁c raft",
+      "▁cr aft",
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+      "▁Lu ke",
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+      "Zygote Init",
+      "ł ów",
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+      "▁Su g",
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+      "▁F rei",
+      "▁Fre i",
+      "▁Fr ei",
+      "▁Mus ée",
+      "▁K urz",
+      "▁Kur z",
+      "▁Ku rz",
+      "▁reg ião",
+      "sw ap",
+      "▁h ate",
+      "▁ha te",
+      "▁hat e",
+      "▁mod ifications",
+      "▁modification s",
+      "▁modific ations",
+      "▁К ом",
+      "▁Ко м",
+      "▁Anto ine",
+      "ug a",
+      "u ga",
+      "RE CT",
+      "R ECT",
+      "ét er",
+      "é ter",
+      "G ROUP",
+      "▁sacr ific",
+      "▁W he",
+      "▁Wh e",
+      "▁Ste vens",
+      "▁Steve ns",
+      "▁Steven s",
+      "olog ische",
+      "Sum mary",
+      "ob s",
+      "o bs",
+      "hn en",
+      "h nen",
+      "< %=",
+      "di enst",
+      "d ienst",
+      "re mark",
+      "rem ark",
+      "r emark",
+      "▁veröff entlicht",
+      "е л",
+      "▁M ock",
+      "▁Mo ck",
+      "▁ Mock",
+      "▁Ль в",
+      "▁tr ês",
+      "g b",
+      "▁celebr ated",
+      "▁E b",
+      "▁c osta",
+      "▁co sta",
+      "▁cost a",
+      "▁cos ta",
+      "▁Ge ographic",
+      "▁att achment",
+      "▁attach ment",
+      "mann schaft",
+      "▁depend ence",
+      "� �",
+      "▁att itude",
+      "et al",
+      "eta l",
+      "e tal",
+      "vi c",
+      "v ic",
+      "ba ut",
+      "bau t",
+      "b aut",
+      "▁д ов",
+      "▁до в",
+      "▁ дов",
+      "▁inter ven",
+      "▁G ü",
+      "ón ica",
+      "ó nica",
+      "▁P on",
+      "▁Po n",
+      "▁dispon ible",
+      "▁F eb",
+      "▁Fe b",
+      "▁wor ship",
+      "▁Specific ally",
+      "H y",
+      "ij u",
+      "i ju",
+      "▁c b",
+      "▁ cb",
+      "▁sp ac",
+      "lev eland",
+      "level and",
+      "▁local idad",
+      "▁prec eding",
+      "▁preced ing",
+      "▁H essen",
+      "x p",
+      "▁W ein",
+      "▁We in",
+      "▁Wei n",
+      "▁Rom â",
+      "▁gi orno",
+      "▁gior no",
+      "▁квіт ня",
+      "lla ços",
+      "▁Academ ia",
+      "▁k ül",
+      "▁Å rs",
+      "▁на ј",
+      "uc lide",
+      "Inter net",
+      "Intern et",
+      "or ton",
+      "ort on",
+      "▁c orn",
+      "▁cor n",
+      "▁co rn",
+      "я ми",
+      "▁\" *",
+      "▁Fel ix",
+      "ap at",
+      "apa t",
+      "a pat",
+      "▁сво и",
+      "MI T",
+      "M IT",
+      "ma de",
+      "mad e",
+      "m ade",
+      "▁lo comot",
+      "хо да",
+      "ход а",
+      "F P",
+      "▁p m",
+      "▁ pm",
+      ".* ;",
+      "▁H amm",
+      "▁Ha mm",
+      "▁Ham m",
+      "` }",
+      "Layout Inflater",
+      "== \"",
+      "= =\"",
+      "▁E ur",
+      "▁Eu r",
+      "▁d ogs",
+      "▁do gs",
+      "▁dog s",
+      "же нии",
+      "▁a zon",
+      "▁az on",
+      "▁ azon",
+      "▁em ulator",
+      "▁r icon",
+      "▁ric on",
+      "▁ri con",
+      "be eld",
+      "▁н у",
+      "▁ ну",
+      "▁approxim ate",
+      "L M",
+      "▁B ond",
+      "▁Bo nd",
+      "▁Bon d",
+      "▁en h",
+      "ęd z",
+      "ę dz",
+      "▁s olit",
+      "▁so lit",
+      "▁sol it",
+      "Relative Layout",
+      "et eor",
+      "ete or",
+      "ament os",
+      "amento s",
+      "▁in direct",
+      "▁ind irect",
+      "ib ől",
+      "▁g ros",
+      "▁gr os",
+      "▁gro s",
+      "▁Original s",
+      "▁Origin als",
+      "▁Orig inals",
+      "comm ands",
+      "command s",
+      "Ex port",
+      "Exp ort",
+      "▁A vec",
+      "▁Av ec",
+      "▁sole mn",
+      "▁solem n",
+      "▁correct ion",
+      "▁corre ction",
+      "▁corr ection",
+      "▁про води",
+      "▁прово ди",
+      "▁Mo sk",
+      "▁Mos k",
+      "▁по до",
+      "▁под о",
+      "▁geb ied",
+      "▁nast ęp",
+      "▁D river",
+      "▁Dr iver",
+      "▁Drive r",
+      "▁ Driver",
+      "▁O ok",
+      "▁V ec",
+      "▁Ve c",
+      "▁ Vec",
+      "▁lung o",
+      "▁lun go",
+      "fi cos",
+      "fic os",
+      "fico s",
+      "f icos",
+      "▁s vol",
+      "▁sv ol",
+      "▁svo l",
+      "▁k id",
+      "▁ki d",
+      "n ja",
+      "▁H r",
+      "▁под дер",
+      "▁vis ibility",
+      "▁ visibility",
+      "▁M éd",
+      "▁Mé d",
+      "▁c pu",
+      "▁cp u",
+      "▁ cpu",
+      "dis cussion",
+      "As set",
+      "Ass et",
+      "▁def ense",
+      "▁Any one",
+      "▁Just in",
+      "is zt",
+      "isz t",
+      "▁Coll ins",
+      "▁Val ent",
+      "▁P ale",
+      "▁Pa le",
+      "▁Pal e",
+      "▁f uel",
+      "▁fue l",
+      "▁fu el",
+      "▁n ose",
+      "▁no se",
+      "▁nos e",
+      "rí guez",
+      "▁Sch les",
+      "▁Schl es",
+      "▁Mal ays",
+      "▁com mut",
+      "▁comm ut",
+      "dr o",
+      "d ro",
+      "ui ng",
+      "u ing",
+      "▁R ico",
+      "▁Ric o",
+      "▁Ri co",
+      "▁Em ma",
+      "or p",
+      "o rp",
+      "▁K irk",
+      "▁Kir k",
+      "▁Qu ando",
+      "▁Ne ue",
+      "▁Neu e",
+      "▁de mande",
+      "▁dem ande",
+      "▁demand e",
+      "▁C over",
+      "▁Co ver",
+      "▁Cov er",
+      "▁res cue",
+      "▁gew ählt",
+      "▁Cal endar",
+      "▁ Calendar",
+      "▁Mad onna",
+      "W P",
+      "os hi",
+      "osh i",
+      "▁M aven",
+      "▁Ma ven",
+      "▁b elle",
+      "▁be lle",
+      "▁bel le",
+      "▁bell e",
+      "▁w x",
+      "▁ wx",
+      "▁su gar",
+      "▁sug ar",
+      "▁Bet rieb",
+      "▁equilib rium",
+      "E AR",
+      "▁text s",
+      "▁tex ts",
+      "сло в",
+      "с лов",
+      "▁czerw ca",
+      "▁D üsseld",
+      "▁EL SE",
+      "▁am ery",
+      "▁amer y",
+      "▁a ni",
+      "▁an i",
+      "▁ ani",
+      "▁o bey",
+      "▁ob ey",
+      "▁N ell",
+      "▁Ne ll",
+      "▁Nel l",
+      "▁in ne",
+      "▁inn e",
+      "▁т ро",
+      "▁ тро",
+      "F D",
+      "cc o",
+      "c co",
+      "▁Z ob",
+      "▁Zo b",
+      "al ette",
+      "ale tte",
+      "alet te",
+      "a lette",
+      "▁má jus",
+      "ect ed",
+      "ec ted",
+      "e cted",
+      "▁Tur key",
+      "▁Turk ey",
+      "▁Wh ether",
+      "▁Whe ther",
+      "q i",
+      "▁ш то",
+      "▁head quarters",
+      "en di",
+      "end i",
+      "ar us",
+      "aru s",
+      "a rus",
+      "op us",
+      "o pus",
+      "▁з оло",
+      "▁зо ло",
+      "▁de stru",
+      "▁dest ru",
+      "▁L ok",
+      "▁Lo k",
+      "▁satisf action",
+      "() \r",
+      "( )\r",
+      "▁Т ер",
+      "▁Те р",
+      "Jo se",
+      "J ose",
+      "▁con quer",
+      "▁conqu er",
+      "▁E ffect",
+      "▁ Effect",
+      "Layout Params",
+      "ie z",
+      "i ez",
+      "▁extern s",
+      "▁gegen über",
+      "▁E SP",
+      "▁ES P",
+      "ol ta",
+      "olt a",
+      "process or",
+      "proc essor",
+      "▁K ult",
+      "▁Ku lt",
+      "▁Atl anta",
+      "▁t ier",
+      "▁ti er",
+      "▁tie r",
+      "Oper ator",
+      "▁ди а",
+      "▁пи сь",
+      "▁gro ß",
+      "▁he arts",
+      "▁heart s",
+      "▁hear ts",
+      "▁mill imeter",
+      "al though",
+      "alth ough",
+      "al les",
+      "all es",
+      "alle s",
+      "a lles",
+      "▁Mag ic",
+      "tr aining",
+      "tra ining",
+      "train ing",
+      "ol ine",
+      "oli ne",
+      "olin e",
+      "o line",
+      "▁орган і",
+      ">\\< ^",
+      "> \\<^",
+      "ці аль",
+      "ex ports",
+      "export s",
+      "Work book",
+      "▁вере сня",
+      "▁t eles",
+      "▁te les",
+      "▁tele s",
+      "▁tel es",
+      "▁econom y",
+      "▁econ omy",
+      "▁ec onomy",
+      "▁t rap",
+      "▁tr ap",
+      "▁tra p",
+      "▁ref use",
+      "▁str anger",
+      "▁strange r",
+      "▁stran ger",
+      "▁inst inct",
+      "по да",
+      "ol an",
+      "ola n",
+      "o lan",
+      "▁n ing",
+      "▁ni ng",
+      "▁nin g",
+      "▁ ning",
+      "inf late",
+      "infl ate",
+      "itat ea",
+      "itate a",
+      "ack s",
+      "ac ks",
+      "a cks",
+      "▁J oy",
+      "▁Jo y",
+      "FL AG",
+      "FLA G",
+      "ail and",
+      "ai land",
+      "▁sort i",
+      "▁sor ti",
+      "▁в пер",
+      "▁p én",
+      "▁pé n",
+      "Not hing",
+      "No thing",
+      "N othing",
+      "▁sz áz",
+      "▁Á ng",
+      "▁A UT",
+      "▁ AUT",
+      "Act ions",
+      "Action s",
+      "A ctions",
+      "E very",
+      "▁чер вня",
+      "▁авто мо",
+      "▁rout ine",
+      "▁e struct",
+      "▁est ruct",
+      "▁G ang",
+      "▁Ga ng",
+      "▁Gan g",
+      "▁h oles",
+      "▁ho les",
+      "▁hol es",
+      "▁hole s",
+      "th esis",
+      "thes is",
+      "▁con cl",
+      "▁conc l",
+      "▁p é",
+      "ri ers",
+      "rie rs",
+      "rier s",
+      "r iers",
+      "ро вой",
+      "рово й",
+      "р овой",
+      "ad ic",
+      "adi c",
+      "a dic",
+      "Sp eed",
+      "Spe ed",
+      "▁command ed",
+      "▁N azionale",
+      "▁Naz ionale",
+      "Man aged",
+      "▁DE CLARE",
+      "▁se dan",
+      "▁sed an",
+      "String s",
+      "Str ings",
+      "▁sa cred",
+      "▁sac red",
+      "▁sacr ed",
+      "ter such",
+      "ters uch",
+      "▁abit anti",
+      "br it",
+      "b rit",
+      "▁N CAA",
+      "▁NC AA",
+      "▁С П",
+      "▁a ged",
+      "▁ag ed",
+      "▁age d",
+      "▁ aged",
+      "▁Ch iesa",
+      "▁Chi esa",
+      "▁re vision",
+      "▁rev ision",
+      "▁revis ion",
+      "op ro",
+      "o pro",
+      "▁over write",
+      "emb ros",
+      "embro s",
+      "▁sort ie",
+      "▁sorti e",
+      "▁ot ten",
+      "▁ott en",
+      "xi v",
+      "x iv",
+      "▁d eli",
+      "▁de li",
+      "▁del i",
+      "▁A sp",
+      "▁As p",
+      "▁b alls",
+      "▁bal ls",
+      "▁ball s",
+      "ka f",
+      "k af",
+      "▁br ave",
+      "▁bra ve",
+      "▁все го",
+      "▁вс его",
+      "eg n",
+      "e gn",
+      "jp eg",
+      "▁O sten",
+      "▁Os ten",
+      "▁Ost en",
+      "Const ants",
+      "▁Inf antry",
+      "▁N ev",
+      "▁Ne v",
+      "▁я ких",
+      "▁як их",
+      "▁му ниципа",
+      "ci ja",
+      "c ija",
+      "▁p oem",
+      "▁po em",
+      "▁ne gro",
+      "▁neg ro",
+      "ха р",
+      "х ар",
+      "▁A sk",
+      "▁As k",
+      "▁a vo",
+      "▁av o",
+      "▁ avo",
+      "▁Me yer",
+      "▁Mey er",
+      "▁W esten",
+      "▁We sten",
+      "▁West en",
+      "▁Wes ten",
+      "▁o ko",
+      "▁ok o",
+      "▁ oko",
+      "ag in",
+      "agi n",
+      "a gin",
+      "▁Süd en",
+      "▁Sü den",
+      "ent ries",
+      "entr ies",
+      "▁Rep ublik",
+      "▁Repub lik",
+      "Collection View",
+      "-- -----",
+      "---- ---",
+      "--- ----",
+      "------ -",
+      "----- --",
+      "- ------",
+      "▁fire fox",
+      "▁alc une",
+      "▁фо то",
+      "▁отри ма",
+      "~~~~ ~~~~",
+      "▁Ра з",
+      "▁Com plex",
+      "▁Comp lex",
+      "▁Comple x",
+      "▁p ia",
+      "▁pi a",
+      "▁public ada",
+      "we i",
+      "w ei",
+      "ced ure",
+      "occup ation",
+      "▁medic ine",
+      "▁dr ove",
+      "▁dro ve",
+      "Pro blem",
+      "▁beg inner",
+      "▁begin ner",
+      "▁thorough ly",
+      "ur ia",
+      "uri a",
+      "u ria",
+      "av ant",
+      "ava nt",
+      "avan t",
+      "uch a",
+      "uc ha",
+      "u cha",
+      "▁l ever",
+      "▁le ver",
+      "▁lev er",
+      "▁te atro",
+      "▁teat ro",
+      "AV A",
+      "A VA",
+      "sq u",
+      "s qu",
+      "tr at",
+      "tra t",
+      "t rat",
+      "iv atal",
+      "iva tal",
+      "▁d irty",
+      "▁dir ty",
+      "▁se conde",
+      "▁second e",
+      "▁sec onde",
+      "▁grav it",
+      "▁pro position",
+      "▁prop osition",
+      "▁propos ition",
+      "h bar",
+      "om ini",
+      "omin i",
+      "omi ni",
+      "▁ ”",
+      "▁C amil",
+      "▁Cam il",
+      "▁Ca mil",
+      "▁qu een",
+      "▁que en",
+      "mod ifier",
+      "J an",
+      "▁l yr",
+      "▁ly r",
+      "Com boBox",
+      "ion ic",
+      "io nic",
+      "ioni c",
+      "i onic",
+      "▁h oly",
+      "▁ho ly",
+      "▁hol y",
+      "▁Sebast ian",
+      "| _{",
+      "▁{ @",
+      "▁мо жно",
+      "▁мож но",
+      "▁Cre ative",
+      "▁inter ess",
+      "▁inte ress",
+      "▁C T",
+      "▁ CT",
+      "i ções",
+      "▁ch ant",
+      "▁cha nt",
+      "▁ chant",
+      "▁wsp ół",
+      "▁Мекси ка",
+      "▁ran ked",
+      "▁rank ed",
+      "▁paździer nika",
+      "▁b rut",
+      "▁br ut",
+      "▁bru t",
+      "▁far ther",
+      "▁V erb",
+      "▁Ver b",
+      "▁Ve rb",
+      "▁S even",
+      "▁Se ven",
+      "lb l",
+      "l bl",
+      "▁mention s",
+      "▁ment ions",
+      "▁F ight",
+      "▁Fig ht",
+      "if en",
+      "ife n",
+      "i fen",
+      "▁b og",
+      "▁bo g",
+      "▁re gres",
+      "▁reg res",
+      "▁sc oring",
+      "ic ane",
+      "ica ne",
+      "ican e",
+      "▁El li",
+      "▁Ell i",
+      "▁pie rw",
+      "▁pier w",
+      "me asure",
+      "ński ej",
+      "ń skiej",
+      "# {",
+      "▁де ся",
+      "▁var maste",
+      "▁Un ix",
+      "I Z",
+      "iti é",
+      "Prim ary",
+      "▁Spring er",
+      "▁Spr inger",
+      "ün g",
+      "ü ng",
+      "▁an v",
+      "▁vers ione",
+      "▁version e",
+      "▁should ers",
+      "▁shoulder s",
+      "▁бри га",
+      "▁j av",
+      "▁ja v",
+      "▁ jav",
+      "lt al",
+      "l tal",
+      "▁kall aste",
+      "▁Mitch ell",
+      "▁wire less",
+      "▁wir eless",
+      "▁Á l",
+      "resp ons",
+      "co uld",
+      "cou ld",
+      "c ould",
+      "▁re lax",
+      "▁rel ax",
+      "▁rela x",
+      "▁ relax",
+      "Lo nd",
+      "L ond",
+      "ń cz",
+      "ство вал",
+      "ствова л",
+      "▁pol ski",
+      "en ç",
+      "za r",
+      "z ar",
+      "▁d type",
+      "▁dt ype",
+      "ow ned",
+      "own ed",
+      "un known",
+      "unk nown",
+      "▁m utable",
+      "▁mu table",
+      "▁mut able",
+      "▁ mutable",
+      "▁si empre",
+      "▁Mont real",
+      "▁loc ate",
+      "▁tr aces",
+      "▁tra ces",
+      "▁trace s",
+      "▁trac es",
+      "▁ins gesamt",
+      "▁N il",
+      "▁Ni l",
+      "▁ Nil",
+      "▁п рода",
+      "▁про да",
+      "▁прод а",
+      "▁War ner",
+      "▁N au",
+      "▁Na u",
+      "tri angle",
+      "▁concentr ation",
+      "▁gentle men",
+      "äch t",
+      "ä cht",
+      "fil ters",
+      "filter s",
+      "inci pal",
+      "VAL ID",
+      "▁де пута",
+      "ad ó",
+      "▁kon st",
+      "gs å",
+      "ag as",
+      "aga s",
+      "a gas",
+      "▁meille ur",
+      "▁дан ным",
+      "є дна",
+      "en coded",
+      "enc oded",
+      "encode d",
+      "< '",
+      "▁she ets",
+      "▁sheet s",
+      "▁ sheets",
+      "cu ador",
+      "▁викори стову",
+      "▁De put",
+      "▁Dep ut",
+      "▁man ière",
+      "ą g",
+      "cs ol",
+      "c sol",
+      ")$ -",
+      ") $-",
+      "UI View",
+      "▁mill ones",
+      "▁E hren",
+      "▁Ehr en",
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+      "▁пол ови",
+      "▁поло ви",
+      "▁` &",
+      "▁o re",
+      "▁or e",
+      "▁ ore",
+      "▁supp lies",
+      "▁parent hes",
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+      "▁vo u",
+      "▁T out",
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+      "Don ald",
+      "▁R ET",
+      "▁RE T",
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+      "m ix",
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+      "▁ent ão",
+      "▁S ister",
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+      "▁na zw",
+      "▁naz w",
+      "▁R av",
+      "▁Ra v",
+      "▁re pro",
+      "▁rep ro",
+      "▁repr o",
+      "▁M ason",
+      "▁Ma son",
+      "▁Mas on",
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+      "▁Plat form",
+      "▁ Platform",
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+      "▁P érez",
+      "▁bl anc",
+      "▁bla nc",
+      "▁blan c",
+      "Be havior",
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+      "(. *",
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+      "ma ker",
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+      "▁el even",
+      "▁ele ven",
+      "▁elev en",
+      "▁part idos",
+      "▁partido s",
+      "în e",
+      "î ne",
+      "▁R egin",
+      "▁Re gin",
+      "▁Reg in",
+      "▁é x",
+      "▁Bu lg",
+      "▁Bul g",
+      "▁network ing",
+      "▁net working",
+      "▁se parator",
+      "▁separ ator",
+      "User Name",
+      "▁edific io",
+      "▁M ie",
+      "▁Mi e",
+      "▁id le",
+      "ye d",
+      "y ed",
+      "▁pass engers",
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+      "▁nice ly",
+      "▁nic ely",
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+      "▁fe wer",
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+      "▁Ha lle",
+      "▁Hall e",
+      "▁Hal le",
+      "▁Maj esty",
+      "▁Sw itch",
+      "▁ Switch",
+      "▁ab road",
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+      "urb ed",
+      "▁о стан",
+      "▁ос тан",
+      "▁оста н",
+      "▁ост ан",
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+      "Read y",
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+      "Br a",
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+      "▁ць ого",
+      "▁pl ut",
+      "▁T rain",
+      "▁Tr ain",
+      "▁Tra in",
+      "▁á prilis",
+      "▁p uesto",
+      "▁pu esto",
+      "▁pue sto",
+      "▁t oss",
+      "▁to ss",
+      "▁irre levant",
+      "▁d ip",
+      "▁di p",
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+      "▁versch ill",
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+      "е на",
+      "▁D oc",
+      "▁Do c",
+      "▁ Doc",
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+      "▁b orders",
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+      "▁bor ders",
+      "▁bord ers",
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+      "▁ri es",
+      "▁ ries",
+      "▁Olymp edia",
+      "▁Gener ation",
+      "met ros",
+      "metro s",
+      "▁hor izon",
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+      "▁Z ahl",
+      "▁Za hl",
+      "▁na he",
+      "▁nah e",
+      "▁B ug",
+      "▁Bu g",
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+      "▁subs tring",
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+      "▁c x",
+      "▁ cx",
+      "▁% )",
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+      "▁p ending",
+      "▁pen ding",
+      "▁En try",
+      "▁Ent ry",
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+      "▁Be rl",
+      "▁Ber l",
+      "▁c ler",
+      "▁cl er",
+      "▁cle r",
+      "▁ cler",
+      "▁S oc",
+      "▁So c",
+      "▁r ounded",
+      "▁round ed",
+      "▁m v",
+      "▁ mv",
+      "ít ett",
+      "▁Di plom",
+      "▁französ ischen",
+      "▁G an",
+      "▁Ga n",
+      "▁Inv estig",
+      "▁index Path",
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+      "▁mol ti",
+      "▁molt i",
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+      "▁XIX e",
+      "▁Elect ron",
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+      "ge le",
+      "gel e",
+      "g ele",
+      "▁M aler",
+      "▁Ma ler",
+      "▁Mal er",
+      "▁Male r",
+      "▁proyect o",
+      "▁B ath",
+      "▁Ba th",
+      "▁Bat h",
+      "el lers",
+      "ell ers",
+      "elle rs",
+      "eller s",
+      "▁G P",
+      "▁ GP",
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+      "o ning",
+      "clou dflare",
+      "▁p ři",
+      "▁př i",
+      "▁d ed",
+      "▁de d",
+      "▁ ded",
+      "▁Od kazy",
+      "▁M sg",
+      "▁ Msg",
+      "▁B eing",
+      "▁Be ing",
+      "▁Bei ng",
+      "▁De puis",
+      "▁Dep uis",
+      "▁Pri mary",
+      "▁Prim ary",
+      "▁Prima ry",
+      "▁ Primary",
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+      "▁prove s",
+      "▁pres iden",
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+      "▁Wat son",
+      "▁web pack",
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+      "ellig ence",
+      "ка м",
+      "▁Office r",
+      "▁Offic er",
+      "▁d elivery",
+      "▁deliver y",
+      "▁deli very",
+      "ж дён",
+      "▁им пе",
+      "▁w il",
+      "▁v esc",
+      "▁ve sc",
+      "▁ves c",
+      "uszt us",
+      "▁Ge off",
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+      "▁For e",
+      "▁Fo re",
+      "▁w enig",
+      "▁we nig",
+      "▁wen ig",
+      "▁A irl",
+      "▁Air l",
+      "▁E fter",
+      "▁Bre ak",
+      "▁St äd",
+      "is miss",
+      "ism iss",
+      "í p",
+      "▁avoid ed",
+      "▁avo ided",
+      "▁assert ion",
+      "D N",
+      "▁te at",
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+      "í na",
+      "▁mechan ical",
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+      "@ {",
+      "▁n ou",
+      "▁no u",
+      "▁ nou",
+      "Ital ie",
+      "source forge",
+      "▁s vo",
+      "▁sv o",
+      "▁kir ály",
+      "▁Re ferences",
+      "▁Refer ences",
+      "▁Reference s",
+      "si x",
+      "s ix",
+      "▁Arch ives",
+      "▁Archiv es",
+      "▁Archive s",
+      "▁fin ishing",
+      "▁finish ing",
+      "ac je",
+      "ét at",
+      "éta t",
+      "é tat",
+      "if fs",
+      "iff s",
+      "▁st ead",
+      "▁ste ad",
+      "▁fe as",
+      "aw are",
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+      "▁di vent",
+      "▁div ent",
+      "▁k é",
+      "▁ ké",
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+      "Check Box",
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+      "p he",
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+      "p ta",
+      "▁s jö",
+      "▁sj ö",
+      "▁розта ш",
+      "▁tedes co",
+      "▁s tal",
+      "▁st al",
+      "▁sta l",
+      "▁ stal",
+      "▁Be ruf",
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+      "▁s vě",
+      "▁sv ě",
+      "▁fl ush",
+      "▁flu sh",
+      "▁ flush",
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+      "▁rad ial",
+      "▁radi al",
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+      "▁Per ry",
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+      "Co ll",
+      "C oll",
+      "li qu",
+      "l iqu",
+      "▁Option al",
+      "▁Opt ional",
+      "▁ Optional",
+      "▁Сан кт",
+      "▁LIN Q",
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+      "▁Fr anc",
+      "▁Fra nc",
+      "ci je",
+      "c ije",
+      "▁Gu illaume",
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+      "k now",
+      "▁Un its",
+      "▁Unit s",
+      "ol k",
+      "▁Syst ème",
+      "▁S ales",
+      "▁Sal es",
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+      "▁ehemal igen",
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+      "set opt",
+      "▁m ellan",
+      "▁mel lan",
+      "▁z ie",
+      "▁ zie",
+      "▁gi ant",
+      "Bo ard",
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+      "▁Cav al",
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+      "▁disput e",
+      "▁disp ute",
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+      "▁she d",
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+      "▁wra pping",
+      "▁wrap ping",
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+      "▁Pos s",
+      "▁Mitg lieder",
+      "▁Mitglied er",
+      "▁n ev",
+      "▁ne v",
+      "Dan iel",
+      "▁t ends",
+      "▁ten ds",
+      "▁tend s",
+      "▁compag nie",
+      "▁liv res",
+      "▁livre s",
+      "lu b",
+      "l ub",
+      "▁ ▁",
+      "▁▁ ▁▁",
+      "▁▁▁ ▁",
+      "▁ ▁▁▁",
+      "▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁",
+      "▁▁▁▁ ▁",
+      "▁▁▁ ▁▁",
+      "▁ ▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁",
+      "▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁",
+      "▁ ▁▁",
+      "▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/orc_model/tokenizer.model b/orc_model/tokenizer.model
new file mode 100644
index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899
--- /dev/null
+++ b/orc_model/tokenizer.model
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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/orc_model/tokenizer_config.json b/orc_model/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d39defd338dee423ae0a71abfd7ea69a44fb1e52
--- /dev/null
+++ b/orc_model/tokenizer_config.json
@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/output_hf_model_openx/.gitattributes b/output_hf_model_openx/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/output_hf_model_openx/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/output_hf_model_openx/README.md b/output_hf_model_openx/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7c77e12a8429944e821fb72e60469b1799493f00
--- /dev/null
+++ b/output_hf_model_openx/README.md
@@ -0,0 +1,99 @@
+---
+library_name: transformers
+tags:
+- robotics
+- vla
+- image-text-to-text
+- multimodal
+- pretraining
+license: mit
+language:
+- en
+pipeline_tag: image-text-to-text
+---
+
+# OpenVLA 7B
+
+OpenVLA 7B (`openvla-7b`) is an open vision-language-action model trained on 970K robot manipulation episodes from the [Open X-Embodiment](https://robotics-transformer-x.github.io/) dataset. 
+The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning.
+
+All OpenVLA checkpoints, as well as our [training codebase](https://github.com/openvla/openvla) are released under an MIT License.
+
+For full details, please read [our paper](https://arxiv.org/abs/2406.09246) and see [our project page](https://openvla.github.io/).
+
+## Model Summary
+
+- **Developed by:** The OpenVLA team consisting of researchers from Stanford, UC Berkeley, Google Deepmind, and the Toyota Research Institute.
+- **Model type:** Vision-language-action (language, image => robot actions)
+- **Language(s) (NLP):** en
+- **License:** MIT
+- **Finetuned from:** [`prism-dinosiglip-224px`](https://github.com/TRI-ML/prismatic-vlms), a VLM trained from:
+  + **Vision Backbone**: DINOv2 ViT-L/14 and SigLIP ViT-So400M/14
+  + **Language Model**: Llama-2
+- **Pretraining Dataset:** [Open X-Embodiment](https://robotics-transformer-x.github.io/) -- specific component datasets can be found [here](https://github.com/openvla/openvla).
+- **Repository:** [https://github.com/openvla/openvla](https://github.com/openvla/openvla)
+- **Paper:** [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/abs/2406.09246)
+- **Project Page & Videos:** [https://openvla.github.io/](https://openvla.github.io/)
+
+## Uses
+
+OpenVLA models take a language instruction and a camera image of a robot workspace as input, and predict (normalized) robot actions consisting of 7-DoF end-effector deltas
+of the form (x, y, z, roll, pitch, yaw, gripper). To execute on an actual robot platform, actions need to be *un-normalized* subject to statistics computed on a per-robot,
+per-dataset basis. See [our repository](https://github.com/openvla/openvla) for more information.
+
+OpenVLA models can be used zero-shot to control robots for specific combinations of embodiments and domains seen in the Open-X pretraining mixture (e.g., for 
+[BridgeV2 environments with a Widow-X robot](https://rail-berkeley.github.io/bridgedata/)). They can also be efficiently *fine-tuned* for new tasks and robot setups
+given minimal demonstration data; [see here](https://github.com/openvla/openvla/blob/main/scripts/finetune.py).
+
+**Out-of-Scope:** OpenVLA models do not zero-shot generalize to new (unseen) robot embodiments, or setups that are not represented in the pretraining mix; in these cases,
+we suggest collecting a dataset of demonstrations on the desired setup, and fine-tuning OpenVLA models instead.
+
+## Getting Started
+
+OpenVLA 7B can be used to control multiple robots for domains represented in the pretraining mixture out-of-the-box. For example,
+here is an example for loading `openvla-7b` for zero-shot instruction following in the [BridgeV2 environments] with a Widow-X robot:
+
+```python
+# Install minimal dependencies (`torch`, `transformers`, `timm`, `tokenizers`, ...)
+# > pip install -r https://raw.githubusercontent.com/openvla/openvla/main/requirements-min.txt
+from transformers import AutoModelForVision2Seq, AutoProcessor
+from PIL import Image
+
+import torch
+
+# Load Processor & VLA
+processor = AutoProcessor.from_pretrained("openvla/openvla-7b", trust_remote_code=True)
+vla = AutoModelForVision2Seq.from_pretrained(
+    "openvla/openvla-7b",
+    attn_implementation="flash_attention_2",  # [Optional] Requires `flash_attn`
+    torch_dtype=torch.bfloat16, 
+    low_cpu_mem_usage=True, 
+    trust_remote_code=True
+).to("cuda:0")
+
+# Grab image input & format prompt
+image: Image.Image = get_from_camera(...)
+prompt = "In: What action should the robot take to {<INSTRUCTION>}?\nOut:"
+
+# Predict Action (7-DoF; un-normalize for BridgeV2)
+inputs = processor(prompt, image).to("cuda:0", dtype=torch.bfloat16)
+action = vla.predict_action(**inputs, unnorm_key="bridge_orig", do_sample=False)
+
+# Execute...
+robot.act(action, ...)
+```
+
+For more examples, including scripts for fine-tuning OpenVLA models on your own robot demonstration datasets, see [our training repository](https://github.com/openvla/openvla).
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{kim24openvla,
+    title={OpenVLA: An Open-Source Vision-Language-Action Model},
+    author={{Moo Jin} Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
+    journal = {arXiv preprint arXiv:2406.09246},
+    year={2024}
+} 
+```
\ No newline at end of file
diff --git a/output_hf_model_openx/added_tokens.json b/output_hf_model_openx/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..8671820d1bb0dfd4dd92d5bd0fa9b5a7773f843e
--- /dev/null
+++ b/output_hf_model_openx/added_tokens.json
@@ -0,0 +1,3 @@
+{
+  "<PAD>": 32000
+}
diff --git a/output_hf_model_openx/config.json b/output_hf_model_openx/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..42ea81c7c783049559839e305afdfe97e30581a3
--- /dev/null
+++ b/output_hf_model_openx/config.json
@@ -0,0 +1,3168 @@
+{
+  "arch_specifier": "no-align+fused-gelu-mlp",
+  "architectures": [
+    "OpenVLAForActionPrediction"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_prismatic.OpenVLAConfig",
+    "AutoModelForVision2Seq": "modeling_prismatic.OpenVLAForActionPrediction"
+  },
+  "hf_llm_id": "meta-llama/Llama-2-7b-hf",
+  "image_resize_strategy": "resize-naive",
+  "image_sizes": [
+    224,
+    224
+  ],
+  "llm_backbone_id": "llama2-7b-pure",
+  "llm_max_length": 2048,
+  "model_type": "openvla",
+  "n_action_bins": 256,
+  "norm_stats": {
+    "austin_buds_dataset_converted_externally_to_rlds": {
+      "action": {
+        "mask": [
+          true,
+          true,
+          true,
+          true,
+          true,
+          true,
+          false
+        ],
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+          1.0,
+          1.0,
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+          0.0,
+          0.0,
+          1.0
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+        "mean": [
+          -0.07678354531526566,
+          0.0036849044263362885,
+          0.05644911900162697,
+          0.0,
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+          0.0,
+          0.3510494828224182
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+          -1.0,
+          -1.0,
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+          0.0,
+          0.0,
+          0.0
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+          -0.8714285492897034,
+          0.0,
+          0.0,
+          0.0,
+          0.0
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+        "q99": [
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+          0.8600000143051147,
+          1.0,
+          0.0,
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+      "num_trajectories": 50,
+      "num_transitions": 34112,
+      "proprio": {
+        "max": [
+          0.0,
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+    "model_type": "llama",
+    "pad_token_id": 32000,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 32064
+  },
+  "timm_model_ids": [
+    "vit_large_patch14_reg4_dinov2.lvd142m",
+    "vit_so400m_patch14_siglip_224"
+  ],
+  "timm_override_act_layers": [
+    null,
+    null
+  ],
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.1",
+  "use_fused_vision_backbone": true,
+  "vision_backbone_id": "dinosiglip-vit-so-224px"
+}
diff --git a/output_hf_model_openx/configuration_prismatic.py b/output_hf_model_openx/configuration_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2625753c4da1a6ef274a02645d4086bc7a7fb2b
--- /dev/null
+++ b/output_hf_model_openx/configuration_prismatic.py
@@ -0,0 +1,140 @@
+"""
+configuration_prismatic.py
+
+HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
+Default configuration specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from transformers import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+
+# === Utilities for Mapping Prismatic names to HF names ===
+# fmt: off
+VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
+    "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
+
+    "clip-vit-l-336px": [336],
+    "siglip-vit-so400m-384px": [384],
+
+    "dinoclip-vit-l-336px": [336, 336],
+    "dinosiglip-vit-so-224px": [224, 224],
+    "dinosiglip-vit-so-384px": [384, 384],
+}
+VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
+    "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
+    "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
+
+    "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
+    "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
+
+    "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
+    "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
+
+    "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
+    "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
+    "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
+}
+TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
+    "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
+    "dinov2-vit-l": [None], "in1k-vit-l": [None],
+    "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
+    "dinoclip-vit-l-336px": [None, "quick_gelu"],
+    "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
+}
+
+LLM_BACKBONE_TO_HF_PATH = {
+    "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
+    "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
+
+    "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
+
+    "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
+    "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
+
+    "phi-2-3b": "microsoft/phi-2",
+}
+LLM_BACKBONE_TO_HF_METACLASS = {
+    "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
+    "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
+
+    "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
+
+    "phi-2-3b": "phi",
+}
+
+VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
+VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
+# fmt: on
+
+
+class PrismaticConfig(PretrainedConfig):
+    model_type: str = "prismatic"
+    is_composition: bool = False
+
+    def __init__(
+        self,
+        vision_backbone_id: str = "siglip-vit-so400m",
+        llm_backbone_id: str = "vicuna-v15-7b",
+        arch_specifier: str = "no-align+gelu-mlp",
+        use_fused_vision_backbone: Optional[bool] = None,
+        image_resize_strategy: str = "letterbox",
+        text_config: Optional[Dict[str, Any]] = None,
+        llm_max_length: int = 2048,
+        pad_token_id: int = 32000,
+        pad_to_multiple_of: int = 64,
+        output_projector_states: bool = False,
+        **kwargs: str,
+    ) -> None:
+        if vision_backbone_id not in VALID_VISION_BACKBONES:
+            raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
+
+        if llm_backbone_id not in VALID_LLM_BACKBONES:
+            raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
+
+        # Set Prismatic Configuration Fields
+        self.vision_backbone_id = vision_backbone_id
+        self.llm_backbone_id = llm_backbone_id
+        self.arch_specifier = arch_specifier
+        self.output_projector_states = output_projector_states
+
+        # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
+        self.use_fused_vision_backbone = (
+            use_fused_vision_backbone
+            if use_fused_vision_backbone is not None
+            else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
+        )
+
+        self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
+        self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
+        self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
+        self.image_resize_strategy = image_resize_strategy
+
+        self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
+        self.llm_max_length = llm_max_length
+        self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
+
+        # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
+        self.text_config = (
+            CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
+            if text_config is not None
+            else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
+        )
+
+        # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+
+class OpenVLAConfig(PrismaticConfig):
+    model_type: str = "openvla"
+
+    def __init__(
+        self,
+        norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
+        n_action_bins: int = 256,
+        **kwargs: str,
+    ) -> None:
+        self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
+
+        super().__init__(**kwargs)
diff --git a/output_hf_model_openx/generation_config.json b/output_hf_model_openx/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2cefc1e51e6ccfe846d2b509ced403df73a8efe2
--- /dev/null
+++ b/output_hf_model_openx/generation_config.json
@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 32000,
+  "transformers_version": "4.40.1"
+}
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diff --git a/output_hf_model_openx/modeling_prismatic.py b/output_hf_model_openx/modeling_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a26c487190fa2359007b30a78094a6c432d6519
--- /dev/null
+++ b/output_hf_model_openx/modeling_prismatic.py
@@ -0,0 +1,562 @@
+"""
+modeling_prismatic.py
+
+Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
+from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
+logic in `prismatic.models.vlms.prismatic.py`.
+
+Note =>> for the time being, not adding the custom HF "docstring" formatting.
+
+References [LLaVa, IDEFICS-2]:
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
+"""
+
+import logging
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import timm
+import tokenizers
+import torch
+import torch.nn as nn
+import transformers
+from timm.models.vision_transformer import LayerScale
+from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import ModelOutput
+
+from .configuration_prismatic import OpenVLAConfig, PrismaticConfig
+
+# Get Logger
+logger = logging.getLogger(__name__)
+
+
+# === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
+IGNORE_INDEX = -100
+
+
+# === Utility Functions for Monkey-Patching ===
+def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        result = fn(*args, **kwargs)
+        return result[0] if isinstance(result, tuple) else result
+
+    return wrapper
+
+
+# HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
+#   =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
+#   =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
+def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
+    return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
+
+
+def ls_apply_patch(ls_module: LayerScale):
+    ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
+    ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
+    del ls_module.gamma
+
+
+# === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
+class PrismaticVisionBackbone(nn.Module):
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool,
+        image_sizes: List[int],
+        timm_model_ids: List[str],
+        timm_override_act_layers: List[Optional[str]],
+    ) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+
+        # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
+        #   =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
+        #               Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
+        assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
+        self.featurizer = timm.create_model(
+            timm_model_ids[0],
+            pretrained=False,
+            num_classes=0,
+            img_size=image_sizes[0],
+            act_layer=timm_override_act_layers[0],
+        )
+        self.featurizer.forward = unpack_tuple(
+            partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
+        )
+        self.embed_dim = self.featurizer.embed_dim
+
+        # If `use_fused_vision_backbone` =>> create "beta" featurizer
+        if self.use_fused_vision_backbone:
+            self.fused_featurizer = timm.create_model(
+                timm_model_ids[1],
+                pretrained=False,
+                num_classes=0,
+                img_size=image_sizes[1],
+                act_layer=timm_override_act_layers[1],
+            )
+            self.fused_featurizer.forward = unpack_tuple(
+                partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
+            )
+            self.embed_dim += self.fused_featurizer.embed_dim
+
+        # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
+        for module in self.featurizer.modules():
+            if isinstance(module, LayerScale):
+                ls_apply_patch(module)
+
+        if self.use_fused_vision_backbone:
+            for module in self.fused_featurizer.modules():
+                if isinstance(module, LayerScale):
+                    ls_apply_patch(module)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
+        if not self.use_fused_vision_backbone:
+            return self.featurizer(pixel_values)
+
+        # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
+        img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
+        patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
+
+        return torch.cat([patches, patches_fused], dim=2)
+
+
+# === Prismatic Projector (nn.Module) Definitions ===
+class PrismaticProjector(nn.Module):
+    def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.vision_dim, self.llm_dim = vision_dim, llm_dim
+
+        # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
+        if not self.use_fused_vision_backbone:
+            self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
+            self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+        else:
+            initial_projection_dim = 4 * vision_dim
+            self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
+            self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
+            self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+            self.act_fn2 = nn.GELU()
+
+    def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
+        if not self.use_fused_vision_backbone:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+        else:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+            projected_features = self.act_fn2(projected_features)
+            projected_features = self.fc3(projected_features)
+
+        return projected_features
+
+
+# === Main HF Class Definitions ===
+@dataclass
+class PrismaticCausalLMOutputWithPast(ModelOutput):
+    """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+    # Additions for VLMs
+    projector_features: Optional[torch.FloatTensor] = None
+
+
+class PrismaticPreTrainedModel(PreTrainedModel):
+    config_class: PretrainedConfig = PrismaticConfig
+    base_model_prefix: str = "model"
+    supports_gradient_checkpointing: bool = True
+
+    _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
+    _skip_keys_device_placement: str = "past_key_values"
+    _supports_flash_attn_2: bool = True
+
+    def _init_weights(self, module: nn.Module) -> None:
+        # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
+        #   => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
+        #      https://github.com/TRI-ML/prismatic-vlms
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @property
+    def _supports_sdpa(self) -> bool:
+        """Check LLM supports SDPA Attention"""
+        return self.language_model._supports_sdpa
+
+
+class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
+    def __init__(self, config: PrismaticConfig) -> None:
+        super().__init__(config)
+
+        # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
+        if config.use_fused_vision_backbone is None:
+            raise ValueError("Missing config field `use_fused_vision_backbone`")
+
+        if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
+            raise NotImplementedError(
+                "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
+                "if you urgently need support for latest TIMM versions."
+            )
+
+        if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
+            logger.warning(
+                f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
+                f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
+                f"there might be inference-time regressions due to dependency changes. If in doubt, please"
+                f"use the above versions."
+            )
+
+        # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
+        self.vision_backbone = PrismaticVisionBackbone(
+            config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
+        )
+
+        # Create Multimodal Projector
+        self.projector = PrismaticProjector(
+            config.use_fused_vision_backbone,
+            vision_dim=self.vision_backbone.embed_dim,
+            llm_dim=config.text_config.hidden_size,
+        )
+
+        # Instantiate LLM Backbone
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
+        self.vocab_size = config.text_config.vocab_size
+        self.pad_token_id = config.pad_token_id
+
+        # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
+        self.post_init()
+
+    # === `PreTrainedModel` Boilerplate ===
+    def get_input_embeddings(self) -> nn.Module:
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value: nn.Module) -> None:
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def get_decoder(self) -> nn.Module:
+        return self.language_model.get_decoder()
+
+    def set_decoder(self, decoder: nn.Module) -> None:
+        self.language_model.set_decoder(decoder)
+
+    def tie_weights(self) -> None:
+        self.language_model.tie_weights()  # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
+
+    def resize_token_embeddings(
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
+    ) -> nn.Embedding:
+        updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+
+        # Update config/instance variables
+        self.config.text_config.vocab_size = updated_embeddings.num_embeddings
+        self.vocab_size = updated_embeddings.num_embeddings
+
+        return updated_embeddings
+
+    # === Core Prismatic VLM `forward()` Logic ===
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_projector_features = output_projector_features if output_projector_features is not None else False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
+        use_cache = use_cache and not self.training
+
+        # Instantiate Placeholder for Projector Features
+        projected_patch_embeddings = None
+
+        # Note :: We only support forward passes with the following cases:
+        #   => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
+        #   => Unimodal Forward :: (pixel_values is None)
+        #   => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
+
+        # === Handle Generation with Cache (`input_ids.shape[1] == 1`) =>> requires `past_keys_values` ===
+        if input_ids.shape[1] == 1:
+            assert input_ids.shape[0] == 1, "Generation is only currently supported for batch size of 1!"
+            assert past_key_values is not None, "You must provide `past_key_values` during cached generation!"
+            assert labels is None, "Unexpected key `labels` provided during cached generation!"
+
+            language_model_output = self.language_model(
+                input_ids=input_ids,
+                attention_mask=None,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Handle Unimodal Forward ===
+        elif pixel_values is None:
+            assert (input_ids is not None) and (inputs_embeds is None), "Missing `input_ids` in language-only forward!"
+            assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
+
+            language_model_output = self.language_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=None,
+                past_key_values=None,
+                inputs_embeds=None,
+                labels=labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Handle Multimodal Forward ===
+        elif (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
+            assert past_key_values is None, "Unexpected key `past_key_values` provided during language-only forward!"
+
+            # Visual Feature Extraction
+            patch_features = self.vision_backbone(pixel_values)
+
+            # Projection Logic =>> Update Attention Mask
+            projected_patch_embeddings = self.projector(patch_features)
+            projected_patch_attention_mask = None
+            if attention_mask is not None:
+                projected_patch_attention_mask = torch.full(
+                    (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                    fill_value=True,
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+
+            # Get Input Embeddings (from Language Model Embeddings)
+            input_embeddings = self.get_input_embeddings()(input_ids)
+
+            # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+            multimodal_embeddings = torch.cat(
+                [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
+            )
+            multimodal_attention_mask = None
+            if attention_mask is not None:
+                multimodal_attention_mask = torch.cat(
+                    [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
+                )
+
+            # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
+            multimodal_labels = None
+            if labels is not None:
+                projected_patch_labels = torch.full(
+                    (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                    fill_value=IGNORE_INDEX,
+                    dtype=labels.dtype,
+                    device=labels.device,
+                )
+                multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
+
+            # Dispatch to Language Model
+            language_model_output = self.language_model(
+                input_ids=None,
+                attention_mask=multimodal_attention_mask,
+                position_ids=None,
+                past_key_values=None,
+                inputs_embeds=multimodal_embeddings,
+                labels=multimodal_labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # === Otherwise =>> Assume Invalid! ===
+        elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
+            raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
+
+        else:
+            raise ValueError(
+                "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
+                f"=> `input_ids` = {input_ids is not None}\n"
+                f"=> `attention_mask` = {attention_mask is not None}\n"
+                f"=> `pixel_values` = {pixel_values is not None}\n"
+                f"=> `labels` = {labels is not None}\n"
+                f"=> `input_embeds` = {inputs_embeds is not None}\n"
+                f"=> `past_key_values` = {past_key_values is not None}\n"
+                f"=> `use_cache` = {use_cache}"
+            )
+
+        # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
+        if not return_dict:
+            if output_projector_features and (projected_patch_embeddings is not None):
+                return *language_model_output, projected_patch_embeddings
+
+            return language_model_output
+
+        return PrismaticCausalLMOutputWithPast(
+            loss=language_model_output.loss,
+            logits=language_model_output.logits,
+            past_key_values=language_model_output.past_key_values,
+            hidden_states=language_model_output.hidden_states,
+            attentions=language_model_output.attentions,
+            projector_features=projected_patch_embeddings,
+        )
+
+    # === GenerationMixin Methods ===
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs: str,
+    ) -> Dict[str, torch.Tensor]:
+        """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
+        if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
+            (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
+        ):
+            raise ValueError("Generation with batch size > 1 is not currently supported!")
+
+        # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        # If `input_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"input_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        # Make sure `pixel_values` are preserved in `model_inputs`
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+            }
+        )
+
+        return model_inputs
+
+    # Defer to Language Model (all handle this differently, with different return types)
+    def _reorder_cache(self, *args, **kwargs) -> Any:
+        return self.language_model._reorder_cache(*args, **kwargs)
+
+
+class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
+    config_class: PretrainedConfig = OpenVLAConfig
+
+    def __init__(self, config: OpenVLAConfig) -> None:
+        super().__init__(config)
+        self.norm_stats = config.norm_stats
+
+        # Compute action bins
+        self.bins = np.linspace(-1, 1, config.n_action_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # Compute vocab size for de-tokenization -- revert added "multiple of"
+        self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
+
+    def predict_action(
+        self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """Thin wrapper around .generate() that decodes predicted actions and unnormalizes them."""
+        # If the special empty token ('') does not already appear after the colon (':') token in the prompt
+        # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
+        if not torch.all(input_ids[:, -1] == 29871):
+            input_ids = torch.cat(
+                (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
+            )
+
+        # Run VLA inference
+        generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
+
+        # Extract predicted action tokens and translate into (normalized) continuous actions
+        predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :].cpu().numpy()
+        discretized_actions = self.vocab_size - predicted_action_token_ids
+        discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+        normalized_actions = self.bin_centers[discretized_actions]
+
+        # Unnormalize actions
+        action_norm_stats = self.get_action_stats(unnorm_key)
+        mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+        action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
+        if unnorm_key is None:
+            assert len(norm_stats) == 1, (
+                f"Your model was trained on more than one dataset, "
+                f"please pass a `unnorm_key` from the following options to choose the statistics "
+                f"used for un-normalizing actions: {norm_stats.keys()}"
+            )
+            unnorm_key = next(iter(norm_stats.keys()))
+
+        assert unnorm_key in norm_stats, (
+            f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
+            f"please choose from: {norm_stats.keys()}"
+        )
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Get the dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return len(self.norm_stats[unnorm_key]["action"]["q01"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return self.norm_stats[unnorm_key]["action"]
diff --git a/output_hf_model_openx/preprocessor_config.json b/output_hf_model_openx/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..92975ace548590d24f65f497d7610af3b92b4dcd
--- /dev/null
+++ b/output_hf_model_openx/preprocessor_config.json
@@ -0,0 +1,114 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "processing_prismatic.PrismaticImageProcessor",
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "image_processor_type": "PrismaticImageProcessor",
+  "image_resize_strategy": "resize-naive",
+  "input_sizes": [
+    [
+      3,
+      224,
+      224
+    ],
+    [
+      3,
+      224,
+      224
+    ]
+  ],
+  "interpolations": [
+    "bicubic",
+    "bicubic"
+  ],
+  "means": [
+    [
+      0.485,
+      0.456,
+      0.406
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "processor_class": "PrismaticProcessor",
+  "stds": [
+    [
+      0.229,
+      0.224,
+      0.225
+    ],
+    [
+      0.5,
+      0.5,
+      0.5
+    ]
+  ],
+  "tvf_crop_params": [
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    },
+    {
+      "output_size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "tvf_do_letterbox": false,
+  "tvf_letterbox_fill": null,
+  "tvf_normalize_params": [
+    {
+      "inplace": false,
+      "mean": [
+        0.484375,
+        0.455078125,
+        0.40625
+      ],
+      "std": [
+        0.228515625,
+        0.2236328125,
+        0.224609375
+      ]
+    },
+    {
+      "inplace": false,
+      "mean": [
+        0.5,
+        0.5,
+        0.5
+      ],
+      "std": [
+        0.5,
+        0.5,
+        0.5
+      ]
+    }
+  ],
+  "tvf_resize_params": [
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    },
+    {
+      "antialias": true,
+      "interpolation": 3,
+      "max_size": null,
+      "size": [
+        224,
+        224
+      ]
+    }
+  ],
+  "use_fused_vision_backbone": true
+}
diff --git a/output_hf_model_openx/processing_prismatic.py b/output_hf_model_openx/processing_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f2daaf82c3b8946fe57596f656f14a031eaef93
--- /dev/null
+++ b/output_hf_model_openx/processing_prismatic.py
@@ -0,0 +1,257 @@
+"""
+processing_prismatic.py
+
+HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
+specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
+import timm.data
+import torch
+import torchvision.transforms.functional as TVF
+from PIL import Image
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from transformers import PreTrainedTokenizerBase
+from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+# === Image Processing ===
+def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
+    """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+    (w, h), max_wh = image.size, max(image.size)
+    horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+    padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+
+    return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
+
+
+class PrismaticImageProcessor(ImageProcessingMixin):
+    model_input_names: ClassVar[List[str]] = ["pixel_values"]
+
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool = False,
+        image_resize_strategy: str = "letterbox",
+        input_sizes: Optional[List[Tuple[int, int, int]]] = None,
+        interpolations: Optional[List[str]] = None,
+        means: Optional[List[Tuple[float, float, float]]] = None,
+        stds: Optional[List[Tuple[float, float, float]]] = None,
+        **kwargs: str,
+    ) -> None:
+        """
+        Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
+        created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
+
+        @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
+        @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
+        @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
+        @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
+        @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
+        @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
+        """
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.image_resize_strategy = image_resize_strategy
+
+        # Handle `None` default values
+        input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
+        means = [(0.5, 0.5, 0.5)] if means is None else means
+        stds = [(0.5, 0.5, 0.5)] if stds is None else stds
+
+        # TIMM `data_cfg` Parameters
+        self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
+
+        # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
+        self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
+        self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+        for idx in range(len(input_sizes)):
+            transform = timm.data.create_transform(
+                input_size=self.input_sizes[idx],
+                interpolation=self.interpolations[idx],
+                mean=self.means[idx],
+                std=self.stds[idx],
+                crop_pct=1.0,  # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
+                crop_mode="center",  # Default crop mode -- no-op when `crop_pct == 1.0`
+                is_training=False,  # No image augmentations when loading the transform!
+            )
+
+            # [Validation] Ensure appropriate transform structure, expected sizes
+            if not (
+                isinstance(transform, Compose)
+                and (len(transform.transforms) == 4)
+                and isinstance(transform.transforms[0], Resize)
+                and isinstance(transform.transforms[1], CenterCrop)
+                and isinstance(transform.transforms[2], ToTensor)
+                and isinstance(transform.transforms[3], Normalize)
+                and (transform.transforms[0].size == self.input_sizes[idx][-1])
+                and (transform.transforms[1].size == self.input_sizes[idx][-2:])
+            ):
+                raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
+
+            # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
+            #   => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
+            resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
+            self.tvf_resize_params.append(
+                {
+                    "size": resize_t.size,
+                    "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
+                    "max_size": None,
+                    "antialias": True,
+                }
+            )
+            self.tvf_crop_params.append({"output_size": crop_t.size})
+            self.tvf_normalize_params.append(
+                {
+                    "mean": norm_t.mean.float().numpy().tolist(),
+                    "std": norm_t.std.float().numpy().tolist(),
+                    "inplace": False,
+                }
+            )
+            self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+            # Handle Prismatic `image_resize_strategy`
+            if self.image_resize_strategy == "resize-naive":
+                self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
+            elif self.image_resize_strategy == "letterbox":
+                self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
+            elif self.image_resize_strategy == "resize-crop":
+                pass
+            else:
+                raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
+
+        # Dispatch **kwargs to super()
+        super().__init__(**kwargs)
+
+    def apply_transform(self, img: Image.Image) -> torch.Tensor:
+        """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
+        if self.tvf_do_letterbox:
+            img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
+
+        # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
+        imgs_t = []
+        for idx in range(len(self.input_sizes)):
+            img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
+            img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
+            img_idx_t = TVF.to_tensor(img_idx)
+            img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
+            imgs_t.append(img_idx_t)
+
+        # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
+        img_t = torch.vstack(imgs_t)
+
+        return img_t
+
+    def preprocess(
+        self,
+        images: Union[Image.Image, List[Image.Image]],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **_: str,
+    ) -> BatchFeature:
+        """
+        Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
+        explicitly only handle PIL.Image.Image instances for simplicity.
+
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
+
+        @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
+        pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
+
+        # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
+        return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
+
+    def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
+        return self.preprocess(images, **kwargs)
+
+
+# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
+#   =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
+class PrismaticProcessor(ProcessorMixin):
+    attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
+    image_processor_class: str = "AutoImageProcessor"
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[ImageProcessingMixin] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ) -> None:
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: Union[Image.Image, List[Image.Image]],
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
+        forwards images to PrismaticImageProcessor.
+
+        @param text: The (batch) of text to encode; must be a string or list of strings.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
+        @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
+        @param max_length: Maximum length (in tokens) to truncate
+        @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
+
+        @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
+        """
+        pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+        text_inputs = self.tokenizer(
+            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+        )
+
+        # [Validate] Need same number of images and text inputs!
+        if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
+            raise ValueError("Batch is malformed; expected same number of images and text inputs!")
+
+        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+
+    # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
+    def batch_decode(
+        self,
+        sequences: Union[List[int], List[List[int]], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> List[str]:
+        return self.tokenizer.batch_decode(
+            sequences=sequences,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> str:
+        return self.tokenizer.decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self) -> List[str]:
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
diff --git a/output_hf_model_openx/processor_config.json b/output_hf_model_openx/processor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f892f8a8f53e2f3603a7cf86043a1add317c1787
--- /dev/null
+++ b/output_hf_model_openx/processor_config.json
@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "processor_class": "PrismaticProcessor"
+}
diff --git a/output_hf_model_openx/special_tokens_map.json b/output_hf_model_openx/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..ab75c8e0a22550f0667501a0c52758f071a9efd8
--- /dev/null
+++ b/output_hf_model_openx/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/output_hf_model_openx/tokenizer.json b/output_hf_model_openx/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..591f1fc6ff695d967513100f707e66d5bf731414
--- /dev/null
+++ b/output_hf_model_openx/tokenizer.json
@@ -0,0 +1,93401 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "<s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "</s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32000,
+      "content": "<PAD>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "Sequence",
+    "normalizers": [
+      {
+        "type": "Prepend",
+        "prepend": "▁"
+      },
+      {
+        "type": "Replace",
+        "pattern": {
+          "String": " "
+        },
+        "content": "▁"
+      }
+    ]
+  },
+  "pre_tokenizer": null,
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      }
+    ],
+    "pair": [
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 1
+        }
+      },
+      {
+        "Sequence": {
+          "id": "B",
+          "type_id": 1
+        }
+      }
+    ],
+    "special_tokens": {
+      "<s>": {
+        "id": "<s>",
+        "ids": [
+          1
+        ],
+        "tokens": [
+          "<s>"
+        ]
+      }
+    }
+  },
+  "decoder": {
+    "type": "Sequence",
+    "decoders": [
+      {
+        "type": "Replace",
+        "pattern": {
+          "String": "▁"
+        },
+        "content": " "
+      },
+      {
+        "type": "ByteFallback"
+      },
+      {
+        "type": "Fuse"
+      },
+      {
+        "type": "Strip",
+        "content": " ",
+        "start": 1,
+        "stop": 0
+      }
+    ]
+  },
+  "model": {
+    "type": "BPE",
+    "dropout": null,
+    "unk_token": "<unk>",
+    "continuing_subword_prefix": null,
+    "end_of_word_suffix": null,
+    "fuse_unk": true,
+    "byte_fallback": true,
+    "ignore_merges": false,
+    "vocab": {
+      "<unk>": 0,
+      "<s>": 1,
+      "</s>": 2,
+      "<0x00>": 3,
+      "<0x01>": 4,
+      "<0x02>": 5,
+      "<0x03>": 6,
+      "<0x04>": 7,
+      "<0x05>": 8,
+      "<0x06>": 9,
+      "<0x07>": 10,
+      "<0x08>": 11,
+      "<0x09>": 12,
+      "<0x0A>": 13,
+      "<0x0B>": 14,
+      "<0x0C>": 15,
+      "<0x0D>": 16,
+      "<0x0E>": 17,
+      "<0x0F>": 18,
+      "<0x10>": 19,
+      "<0x11>": 20,
+      "<0x12>": 21,
+      "<0x13>": 22,
+      "<0x14>": 23,
+      "<0x15>": 24,
+      "<0x16>": 25,
+      "<0x17>": 26,
+      "<0x18>": 27,
+      "<0x19>": 28,
+      "<0x1A>": 29,
+      "<0x1B>": 30,
+      "<0x1C>": 31,
+      "<0x1D>": 32,
+      "<0x1E>": 33,
+      "<0x1F>": 34,
+      "<0x20>": 35,
+      "<0x21>": 36,
+      "<0x22>": 37,
+      "<0x23>": 38,
+      "<0x24>": 39,
+      "<0x25>": 40,
+      "<0x26>": 41,
+      "<0x27>": 42,
+      "<0x28>": 43,
+      "<0x29>": 44,
+      "<0x2A>": 45,
+      "<0x2B>": 46,
+      "<0x2C>": 47,
+      "<0x2D>": 48,
+      "<0x2E>": 49,
+      "<0x2F>": 50,
+      "<0x30>": 51,
+      "<0x31>": 52,
+      "<0x32>": 53,
+      "<0x33>": 54,
+      "<0x34>": 55,
+      "<0x35>": 56,
+      "<0x36>": 57,
+      "<0x37>": 58,
+      "<0x38>": 59,
+      "<0x39>": 60,
+      "<0x3A>": 61,
+      "<0x3B>": 62,
+      "<0x3C>": 63,
+      "<0x3D>": 64,
+      "<0x3E>": 65,
+      "<0x3F>": 66,
+      "<0x40>": 67,
+      "<0x41>": 68,
+      "<0x42>": 69,
+      "<0x43>": 70,
+      "<0x44>": 71,
+      "<0x45>": 72,
+      "<0x46>": 73,
+      "<0x47>": 74,
+      "<0x48>": 75,
+      "<0x49>": 76,
+      "<0x4A>": 77,
+      "<0x4B>": 78,
+      "<0x4C>": 79,
+      "<0x4D>": 80,
+      "<0x4E>": 81,
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+      "▁+\\": 17501,
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+      "cji": 17624,
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+      "▁russ": 17626,
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+      "HI": 17628,
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+      "cca": 17630,
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+      "▁sondern": 17665,
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+      "▁permanent": 17667,
+      "▁conclude": 17668,
+      "Seg": 17669,
+      "▁акаде": 17670,
+      "}\",": 17671,
+      "▁февраля": 17672,
+      "řed": 17673,
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+      "znam": 17706,
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+      "uliar": 17709,
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+      "▁resistance": 17711,
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+      "▁HE": 17714,
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+      "▁contrib": 17737,
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+      "▁publication": 17745,
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+      "▁detected": 17809,
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+      "Complete": 17813,
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+      "');\r": 17822,
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+      "▁periodo": 17848,
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+      "▁Abd": 17860,
+      "▁CF": 17861,
+      "arks": 17862,
+      "resolve": 17863,
+      "wedge": 17864,
+      "▁initialization": 17865,
+      "▁Véase": 17866,
+      "▁приня": 17867,
+      "stmt": 17868,
+      "▁income": 17869,
+      "MY": 17870,
+      "▁odkazy": 17871,
+      "▁Siehe": 17872,
+      "▁bodies": 17873,
+      "▁soc": 17874,
+      "Random": 17875,
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+      "ablo": 17877,
+      "▁regarded": 17878,
+      "onCreate": 17879,
+      "▁Magazine": 17880,
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+      "▁Buenos": 17882,
+      "ил": 17883,
+      ")));": 17884,
+      "capt": 17885,
+      "redirect": 17886,
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+      "▁farm": 17888,
+      "▁rôle": 17889,
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+      "    ": 17891,
+      "subfigure": 17892,
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+      "ziel": 17894,
+      "▁окон": 17895,
+      "EE": 17896,
+      "mee": 17897,
+      "▁perten": 17898,
+      "▁représent": 17899,
+      "▁LA": 17900,
+      "?'": 17901,
+      "▁тру": 17902,
+      "▁rational": 17903,
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+      "▁artists": 17906,
+      "Flow": 17907,
+      "▁Аль": 17908,
+      "izard": 17909,
+      "▁numero": 17910,
+      "actic": 17911,
+      "▁destruct": 17912,
+      "▁Пра": 17913,
+      "onsieur": 17914,
+      "qt": 17915,
+      "abestanden": 17916,
+      "ność": 17917,
+      "Connect": 17918,
+      "▁oracle": 17919,
+      "▁Stockholm": 17920,
+      "sizeof": 17921,
+      "▁gemäß": 17922,
+      "ACT": 17923,
+      "▁expert": 17924,
+      "utions": 17925,
+      "▁hacia": 17926,
+      "▁logger": 17927,
+      "▁fool": 17928,
+      "rypto": 17929,
+      "ær": 17930,
+      "▁cidade": 17931,
+      "▁составе": 17932,
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+      "▁cu rr",
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+      "▁serv ing",
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+      "s erve",
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+      "▁trou ve",
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+      "л ез",
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+      "дин а",
+      "ather ine",
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+      "s ync",
+      "az zo",
+      "azz o",
+      "ab stract",
+      "as sets",
+      "ass ets",
+      "asse ts",
+      "asset s",
+      "▁D ru",
+      "▁Dr u",
+      "з д",
+      "ord net",
+      "▁b igger",
+      "▁big ger",
+      "▁initial ized",
+      "▁initialize d",
+      "ка з",
+      "og ene",
+      "ogen e",
+      "oge ne",
+      "vi ously",
+      "vious ly",
+      "v iously",
+      "▁g uid",
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+      "scheid ung",
+      "▁Z ent",
+      "▁Ze nt",
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+      "▁frame s",
+      "▁fra mes",
+      "▁fram es",
+      "▁ frames",
+      "ri eben",
+      "rie ben",
+      "rieb en",
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+      "▁iss ued",
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+      "▁issu ed",
+      "▁d ow",
+      "▁do w",
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+      "ils t",
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+      "M ove",
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+      "▁ dor",
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+      "▁R O",
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+      "ac ent",
+      "ace nt",
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+      "▁str ateg",
+      "▁strat eg",
+      "▁mån aden",
+      "LO C",
+      "L OC",
+      "▁c atalog",
+      "▁cat alog",
+      "▁catal og",
+      "▁ catalog",
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+      "ust ed",
+      "u sted",
+      "▁Frame work",
+      "▁ Framework",
+      "EC K",
+      "E CK",
+      "▁a té",
+      "▁at é",
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+      "▁Be rt",
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+      "▁ge bru",
+      "▁geb ru",
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+      "ican s",
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+      "▁impr oved",
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+      "▁L abor",
+      "▁La bor",
+      "▁Lab or",
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+      "▁s wo",
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+      "g ood",
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+      "▁Ps ych",
+      "▁ro ots",
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+      "▁го дах",
+      "▁года х",
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+      "▁pia n",
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+      "▁t hin",
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+      "i eval",
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+      "▁вы пу",
+      "▁I X",
+      "▁ IX",
+      "▁bl ind",
+      "▁Mo tor",
+      "▁Mot or",
+      "▁ш а",
+      "▁ ша",
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+      "▁f og",
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+      "▁W rit",
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+      "▁lin g",
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+      "▁M ars",
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+      "ott i",
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+      "▁Tri b",
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+      "▁me rc",
+      "▁mer c",
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+      "z ung",
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+      "c fg",
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+      "sc hen",
+      "sche n",
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+      "▁ste r",
+      "▁ ster",
+      "j p",
+      "▁R ap",
+      "▁Ra p",
+      "▁rec ording",
+      "▁record ing",
+      "▁pe int",
+      "▁l ets",
+      "▁le ts",
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+      "▁ti ck",
+      "▁ tick",
+      "▁Col onel",
+      "Th anks",
+      "Thank s",
+      "▁f erm",
+      "▁fe rm",
+      "▁fer m",
+      "▁gr anted",
+      "▁gran ted",
+      "▁grant ed",
+      "▁th reshold",
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+      "▁H un",
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+      "▁п рав",
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+      "▁я кі",
+      "▁як і",
+      "P G",
+      "▁w s",
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+      "i ba",
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+      "▁Sa ve",
+      "▁Sav e",
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+      "▁в ъ",
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+      "▁me jor",
+      "▁с лова",
+      "▁сло ва",
+      "▁rep lacement",
+      "▁replace ment",
+      "▁repla cement",
+      "▁im pr",
+      "▁imp r",
+      "▁Frances co",
+      "▁Ho tel",
+      "▁Hot el",
+      "▁UP DATE",
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+      "▁му зы",
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+      "u gs",
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+      "▁f az",
+      "▁fa z",
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+      "▁significant ly",
+      "▁L ag",
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+      "note s",
+      "n otes",
+      "▁s ele",
+      "▁se le",
+      "▁sel e",
+      "▁dedic ated",
+      "▁H ost",
+      "▁Ho st",
+      "▁ Host",
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+      "wi ng",
+      "win g",
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+      "▁T itel",
+      "▁Tit el",
+      "▁Ti tel",
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+      "larg e",
+      "▁con ten",
+      "▁cont en",
+      "▁co nten",
+      "▁conte n",
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+      "▁de ser",
+      "▁des er",
+      "▁G ordon",
+      "▁Gor don",
+      "с пе",
+      "▁p atri",
+      "▁pat ri",
+      "▁pa tri",
+      "▁patr i",
+      "▁R andom",
+      "▁Rand om",
+      "▁Ran dom",
+      "▁ Random",
+      "▁Return s",
+      "ы м",
+      "ро ма",
+      "ром а",
+      "▁Stud ies",
+      "S l",
+      "▁fr ü",
+      "TE XT",
+      "T EXT",
+      "in ate",
+      "ina te",
+      "▁T ol",
+      "▁To l",
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+      "ar ta",
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+      "▁or bit",
+      "▁orb it",
+      "▁A ires",
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+      "▁I ss",
+      "▁Is s",
+      "▁te ż",
+      "▁d iverse",
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+      "▁divers e",
+      "▁diver se",
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+      "▁m ise",
+      "▁mi se",
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+      "▁batt ery",
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+      "▁bat tery",
+      "▁A kadem",
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+      "▁В ід",
+      "▁Ві д",
+      "▁Re form",
+      "▁Ref orm",
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+      "er b",
+      "e rb",
+      "▁mon aster",
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+      "▁Ди в",
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+      "▁indic ating",
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+      "▁ur ls",
+      "▁ urls",
+      "▁k ein",
+      "▁ke in",
+      "об раз",
+      "обра з",
+      "pi ck",
+      "pic k",
+      "p ick",
+      "▁Ad mir",
+      "▁old est",
+      "▁ol dest",
+      "▁m uz",
+      "▁mu z",
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+      "▁contrad iction",
+      "▁contradict ion",
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+      "illi ant",
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+      "▁pa v",
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+      "▁pap el",
+      "ub s",
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+      "▁же на",
+      "▁жен а",
+      "▁ жена",
+      "AM L",
+      "A ML",
+      "▁re cip",
+      "▁rec ip",
+      "▁reci p",
+      "▁C OL",
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+      "▁cl ue",
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+      "w ür",
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+      "Dra g",
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+      "en ium",
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+      "▁re serve",
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+      "▁ren t",
+      "▁ rent",
+      "▁s unt",
+      "▁su nt",
+      "▁sun t",
+      "▁G irls",
+      "▁Girl s",
+      "▁Gir ls",
+      "▁Er nest",
+      "▁Ern est",
+      "▁f its",
+      "▁fi ts",
+      "▁fit s",
+      "▁op pon",
+      "▁opp on",
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+      "▁Flo rence",
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+      "▁eng ines",
+      "▁engine s",
+      "D ynamic",
+      "▁stycz nia",
+      "▁b ias",
+      "▁bi as",
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+      "ди й",
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+      "▁historique s",
+      "▁H ä",
+      "ho d",
+      "h od",
+      "▁w ł",
+      "sch ap",
+      "▁l ac",
+      "▁la c",
+      "▁ lac",
+      "▁F oi",
+      "▁Fo i",
+      "▁d well",
+      "▁dw ell",
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+      "fer n",
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+      "▁ru olo",
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+      "▁f orum",
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+      "re no",
+      "ren o",
+      "r eno",
+      "un ya",
+      "▁у ез",
+      "▁Mill ionen",
+      "▁listop ada",
+      "▁M aine",
+      "▁Ma ine",
+      "▁Main e",
+      "▁Mai ne",
+      "▁gru pos",
+      "▁grupo s",
+      "▁grup os",
+      "▁St orage",
+      "▁Sto rage",
+      "▁ Storage",
+      "▁app le",
+      "▁ap ple",
+      "▁ apple",
+      "▁L ö",
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+      "ous ed",
+      "ouse d",
+      "o used",
+      "д ро",
+      "sc i",
+      "s ci",
+      "▁hi bernate",
+      "▁ hibernate",
+      "do g",
+      "d og",
+      "▁во сто",
+      "▁вос то",
+      "▁ восто",
+      "▁intens ity",
+      "leg end",
+      "lege nd",
+      "legen d",
+      "▁W ille",
+      "▁Will e",
+      "▁Wil le",
+      "▁Wi lle",
+      "▁szer int",
+      "ges ellschaft",
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+      "▁Li ving",
+      "▁Liv ing",
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+      "n eed",
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+      "▁sp raw",
+      "▁spr aw",
+      "▁be ho",
+      "▁beh o",
+      "▁fot ograf",
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+      "▁k is",
+      "▁ki s",
+      "▁sign ing",
+      "▁sig ning",
+      "ak ult",
+      "aku lt",
+      "▁index ing",
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+      "a por",
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+      "▁concept ion",
+      "▁conce ption",
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+      "a ggreg",
+      "▁Са вез",
+      "▁aff air",
+      "ě ní",
+      "A ugust",
+      "▁се кре",
+      "▁miesz kań",
+      "UI Image",
+      "▁b ishop",
+      "▁bi shop",
+      "▁ bishop",
+      "▁serv ants",
+      "▁servant s",
+      "▁tr ail",
+      "▁tra il",
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+      "▁jo ins",
+      "▁join s",
+      "▁N ear",
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+      "▁B ien",
+      "▁Bi en",
+      "▁пер вый",
+      "▁первы й",
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+      "▁ge ometric",
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+      "▁k W",
+      "▁decom position",
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+      "▁come dy",
+      "▁activ ation",
+      "▁an gry",
+      "▁ang ry",
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+      "▁inst antly",
+      "▁instant ly",
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+      "▁Cl ay",
+      "▁Cla y",
+      "co t",
+      "c ot",
+      "▁G én",
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+      "( $(",
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+      "▁lif ted",
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+      "▁K it",
+      "▁Ki t",
+      "▁ Kit",
+      "▁l inea",
+      "▁li nea",
+      "▁line a",
+      "▁lin ea",
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+      "ha rt",
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+      "▁nu it",
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+      "ли и",
+      "▁r öm",
+      "Task s",
+      "▁S r",
+      "▁se is",
+      "▁sei s",
+      "as ia",
+      "asi a",
+      "}} $.",
+      "}}$ .",
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+      "control s",
+      "contr ols",
+      "▁S tim",
+      "▁St im",
+      "▁Re cht",
+      "▁Rec ht",
+      "ocia ción",
+      "oci ación",
+      "▁N atal",
+      "▁Na tal",
+      "▁Nat al",
+      "▁Philipp ines",
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+      "ule n",
+      "u len",
+      "F ixed",
+      "▁switch ed",
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+      "▁нача ле",
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+      "urs t",
+      "▁aut our",
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+      "C a",
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+      "▁Fre i",
+      "▁Fr ei",
+      "▁Mus ée",
+      "▁K urz",
+      "▁Kur z",
+      "▁Ku rz",
+      "▁reg ião",
+      "sw ap",
+      "▁h ate",
+      "▁ha te",
+      "▁hat e",
+      "▁mod ifications",
+      "▁modification s",
+      "▁modific ations",
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+      "▁Ко м",
+      "▁Anto ine",
+      "ug a",
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+      "RE CT",
+      "R ECT",
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+      "é ter",
+      "G ROUP",
+      "▁sacr ific",
+      "▁W he",
+      "▁Wh e",
+      "▁Ste vens",
+      "▁Steve ns",
+      "▁Steven s",
+      "olog ische",
+      "Sum mary",
+      "ob s",
+      "o bs",
+      "hn en",
+      "h nen",
+      "< %=",
+      "di enst",
+      "d ienst",
+      "re mark",
+      "rem ark",
+      "r emark",
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+      "е л",
+      "▁M ock",
+      "▁Mo ck",
+      "▁ Mock",
+      "▁Ль в",
+      "▁tr ês",
+      "g b",
+      "▁celebr ated",
+      "▁E b",
+      "▁c osta",
+      "▁co sta",
+      "▁cost a",
+      "▁cos ta",
+      "▁Ge ographic",
+      "▁att achment",
+      "▁attach ment",
+      "mann schaft",
+      "▁depend ence",
+      "� �",
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+      "v ic",
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+      "▁inter ven",
+      "▁G ü",
+      "ón ica",
+      "ó nica",
+      "▁P on",
+      "▁Po n",
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+      "▁Fe b",
+      "▁wor ship",
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+      "i ju",
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+      "▁sp ac",
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+      "level and",
+      "▁local idad",
+      "▁prec eding",
+      "▁preced ing",
+      "▁H essen",
+      "x p",
+      "▁W ein",
+      "▁We in",
+      "▁Wei n",
+      "▁Rom â",
+      "▁gi orno",
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+      "▁квіт ня",
+      "lla ços",
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+      "▁k ül",
+      "▁Å rs",
+      "▁на ј",
+      "uc lide",
+      "Inter net",
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+      "or ton",
+      "ort on",
+      "▁c orn",
+      "▁cor n",
+      "▁co rn",
+      "я ми",
+      "▁\" *",
+      "▁Fel ix",
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+      "apa t",
+      "a pat",
+      "▁сво и",
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+      "ma de",
+      "mad e",
+      "m ade",
+      "▁lo comot",
+      "хо да",
+      "ход а",
+      "F P",
+      "▁p m",
+      "▁ pm",
+      ".* ;",
+      "▁H amm",
+      "▁Ha mm",
+      "▁Ham m",
+      "` }",
+      "Layout Inflater",
+      "== \"",
+      "= =\"",
+      "▁E ur",
+      "▁Eu r",
+      "▁d ogs",
+      "▁do gs",
+      "▁dog s",
+      "же нии",
+      "▁a zon",
+      "▁az on",
+      "▁ azon",
+      "▁em ulator",
+      "▁r icon",
+      "▁ric on",
+      "▁ri con",
+      "be eld",
+      "▁н у",
+      "▁ ну",
+      "▁approxim ate",
+      "L M",
+      "▁B ond",
+      "▁Bo nd",
+      "▁Bon d",
+      "▁en h",
+      "ęd z",
+      "ę dz",
+      "▁s olit",
+      "▁so lit",
+      "▁sol it",
+      "Relative Layout",
+      "et eor",
+      "ete or",
+      "ament os",
+      "amento s",
+      "▁in direct",
+      "▁ind irect",
+      "ib ől",
+      "▁g ros",
+      "▁gr os",
+      "▁gro s",
+      "▁Original s",
+      "▁Origin als",
+      "▁Orig inals",
+      "comm ands",
+      "command s",
+      "Ex port",
+      "Exp ort",
+      "▁A vec",
+      "▁Av ec",
+      "▁sole mn",
+      "▁solem n",
+      "▁correct ion",
+      "▁corre ction",
+      "▁corr ection",
+      "▁про води",
+      "▁прово ди",
+      "▁Mo sk",
+      "▁Mos k",
+      "▁по до",
+      "▁под о",
+      "▁geb ied",
+      "▁nast ęp",
+      "▁D river",
+      "▁Dr iver",
+      "▁Drive r",
+      "▁ Driver",
+      "▁O ok",
+      "▁V ec",
+      "▁Ve c",
+      "▁ Vec",
+      "▁lung o",
+      "▁lun go",
+      "fi cos",
+      "fic os",
+      "fico s",
+      "f icos",
+      "▁s vol",
+      "▁sv ol",
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+      "▁k id",
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+      "▁H r",
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+      "▁M éd",
+      "▁Mé d",
+      "▁c pu",
+      "▁cp u",
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+      "▁def ense",
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+      "▁P ale",
+      "▁Pa le",
+      "▁Pal e",
+      "▁f uel",
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+      "▁fu el",
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+      "▁K irk",
+      "▁Kir k",
+      "▁Qu ando",
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+      "▁Co ver",
+      "▁Cov er",
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+      "▁gew ählt",
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+      "▁ Calendar",
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+      "W P",
+      "os hi",
+      "osh i",
+      "▁M aven",
+      "▁Ma ven",
+      "▁b elle",
+      "▁be lle",
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+      "▁bell e",
+      "▁w x",
+      "▁ wx",
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+      "▁sug ar",
+      "▁Bet rieb",
+      "▁equilib rium",
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+      "▁text s",
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+      "сло в",
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+      "▁czerw ca",
+      "▁D üsseld",
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+      "▁am ery",
+      "▁amer y",
+      "▁a ni",
+      "▁an i",
+      "▁ ani",
+      "▁o bey",
+      "▁ob ey",
+      "▁N ell",
+      "▁Ne ll",
+      "▁Nel l",
+      "▁in ne",
+      "▁inn e",
+      "▁т ро",
+      "▁ тро",
+      "F D",
+      "cc o",
+      "c co",
+      "▁Z ob",
+      "▁Zo b",
+      "al ette",
+      "ale tte",
+      "alet te",
+      "a lette",
+      "▁má jus",
+      "ect ed",
+      "ec ted",
+      "e cted",
+      "▁Tur key",
+      "▁Turk ey",
+      "▁Wh ether",
+      "▁Whe ther",
+      "q i",
+      "▁ш то",
+      "▁head quarters",
+      "en di",
+      "end i",
+      "ar us",
+      "aru s",
+      "a rus",
+      "op us",
+      "o pus",
+      "▁з оло",
+      "▁зо ло",
+      "▁de stru",
+      "▁dest ru",
+      "▁L ok",
+      "▁Lo k",
+      "▁satisf action",
+      "() \r",
+      "( )\r",
+      "▁Т ер",
+      "▁Те р",
+      "Jo se",
+      "J ose",
+      "▁con quer",
+      "▁conqu er",
+      "▁E ffect",
+      "▁ Effect",
+      "Layout Params",
+      "ie z",
+      "i ez",
+      "▁extern s",
+      "▁gegen über",
+      "▁E SP",
+      "▁ES P",
+      "ol ta",
+      "olt a",
+      "process or",
+      "proc essor",
+      "▁K ult",
+      "▁Ku lt",
+      "▁Atl anta",
+      "▁t ier",
+      "▁ti er",
+      "▁tie r",
+      "Oper ator",
+      "▁ди а",
+      "▁пи сь",
+      "▁gro ß",
+      "▁he arts",
+      "▁heart s",
+      "▁hear ts",
+      "▁mill imeter",
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+      "alth ough",
+      "al les",
+      "all es",
+      "alle s",
+      "a lles",
+      "▁Mag ic",
+      "tr aining",
+      "tra ining",
+      "train ing",
+      "ol ine",
+      "oli ne",
+      "olin e",
+      "o line",
+      "▁орган і",
+      ">\\< ^",
+      "> \\<^",
+      "ці аль",
+      "ex ports",
+      "export s",
+      "Work book",
+      "▁вере сня",
+      "▁t eles",
+      "▁te les",
+      "▁tele s",
+      "▁tel es",
+      "▁econom y",
+      "▁econ omy",
+      "▁ec onomy",
+      "▁t rap",
+      "▁tr ap",
+      "▁tra p",
+      "▁ref use",
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+      "▁strange r",
+      "▁stran ger",
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+      "ol an",
+      "ola n",
+      "o lan",
+      "▁n ing",
+      "▁ni ng",
+      "▁nin g",
+      "▁ ning",
+      "inf late",
+      "infl ate",
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+      "itate a",
+      "ack s",
+      "ac ks",
+      "a cks",
+      "▁J oy",
+      "▁Jo y",
+      "FL AG",
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+      "ai land",
+      "▁sort i",
+      "▁sor ti",
+      "▁в пер",
+      "▁p én",
+      "▁pé n",
+      "Not hing",
+      "No thing",
+      "N othing",
+      "▁sz áz",
+      "▁Á ng",
+      "▁A UT",
+      "▁ AUT",
+      "Act ions",
+      "Action s",
+      "A ctions",
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+      "▁чер вня",
+      "▁авто мо",
+      "▁rout ine",
+      "▁e struct",
+      "▁est ruct",
+      "▁G ang",
+      "▁Ga ng",
+      "▁Gan g",
+      "▁h oles",
+      "▁ho les",
+      "▁hol es",
+      "▁hole s",
+      "th esis",
+      "thes is",
+      "▁con cl",
+      "▁conc l",
+      "▁p é",
+      "ri ers",
+      "rie rs",
+      "rier s",
+      "r iers",
+      "ро вой",
+      "рово й",
+      "р овой",
+      "ad ic",
+      "adi c",
+      "a dic",
+      "Sp eed",
+      "Spe ed",
+      "▁command ed",
+      "▁N azionale",
+      "▁Naz ionale",
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+      "▁se dan",
+      "▁sed an",
+      "String s",
+      "Str ings",
+      "▁sa cred",
+      "▁sac red",
+      "▁sacr ed",
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+      "ters uch",
+      "▁abit anti",
+      "br it",
+      "b rit",
+      "▁N CAA",
+      "▁NC AA",
+      "▁С П",
+      "▁a ged",
+      "▁ag ed",
+      "▁age d",
+      "▁ aged",
+      "▁Ch iesa",
+      "▁Chi esa",
+      "▁re vision",
+      "▁rev ision",
+      "▁revis ion",
+      "op ro",
+      "o pro",
+      "▁over write",
+      "emb ros",
+      "embro s",
+      "▁sort ie",
+      "▁sorti e",
+      "▁ot ten",
+      "▁ott en",
+      "xi v",
+      "x iv",
+      "▁d eli",
+      "▁de li",
+      "▁del i",
+      "▁A sp",
+      "▁As p",
+      "▁b alls",
+      "▁bal ls",
+      "▁ball s",
+      "ka f",
+      "k af",
+      "▁br ave",
+      "▁bra ve",
+      "▁все го",
+      "▁вс его",
+      "eg n",
+      "e gn",
+      "jp eg",
+      "▁O sten",
+      "▁Os ten",
+      "▁Ost en",
+      "Const ants",
+      "▁Inf antry",
+      "▁N ev",
+      "▁Ne v",
+      "▁я ких",
+      "▁як их",
+      "▁му ниципа",
+      "ci ja",
+      "c ija",
+      "▁p oem",
+      "▁po em",
+      "▁ne gro",
+      "▁neg ro",
+      "ха р",
+      "х ар",
+      "▁A sk",
+      "▁As k",
+      "▁a vo",
+      "▁av o",
+      "▁ avo",
+      "▁Me yer",
+      "▁Mey er",
+      "▁W esten",
+      "▁We sten",
+      "▁West en",
+      "▁Wes ten",
+      "▁o ko",
+      "▁ok o",
+      "▁ oko",
+      "ag in",
+      "agi n",
+      "a gin",
+      "▁Süd en",
+      "▁Sü den",
+      "ent ries",
+      "entr ies",
+      "▁Rep ublik",
+      "▁Repub lik",
+      "Collection View",
+      "-- -----",
+      "---- ---",
+      "--- ----",
+      "------ -",
+      "----- --",
+      "- ------",
+      "▁fire fox",
+      "▁alc une",
+      "▁фо то",
+      "▁отри ма",
+      "~~~~ ~~~~",
+      "▁Ра з",
+      "▁Com plex",
+      "▁Comp lex",
+      "▁Comple x",
+      "▁p ia",
+      "▁pi a",
+      "▁public ada",
+      "we i",
+      "w ei",
+      "ced ure",
+      "occup ation",
+      "▁medic ine",
+      "▁dr ove",
+      "▁dro ve",
+      "Pro blem",
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+      "▁begin ner",
+      "▁thorough ly",
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+      "u ria",
+      "av ant",
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+      "avan t",
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+      "▁Le ist",
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+      "a rial",
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+      "▁optimize d",
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+      "▁H ass",
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+      "▁dire kt",
+      "▁t m",
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+      "▁Bo m",
+      "▁k unst",
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+      "▁sh elter",
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+      "▁ra v",
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+      "▁św iat",
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+      "▁m esh",
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+      "да го",
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+      "▁passe r",
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+      "▁cos m",
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+      "▁redund ant",
+      "▁G ros",
+      "▁Gr os",
+      "▁Gro s",
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+      "▁v io",
+      "▁vi o",
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+      "▁G am",
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+      "▁j ack",
+      "▁ja ck",
+      "▁ jack",
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+      "H int",
+      "▁A pol",
+      "▁Ap ol",
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+      "▁were ld",
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+      "▁nar od",
+      "▁это т",
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+      "▁от ме",
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+      "▁wor n",
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+      "▁Gil l",
+      "▁S ent",
+      "▁Se nt",
+      "▁Sen t",
+      "ie lla",
+      "iel la",
+      "i ella",
+      "▁Cra ig",
+      "▁S ele",
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+      "▁Indep end",
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+      "▁` &",
+      "▁o re",
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+      "▁ ore",
+      "▁supp lies",
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+      "Don ald",
+      "▁R ET",
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+      "m ix",
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+      "▁na zw",
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+      "▁rep ro",
+      "▁repr o",
+      "▁M ason",
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+      "▁bla nc",
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+      "▁elev en",
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+      "▁R egin",
+      "▁Re gin",
+      "▁Reg in",
+      "▁é x",
+      "▁Bu lg",
+      "▁Bul g",
+      "▁network ing",
+      "▁net working",
+      "▁se parator",
+      "▁separ ator",
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+      "▁M ie",
+      "▁Mi e",
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+      "▁nice ly",
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+      "▁Ha lle",
+      "▁Hall e",
+      "▁Hal le",
+      "▁Maj esty",
+      "▁Sw itch",
+      "▁ Switch",
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+      "▁о стан",
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+      "▁pl ut",
+      "▁T rain",
+      "▁Tr ain",
+      "▁Tra in",
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+      "▁p uesto",
+      "▁pu esto",
+      "▁pue sto",
+      "▁t oss",
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+      "▁irre levant",
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+      "▁D oc",
+      "▁Do c",
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+      "▁Be rl",
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+      "▁S oc",
+      "▁So c",
+      "▁r ounded",
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+      "▁G an",
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+      "▁Inv estig",
+      "▁index Path",
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+      "▁M aler",
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+      "▁proyect o",
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+      "▁G P",
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+      "▁ ded",
+      "▁Od kazy",
+      "▁M sg",
+      "▁ Msg",
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+      "▁Pri mary",
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+      "▁Office r",
+      "▁Offic er",
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+      "▁deliver y",
+      "▁deli very",
+      "ж дён",
+      "▁им пе",
+      "▁w il",
+      "▁v esc",
+      "▁ve sc",
+      "▁ves c",
+      "uszt us",
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+      "▁Fo re",
+      "▁w enig",
+      "▁we nig",
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+      "▁A irl",
+      "▁Air l",
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+      "▁Bre ak",
+      "▁St äd",
+      "is miss",
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+      "▁n ou",
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+      "Ital ie",
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+      "▁sv o",
+      "▁kir ály",
+      "▁Re ferences",
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+      "si x",
+      "s ix",
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+      "▁Archiv es",
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+      "▁spole č",
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+      "zi ale",
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+      "z iale",
+      "ce des",
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+      "c edes",
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+      "▁кра й",
+      "▁d ivent",
+      "▁di vent",
+      "▁div ent",
+      "▁k é",
+      "▁ ké",
+      "qu it",
+      "qui t",
+      "q uit",
+      "то ром",
+      "тор ом",
+      "Check Box",
+      "▁Zob acz",
+      "ph e",
+      "p he",
+      "pt a",
+      "p ta",
+      "▁s jö",
+      "▁sj ö",
+      "▁розта ш",
+      "▁tedes co",
+      "▁s tal",
+      "▁st al",
+      "▁sta l",
+      "▁ stal",
+      "▁Be ruf",
+      "▁Ber uf",
+      "ова я",
+      "о вая",
+      "▁s vě",
+      "▁sv ě",
+      "▁fl ush",
+      "▁flu sh",
+      "▁ flush",
+      "▁від бу",
+      "▁rad ial",
+      "▁radi al",
+      "▁différ entes",
+      "ан та",
+      "▁Per ry",
+      "Col l",
+      "Co ll",
+      "C oll",
+      "li qu",
+      "l iqu",
+      "▁Option al",
+      "▁Opt ional",
+      "▁ Optional",
+      "▁Сан кт",
+      "▁LIN Q",
+      "▁Fran c",
+      "▁Fr anc",
+      "▁Fra nc",
+      "ci je",
+      "c ije",
+      "▁Gu illaume",
+      "kn ow",
+      "k now",
+      "▁Un its",
+      "▁Unit s",
+      "ol k",
+      "▁Syst ème",
+      "▁S ales",
+      "▁Sal es",
+      "▁Sa les",
+      "▁ehemal igen",
+      "ми рова",
+      "мир ова",
+      "x html",
+      "set opt",
+      "▁m ellan",
+      "▁mel lan",
+      "▁z ie",
+      "▁ zie",
+      "▁gi ant",
+      "Bo ard",
+      "▁C aval",
+      "▁Ca val",
+      "▁Cav al",
+      "▁def ence",
+      "-- --------",
+      "---- ------",
+      "-------- --",
+      "--- -------",
+      "------ ----",
+      "----- -----",
+      "------- ---",
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+      "▁de vil",
+      "▁dev il",
+      "▁devi l",
+      "▁Ф о",
+      "▁N är",
+      "▁Nä r",
+      "▁... )",
+      "▁.. .)",
+      "▁ ...)",
+      "▁v ois",
+      "▁vo is",
+      "▁ab bre",
+      "▁abb re",
+      "▁M änner",
+      "xim o",
+      "xi mo",
+      "x imo",
+      "▁intellect ual",
+      "▁t ales",
+      "▁tal es",
+      "▁ta les",
+      "▁tale s",
+      "sim ilar",
+      "ne um",
+      "▁O rig",
+      "▁Or ig",
+      "▁Ori g",
+      "▁po stal",
+      "▁pos tal",
+      "▁post al",
+      "▁h vor",
+      "▁ident ification",
+      "▁identific ation",
+      "▁О д",
+      "ue sto",
+      "ues to",
+      "uest o",
+      "u esto",
+      "▁. ./",
+      "▁.. /",
+      "▁ ../",
+      "▁b ir",
+      "▁bi r",
+      "▁ bir",
+      "▁Л он",
+      "▁Ло н",
+      "▁es empio",
+      "▁E ing",
+      "▁Ein g",
+      "Exp and",
+      "▁PR IMARY",
+      "▁J in",
+      "▁Ji n",
+      "▁vš ak",
+      "ours es",
+      "ourse s",
+      "▁Be tty",
+      "▁Bet ty",
+      "▁W M",
+      "▁ WM",
+      "▁fl ask",
+      "▁fla sk",
+      "hl en",
+      "h len",
+      "▁A del",
+      "▁Ad el",
+      "lar avel",
+      "▁д ет",
+      "▁де т",
+      "сь кою",
+      "сько ю",
+      "▁M undo",
+      "▁Mun do",
+      "ic zn",
+      "icz n",
+      "ifi é",
+      "▁М ор",
+      "▁Мо р",
+      "▁д рев",
+      "▁др ев",
+      "Date Format",
+      "сь ким",
+      "ськ им",
+      "▁d ated",
+      "▁da ted",
+      "▁dat ed",
+      "▁date d",
+      "▁ dated",
+      "ко ли",
+      "кол и",
+      "▁результа те",
+      "\\) .",
+      "\\ ).",
+      "▁delay ed",
+      "so und",
+      "s ound",
+      "▁Ма к",
+      "▁\" ...",
+      "▁\". ..",
+      "▁b innen",
+      "▁bin nen",
+      "▁фа куль",
+      "▁pol ygon",
+      "▁poly gon",
+      "▁eg gs",
+      "▁egg s",
+      "At IndexPath",
+      "AtIndex Path",
+      "мен таль",
+      "мент аль",
+      "мента ль",
+      "▁in cred",
+      "▁incre d",
+      "▁inc red",
+      "ch unk",
+      "web driver",
+      "▁с вобо",
+      "▁сво бо",
+      "▁mi ędzy",
+      "Rece ived",
+      "Receive d",
+      "▁M onde",
+      "▁Mon de",
+      "▁Mo nde",
+      "▁Mond e",
+      "▁J Query",
+      "Bu tt",
+      "But t",
+      "B utt",
+      "▁P DO",
+      "▁for ec",
+      "▁fo rec",
+      "▁fore c",
+      "▁discipl ine",
+      "ch ev",
+      "che v",
+      "на т",
+      "н ат",
+      "▁re dis",
+      "▁red is",
+      "▁hun ting",
+      "▁al k",
+      "▁ alk",
+      "▁proof s",
+      "PR I",
+      "P RI",
+      "▁c hip",
+      "▁ch ip",
+      "▁chi p",
+      "és ie",
+      "▁H O",
+      "▁ HO",
+      "▁r ug",
+      "▁ru g",
+      "▁ rug",
+      "zo s",
+      "z os",
+      "▁s orte",
+      "▁sort e",
+      "▁sor te",
+      "▁ze igt",
+      "▁Phys ics",
+      "leg te",
+      "legt e",
+      "▁proport ional",
+      "▁proportion al",
+      "▁tool bar",
+      "ve ment",
+      "v ement",
+      "not in",
+      "▁prv ní",
+      "bl ah",
+      "bla h",
+      "b lah",
+      "▁prés ence",
+      "▁l loc",
+      "▁ll oc",
+      "▁lí der",
+      "▁Ac cept",
+      "▁ Accept",
+      "▁Al ways",
+      "▁\" {",
+      "▁divers i",
+      "▁diver si",
+      "ik or",
+      "iko r",
+      "i kor",
+      "Per iod",
+      "ж ён",
+      "▁Al liance",
+      "▁All iance",
+      "▁re lay",
+      "▁rel ay",
+      "▁rela y",
+      "Br o",
+      "B ro",
+      "jö n",
+      "j ön",
+      "▁B aud",
+      "▁Ba ud",
+      "▁Bau d",
+      "▁B ian",
+      "▁Bi an",
+      "') [",
+      "' )[",
+      "чи в",
+      "▁P oss",
+      "▁Po ss",
+      "▁Pos s",
+      "▁Mitg lieder",
+      "▁Mitglied er",
+      "▁n ev",
+      "▁ne v",
+      "Dan iel",
+      "▁t ends",
+      "▁ten ds",
+      "▁tend s",
+      "▁compag nie",
+      "▁liv res",
+      "▁livre s",
+      "lu b",
+      "l ub",
+      "▁ ▁",
+      "▁▁ ▁▁",
+      "▁▁▁ ▁",
+      "▁ ▁▁▁",
+      "▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁",
+      "▁▁▁▁ ▁",
+      "▁▁▁ ▁▁",
+      "▁ ▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁",
+      "▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁",
+      "▁ ▁▁",
+      "▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁▁▁"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/output_hf_model_openx/tokenizer.model b/output_hf_model_openx/tokenizer.model
new file mode 100644
index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899
--- /dev/null
+++ b/output_hf_model_openx/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/output_hf_model_openx/tokenizer_config.json b/output_hf_model_openx/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d39defd338dee423ae0a71abfd7ea69a44fb1e52
--- /dev/null
+++ b/output_hf_model_openx/tokenizer_config.json
@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/prismatic/__init__.py b/prismatic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fad1d6a59fcb09f71bf70a2a9f3b890f8476c18f
--- /dev/null
+++ b/prismatic/__init__.py
@@ -0,0 +1 @@
+from .models import available_model_names, available_models, get_model_description, load
diff --git a/prismatic/conf/__init__.py b/prismatic/conf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0af60ce04bf5b23d2cec9380f575d523e61997f
--- /dev/null
+++ b/prismatic/conf/__init__.py
@@ -0,0 +1,3 @@
+from .datasets import DatasetConfig, DatasetRegistry
+from .models import ModelConfig, ModelRegistry
+from .vla import VLAConfig, VLARegistry
diff --git a/prismatic/conf/datasets.py b/prismatic/conf/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..897ab3092e232321628f284a5e1926db21feb2bf
--- /dev/null
+++ b/prismatic/conf/datasets.py
@@ -0,0 +1,133 @@
+"""
+datasets.py
+
+Draccus Dataclass Definition for a DatasetConfig object, with various registered subclasses for each dataset variant
+and processing scheme. A given dataset variant (e.g., `llava-lightning`) configures the following attributes:
+    - Dataset Variant (Identifier) --> e.g., "llava-v15"
+    - Align Stage Dataset Components (annotations, images)
+    - Finetune Stage Dataset Components (annotations, images)
+    - Dataset Root Directory (Path)
+"""
+
+from dataclasses import dataclass
+from enum import Enum, unique
+from pathlib import Path
+from typing import Tuple
+
+from draccus import ChoiceRegistry
+
+
+@dataclass
+class DatasetConfig(ChoiceRegistry):
+    # fmt: off
+    dataset_id: str                                 # Unique ID that fully specifies a dataset variant
+
+    # Dataset Components for each Stage in < align | finetune >
+    align_stage_components: Tuple[Path, Path]       # Path to annotation file and images directory for `align` stage
+    finetune_stage_components: Tuple[Path, Path]    # Path to annotation file and images directory for `finetune` stage
+
+    dataset_root_dir: Path                          # Path to dataset root directory; others paths are relative to root
+    # fmt: on
+
+
+# [Reproduction] LLaVa-v15 (exact dataset used in all public LLaVa-v15 models)
+@dataclass
+class LLaVa_V15_Config(DatasetConfig):
+    dataset_id: str = "llava-v15"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_mix665k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# [Multimodal-Only] LLava-v15 WITHOUT the Language-Only ShareGPT Data (No Co-Training)
+@dataclass
+class LLaVa_Multimodal_Only_Config(DatasetConfig):
+    dataset_id: str = "llava-multimodal"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_stripped625k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LVIS-Instruct-4V
+@dataclass
+class LLaVa_LVIS4V_Config(DatasetConfig):
+    dataset_id: str = "llava-lvis4v"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lvis4v_mix888k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LRV-Instruct
+@dataclass
+class LLaVa_LRV_Config(DatasetConfig):
+    dataset_id: str = "llava-lrv"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lrv_mix1008k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# LLaVa-v15 + LVIS-Instruct-4V + LRV-Instruct
+@dataclass
+class LLaVa_LVIS4V_LRV_Config(DatasetConfig):
+    dataset_id: str = "llava-lvis4v-lrv"
+
+    align_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-laion-cc-sbu-558k/chat.json"),
+        Path("download/llava-laion-cc-sbu-558k/"),
+    )
+    finetune_stage_components: Tuple[Path, Path] = (
+        Path("download/llava-v1.5-instruct/llava_v1_5_lvis4v_lrv_mix1231k.json"),
+        Path("download/llava-v1.5-instruct/"),
+    )
+    dataset_root_dir: Path = Path("/mnt/fsx/skaramcheti/datasets/prismatic-vlms")
+
+
+# === Define a Dataset Registry Enum for Reference & Validation =>> all *new* datasets must be added here! ===
+@unique
+class DatasetRegistry(Enum):
+    # === LLaVa v1.5 ===
+    LLAVA_V15 = LLaVa_V15_Config
+
+    LLAVA_MULTIMODAL_ONLY = LLaVa_Multimodal_Only_Config
+
+    LLAVA_LVIS4V = LLaVa_LVIS4V_Config
+    LLAVA_LRV = LLaVa_LRV_Config
+
+    LLAVA_LVIS4V_LRV = LLaVa_LVIS4V_LRV_Config
+
+    @property
+    def dataset_id(self) -> str:
+        return self.value.dataset_id
+
+
+# Register Datasets in Choice Registry
+for dataset_variant in DatasetRegistry:
+    DatasetConfig.register_subclass(dataset_variant.dataset_id, dataset_variant.value)
diff --git a/prismatic/conf/models.py b/prismatic/conf/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f507b0dd0d7df45f1d12de304425753a04aa732
--- /dev/null
+++ b/prismatic/conf/models.py
@@ -0,0 +1,584 @@
+"""
+models.py
+
+Draccus Dataclass Definition for a ModelConfig object, with various registered subclasses for each model family and
+variant thereof. A given model variant configures the following attributes:
+    - Pretrained Visual Representation (e.g., OpenAI CLIP ViT-L/14) + Pretrained LLM Backbone (e.g., LLaMa-2 7B)
+    - VLM Configuration + Parameters (e.g., MLP Projector, Image Preprocessing, etc.)
+    - [Optional] Stage 1 (`align`) Optimization Hyperparameters
+    - Stage 2 (`finetune`) Optimization Hyperparameters
+"""
+
+from dataclasses import dataclass
+from enum import Enum, unique
+from typing import Optional
+
+from draccus import ChoiceRegistry
+
+
+@dataclass
+class ModelConfig(ChoiceRegistry):
+    # fmt: off
+    model_id: str                                           # Unique Model ID that fully specifies a given variant
+    arch_specifier: str                                     # Architecture specifier string (e.g., "gelu-mlp")
+
+    # Pretrained Backbones
+    vision_backbone_id: str                                 # Pretrained Visual Featurizer (from TIMM) to load
+    llm_backbone_id: str                                    # Pretrained LLM (from HF Transformers) to load
+
+    # Backbone Parameters
+    image_resize_strategy: str                              # Resizing strategy in < crop | letterbox | corner-pad >
+    llm_max_length: int                                     # Maximum context length for LLM (can be < than max!)
+
+    # === Multi-Stage Optimization Hyperparameters ===
+    # By default, we assume an AdamW optimizer with FSDP (Gradient Sharding or Full Sharding depending on stage)
+
+    # Align Stage Optimization Parameters
+    align_epochs: int                                       # Epochs to Run (in case `max_steps` is not specified)
+    align_max_steps: Optional[int]                          # [Optional] Max Gradient Steps (overrides epochs)
+    align_global_batch_size: int                            # Global Batch Size (divided across processes)
+    align_per_device_batch_size: int                        # Per-Device Batch Size (per-process)
+                                                            #   => # of accumulation steps is auto-computed
+
+    align_learning_rate: float                              # Peak Learning Rate (lr_scheduler sets warmup/decay)
+    align_weight_decay: float                               # Weight Decay for AdamW Optimizer
+    align_max_grad_norm: float                              # Max Grad Norm (for global gradient clipping)
+    align_lr_scheduler_type: str                            # LR Scheduler (default: "linear-warmup+cosine-decay")
+    align_warmup_ratio: float                               # Fraction of total steps to warmup
+
+    align_train_strategy: str                               # Align Train Strategy (default: "fsdp-shard-grad-op")
+
+    # Finetune Stage Optimization Parameters
+    finetune_epochs: int                                    # Epochs to Run (in case `max_steps` is not specified)
+    finetune_max_steps: Optional[int]                       # [Optional] Max Gradient Steps (overrides epochs)
+    finetune_global_batch_size: int                         # Global Batch Size (divided across processes)
+    finetune_per_device_batch_size: int                     # Per-Device Batch Size (per-process)
+                                                            #   => # of accumulation steps is auto-computed
+
+    finetune_learning_rate: float                           # Peak Learning Rate (lr_scheduler sets warmup/decay)
+    finetune_weight_decay: float                            # Weight Decay for AdamW Optimizer
+    finetune_max_grad_norm: float                           # Max Grad Norm (for global gradient clipping)
+    finetune_lr_scheduler_type: str                         # LR Scheduler (default: "linear-warmup+cosine-decay")
+    finetune_warmup_ratio: float                            # Fraction of total steps to warmup
+
+    finetune_train_strategy: str                            # Finetune Train Strategy (default: "fsdp-full-shard")
+
+    # Enable Gradient/Activation Checkpointing (for the LLM Backbone)
+    enable_gradient_checkpointing: bool = True
+
+    # Enable Traditional Mixed Precision Training via Torch Native AMP (`autocast`)
+    enable_mixed_precision_training: bool = True            # Whether to enable mixed precision training
+    reduce_in_full_precision: bool = False                  # Whether to run gradient reduction in FP32
+
+    # fmt: on
+
+
+# === LLaVa v1.5 Reproduction - Fully Specified Configurations ===
+@dataclass
+class LLaVa_v15_Reproduction_7B(ModelConfig):
+    model_id: str = "reproduction-llava-v15+7b"
+    arch_specifier: str = "gelu-mlp"
+
+    vision_backbone_id: str = "clip-vit-l-336px"
+    llm_backbone_id: str = "vicuna-v15-7b"
+
+    image_resize_strategy: str = "letterbox"
+    llm_max_length: int = 2048
+
+    # Align Stage Optimization Parameters
+    align_epochs: int = 1
+    align_max_steps: Optional[int] = None
+    align_global_batch_size: int = 256
+    align_per_device_batch_size: int = 16
+
+    align_learning_rate: float = 1e-3
+    align_weight_decay: float = 0.0
+    align_max_grad_norm: float = 1.0
+    align_lr_scheduler_type: str = "linear-warmup+cosine-decay"
+    align_warmup_ratio: float = 0.03
+
+    align_train_strategy: str = "fsdp-shard-grad-op"
+
+    # Finetune Stage Optimization Parameters
+    finetune_epochs: int = 1
+    finetune_max_steps: Optional[int] = None
+    finetune_global_batch_size: int = 128
+    finetune_per_device_batch_size: int = 16
+
+    finetune_learning_rate: float = 2e-5
+    finetune_weight_decay: float = 0.1
+    finetune_max_grad_norm: float = 1.0
+    finetune_lr_scheduler_type: str = "linear-warmup+cosine-decay"
+    finetune_warmup_ratio: float = 0.03
+
+    finetune_train_strategy: str = "fsdp-full-shard"
+
+
+@dataclass
+class LLaVa_v15_Reproduction_13B(LLaVa_v15_Reproduction_7B):
+    model_id: str = "reproduction-llava-v15+13b"
+    llm_backbone_id: str = "vicuna-v15-13b"
+
+
+# === Section 4.1 :: Optimization Procedure ===
+
+
+# Section 4.1A :: 🚀 --> Necessity of Multi-Stage Training
+@dataclass
+class Exp_7B_One_Stage(LLaVa_v15_Reproduction_7B):
+    model_id: str = "one-stage+7b"
+    arch_specifier: str = "no-align+gelu-mlp"
+
+
+@dataclass
+class Exp_13B_One_Stage(LLaVa_v15_Reproduction_13B):
+    model_id: str = "one-stage+13b"
+    arch_specifier: str = "no-align+gelu-mlp"
+
+
+# Section 4.1B :: 🛠️ --> Full Finetuning through Visual Backbones
+#   =>> Note :: Run with `--stage full-finetune`
+@dataclass
+class Exp_7B_Full_Finetune_Multi_Stage(LLaVa_v15_Reproduction_7B):
+    model_id: str = "full-ft-multi-stage+7b"
+
+
+@dataclass
+class Exp_7B_Full_Finetune_One_Stage(Exp_7B_One_Stage):
+    model_id: str = "full-ft-one-stage+7b"
+
+
+# === Section 4.2 :: Image Processing and Visual Representations ===
+
+
+# Section 4.2A :: 📸 --> Choosing a Pretrained Representation
+@dataclass
+class Exp_7B_IN1K_ViT_L_p16_224px(Exp_7B_One_Stage):
+    model_id: str = "in1k-224px+7b"
+    vision_backbone_id: str = "in1k-vit-l"
+
+
+@dataclass
+class Exp_7B_DINOv2_ViT_L_p14_224px(Exp_7B_One_Stage):
+    model_id: str = "dinov2-224px+7b"
+    vision_backbone_id: str = "dinov2-vit-l"
+
+
+@dataclass
+class Exp_7B_CLIP_ViT_L_p14_224px(Exp_7B_One_Stage):
+    model_id: str = "clip-224px+7b"
+    vision_backbone_id: str = "clip-vit-l"
+
+
+@dataclass
+class Exp_7B_SigLIP_ViT_SO_p14_224px(Exp_7B_One_Stage):
+    model_id: str = "siglip-224px+7b"
+    vision_backbone_id: str = "siglip-vit-so400m"
+
+
+# Section 4.2B :: 📐 --> Choosing an Image Preprocessing Strategy
+@dataclass
+class Exp_7B_CLIP_ViT_L_p14_336px_Resize_Crop(Exp_7B_One_Stage):
+    model_id: str = "clip-336px-resize-crop+7b"
+    image_resize_strategy: str = "resize-crop"
+
+
+@dataclass
+class Exp_7B_CLIP_ViT_L_p14_336px_Resize_Naive(Exp_7B_One_Stage):
+    model_id: str = "clip-336px-resize-naive+7b"
+    image_resize_strategy: str = "resize-naive"
+
+
+@dataclass
+class Exp_7B_SigLIP_ViT_SO_p14_384px_Letterbox(Exp_7B_One_Stage):
+    model_id: str = "siglip-384px-letterbox+7b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "letterbox"
+
+
+@dataclass
+class Exp_7B_SigLIP_ViT_SO_p14_384px_Resize_Crop(Exp_7B_One_Stage):
+    model_id: str = "siglip-384px-resize-crop+7b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "resize-crop"
+
+
+@dataclass
+class Exp_7B_SigLIP_ViT_SO_p14_384px_Resize_Naive(Exp_7B_One_Stage):
+    model_id: str = "siglip-384px-resize-naive+7b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "resize-naive"
+
+
+# Section 4.2D :: 🥞 --> Stacking/Ensembling Visual Representations
+@dataclass
+class Exp_7B_DINOCLIP_ViT_L_p14_336px_Letterbox(Exp_7B_One_Stage):
+    model_id: str = "dinoclip-336px-letterbox+7b"
+    vision_backbone_id: str = "dinoclip-vit-l-336px"
+    image_resize_strategy: str = "letterbox"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+@dataclass
+class Exp_7B_DINOCLIP_ViT_L_p14_336px_Resize_Naive(Exp_7B_One_Stage):
+    model_id: str = "dinoclip-336px-resize-naive+7b"
+    vision_backbone_id: str = "dinoclip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+@dataclass
+class Exp_7B_DINOSigLIP_ViT_L_p14_384px_Letterbox(Exp_7B_One_Stage):
+    model_id: str = "dinosiglip-384px-letterbox+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "letterbox"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+@dataclass
+class Exp_7B_DINOSigLIP_ViT_L_p14_384px_Resize_Naive(Exp_7B_One_Stage):
+    model_id: str = "dinosiglip-384px-resize-naive+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "resize-naive"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+# === Section 4.3 :: Language Models ===
+
+
+# Section 4.3A :: 📝 --> Base vs. Instruct-Tuned (Chat) LLMs
+@dataclass
+class Exp_7B_Llama2(Exp_7B_One_Stage):
+    model_id: str = "llama2+7b"
+    llm_backbone_id: str = "llama2-7b-pure"
+
+
+@dataclass
+class Exp_13B_Llama2(Exp_13B_One_Stage):
+    model_id: str = "llama2+13b"
+    llm_backbone_id: str = "llama2-13b-pure"
+
+
+# ~ Additional LLM Backbones :: LLaMa-2 Chat, Mistral v0.1, Mistral v0.1 Instruct, Phi-2 ~
+@dataclass
+class Ext_Exp_7B_Llama2_Chat(Exp_7B_One_Stage):
+    model_id: str = "llama2-chat+7b"
+    llm_backbone_id: str = "llama2-7b-chat"
+
+
+@dataclass
+class Ext_Exp_13B_Llama2_Chat(Exp_13B_One_Stage):
+    model_id: str = "llama2-chat+13b"
+    llm_backbone_id: str = "llama2-13b-chat"
+
+
+@dataclass
+class Ext_Exp_7B_Mistral_V1(Exp_7B_One_Stage):
+    model_id: str = "mistral-v0.1+7b"
+    llm_backbone_id: str = "mistral-v0.1-7b-pure"
+
+
+@dataclass
+class Ext_Exp_7B_Mistral_Instruct_V1(Exp_7B_One_Stage):
+    model_id: str = "mistral-instruct-v0.1+7b"
+    llm_backbone_id: str = "mistral-v0.1-7b-instruct"
+
+
+@dataclass
+class Ext_Exp_3B_Phi_2(Exp_7B_One_Stage):
+    model_id: str = "phi-2+3b"
+    llm_backbone_id: str = "phi-2-3b"
+
+
+# Section 4.3B :: ✌️ --> Co-training on Language-only Data
+#   =>> Note :: Run with `--dataset.type "llava-multimodal" (multimodal data only / no co-training)
+@dataclass
+class Exp_7B_Vicuna_No_Cotraining(Exp_7B_One_Stage):
+    model_id: str = "vicuna-no-cotraining+7b"
+
+
+@dataclass
+class Exp_7B_Llama2_No_Cotraining(Exp_7B_One_Stage):
+    model_id: str = "llama2-no-cotraining+7b"
+    llm_backbone_id: str = "llama2-7b-pure"
+
+
+# === Section 4.4 :: Scaling Properties - Train Time & Data ===
+
+
+# Section 4.4A :: ⏰ --> Scaling Train Time
+@dataclass
+class Exp_7B_1p25_Epochs(Exp_7B_One_Stage):
+    model_id: str = "train-1.25-epochs+7b"
+    finetune_max_steps: int = 6500
+
+
+@dataclass
+class Exp_7B_1p5_Epochs(Exp_7B_One_Stage):
+    model_id: str = "train-1.5-epochs+7b"
+    finetune_max_steps: int = 7800
+
+
+@dataclass
+class Exp_7B_2_Epochs(Exp_7B_One_Stage):
+    model_id: str = "train-2-epochs+7b"
+    finetune_epochs: int = 2
+
+
+@dataclass
+class Exp_7B_3_Epochs(Exp_7B_One_Stage):
+    model_id: str = "train-3-epochs+7b"
+    finetune_epochs: int = 3
+
+
+# Section 4.4B :: 📚 --> Scaling Data
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v"`
+@dataclass
+class Exp_7B_LLaVa_LVIS4V(Exp_7B_One_Stage):
+    model_id: str = "llava-lvis4v+7b"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lrv"`
+@dataclass
+class Exp_7B_LLaVa_LRV(Exp_7B_One_Stage):
+    model_id: str = "llava-lrv+7b"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Exp_7B_LLaVa_LVIS4V_LRV(Exp_7B_One_Stage):
+    model_id: str = "llava-lvis4v-lrv+7b"
+
+
+# === Section 5 :: Prisms ===
+
+
+# Prism-CLIP
+@dataclass
+class Prism_7B_CLIP_Controlled(Exp_7B_One_Stage):
+    model_id: str = "prism-clip-controlled+7b"
+    vision_backbone_id: str = "clip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+
+
+@dataclass
+class Prism_13B_CLIP_Controlled(Exp_13B_One_Stage):
+    model_id: str = "prism-clip-controlled+13b"
+    vision_backbone_id: str = "clip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_7B_CLIP(Exp_7B_One_Stage):
+    model_id: str = "prism-clip+7b"
+    vision_backbone_id: str = "clip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    finetune_epochs: int = 2
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_13B_CLIP(Exp_13B_One_Stage):
+    model_id: str = "prism-clip+13b"
+    vision_backbone_id: str = "clip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+    finetune_epochs: int = 2
+
+
+# Prism-SigLIP
+@dataclass
+class Prism_7B_SigLIP_Controlled(Exp_7B_One_Stage):
+    model_id: str = "prism-siglip-controlled+7b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+
+
+@dataclass
+class Prism_13B_SigLIP_Controlled(Exp_13B_One_Stage):
+    model_id: str = "prism-siglip-controlled+13b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_7B_SigLIP(Exp_7B_One_Stage):
+    model_id: str = "prism-siglip+7b"
+    vision_backbone_id: str = "siglip-vit-so400m-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    finetune_epochs: int = 2
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_13B_SigLIP(Exp_13B_One_Stage):
+    model_id: str = "prism-siglip+13b"
+    vision_backbone_id: str = "clip-vit-l-336px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+    finetune_epochs: int = 2
+
+
+# Prism-DINOSigLIP
+@dataclass
+class Prism_7B_DINOSigLIP_Controlled(Exp_7B_One_Stage):
+    model_id: str = "prism-dinosiglip-controlled+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+@dataclass
+class Prism_13B_DINOSigLIP_Controlled(Exp_13B_One_Stage):
+    model_id: str = "prism-dinosiglip-controlled+13b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_7B_DINOSigLIP(Exp_7B_One_Stage):
+    model_id: str = "prism-dinosiglip+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+    finetune_epochs: int = 2
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_13B_DINOSigLIP(Exp_13B_One_Stage):
+    model_id: str = "prism-dinosiglip+13b"
+    vision_backbone_id: str = "dinosiglip-vit-so-384px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-13b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+    finetune_epochs: int = 2
+
+
+# [Inference-Optimized] 224px Prisms
+@dataclass
+class Opt_7B_DINOSigLIP_ViT_SO_p14_224px_Resize_Naive(Exp_7B_One_Stage):
+    model_id: str = "dinosiglip-224px-resize-naive+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-224px"
+    image_resize_strategy: str = "resize-naive"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+@dataclass
+class Prism_7B_DINOSigLIP_224px_Controlled(Exp_7B_One_Stage):
+    model_id: str = "prism-dinosiglip-224px-controlled+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-224px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+
+
+#   =>> Note :: Run with `--dataset.type "llava-lvis4v-lrv"`
+@dataclass
+class Prism_7B_DINOSigLIP_224px(Exp_7B_One_Stage):
+    model_id: str = "prism-dinosiglip-224px+7b"
+    vision_backbone_id: str = "dinosiglip-vit-so-224px"
+    image_resize_strategy: str = "resize-naive"
+    llm_backbone_id: str = "llama2-7b-pure"
+    arch_specifier: str = "no-align+fused-gelu-mlp"
+    finetune_epochs: int = 2
+
+
+# === Define a Model Registry Enum for Reference & Validation ===
+@unique
+class ModelRegistry(Enum):
+    # === LLaVa v1.5 Base Reproductions ===
+    REPRODUCTION_7B = LLaVa_v15_Reproduction_7B
+    REPRODUCTION_13B = LLaVa_v15_Reproduction_13B
+
+    # === Section 4.1 :: Optimization Procedure ===
+    EXP_ONE_STAGE_7B = Exp_7B_One_Stage
+    EXP_ONE_STAGE_13B = Exp_13B_One_Stage
+
+    EXP_FULL_FT_MULTI_STAGE = Exp_7B_Full_Finetune_Multi_Stage
+    EXP_FULL_FT_ONE_STAGE = Exp_7B_Full_Finetune_One_Stage
+
+    # === Section 4.2 :: Image Processing and Visual Representations ===
+    EXP_IN1K_224PX = Exp_7B_IN1K_ViT_L_p16_224px
+    EXP_DINOV2_224PX = Exp_7B_DINOv2_ViT_L_p14_224px
+    EXP_CLIP_224PX = Exp_7B_CLIP_ViT_L_p14_224px
+    EXP_SIGLIP_224PX = Exp_7B_SigLIP_ViT_SO_p14_224px
+
+    EXP_CLIP_336PX_RESIZE_CROP = Exp_7B_CLIP_ViT_L_p14_336px_Resize_Crop
+    EXP_CLIP_336PX_RESIZE_NAIVE = Exp_7B_CLIP_ViT_L_p14_336px_Resize_Naive
+    EXP_SIGLIP_384PX_LETTERBOX = Exp_7B_SigLIP_ViT_SO_p14_384px_Letterbox
+    EXP_SIGLIP_384PX_RESIZE_CROP = Exp_7B_SigLIP_ViT_SO_p14_384px_Resize_Crop
+    EXP_SIGLIP_384PX_RESIZE_NAIVE = Exp_7B_SigLIP_ViT_SO_p14_384px_Resize_Naive
+
+    EXP_DINOCLIP_336PX_LETTERBOX = Exp_7B_DINOCLIP_ViT_L_p14_336px_Letterbox
+    EXP_DINOCLIP_336PX_RESIZE_NAIVE = Exp_7B_DINOCLIP_ViT_L_p14_336px_Resize_Naive
+    EXP_DINOSIGLIP_384PX_LETTERBOX = Exp_7B_DINOSigLIP_ViT_L_p14_384px_Letterbox
+    EXP_DINOSIGLIP_384PX_RESIZE_NAIVE = Exp_7B_DINOSigLIP_ViT_L_p14_384px_Resize_Naive
+
+    # === Section 4.3 :: Language Models ===
+    EXP_LLAMA2_7B = Exp_7B_Llama2
+    EXP_LLAMA2_13B = Exp_13B_Llama2
+
+    # ~ Additional LLM Backbone Experiments :: LLaMa-2 Chat, Mistral v0.1, Mistral v0.1 Instruct ~
+    EXT_EXP_LLAMA2_CHAT_7B = Ext_Exp_7B_Llama2_Chat
+    EXT_EXP_LLAMA2_CHAT_13B = Ext_Exp_13B_Llama2_Chat
+    EXT_EXP_MISTRAL_V1_7B = Ext_Exp_7B_Mistral_V1
+    EXT_EXP_MISTRAL_INSTRUCT_V1_7B = Ext_Exp_7B_Mistral_Instruct_V1
+    EXT_EXP_PHI_2_3B = Ext_Exp_3B_Phi_2
+
+    # Cotraining w/ Unimodal Data
+    EXP_VICUNA_NO_COTRAINING_7B = Exp_7B_Vicuna_No_Cotraining
+    EXP_LLAMA2_NO_COTRAINING_7B = Exp_7B_Llama2_No_Cotraining
+
+    # === Section 4.4 :: Scaling Properties - Train Time & Data ===
+    EXP_1P25_EPOCHS = Exp_7B_1p25_Epochs
+    EXP_1P5_EPOCHS = Exp_7B_1p5_Epochs
+    EXP_2_EPOCHS = Exp_7B_2_Epochs
+    EXP_3_EPOCHS = Exp_7B_3_Epochs
+
+    EXP_LLAVA_LVIS4V = Exp_7B_LLaVa_LVIS4V
+    EXP_LLAVA_LRV = Exp_7B_LLaVa_LRV
+    EXP_LLAVA_LVIS4V_LRV = Exp_7B_LLaVa_LVIS4V_LRV
+
+    # === Section 5 :: Prisms ===
+    PRISM_CLIP_CONTROLLED_7B = Prism_7B_CLIP_Controlled
+    PRISM_CLIP_CONTROLLED_13B = Prism_13B_CLIP_Controlled
+    PRISM_CLIP_7B = Prism_7B_CLIP
+    PRISM_CLIP_13B = Prism_13B_CLIP
+
+    PRISM_SIGLIP_CONTROLLED_7B = Prism_7B_SigLIP_Controlled
+    PRISM_SIGLIP_CONTROLLED_13B = Prism_13B_SigLIP_Controlled
+    PRISM_SIGLIP_7B = Prism_7B_SigLIP
+    PRISM_SIGLIP_13B = Prism_13B_SigLIP
+
+    PRISM_DINOSIGLIP_CONTROLLED_7B = Prism_7B_DINOSigLIP_Controlled
+    PRISM_DINOSIGLIP_CONTROLLED_13B = Prism_13B_DINOSigLIP_Controlled
+    PRISM_DINOSIGLIP_7B = Prism_7B_DINOSigLIP
+    PRISM_DINOSIGLIP_13B = Prism_13B_DINOSigLIP
+
+    # === Inference Optimized :: 224px Prisms ===
+    OPT_DINOSIGLIP_224PX_RESIZE_NAIVE = Opt_7B_DINOSigLIP_ViT_SO_p14_224px_Resize_Naive
+    PRISM_DINOSIGLIP_224PX_CONTROLLED_7B = Prism_7B_DINOSigLIP_224px_Controlled
+    PRISM_DINOSIGLIP_224PX_7B = Prism_7B_DINOSigLIP_224px
+
+    @property
+    def model_id(self) -> str:
+        return self.value.model_id
+
+
+# Register Models in Choice Registry
+for model_variant in ModelRegistry:
+    ModelConfig.register_subclass(model_variant.model_id, model_variant.value)
diff --git a/prismatic/conf/vla.py b/prismatic/conf/vla.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d057076dd1694fbc1dfdabea71fb52f9a6b8be9
--- /dev/null
+++ b/prismatic/conf/vla.py
@@ -0,0 +1,315 @@
+"""
+vla.py
+
+Draccus Dataclass Definition for a VLAConfig object, with various registered subclasses for each VLA experiment and
+model configuration thereof. A given VLA model (`policy`) configures the following attributes:
+    - Data Mixture (e.g., Bridge, OXE_MAGIC_SOUP, etc.)
+    - Base VLM from Prismatic Registry (e.g., `prism-dinosiglip+7b`)
+    - VLA Model Architecture / Parameters (e.g., freeze vision encoder, last layer finetuning)
+    - Training / Optimization Hyperparameters
+"""
+
+from dataclasses import dataclass
+from enum import Enum, unique
+from pathlib import Path
+from typing import Optional, Union
+
+from draccus import ChoiceRegistry
+
+
+@dataclass
+class VLAConfig(ChoiceRegistry):
+    # fmt: off
+    vla_id: str                                     # Unique VLA Policy ID that fully specifies a configuration variant
+    base_vlm: Union[str, Path]                      # Base VLM as ID/Path to Run Directory (e.g., `prism-dinosiglip+7b`)
+    freeze_vision_backbone: bool                    # Freeze Vision Backbone Parameters (akin to pretraining)
+    freeze_llm_backbone: bool                       # Freeze LLM Backbone parameters
+    unfreeze_last_llm_layer: bool                   # Unfreeze final layer of LLM (only takes effect if LLM is frozen)
+
+    # Data Mixture Parameters
+    data_mix: str                                   # Open-X Embodiment Dataset =>> Unique Mixture ID (e.g., `bridge`)
+    shuffle_buffer_size: int                        # Size of Shuffle Buffer (100K for Bridge, 1M for OXE)
+
+    # Optimization Parameters
+    epochs: int                                     # Epochs to Run (in case `max_steps` is not specified)
+    max_steps: Optional[int]                        # [Optional] Max Gradient Steps to Run (overrides `epochs`)
+
+    expected_world_size: int                        # Expected # of GPUs =>> allows us to gate training on hardware
+    global_batch_size: int                          # Global Batch Size (divided across processes / world size)
+    per_device_batch_size: int                      # Per-Device Batch Size (per-process / individual GPU)
+                                                    #   =>> # of accumulation steps is auto-computed
+
+    learning_rate: float                            # Peak Learning Rate (`lr_scheduler_type` sets warmup/decay)
+    weight_decay: float                             # Weight Decay for AdamW Optimizer
+    max_grad_norm: float                            # Max Grad Norm (for global gradient clipping)
+    lr_scheduler_type: str                          # LR Scheduler (usually: "constant" | "linear-warmup+cosine-decay")
+    warmup_ratio: float                             # Fraction of Steps to Warmup (for warmup LR schedulers)
+
+    train_strategy: str                             # Train Strategy (default "fsdp-full-shard")
+
+    # Enable Gradient/Activation Checkpointing (for the LLM Backbone)
+    enable_gradient_checkpointing: bool = True      # Enable Gradient/Activation Checkpointing during Training
+
+    # Mixed Precision Training via Torch Native AMP (`autocast`)
+    enable_mixed_precision_training: bool = True    # Enable Traditional BF16 Mixed Precision
+    reduce_in_full_precision: bool = True           # Accumulate/Reduce All-Gather Gradients in FP32 Full Precision
+
+    # fmt: on
+
+
+# === OpenVLA Training Configurations ===
+
+
+# = [8 GPU] Fast Iteration =>> SigLIP 224px + Bridge =
+@dataclass
+class Exp_SigLIP_224px_Bridge(VLAConfig):
+    vla_id: str = "siglip-224px+mx-bridge"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+
+    freeze_vision_backbone: bool = False
+    freeze_llm_backbone: bool = False
+    unfreeze_last_llm_layer: bool = False
+
+    # Data Mixture Parameters
+    data_mix: str = "droid"
+    shuffle_buffer_size: int = 256_000
+
+    # Optimization Parameters
+    epochs: int = 1000
+    max_steps: Optional[int] = 20000
+
+    expected_world_size: int = 2
+    global_batch_size: int = 2
+    per_device_batch_size: int = 1
+
+    learning_rate: float = 2e-5
+    weight_decay: float = 0.0
+    max_grad_norm: float = 1.0
+    lr_scheduler_type: str = "constant"
+    warmup_ratio: float = 0.0
+
+    train_strategy: str = "fsdp-full-shard"
+
+# = [1 GPU] Testing validation adding
+@dataclass
+class Exp_DinoSigLIP_SlotAtt_224px_Droid_TrainValTest(VLAConfig):
+    vla_id: str = "prism-dinosiglip-slotatt-224px+mx-droid-trainval"
+    base_vlm: Union[str, Path] = "prism-dinosiglip-slotatt-224px+7b"
+
+    freeze_vision_backbone: bool = True
+    freeze_llm_backbone: bool = False
+    unfreeze_last_llm_layer: bool = False
+
+    # Note =>> We adopt two stages, training on a mixture including DROID for 70% of training, before resampling!
+    data_mix: str = "droid"
+    shuffle_buffer_size: int = 100 # 256_000 # 256_000
+
+    # Optimization Parameters
+    epochs: int = 20
+    max_steps: Optional[int] = 1000000
+
+    expected_world_size: int = 1
+    global_batch_size: int = 1
+    per_device_batch_size: int = 1
+
+    learning_rate: float = 2e-5
+    weight_decay: float = 0.0
+    max_grad_norm: float = 1.0
+    lr_scheduler_type: str = "constant"
+    warmup_ratio: float = 0.0
+
+    train_strategy: str = "fsdp-full-shard"
+
+# = [1 GPU] Testing validation adding
+@dataclass
+class Exp_DinoSigLIP_224px_Droid_TrainValTest(VLAConfig):
+    vla_id: str = "prism-dinosiglip-224px+mx-droid-trainval"
+    base_vlm: Union[str, Path] = "prism-dinosiglip-224px+7b"
+
+    freeze_vision_backbone: bool = False
+    freeze_llm_backbone: bool = False
+    unfreeze_last_llm_layer: bool = False
+
+    # Note =>> We adopt two stages, training on a mixture including DROID for 70% of training, before resampling!
+    data_mix: str = "droid"
+    shuffle_buffer_size: int = 256_000 # 256_000
+
+    # Optimization Parameters
+    epochs: int = 20
+    max_steps: Optional[int] = 21000 # 1000000
+
+    expected_world_size: int = 2
+    global_batch_size: int = 2
+    per_device_batch_size: int = 1
+
+    learning_rate: float = 2e-5
+    weight_decay: float = 0.0
+    max_grad_norm: float = 1.0
+    lr_scheduler_type: str = "constant"
+    warmup_ratio: float = 0.0
+
+    train_strategy: str = "fsdp-full-shard"
+
+# = [8 GPU] SigLIP 224px Frozen Vision Backbone + Bridge =
+@dataclass
+class Exp_FreezeVIT_SigLIP_224px_Bridge(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px-icy+mx-bridge"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+    freeze_vision_backbone: bool = True
+
+
+# = [8 GPU] Fast Iteration =>> DINO-SigLIP 224px + Bridge =
+@dataclass
+class Exp_DinoSigLIP_224px_Bridge(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "prism-dinosiglip-224px+mx-bridge"
+    base_vlm: Union[str, Path] = "prism-dinosiglip-224px+7b"
+
+    data_mix: str = "bridge"
+
+
+# = [64 GPU] SigLIP 224px + OXE Magic Soup =
+@dataclass
+class Exp_SigLIP_224px_OXE_Magic_Soup(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px+mx-oxe-magic-soup"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+
+    data_mix: str = "oxe_magic_soup"
+
+    expected_world_size: int = 64
+    global_batch_size: int = 2048
+    per_device_batch_size: int = 32
+
+# = [04 GPU] DINO-SigLIP 224px + DROID =
+@dataclass
+class Exp_DinoSigLIP_224px_Droid(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "prism-dinosiglip-224px+mx-droid"
+    base_vlm: Union[str, Path] = "prism-dinosiglip-224px+7b"
+
+    # Note =>> We adopt two stages, training on a mixture including DROID for 70% of training, before resampling!
+    data_mix: str = "droid"
+
+    expected_world_size: int = 2
+    global_batch_size: int = 2
+    per_device_batch_size: int = 1
+
+# = [64 GPU] DINO-SigLIP 224px + OXE Magic Soup++ =
+@dataclass
+class Exp_DinoSigLIP_224px_OXE_Magic_Soup_Plus(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "prism-dinosiglip-224px+mx-oxe-magic-soup-plus"
+    base_vlm: Union[str, Path] = "prism-dinosiglip-224px+7b"
+
+    # Note =>> We adopt two stages, training on a mixture including DROID for 70% of training, before resampling!
+    # data_mix: str = "oxe_magic_soup_plus"
+    data_mix: str = "oxe_magic_soup_plus_minus"
+
+    expected_world_size: int = 4
+    global_batch_size: int = 4
+    per_device_batch_size: int = 1
+
+
+# === OpenVLA Fine-tuning Configurations ===
+
+
+# = [8 GPU] SigLIP 224px + T-DROID =
+@dataclass
+class Exp_SigLIP_224px_TDROID_CarrotInBowl(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px+mx-tdroid_carrot_in_bowl"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+
+    data_mix: str = "tdroid_carrot_in_bowl"
+
+
+@dataclass
+class Exp_SigLIP_224px_TDROID_PourCornInPot(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px+mx-tdroid_pour_corn_in_pot"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+
+    data_mix: str = "tdroid_pour_corn_in_pot"
+
+
+# = [8 GPU] SigLIP 224px + T-DROID -- Partial Finetuning =
+@dataclass
+class Exp_SigLIP_224px_Icy_TDROID_CarrotInBowl(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px-icy+mx-tdroid_carrot_in_bowl"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+    freeze_vision_backbone: bool = True
+    freeze_llm_backbone: bool = False
+
+    data_mix: str = "tdroid_carrot_in_bowl"
+
+
+@dataclass
+class Exp_SigLIP_224px_LastLayer_TDROID_CarrotInBowl(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px-last_layer+mx-tdroid_carrot_in_bowl"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+    freeze_vision_backbone: bool = True
+    freeze_llm_backbone: bool = True
+    unfreeze_last_llm_layer: bool = True
+
+    data_mix: str = "tdroid_carrot_in_bowl"
+
+
+@dataclass
+class Exp_SigLIP_224px_Sandwich_TDROID_CarrotInBowl(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px-sandwich+mx-tdroid_carrot_in_bowl"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+    freeze_vision_backbone: bool = False
+    freeze_llm_backbone: bool = True
+    unfreeze_last_llm_layer: bool = True
+
+    data_mix: str = "tdroid_carrot_in_bowl"
+
+
+# === [8 GPU] SigLIP 224px + FrankaWipe ===
+@dataclass
+class Exp_SigLIP_224px_Droid_Wipe(Exp_SigLIP_224px_Bridge):
+    vla_id: str = "siglip-224px+mx-droid_wipe"
+    base_vlm: Union[str, Path] = "siglip-224px+7b"
+
+    data_mix: str = "droid_wipe"
+
+
+# === Define a VLA Registry Enum for Reference & Validation ===
+@unique
+class VLARegistry(Enum):
+    # Sanity Check Configurations =>> BridgeV2
+    SIGLIP_224PX_MX_BRIDGE = Exp_SigLIP_224px_Bridge
+    DINOSIGLIP_224PX_MX_BRIDGE = Exp_DinoSigLIP_224px_Bridge
+
+    # SigLIP Frozen Backbone Experiment
+    FREEZE_SIGLIP_224PX_MX_BRIDGE = Exp_FreezeVIT_SigLIP_224px_Bridge
+
+    # [OpenVLA v0.1 7B] SigLIP 224px + OXE Magic Soup
+    SIGLIP_224PX_MX_OXE_MAGIC_SOUP = Exp_SigLIP_224px_OXE_Magic_Soup
+
+    # [OpenVLA 7B] DINO + SigLIP 224px + DROID TRAINVALTEST
+    DINOSIGLIP_224PX_MX_DROID_TRAINVALTEST = Exp_DinoSigLIP_224px_Droid_TrainValTest
+
+    # [OpenVLA 7B] DINO + SigLIP 224px + DROID TRAINVALTEST
+    DINOSIGLIP_SLOTATT_224PX_MX_DROID_TRAINVALTEST = Exp_DinoSigLIP_SlotAtt_224px_Droid_TrainValTest
+
+    # [OpenVLA 7B] DINO + SigLIP 224px + DROID
+    DINOSIGLIP_224PX_MX_DROID = Exp_DinoSigLIP_224px_Droid
+
+    # [OpenVLA 7B] DINO + SigLIP 224px + OXE Magic Soup++
+    DINOSIGLIP_224PX_MX_OXE_MAGIC_SOUP_PLUS = Exp_DinoSigLIP_224px_OXE_Magic_Soup_Plus
+
+    # === TDROID Fine-tuning Configs ===
+    SIGLIP_224PX_MX_TDROID_CARROT_IN_BOWL = Exp_SigLIP_224px_TDROID_CarrotInBowl
+    SIGLIP_224PX_MX_TDROID_POUR_CORN_IN_POT = Exp_SigLIP_224px_TDROID_PourCornInPot
+
+    SIGLIP_224PX_ICY_MX_TDROID_CARROT_IN_BOWL = Exp_SigLIP_224px_Icy_TDROID_CarrotInBowl
+    SIGLIP_224PX_LASTLAYER_MX_TDROID_CARROT_IN_BOWL = Exp_SigLIP_224px_LastLayer_TDROID_CarrotInBowl
+    SIGLIP_224PX_SANDWICH_MX_TDROID_CARROT_IN_BOWL = Exp_SigLIP_224px_Sandwich_TDROID_CarrotInBowl
+
+    # === DROID Fine-tuning Configs ===
+    SIGLIP_224PX_MX_DROID_WIPE = Exp_SigLIP_224px_Droid_Wipe
+
+    @property
+    def vla_id(self) -> str:
+        return self.value.vla_id
+
+
+# Register VLAs in Choice Registry
+for vla_variant in VLARegistry:
+    VLAConfig.register_subclass(vla_variant.vla_id, vla_variant.value)
diff --git a/prismatic/debug_tools.py b/prismatic/debug_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..f66a836ab003e1799ebd4ac2c31ddace9641fc55
--- /dev/null
+++ b/prismatic/debug_tools.py
@@ -0,0 +1,277 @@
+import torch
+import cv2
+import numpy as np
+
+# Define the denormalization function
+def denormalize_from_DINO(tensor, mean = (0.484375, 0.455078125, 0.40625), std = (0.228515625, 0.2236328125, 0.224609375)):
+    if len(tensor.shape) == 3:
+        dtype = tensor.dtype
+        mean = torch.tensor(mean, dtype=dtype).reshape(3, 1, 1)
+        std = torch.tensor(std, dtype=dtype).reshape(3, 1, 1)
+        return tensor * std + mean
+    elif len(tensor.shape) == 4:
+        dtype = tensor.dtype
+        mean = torch.tensor(mean, dtype=dtype).reshape(1, 3, 1, 1)
+        std = torch.tensor(std, dtype=dtype).reshape(1, 3, 1, 1)
+        return tensor * std + mean
+    elif len(tensor.shape) == 5: # bz, tz, cz, hz, wz
+        dtype = tensor.dtype
+        mean = torch.tensor(mean, dtype=dtype).reshape(1, 1, 3, 1, 1)
+        std = torch.tensor(std, dtype=dtype).reshape(1, 1, 3, 1, 1)
+        return tensor * std + mean
+
+    else:
+        print('Error shape', tensor.shape)
+        1/0
+
+def torch2numpy(tensor):
+    if not torch.is_tensor(tensor) and len(tensor.shape) == 3:
+        raise TypeError('Input should be a 2D image tensor [C, H, W].')
+    tensor = torch.permute(tensor.detach(), [1, 2, 0]).cpu().numpy()
+    tensor = (tensor * 255).astype(np.uint8)
+    return tensor
+
+def draw_bbox(image, bbox, label=None):    
+    # Get the image dimensions
+    img_height, img_width, _ = image.shape
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # Denormalize the bbox coordinates
+    x_center, y_center, width, height = bbox.tolist()
+    x_center, y_center, width, height = (
+        x_center * img_width,
+        y_center * img_height,
+        width * img_width,
+        height * img_height,
+    )
+
+    # Calculate the top-left and bottom-right coordinates
+    x_min = int(x_center - width / 2)
+    y_min = int(y_center - height / 2)
+    x_max = int(x_center + width / 2)
+    y_max = int(y_center + height / 2)
+
+    # Ensure coordinates are within bounds
+    x_min = max(0, min(img_width - 1, x_min))
+    y_min = max(0, min(img_height - 1, y_min))
+    x_max = max(0, min(img_width - 1, x_max))
+    y_max = max(0, min(img_height - 1, y_max))
+
+    # Draw the bounding box
+    color = (0,255,0)
+    thickness = 2
+    image = cv2.rectangle(image, pt1=(x_min,y_min), pt2=(x_max,y_max), 
+                          color=color, thickness=thickness)
+
+    if label is not None:
+        # Place the label above the bounding box
+        label_position = (x_min, max(y_min - 10, 0))  # Offset above the top-left corner
+        image = cv2.putText(image, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 
+                    0.5, (0, 255, 0), 1, cv2.LINE_AA)
+        
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image
+
+
+def write_image(img, name, bboxes=None, labels=None):
+    if bboxes is None:
+        cv2.imwrite('./DEBUG/'+name+'.png', cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+    elif labels is None:
+        for ind in range(bboxes.shape[0]):
+            bbox = bboxes[ind]
+            img = draw_bbox(img, bbox)
+        cv2.imwrite('./DEBUG/'+name+'.png', cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+    else:
+        for label, bbox in zip(labels, bboxes):
+            img = draw_bbox(img, bbox, label)
+        cv2.imwrite('./DEBUG/'+name+'.png', cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+
+
+import torch
+import numpy as np
+import os
+from PIL import Image, ImageDraw
+
+def generate_colors(num_colors):
+    """Generate a set of distinguishable colors."""
+    np.random.seed(42)  # Ensure consistent colors
+    colors = [
+        tuple(np.random.randint(0, 255, size=3).tolist()) for _ in range(num_colors)
+    ]
+    return colors
+
+def draw_bboxes_on_images(
+    images, bboxes, save_dir="output_images", objectness_threshold=None
+):
+    """
+    Draw bounding boxes on images, concatenate images across the window dimension, and save the results.
+    Uses predefined colors for consistency across the window and includes the objectness threshold in filenames.
+
+    Args:
+        images (torch.Tensor): Tensor of shape [bz, window, cc, h, w] with values in range [0, 1].
+        bboxes (torch.Tensor): Tensor of shape [bz, window, box_num, 5] (cx, cy, w, h, objectness).
+        save_dir (str): Directory to save the output images.
+        objectness_threshold (float, optional): If set, only boxes with objectness >= threshold are drawn.
+
+    Returns:
+        None (images are saved to `save_dir`).
+    """
+    bz, window, cc, h, w = images.shape
+    box_num = bboxes.shape[2]  # Get number of objects per window
+    os.makedirs(save_dir, exist_ok=True)
+
+    # Generate predefined colors (1 color per object index)
+    predefined_colors = generate_colors(box_num)
+
+    # Convert threshold to string for filename
+    threshold_str = f"th{objectness_threshold:.2f}" if objectness_threshold is not None else "thNone"
+
+    for b in range(bz):
+        images_list = []
+
+        for w_idx in range(window):
+            # Convert image tensor to numpy and scale to [0, 255]
+            img_np = (images[b, w_idx].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
+            img_pil = Image.fromarray(img_np)
+
+            # Draw bounding boxes
+            draw = ImageDraw.Draw(img_pil)
+            for obj_idx, box in enumerate(bboxes[b, w_idx]):
+                cx, cy, bw, bh, objectness = box.cpu().numpy()
+
+                # Apply objectness filtering if threshold is set
+                if objectness_threshold is None or objectness >= objectness_threshold:
+                    x1 = int((cx - bw / 2) * w)
+                    y1 = int((cy - bh / 2) * h)
+                    x2 = int((cx + bw / 2) * w)
+                    y2 = int((cy + bh / 2) * h)
+
+                    # Assign a predefined color based on object index
+                    color = predefined_colors[obj_idx]
+
+                    draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
+
+            images_list.append(img_pil)
+
+        # Concatenate images horizontally across the window dimension
+        concatenated_image = np.hstack([np.array(img) for img in images_list])
+        concatenated_pil = Image.fromarray(concatenated_image)
+
+        # Save the concatenated image with threshold in filename
+        filename = f"batch_{b}_{threshold_str}.png"
+        concatenated_pil.save(os.path.join(save_dir, filename))
+
+    print(f"Images saved in {save_dir}")
+
+
+def apply_colormap(depth_map):
+    """Applies a colormap to a grayscale depth map (normalized between 0-255)."""
+    depth_map = (depth_map * 255).astype(np.uint8)  # Normalize to [0, 255]
+    depth_map_color = cv2.applyColorMap(depth_map, cv2.COLORMAP_JET)  # Apply color mapping
+    return depth_map_color
+
+import pickle
+def draw_bboxes_with_depth(
+    images, bboxes, depths=None, window_step=1, save_dir="output_images", objectness_threshold=None, interactable_only=False, **kwargs
+):
+    """
+    Draw bounding boxes on images, optionally overlay depth maps inside bounding box regions, 
+    concatenate images across the window, and save.
+
+    Args:
+        images (torch.Tensor): Tensor of shape [bz, window, cc, h, w] with values in range [0, 1].
+        bboxes (torch.Tensor): Tensor of shape [bz, window, box_num, 5] (cx, cy, w, h, objectness).
+        depths (torch.Tensor or None): Tensor of shape [bz, window, box_num, 1, 40, 40] with values in range [0, 1], or None.
+        save_dir (str): Directory to save the output images.
+        objectness_threshold (float, optional): If set, only boxes with objectness >= threshold are drawn.
+
+    Returns:
+        None (images are saved to `save_dir`).
+    """
+    bz, window, cc, h, w = images.shape
+    box_num = bboxes.shape[2]  # Get number of objects per window
+    os.makedirs(save_dir, exist_ok=True)
+
+    # Generate predefined colors (1 color per object index)
+    predefined_colors = generate_colors(box_num)
+
+    # Convert threshold to string for filename
+    threshold_str = f"th{objectness_threshold:.2f}" if objectness_threshold is not None else "thNone"
+    depth_str = "depth" if depths is not None else "nodepth"  # Include depth status in filename
+
+    for b in range(bz):
+        raw_images_list = []
+        images_list = []
+
+        for w_idx in np.arange(0, window, window_step):
+            # Convert image tensor to numpy and scale to [0, 255]
+            img_np = (images[b, w_idx].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
+            img_pil = img_np.copy()
+
+            # Draw bounding boxes
+            for obj_idx, box in enumerate(bboxes[b, w_idx]):
+                if not interactable_only and obj_idx not in [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 14, 15]:
+                    continue      
+                cx, cy, bw, bh, objectness = box.cpu().numpy()[:5]
+                if len(box.cpu().numpy()) == 6:
+                    interactable_score = box.cpu().numpy()[5]
+                else:
+                    interactable_score = 1
+
+                # Apply objectness filtering if threshold is set
+                if objectness_threshold is None or objectness >= objectness_threshold:
+                    if (interactable_only and interactable_score < 0.5):
+                        continue
+                    x1 = int((cx - bw / 2) * w)
+                    y1 = int((cy - bh / 2) * h)
+                    x2 = int((cx + bw / 2) * w)
+                    y2 = int((cy + bh / 2) * h)
+
+                    # Assign a predefined color based on object index
+                    color = predefined_colors[obj_idx]
+                    cv2.rectangle(img_pil, (x1, y1), (x2, y2), color, thickness=3)
+                        
+                    # Overlay depth map inside the bounding box (only if depths is provided)
+                    if depths is not None:
+                        depth_map = depths[b, w_idx, obj_idx, 0].cpu().numpy()  # Shape (40, 40)
+                        depth_resized = cv2.resize((depth_map*255).astype(np.uint8), (x2 - x1, y2 - y1))
+                        depth_colored = cv2.applyColorMap(depth_resized, cv2.COLORMAP_JET)  # Apply colormap
+                        alpha = 0.5
+                        img_pil[y1:y2, x1:x2] = cv2.addWeighted(img_pil[y1:y2, x1:x2], 1 - alpha, depth_colored, alpha, 0)
+
+                    if interactable_only and depths is not None:
+                        tmp = img_np.copy()
+                        cv2.rectangle(tmp, (x1, y1), (x2, y2), color, thickness=3)
+                        tmp[y1:y2, x1:x2] = cv2.addWeighted(tmp[y1:y2, x1:x2], 1 - alpha, depth_colored, alpha, 0)
+                        filename = f"batch_{b}_obj{obj_idx}.png"
+                        tmp = Image.fromarray(tmp)
+                        tmp.save(os.path.join(save_dir, filename))
+                        print("interaction saved at", os.path.join(save_dir, filename))
+
+
+            images_list.append(img_pil)
+            raw_images_list.append(img_np)
+
+        # Concatenate images horizontally across the window dimension
+        concatenated_image = np.hstack([np.array(img) for img in images_list])
+        concatenated_pil = Image.fromarray(concatenated_image)
+        raw_concatenated_image = np.hstack([np.array(img) for img in raw_images_list])
+        raw_concatenated_pil = Image.fromarray(raw_concatenated_image)
+
+        # Save the concatenated image with threshold & depth status in filename
+        filename = f"batch_{b}_{threshold_str}_{depth_str}.png"
+        if 'wrist' in kwargs:
+            filename = filename.replace('batch_', 'batch_wrist_')
+        concatenated_pil.save(os.path.join(save_dir, filename))
+        filename = f"batch_{b}_raw.png"
+        if 'wrist' in kwargs:
+            filename = filename.replace('batch_', 'batch_wrist_')
+        raw_concatenated_pil.save(os.path.join(save_dir, filename))
+        if 'features' in kwargs:
+            print("saving features...")
+            pred_feat = kwargs['features'].detach().cpu()
+            # Save tensor using pickle
+            with open(os.path.join(save_dir, f"batch_{b}_{threshold_str}_{depth_str}.pkl"), "wb") as f:
+                pickle.dump(pred_feat[b], f)
+
+    print(f"Images saved in {save_dir}")
\ No newline at end of file
diff --git a/prismatic/extern/__init__.py b/prismatic/extern/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/extern/hf/__init__.py b/prismatic/extern/hf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/extern/hf/configuration_prismatic.py b/prismatic/extern/hf/configuration_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4c6121ab276ed13195f47b6372b4d3c4b31f070
--- /dev/null
+++ b/prismatic/extern/hf/configuration_prismatic.py
@@ -0,0 +1,177 @@
+"""
+configuration_prismatic.py
+
+HuggingFace-style configuration definition for Prismatic VLMs, inheriting from `transformers.PretrainedConfig`.
+Default configuration specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from transformers import PretrainedConfig
+from transformers.models.auto import CONFIG_MAPPING
+
+# === Utilities for Mapping Prismatic names to HF names ===
+# fmt: off
+VISION_BACKBONE_TO_RESOLUTION: Dict[str, List[int]] = {
+    "clip-vit-l": [224], "siglip-vit-so400m": [224], "dinov2-vit-l": [224], "in1k-vit-l": [224],
+
+    "clip-vit-l-336px": [336],
+    "siglip-vit-so400m-384px": [384],
+
+    "dinoclip-vit-l-336px": [336, 336],
+    "dinosiglip-vit-so-224px": [224, 224],
+    "dinosiglip-vit-so-384px": [384, 384],
+}
+VISION_BACKBONE_TO_TIMM_ID: Dict[str, List[str]] = {
+    "clip-vit-l": ["vit_large_patch14_clip_224.openai"],
+    "clip-vit-l-336px": ["vit_large_patch14_clip_336.openai"],
+
+    "dinov2-vit-l": ["vit_large_patch14_reg4_dinov2.lvd142m"],
+    "in1k-vit-l": ["vit_large_patch16_224.augreg_in21k_ft_in1k"],
+
+    "siglip-vit-so400m": ["vit_so400m_patch14_siglip_224"],
+    "siglip-vit-so400m-384px": ["vit_so400m_patch14_siglip_384"],
+
+    "dinoclip-vit-l-336px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_large_patch14_clip_336.openai"],
+    "dinosiglip-vit-so-224px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_224"],
+    "dinosiglip-vit-so-384px": ["vit_large_patch14_reg4_dinov2.lvd142m", "vit_so400m_patch14_siglip_384"],
+}
+TIMM_OVERRIDE_ACT_LAYER: Dict[str, List[Optional[str]]] = {
+    "clip-vit-l": ["quick_gelu"], "clip-vit-l-336px": ["quick_gelu"],
+    "dinov2-vit-l": [None], "in1k-vit-l": [None],
+    "siglip-vit-so400m": [None], "siglip-vit-so400m-384px": [None],
+    "dinoclip-vit-l-336px": [None, "quick_gelu"],
+    "dinosiglip-vit-so-224px": [None, None], "dinosiglip-vit-so-384px": [None, None]
+}
+
+LLM_BACKBONE_TO_HF_PATH = {
+    "llama2-7b-pure": "meta-llama/Llama-2-7b-hf", "llama2-13b-pure": "meta-llama/Llama-2-13b-hf",
+    "llama2-7b-chat": "meta-llama/Llama-2-7b-chat-hf", "llama2-13b-chat": "meta-llama/Llama-2-13b-chat-hf",
+
+    "vicuna-v15-7b": "lmsys/vicuna-7b-v1.5", "vicuna-v15-13b": "lmsys/vicuna-13b-v1.5",
+
+    "mistral-v0.1-7b-pure": "mistralai/Mistral-7B-v0.1",
+    "mistral-v0.1-7b-instruct": "mistralai/Mistral-7B-Instruct-v0.1",
+
+    "phi-2-3b": "microsoft/phi-2",
+}
+LLM_BACKBONE_TO_HF_METACLASS = {
+    "llama2-7b-pure": "llama", "llama2-13b-pure": "llama", "llama2-7b-chat": "llama", "llama2-13b-chat": "llama",
+    "vicuna-v15-7b": "llama", "vicuna-v15-13b": "llama",
+
+    "mistral-v0.1-7b-pure": "mistral", "mistral-v0.1-7b-instruct": "mistral",
+
+    "phi-2-3b": "phi",
+}
+
+VALID_VISION_BACKBONES = set(VISION_BACKBONE_TO_RESOLUTION.keys())
+VALID_LLM_BACKBONES = set(LLM_BACKBONE_TO_HF_PATH)
+# fmt: on
+
+
+class PrismaticConfig(PretrainedConfig):
+    model_type: str = "prismatic"
+    is_composition: bool = False
+
+    def __init__(
+        self,
+        vision_backbone_id: str = "siglip-vit-so400m",
+        llm_backbone_id: str = "vicuna-v15-7b",
+        arch_specifier: str = "no-align+gelu-mlp",
+        use_fused_vision_backbone: Optional[bool] = None,
+        image_resize_strategy: str = "letterbox",
+        text_config: Optional[Dict[str, Any]] = None,
+        llm_max_length: int = 2048,
+        pad_token_id: int = 32000,
+        pad_to_multiple_of: int = 64,
+        output_projector_states: bool = False,
+        **kwargs: str,
+    ) -> None:
+        if vision_backbone_id not in VALID_VISION_BACKBONES:
+            raise ValueError(f"Vision backbone `{vision_backbone_id}` not in {VALID_VISION_BACKBONES = }")
+
+        if llm_backbone_id not in VALID_LLM_BACKBONES:
+            raise ValueError(f"LLM backbone `{llm_backbone_id}` not in {VALID_LLM_BACKBONES = }")
+
+        # Set Prismatic Configuration Fields
+        self.vision_backbone_id = vision_backbone_id
+        self.llm_backbone_id = llm_backbone_id
+        self.arch_specifier = arch_specifier
+        self.output_projector_states = output_projector_states
+
+        # [Contract] All vision backbone parameters are lists =>> supports fused backbones with different preprocessing
+        self.use_fused_vision_backbone = (
+            use_fused_vision_backbone
+            if use_fused_vision_backbone is not None
+            else any(self.vision_backbone_id.startswith(v) for v in ["dinoclip", "dinosiglip"])
+        )
+
+        self.timm_model_ids = VISION_BACKBONE_TO_TIMM_ID[self.vision_backbone_id]
+        self.timm_override_act_layers = TIMM_OVERRIDE_ACT_LAYER[self.vision_backbone_id]
+        self.image_sizes = VISION_BACKBONE_TO_RESOLUTION[self.vision_backbone_id]
+        self.image_resize_strategy = image_resize_strategy
+
+        self.hf_llm_id = LLM_BACKBONE_TO_HF_PATH[self.llm_backbone_id]
+        self.llm_max_length = llm_max_length
+        self.pad_token_id, self.pad_to_multiple_of = pad_token_id, pad_to_multiple_of
+
+        # [IMPORTANT] HF Utilities actually look for a `text_config` field... we need to use that specific naming!
+        self.text_config = (
+            CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]](**text_config)
+            if text_config is not None
+            else CONFIG_MAPPING[LLM_BACKBONE_TO_HF_METACLASS[self.llm_backbone_id]]()
+        )
+
+        # Dispatch **kwargs to super() =>> note that `pad_token_id` collides, so we pass it in here as well...
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+
+class OpenVLAConfig(PrismaticConfig):
+    model_type: str = "openvla"
+
+    def __init__(
+        self,
+        norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
+        n_action_bins: int = 256,
+        **kwargs: str,
+    ) -> None:
+        self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
+
+        super().__init__(**kwargs)
+
+class SlotVLAConfig(OpenVLAConfig):
+    model_type: str = "slotvla"
+
+    def __init__(
+        self,
+        **kwargs: str,
+    ) -> None:
+        super().__init__(**kwargs)
+
+class SlotVLAV2Config(OpenVLAConfig):
+    model_type: str = "slotvlav2"
+
+    def __init__(
+        self,
+        **kwargs: str,
+    ) -> None:
+        super().__init__(**kwargs)
+
+class CustomOpenVLAConfig(OpenVLAConfig):
+    model_type: str = "customvla"
+
+    def __init__(
+        self,
+        **kwargs: str,
+    ) -> None:
+        super().__init__(**kwargs)
+
+
+class ObjectCentricVLAConfig(OpenVLAConfig):
+    model_type: str = "objectvla"
+
+    def __init__(
+        self,
+        **kwargs: str,
+    ) -> None:
+        super().__init__(**kwargs)
diff --git a/prismatic/extern/hf/modeling_prismatic.py b/prismatic/extern/hf/modeling_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..8649306dde50f63df62c5490f891e4b33de8281d
--- /dev/null
+++ b/prismatic/extern/hf/modeling_prismatic.py
@@ -0,0 +1,6044 @@
+"""
+modeling_prismatic.py
+
+Core HuggingFace-style PrismaticPreTrainedModel and PrismaticForConditionalGeneration class definitions, inheriting
+from the default `transformers.PretrainedModel`. Meant to be standalone and self-contained, but exactly replicate the
+logic in `prismatic.models.vlms.prismatic.py`.
+
+Note =>> for the time being, not adding the custom HF "docstring" formatting.
+
+# CUDA_VISIBLE_DEVICES=2 torchrun --standalone --nnodes 1 --nproc-per-node 1 vla-scripts/finetune-sco_slotssm_to_embodied_bwd-to-fwd.py  --vla_path "output_hf_model_openx"   --data_root_dir "/cm/archive/nhatcm3"  --dataset_name libero_goal    --run_root_dir "/cm/shared/nhatcm3/weights/openvla/ckpts"     --adapter_tmp_dir "/cm/shared/nhatcm3/weights/openvla/adapters"      --lora_rank 64        --batch_size 4          --grad_accumulation_steps 1            --learning_rate 2e-5              --image_aug True                --wandb_project vla_baseline                  --wandb_entity nhat                   --save_steps 1000                      --modality_key 1000
+
+References [LLaVa, IDEFICS-2]:
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/modeling_llava.py
+    => https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
+"""
+
+import logging
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import timm
+import tokenizers
+import torch
+import torch.nn as nn
+import transformers
+from timm.models.vision_transformer import LayerScale
+from transformers import AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import ModelOutput
+
+from .configuration_prismatic import OpenVLAConfig, PrismaticConfig, ObjectCentricVLAConfig
+from .configuration_prismatic import SlotVLAConfig, SlotVLAV2Config, CustomOpenVLAConfig
+
+# Get Logger
+logger = logging.getLogger(__name__)
+
+# === Dummy class ===
+class Dummy(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.logits = torch.zeros(1, 2, 1).to('cuda:0')
+        self.past_key_values = torch.zeros(1, 2, 1).to('cuda:0')
+
+    def __contains__(self, item):
+        return hasattr(self, item)
+
+# === PyTorch/HuggingFace Default IGNORE_INDEX (for CrossEntropyLoss labels)
+IGNORE_INDEX = -100
+
+
+# === Utility Functions for Monkey-Patching ===
+def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        result = fn(*args, **kwargs)
+        return result[0] if isinstance(result, tuple) else result
+
+    return wrapper
+
+
+# HF Transformers overwrites parameters with names containing `gamma`; we're going to patch VisionBackbone.LayerScale.
+#   =>> TIMM :: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L109
+#   =>> Transformers :: https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3960
+def _ls_new_forward(self, x: torch.Tensor) -> torch.Tensor:
+    return x.mul_(self.scale_factor) if self.inplace else x * self.scale_factor
+
+
+def ls_apply_patch(ls_module: LayerScale):
+    ls_module.scale_factor = nn.Parameter(ls_module.gamma.clone())
+    ls_module.forward = _ls_new_forward.__get__(ls_module, LayerScale)
+    del ls_module.gamma
+
+
+def resize_visual_features(features, upsample_rate=2, channel_reduction_rate=2):
+    # assuming features : [N, P, D]
+    N, P, D = features.shape
+    features = F.avg_pool1d(features.transpose(1, 2), 
+                            kernel_size=channel_reduction_rate, 
+                            stride=channel_reduction_rate).transpose(1, 2)
+    features = features.view(N, 16, 16, D//channel_reduction_rate)
+    features = F.interpolate(
+        features.permute(0, 3, 1, 2),
+        scale_factor=upsample_rate,
+        mode='bilinear',
+        align_corners=False
+    ).permute(0, 2, 3, 1)
+
+    return features
+
+
+# === Prismatic Vision Backbone (nn.Module) Definitions (w/ Fused Backbone Support) ===
+class PrismaticVisionBackbone(nn.Module):
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool,
+        image_sizes: List[int],
+        timm_model_ids: List[str],
+        timm_override_act_layers: List[Optional[str]],
+    ) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+
+        # [Contract] Validate number of (fused) vision backbones, create "alpha" featurizer and Instantiate
+        #   =>> Note :: Monkey-Patch the `forward()` function of the backbone to ensure FSDP-compatibility
+        #               Hardcodes `get_intermediate_layers` to return the **SECOND-TO-LAST** layer patches!
+        assert len(timm_model_ids) <= 2, "Prismatic models only support up to 2 (fused) vision backbones!"
+        self.featurizer = timm.create_model(
+            timm_model_ids[0],
+            pretrained=False,
+            num_classes=0,
+            img_size=image_sizes[0],
+            act_layer=timm_override_act_layers[0],
+        )
+        self.featurizer.forward = unpack_tuple(
+            partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
+        )
+        self.embed_dim = self.featurizer.embed_dim
+
+        # If `use_fused_vision_backbone` =>> create "beta" featurizer
+        if self.use_fused_vision_backbone:
+            self.fused_featurizer = timm.create_model(
+                timm_model_ids[1],
+                pretrained=False,
+                num_classes=0,
+                img_size=image_sizes[1],
+                act_layer=timm_override_act_layers[1],
+            )
+            self.fused_featurizer.forward = unpack_tuple(
+                partial(self.fused_featurizer.get_intermediate_layers, n={len(self.fused_featurizer.blocks) - 2})
+            )
+            self.embed_dim += self.fused_featurizer.embed_dim
+
+        # Patch `vision_backbone.featurizer` and `vision_backbone.fused_featurizer` with HF-Compatible LayerScale
+        for module in self.featurizer.modules():
+            if isinstance(module, LayerScale):
+                ls_apply_patch(module)
+
+        if self.use_fused_vision_backbone:
+            for module in self.fused_featurizer.modules():
+                if isinstance(module, LayerScale):
+                    ls_apply_patch(module)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Run image (`pixel_values`) through featurizer; if channel-stacked, then dispatch and sequence stack."""
+        if not self.use_fused_vision_backbone:
+            return self.featurizer(pixel_values)
+
+        # Split `pixel_values :: [bsz, 2 * 3, resolution, resolution]` =>> featurize =>> channel stack
+        img, img_fused = torch.split(pixel_values, [3, 3], dim=1)
+        patches, patches_fused = self.featurizer(img), self.fused_featurizer(img_fused)
+
+        return torch.cat([patches, patches_fused], dim=2)
+
+
+# === Prismatic Projector (nn.Module) Definitions ===
+class PrismaticProjector(nn.Module):
+    def __init__(self, use_fused_vision_backbone: bool, vision_dim: int, llm_dim: int) -> None:
+        super().__init__()
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.vision_dim, self.llm_dim = vision_dim, llm_dim
+
+        # Switch on `use_fused_vision_backbone` =>> use slightly different MLPs and projection factors!
+        if not self.use_fused_vision_backbone:
+            self.fc1 = nn.Linear(self.vision_dim, self.llm_dim, bias=True)
+            self.fc2 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+        else:
+            initial_projection_dim = 4 * vision_dim
+            self.fc1 = nn.Linear(self.vision_dim, initial_projection_dim, bias=True)
+            self.fc2 = nn.Linear(initial_projection_dim, self.llm_dim, bias=True)
+            self.fc3 = nn.Linear(self.llm_dim, self.llm_dim, bias=True)
+            self.act_fn1 = nn.GELU()
+            self.act_fn2 = nn.GELU()
+
+    def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
+        if not self.use_fused_vision_backbone:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+        else:
+            projected_features = self.fc1(img_patches)
+            projected_features = self.act_fn1(projected_features)
+            projected_features = self.fc2(projected_features)
+            projected_features = self.act_fn2(projected_features)
+            projected_features = self.fc3(projected_features)
+
+        return projected_features
+
+
+# === Main HF Class Definitions ===
+@dataclass
+class PrismaticCausalLMOutputWithPast(ModelOutput):
+    """Base class for Prismatic casual (visually-conditioned) language model outputs; also exposes visual features."""
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+    # Additions for VLMs
+    projector_features: Optional[torch.FloatTensor] = None
+
+
+class PrismaticPreTrainedModel(PreTrainedModel):
+    config_class: PretrainedConfig = PrismaticConfig
+    base_model_prefix: str = "model"
+    supports_gradient_checkpointing: bool = True
+
+    _no_split_modules: ClassVar[List[str]] = ["PrismaticProjector"]
+    _skip_keys_device_placement: str = "past_key_values"
+    _supports_flash_attn_2: bool = True
+
+    def _init_weights(self, module: nn.Module) -> None:
+        # Important :: this HF ported version is *not* meant for training from scratch; only inference and fine-tuning!
+        #   => As such, this init_weights code is not correct; if training VLMs from scratch, use the main codebase at
+        #      https://github.com/TRI-ML/prismatic-vlms
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @property
+    def _supports_sdpa(self) -> bool:
+        """Check LLM supports SDPA Attention"""
+        return self.language_model._supports_sdpa
+
+
+class PrismaticForConditionalGeneration(PrismaticPreTrainedModel):
+    def __init__(self, config: PrismaticConfig) -> None:
+        super().__init__(config)
+
+        # [Validation] Lightweight Validate on `config` Fields + Dependency Versions
+        if config.use_fused_vision_backbone is None:
+            raise ValueError("Missing config field `use_fused_vision_backbone`")
+
+        if timm.__version__ not in {"0.9.10", "0.9.11", "0.9.12", "0.9.16"}:
+            raise NotImplementedError(
+                "TIMM Version must be >= 0.9.10 and < 1.0.0 (breaking); please raise a GitHub Issue "
+                "if you urgently need support for latest TIMM versions."
+            )
+
+        if (transformers.__version__ != "4.40.1") or (tokenizers.__version__ != "0.19.1"):
+            logger.warning(
+                f"Expected `transformers==4.40.1` and `tokenizers==0.19.1` but got "
+                f"`transformers=={transformers.__version__}` and `tokenizers=={tokenizers.__version__}`; "
+                f"there might be inference-time regressions due to dependency changes. If in doubt, please"
+                f"use the above versions."
+            )
+
+        # Instantiate PrismaticVisionBackbone (w/ Potential Fused Backbone)
+        self.vision_backbone = PrismaticVisionBackbone(
+            config.use_fused_vision_backbone, config.image_sizes, config.timm_model_ids, config.timm_override_act_layers
+        )
+
+        # Create Multimodal Projector
+        self.projector = PrismaticProjector(
+            config.use_fused_vision_backbone,
+            vision_dim=self.vision_backbone.embed_dim,
+            llm_dim=config.text_config.hidden_size,
+        )
+
+        # Instantiate LLM Backbone
+        self.language_model = AutoModelForCausalLM.from_config(
+            config.text_config, attn_implementation=config._attn_implementation
+        )
+        print(self.language_model)
+        # print(self.language_model); 1/0
+        self.vocab_size = config.text_config.vocab_size
+        self.pad_token_id = config.pad_token_id
+
+        token_weights = np.full(shape=self.language_model.config.vocab_size, fill_value=1.0)  # Uniform weight for all
+        token_weights[31872] = 0.1  # Set weight for the frequent class, adjust index if necessary
+        self.token_weights = torch.FloatTensor(token_weights)
+
+        # HF Boilerplate =>> initializes weights via `_init_weights()` and sets gradient checkpointing
+        self.post_init()
+
+    # === `PreTrainedModel` Boilerplate ===
+    def get_input_embeddings(self) -> nn.Module:
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value: nn.Module) -> None:
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings: nn.Module) -> None:
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def get_decoder(self) -> nn.Module:
+        return self.language_model.get_decoder()
+
+    def set_decoder(self, decoder: nn.Module) -> None:
+        self.language_model.set_decoder(decoder)
+
+    def tie_weights(self) -> None:
+        self.language_model.tie_weights()  # Note: `Llama-2` and `Mistral` don't tie weights (no-op)
+
+    def resize_token_embeddings(
+        self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None
+    ) -> nn.Embedding:
+        updated_embeddings = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+
+        # Update config/instance variables
+        self.config.text_config.vocab_size = updated_embeddings.num_embeddings
+        self.vocab_size = updated_embeddings.num_embeddings
+
+        return updated_embeddings
+
+    def calculate_loss(self, logits, labels, weight=None):
+        # tested to find that this loss (when without weight is the same as that in transformers)
+        # Shift so that tokens < n predict n
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        # Flatten the tokens
+        loss_fct = nn.CrossEntropyLoss() # weight=weight
+        shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
+        shift_labels = shift_labels.view(-1)
+        # Enable model parallelism
+        shift_labels = shift_labels.to(shift_logits.device)
+        loss = loss_fct(shift_logits, shift_labels)
+        return loss
+
+    # === Core Prismatic VLM `forward()` Logic ===
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_projector_features = output_projector_features if output_projector_features is not None else False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
+        use_cache = use_cache and not self.training
+
+        # Instantiate Placeholder for Projector Features
+        projected_patch_embeddings = None
+
+        # Note :: We only support forward passes with the following cases:
+        #   => Cached Generation :: (input_ids.shape[1] == 1) and (past_key_values is not None)
+        #   => Unimodal Forward :: (pixel_values is None)
+        #   => Multimodal Forward :: (pixel_values is not None) and (input_ids/embeds.shape[0] == pixel_values.shape[0])
+        
+        # import time
+        # start = time.time()
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Visual Feature Extraction
+            patch_features = self.vision_backbone(pixel_values)
+        else: # [bz, num_view, horizon, cc, h, w]
+            # Visual Feature Extraction With Wrist Camera
+            bz, num_view, horizon, cc, h, w = pixel_values.shape
+            cur_pixel_values = pixel_values[:,:,0,:,:,:].reshape(-1, 6, 224, 224)
+            patch_features = self.vision_backbone(cur_pixel_values)
+
+            patch_features = patch_features.reshape(bz, -1, patch_features.shape[-1])
+
+
+        # start = time.time()
+        # Projection Logic =>> Update Attention Mask
+        projected_patch_embeddings = self.projector(patch_features)
+        projected_patch_attention_mask = None
+        if attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                fill_value=True,
+                dtype=attention_mask.dtype,
+                device=attention_mask.device,
+            )
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        input_embeddings = self.get_input_embeddings()(input_ids)
+
+        # start = time.time()
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [input_embeddings[:, :1, :], projected_patch_embeddings, input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [attention_mask[:, :1], projected_patch_attention_mask, attention_mask[:, 1:]], dim=1
+            )
+
+        # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
+        multimodal_labels = None
+        if labels is not None:
+            projected_patch_labels = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                fill_value=IGNORE_INDEX,
+                dtype=labels.dtype,
+                device=labels.device,
+            )
+            multimodal_labels = torch.cat([labels[:, :1], projected_patch_labels, labels[:, 1:]], dim=1)
+
+        # Dispatch to Language Model
+        language_model_output = self.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=False,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.training:
+            loss = self.calculate_loss(logits=language_model_output.logits, 
+                                    labels=multimodal_labels, 
+                                    weight=self.token_weights.to(multimodal_labels.device)) 
+        else:
+            loss = None
+
+        # Unpack `language_model_output` and return PrismaticCausalLMOutputWithPast (or tuple if not `return_dict`)
+        if not return_dict:
+            if output_projector_features and (projected_patch_embeddings is not None):
+                return *language_model_output, projected_patch_embeddings
+
+            return language_model_output
+
+        return PrismaticCausalLMOutputWithPast(
+            loss=loss,
+            logits=language_model_output.logits,
+            past_key_values=language_model_output.past_key_values,
+            hidden_states=language_model_output.hidden_states,
+            attentions=language_model_output.attentions,
+            projector_features=projected_patch_embeddings,
+        )
+
+    # === GenerationMixin Methods ===
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs: str,
+    ) -> Dict[str, torch.Tensor]:
+        """Borrowed from `LlamaForCausalLM` and simplified for batch size = 1; mirrors original PrismaticVLM logic."""
+        if ((input_ids is not None) and (input_ids.shape[0] > 1)) or (
+            (inputs_embeds is not None) and (inputs_embeds.shape[0] > 1)
+        ):
+            raise ValueError("Generation with batch size > 1 is not currently supported!")
+
+        # Handle `past_key_values` (cache) =>> assume `input_ids` just has unprocessed tokens
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+
+        # If `input_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"input_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        # Make sure `pixel_values` are preserved in `model_inputs`
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+            }
+        )
+
+        return model_inputs
+
+    # Defer to Language Model (all handle this differently, with different return types)
+    def _reorder_cache(self, *args, **kwargs) -> Any:
+        return self.language_model._reorder_cache(*args, **kwargs)
+
+
+class OpenVLAForActionPrediction(PrismaticForConditionalGeneration):
+    config_class: PretrainedConfig = OpenVLAConfig
+
+    def __init__(self, config: OpenVLAConfig) -> None:
+        super().__init__(config)
+        self.norm_stats = config.norm_stats
+        
+        # Compute action bins
+        self.bins = np.linspace(-1, 1, config.n_action_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # Compute vocab size for de-tokenization -- revert added "multiple of"
+        self.vocab_size = self.config.text_config.vocab_size - self.config.pad_to_multiple_of
+        self.time_logs = []
+    
+    def predict_action(
+        self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """Thin wrapper around super().generate() that decodes predicted actions and de-normalizes them."""
+
+        # If the special empty token ('') does not already appear after the colon (':') token in the prompt
+        # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
+        # if not torch.all(input_ids[:, -1] == 29871):
+        #     input_ids = torch.cat(
+        #         (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
+        #     )
+
+        # Run VLA inference
+        generated_ids = self.forward(input_ids=input_ids, **kwargs).logits[0:1].argmax(dim=2)
+
+        predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key)-2 :-2].cpu().numpy()
+        discretized_actions = self.vocab_size - predicted_action_token_ids
+        discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+        normalized_actions = self.bin_centers[discretized_actions]
+
+        # Unnormalize actions
+        action_norm_stats = self.get_action_stats(unnorm_key)
+        mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+        action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+
+    def predict_visually(
+        self, input_ids: Optional[torch.LongTensor] = None, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """Thin wrapper around super().generate() that decodes predicted actions and de-normalizes them."""
+        # If the special empty token ('') does not already appear after the colon (':') token in the prompt
+        # (after "OUT:" or "ASSISTANT:"), insert it to match the inputs seen at training time
+        if not torch.all(input_ids[:, -1] == 29871):
+            input_ids = torch.cat(
+                (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(input_ids.device)), dim=1
+            )
+        
+        try:
+            # Run VLA inference
+            generated_ids = self.generate(input_ids, max_new_tokens=self.get_action_dim(unnorm_key), **kwargs)
+            return generated_ids
+        except:
+            return None
+        
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
+        if unnorm_key is None and len(norm_stats) != 1:
+            raise ValueError(
+                f"Your model was trained on more than one dataset. "
+                f"Please pass a `unnorm_key` from the following options to choose the statistics used for "
+                f"de-normalizing actions: {norm_stats.keys()}"
+            )
+
+        # If None, grab the (singular) dataset in `norm_stats` to use as `unnorm_key`
+        unnorm_key = unnorm_key if unnorm_key is not None else next(iter(norm_stats.keys()))
+        if unnorm_key not in norm_stats:
+            raise ValueError(
+                f"The `unnorm_key` you chose ({unnorm_key = }) is not in the available statistics. "
+                f"Please choose from: {norm_stats.keys()}"
+            )
+
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Get the dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return len(self.norm_stats[unnorm_key]["action"]["q01"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+######################################################################################
+######################################################################################
+######################################################################################
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        return self.fn(x, *args, **kwargs) + x
+
+class LearnableConditioning(nn.Module):
+    def __init__(
+        self,
+        object_dim: int,
+        n_slots: int,
+        init_const: float
+    ):
+        super().__init__()
+        self.n_slots = n_slots
+        self.object_dim = object_dim
+        self.init_const = init_const
+        self.tokens = nn.Parameter(
+            torch.randn(1, n_slots, object_dim) * init_const
+        )
+    
+    def forward(self, batch_size: int):
+        condition = self.tokens.expand(batch_size, -1, -1)
+        return condition
+
+INIT_CONST = 0.02
+class SlotAttention(nn.Module):
+    """Implementation of SlotAttention.
+
+    Based on the slot attention implementation of Phil Wang available at:
+    https://github.com/lucidrains/slot-attention
+    """
+
+    def __init__(
+        self,
+        n_slots: int,
+        in_dim: int,
+        feature_dim: int,
+        kvq_dim: Optional[int] = None,
+        n_heads: int = 1,
+        iters: int = 3,
+        eps: float = 1e-8,
+        use_projection_bias: bool = False,
+        use_implicit_differentiation: bool = False,
+    ):
+        super().__init__()
+
+        self.in_dim = in_dim
+        self.n_heads = n_heads
+        self.iters = iters
+        self.eps = eps
+        self.use_implicit_differentiation = use_implicit_differentiation
+        self.feature_dim = feature_dim
+        if kvq_dim is None:
+            self.kvq_dim = feature_dim
+        else:
+            self.kvq_dim = kvq_dim
+
+        if self.kvq_dim % self.n_heads != 0:
+            raise ValueError("Key, value, query dimensions must be divisible by number of heads.")
+        self.dims_per_head = self.kvq_dim // self.n_heads
+        self.scale = self.dims_per_head**-0.5
+
+        # perceptual grouping
+        self.init_proj = nn.Linear(in_dim, feature_dim, bias=use_projection_bias)
+        # self.out_proj = nn.Linear(feature_dim, in_dim, bias=use_projection_bias)
+        self.conditioning = LearnableConditioning(object_dim=feature_dim, n_slots=n_slots, init_const=INIT_CONST)
+
+        self.to_q = nn.Linear(feature_dim, self.kvq_dim, bias=use_projection_bias)
+        self.to_k = nn.Linear(feature_dim, self.kvq_dim, bias=use_projection_bias)
+        self.to_v = nn.Linear(feature_dim, self.kvq_dim, bias=use_projection_bias)
+
+        self.gru = nn.GRUCell(self.kvq_dim, feature_dim)
+
+        self.norm_input = nn.LayerNorm(feature_dim)
+        self.norm_slots = nn.LayerNorm(feature_dim)
+        self.ff_mlp = self.build_two_layer_mlp(input_dim=feature_dim, output_dim=feature_dim, hidden_dim=feature_dim*4,
+                                            initial_layer_norm=True, residual=True)
+
+    def step(self, slots, k, v, masks=None):
+        bs, n_slots, _ = slots.shape
+        slots_prev = slots
+
+        slots = self.norm_slots(slots)
+        q = self.to_q(slots).view(bs, n_slots, self.n_heads, self.dims_per_head)
+
+        dots = torch.einsum("bihd,bjhd->bihj", q, k) * self.scale
+        if masks is not None:
+            # Masked slots should not take part in the competition for features. By replacing their
+            # dot-products with -inf, their attention values will become zero within the softmax.
+            dots.masked_fill_(masks.to(torch.bool).view(bs, n_slots, 1, 1), float("-inf"))
+
+        attn = dots.flatten(1, 2).softmax(dim=1)  # Take softmax over slots and heads
+        attn = attn.view(bs, n_slots, self.n_heads, -1)
+        attn_before_reweighting = attn
+        attn = attn + self.eps
+        attn = attn / attn.sum(dim=-1, keepdim=True)
+
+        updates = torch.einsum("bjhd,bihj->bihd", v, attn)
+
+        slots = self.gru(updates.reshape(-1, self.kvq_dim), slots_prev.reshape(-1, self.feature_dim))
+
+        slots = slots.reshape(bs, -1, self.feature_dim)
+
+        if self.ff_mlp:
+            slots = self.ff_mlp(slots)
+
+        return slots, attn_before_reweighting #.mean(dim=2)
+
+    def iterate(self, slots, k, v, masks=None):
+        # Slot update.
+        if self.training:
+            with torch.autocast(device_type='cuda', dtype=torch.float32):
+                for _ in range(self.iters):
+                    slots, attn = self.step(slots, k, v, masks)
+            slots, attn = slots.to(torch.bfloat16), attn.to(torch.bfloat16)
+        else:
+            with torch.autocast(device_type='cuda', dtype=torch.float32):
+                for _ in range(self.iters):
+                    slots, attn = self.step(slots, k, v, masks)
+            slots, attn = slots.to(torch.bfloat16), attn.to(torch.bfloat16)
+        return slots, attn
+
+    def forward(
+        self, inputs: torch.Tensor, masks: Optional[torch.Tensor] = None,
+        past_slots: torch.Tensor = None
+    ):
+        device = inputs.device
+        b, n, d = inputs.shape
+        if past_slots is None:
+            slots = self.conditioning(b)
+        else:
+            slots = past_slots[:,0]
+        inputs = self.init_proj(inputs)
+        inputs = self.norm_input(inputs)
+        k = self.to_k(inputs).view(b, n, self.n_heads, self.dims_per_head)
+        v = self.to_v(inputs).view(b, n, self.n_heads, self.dims_per_head)
+
+        if self.use_implicit_differentiation:
+            slots, attn = self.iterate(slots, k, v, masks)
+            slots, attn = self.step(slots.detach(), k, v, masks)
+        else:
+            slots, attn = self.iterate(slots, k, v, masks)
+
+        # # ##########################################################################
+        # # count at __embeddings
+        # import os
+        # import pickle
+        # embedding_dir = '__embeddings_demo'
+        # embeddings = os.listdir(embedding_dir)
+        # # save at __embeddings
+        # # Open a file and use dump() 
+        # data = {
+        #     'slots': slots,
+        #     'attn': attn
+        # }
+        # # A new file will be created 
+        # with open(os.path.join(embedding_dir, 'embed_' + str(len(embeddings)//2).zfill(5) + '.pkl'), 'wb') as file:
+        #     pickle.dump(data, file)
+        # # ##########################################################################
+
+        upsampled_slots = None
+        return slots, attn, upsampled_slots
+
+    def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+        if callable(name):
+            return name
+
+        name = name.lower()
+        if name == "relu":
+            return nn.ReLU(inplace=inplace)
+        elif name == "leaky_relu":
+            if leaky_relu_slope is None:
+                raise ValueError("Slope of leaky ReLU was not defined")
+            return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+        elif name == "tanh":
+            return nn.Tanh()
+        elif name == "sigmoid":
+            return nn.Sigmoid()
+        elif name == "identity":
+            return nn.Identity()
+        else:
+            raise ValueError(f"Unknown activation function {name}")
+
+    def build_mlp(
+        self,
+        input_dim: int,
+        output_dim: int,
+        features: List[int],
+        activation_fn: Optional[Union[str, Callable]] = "relu",
+        final_activation_fn: Optional[Union[str, Callable]] = None,
+        initial_layer_norm: bool = False,
+        residual: bool = False,
+    ) -> nn.Sequential:
+        layers = []
+        current_dim = input_dim
+        if initial_layer_norm:
+            layers.append(nn.LayerNorm(current_dim))
+
+        for n_features in features:
+            layers.append(nn.Linear(current_dim, n_features))
+            nn.init.xavier_uniform_(layers[-1].weight)
+            nn.init.zeros_(layers[-1].bias)
+            if activation_fn is not None:
+                layers.append(self.get_activation_fn(activation_fn))
+            current_dim = n_features
+
+        layers.append(nn.Linear(current_dim, output_dim))
+        nn.init.xavier_uniform_(layers[-1].weight)
+        nn.init.zeros_(layers[-1].bias)
+        if final_activation_fn is not None:
+            layers.append(self.get_activation_fn(final_activation_fn))
+
+        if residual:
+            return Residual(nn.Sequential(*layers))
+        return nn.Sequential(*layers)
+
+    def build_two_layer_mlp(
+        self, input_dim, output_dim, hidden_dim, initial_layer_norm: bool = False, residual: bool = False
+    ):
+        """Build a two layer MLP, with optional initial layer norm.
+
+        Separate class as this type of construction is used very often for slot attention and
+        transformers.
+        """
+        return self.build_mlp(
+            input_dim, output_dim, [hidden_dim], initial_layer_norm=initial_layer_norm, residual=residual
+        )
+
+
+""" The MLP class for building linear nns for bbox predictions
+"""
+import torch.nn.functional as F
+class MLP(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+""" The mask prediction head for projecting each slot representations into a binary mask
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class MaskPredictionHead(nn.Module):
+    def __init__(self, slot_dim, hidden_dim=64, mask_size=(224, 224)):
+        super().__init__()
+        self.H, self.W = mask_size
+
+        # Linear projection to conv-friendly shape
+        self.proj = nn.Linear(slot_dim, hidden_dim)
+
+        # Conv decoder
+        self.decoder = nn.Sequential(
+            nn.Conv2d(hidden_dim + 2, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 1, kernel_size=1),  # Output: mask logits
+        )
+
+        # Positional encoding: 2 channels (x, y)
+        self.positional_encoding = self._build_2d_pos_enc(self.H, self.W)
+
+    def _build_2d_pos_enc(self, H, W):
+        y, x = torch.meshgrid(
+            torch.linspace(-1, 1, H),
+            torch.linspace(-1, 1, W),
+            indexing='ij'
+        )
+        pos = torch.stack([x, y], dim=0)  # Shape: [2, H, W]
+        return pos  # Not learnable, but can be made learnable
+
+    def forward(self, slot_features):
+        """
+        slot_features: [B, num_slots, slot_dim]
+        Returns: binary_masks [B, num_slots, H, W]
+        """
+        B, num_slots, slot_dim = slot_features.shape
+
+        # print('input', torch.min(slot_features), torch.max(slot_features))
+        # Project and spatially broadcast
+        slot_proj = self.proj(slot_features)  # [B, num_slots, hidden_dim]
+        # print('projs', torch.min(slot_proj), torch.max(slot_proj))
+
+        slot_proj = slot_proj.view(B * num_slots, -1, 1, 1)
+        slot_proj = slot_proj.expand(-1, -1, self.H, self.W)  # [B*num_slots, hidden_dim, H, W]
+
+        # Add positional encoding
+        pos_enc = self.positional_encoding.to(slot_proj.device)  # [2, H, W]
+        pos_enc = pos_enc.unsqueeze(0).expand(B * num_slots, -1, -1, -1)  # [B*num_slots, 2, H, W]
+        x = torch.cat([slot_proj, pos_enc], dim=1)  # [B*num_slots, hidden_dim+2, H, W]
+
+        logits = self.decoder(x)  # [B*num_slots, 1, H, W]
+        masks = logits.view(B, num_slots, self.H, self.W)  # [B, num_slots, H, W]
+        # print('masks', torch.min(masks), torch.max(masks))
+        # print('sigmd', torch.min(masks.sigmoid()), torch.max(masks.sigmoid()))
+        # print('')
+        return masks
+
+
+class OpenVLAForActionPrediction_SlotAtt(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, model: OpenVLAForActionPrediction = None, number_of_slots = 16) -> None:
+        if config is None and model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif model is not None:
+            super().__init__()
+            self.model = model
+
+        self.object_token_num = number_of_slots
+
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = None
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        return output
+
+from prismatic.models.addons.multimodal_transformer import AfdMultimodalTransformer
+class OpenVLAForActionPrediction_LangSlotAtt(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, model: OpenVLAForActionPrediction = None, number_of_slots = 16) -> None:
+        if config is None and model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif model is not None:
+            super().__init__()
+            self.model = model
+
+        self.object_token_num = number_of_slots
+
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        # cross attention
+        self.object_centric_afd_interact_head = AfdMultimodalTransformer(d_model=512, num_encoder_layers=3)
+
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[str] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': [], 'interact': []}
+        
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = None
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            interactability = self.object_centric_afd_interact_head(suboutput['visual_tokens'].squeeze(1), 
+                                                                    texts.squeeze(1)) #, attention=texts_attn)
+            suboutput['interact'] = torch.unsqueeze(interactability, dim=1)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        return output
+
+
+""" From here begins the modeling code for SlotSSM into OpenVLA (but without action modeling yet)
+"""
+class MultiHeadAttention(nn.Module):
+
+    def __init__(self, d_model, num_heads, dropout=0., inverted=False, bias=True,
+                 norm_over_input=True, epsilon=1e-5, d_model_hidden=None):
+        super().__init__()
+
+        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.inverted = inverted
+        self.norm_over_input = norm_over_input
+
+        self.epsilon = epsilon
+
+        self.attn_dropout = nn.Dropout(dropout)
+        self.output_dropout = nn.Dropout(dropout)
+
+        if d_model_hidden is None:
+            d_model_hidden = d_model
+
+        self.proj_q = nn.Linear(d_model, d_model_hidden, bias=bias)
+        self.proj_k = nn.Linear(d_model, d_model_hidden, bias=bias)
+        self.proj_v = nn.Linear(d_model, d_model_hidden, bias=bias)
+        self.proj_o = nn.Linear(d_model_hidden, d_model, bias=bias)
+
+    def forward(self, q, k, v, attn_mask=None, attn_bias=None, output_attentions=False):
+        """
+        q: batch_size x target_len x d_model
+        k: batch_size x source_len x d_model
+        v: batch_size x source_len x d_model·
+        attn_mask: target_len x source_len
+        return: batch_size x target_len x d_model
+        """
+        B, T, _ = q.shape
+        _, S, _ = k.shape
+
+        q = self.proj_q(q).view(B, T, self.num_heads, -1).transpose(1, 2)
+        k = self.proj_k(k).view(B, S, self.num_heads, -1).transpose(1, 2)
+        v = self.proj_v(v).view(B, S, self.num_heads, -1).transpose(1, 2)
+
+        q = q * (q.shape[-1] ** (-0.5))
+        attn = torch.matmul(q, k.transpose(-1, -2))
+
+        if attn_bias is not None:
+            attn = attn + attn_bias
+
+        if attn_mask is not None:
+            attn = attn.masked_fill(attn_mask, float('-inf'))
+
+        if self.inverted:
+            attn = F.softmax(attn.flatten(start_dim=1, end_dim=2), dim=1).reshape(B, self.num_heads, T, S)
+            attn_vis = attn.detach()
+
+            if attn_mask is not None:
+                attn = attn.masked_fill(attn_mask, float('0.'))
+
+            # attn /= attn.sum(dim=-1, keepdim=True) + self.epsilon
+            if self.norm_over_input:
+                attn = attn / (attn.sum(dim=-1, keepdim=True) + self.epsilon)
+        else:
+            # print('attn b4', attn.shape, torch.min(attn), torch.max(attn))
+            attn = F.softmax(attn, dim=-1)
+            # print('attn af', attn.shape, torch.min(attn), torch.max(attn))
+            attn_vis = attn.detach()
+
+        attn = self.attn_dropout(attn)
+
+        output = torch.matmul(attn, v).transpose(1, 2).reshape(B, T, -1)
+        output = self.proj_o(output)
+        output = self.output_dropout(output)
+
+        outputs = (output, attn_vis) if output_attentions else (output,)
+        return outputs
+
+""" CLIP text encoder for open vocab chunks
+"""
+TASK_DECOMP = {
+    'pick the bowl from the plate and place it back 7 times': ['pick', 'bowl', 'from plate', 'place', 'it', '7 times'], 
+    'pick the bowl from the plate and place it back 5 times':  ['pick', 'bowl', 'from plate', 'place', 'it', '5 times'], 
+    'pick the bowl from the plate and place it back 3 times':  ['pick', 'bowl', 'from plate', 'place', 'it', '3 times'], 
+    'pick the bowl from the plate and place it back 1 time':   ['pick', 'bowl', 'from plate', 'place', 'it', '1 time'],
+    'swap the 3 bowls from left to right using the intermediary plate': ['swap', '3 bowls', 'from left to right', 'using', 'intermediary plate'],
+    'swap the 2 bowls using the intermediary plate': ['swap', '2 bowls', 'using', 'intermediary plate'],
+
+    'robot open the middle drawer of the cabinet': ['robot', 'open', 'middle drawer', 'of', 'cabinet'],
+    'robot open the top drawer and put the bowl inside': ['robot', 'open', 'top drawer', 'and', 'put', 'bowl', 'inside'],
+    'robot push the plate to the front of the stove': ['robot', 'push', 'plate', 'to', 'front of', 'stove'],
+    'robot put the bowl on the plate': ['robot', 'put', 'bowl', 'on', 'plate'],
+    'robot put the bowl on the stove': ['robot', 'put', 'bowl', 'on', 'stove'],
+    'robot put the bowl on top of the cabinet': ['robot', 'put', 'bowl', 'on top of', 'cabinet'],
+    'robot put the cream cheese in the bowl': ['robot', 'put', 'cream cheese', 'in', 'bowl'],
+    'robot put the wine bottle on the rack': ['robot', 'put', 'wine bottle', 'on', 'rack'],
+    'robot put the wine bottle on top of the cabinet': ['robot', 'put', 'wine bottle', 'on top of', 'cabinet'],
+    'robot turn on the stove': ['robot', 'turn on', 'stove'],
+
+    "robot turn on the stove and put the moka pot on it": ['robot', 'stove', 'moka pot'],
+    "robot put the black bowl in the bottom drawer of the cabinet and close it": ['robot', 'bowl', 'cabinet'],
+    "robot put the yellow and white mug in the microwave and close it": ['robot', 'yellow and white mug', 'microwave'],
+    "robot put both moka pots on the stove": ['robot', 'moka pot', 'stove'],
+    "robot put both the alphabet soup and the cream cheese box in the basket": ['robot', 'alphabet soup', 'cream cheese'],
+    "robot put both the alphabet soup and the tomato sauce in the basket": ['robot', 'alphabet soup', 'tomato'],
+    "robot put both the cream cheese box and the butter in the basket": ['robot', 'cream cheese', 'butter', 'basket'],
+    "robot put the white mug on the left plate and put the yellow and white mug on the right plate": ['robot', 'white mug', 'plate', 'yellow and white mug'],
+    "robot put the white mug on the plate and put the chocolate pudding to the right of the plate": ['robot', 'white mug', 'plate', 'chocolate pudding'],
+    "robot pick up the book and place it in the back compartment of the caddy": ['robot', 'book', 'caddy'],
+
+    "robot pick up the bowl and place it back on the plate": ['robot', 'pick up', 'bowl', 'place back on', 'plate'],
+    "robot lift the bottle and put it down on the plate": ['robot', 'lift', 'bottle', 'put down on', 'plate'],
+    "robot lift the bowl and place it back on the plate 3 times": ['robot', 'lift', 'bowl', 'place back on', 'plate', 'three times'],
+    "robot pick up the bottle and put it down the plate 3 times": ['robot', 'pick up', 'bottle', 'put down', 'plate', 'three times'],
+    "robot lift the bowl and place it back on the plate 5 times": ['robot', 'lift', 'bowl', 'place back on', 'plate', 'five times'],
+    "robot pick up the bowl and place it on the plate 7 times": ['robot', 'pick up', 'bowl', 'place on', 'plate', 'seven times'],
+    "robot swap the 2 bowls on their plates using the empty plate": ['robot', 'swap', '2 bowls', 'on their plates', 'using', 'empty plate'],
+    "robot rotate the 3 bowls on their plates from left to right using the empty plate": ['robot', 'rotate', '3 bowls', 'on their plates', 'from left to right', 'using', 'empty plate'],
+    "robot put the cream cheese in the nearest basket and place that basket in the center": ['robot', 'put', 'cream cheese', 'in', 'nearest basket', 'place', 'that basket', 'in the center'],
+    "robot put the cream cheese in the nearest basket and place the empty basket in the center": ['robot', 'put', 'cream cheese', 'in', 'nearest basket', 'place', 'empty basket', 'in the center'],
+
+    "robot pick up the black bowl between the plate and the ramekin and place it on the plate": ['robot', 'bowl', 'plate', 'ramekin'],
+    "robot pick up the black bowl from table center and place it on the plate": ['robot', 'bowl', 'plate'],
+    "robot pick up the black bowl in the top drawer of the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+    "robot pick up the black bowl next to the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "robot pick up the black bowl next to the plate and place it on the plate": ['robot', 'bowl', 'plate'],
+    "robot pick up the black bowl next to the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "robot pick up the black bowl on the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "robot pick up the black bowl on the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "robot pick up the black bowl on the stove and place it on the plate": ['robot', 'bowl', 'stove', 'plate'],
+    "robot pick up the black bowl on the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+
+    "robot pick up the butter and place it in the basket": ['robot', 'pick up', 'butter', 'place', 'in', 'basket'],
+    "robot pick up the bbq sauce and place it in the basket": ['robot', 'pick up', 'bbq sauce', 'place', 'in', 'basket'],
+    "robot pick up the cream cheese and place it in the basket": ['robot', 'pick up', 'cream cheese', 'place', 'in', 'basket'],
+    "robot pick up the salad dressing and place it in the basket": ['robot', 'pick up', 'salad dressing', 'place', 'in', 'basket'],
+    "robot pick up the orange juice and place it in the basket": ['robot', 'pick up', 'orange juice', 'place', 'in', 'basket'],
+    "robot pick up the alphabet soup and place it in the basket": ['robot', 'pick up', 'alphabet soup', 'place', 'in', 'basket'],
+    "robot pick up the tomato sauce and place it in the basket": ['robot', 'pick up', 'tomato sauce', 'place', 'in', 'basket'],
+    "robot pick up the ketchup and place it in the basket": ['robot', 'pick up', 'ketchup', 'place', 'in', 'basket'],
+    "robot pick up the chocolate pudding and place it in the basket": ['robot', 'pick up', 'chocolate pudding', 'place', 'in', 'basket'],
+    "robot pick up the milk and place it in the basket": ['robot', 'pick up', 'milk', 'place', 'in', 'basket'],
+
+    "pick up the black bowl between the plate and the ramekin and place it on the plate": ['robot', 'bowl', 'plate', 'ramekin'],
+    "pick up the black bowl from table center and place it on the plate": ['robot', 'bowl', 'plate'],
+    "pick up the black bowl in the top drawer of the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+    "pick up the black bowl next to the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "pick up the black bowl next to the plate and place it on the plate": ['robot', 'bowl', 'plate'],
+    "pick up the black bowl next to the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "pick up the black bowl on the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "pick up the black bowl on the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "pick up the black bowl on the stove and place it on the plate": ['robot', 'bowl', 'stove', 'plate'],
+    "pick up the black bowl on the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+
+    "robot pick up mayo and place in the blue bowl": ['robot', 'pick up', 'mayo', 'place in', 'blue bowl'],
+    "robot place mayo and mustard in the blue bowl": ['robot', 'place', 'mayo', 'and', 'mustard', 'in', 'blue bowl'],
+    "robot pick up mayo and move it to the right of the blue bowl": ['robot', 'pick up', 'mayo', 'move it', 'to right of', 'blue bowl'],
+}
+import clip
+class CLIPBasedTextEncoder:
+    def __init__(self, model, preprocessor):
+        self.model = model
+        self.preprocessor = preprocessor
+
+    def get_text_decomposition(self, text):
+        return TASK_DECOMP[text]
+
+    def encode(self, texts, device):
+        all_masks = []
+        all_feats = []
+        all_feats_gen = []
+
+        for text in texts:
+            decomposed = self.get_text_decomposition(text)
+            with torch.no_grad():
+                tokenized = clip.tokenize(decomposed).to(device)
+                text_features = self.model.encode_text(tokenized)
+                masks = torch.zeros(text_features.shape[0], dtype=torch.bool, device=device)
+                all_masks.append(masks)
+                all_feats.append(text_features)
+                all_feats_gen.append(
+                    self.model.encode_text(
+                        clip.tokenize([text]).to(device)
+                    )
+                )
+
+        # Pad features and masks to the same length
+        max_chunks = max(len(seq) for seq in all_feats)
+        feat_dim = all_feats[0].shape[-1]
+
+        padded_features = []
+        padded_masks = []
+
+        for feats, masks in zip(all_feats, all_masks):
+            token_len = feats.shape[0]
+            pad_len = max_chunks - token_len
+            pad_feats = torch.zeros((pad_len, feat_dim), device=device)
+            pad_masks = torch.ones(pad_len, dtype=torch.bool, device=device) # ones for -infinity masking
+
+            feats = torch.cat([feats, pad_feats])
+            masks = torch.cat([masks, pad_masks])
+
+            padded_features.append(feats)
+            padded_masks.append(masks)
+
+        features_gen_tensor = torch.stack(all_feats_gen) # [B, feat_dim]
+        features_tensor = torch.stack(padded_features)   # [B, max_chunks, feat_dim]
+        masks_tensor = torch.stack(padded_masks)         # [B, max_chunks]
+
+        return features_gen_tensor, features_tensor, masks_tensor
+
+    def encode_simple(self, texts, device):
+        all_feats = []
+
+        for text in texts:
+            decomposed = [text]
+            with torch.no_grad():
+                tokenized = clip.tokenize(decomposed).to(device)
+                text_features = self.model.encode_text(tokenized)
+                all_feats.append(text_features)
+
+        features_tensor = torch.stack(all_feats)  # [B, max_chunks, feat_dim]
+        return features_tensor
+
+    def __call__(self, texts, temporal_length, device):
+        features_gen_tensor, features_tensor, masks_tensor = self.encode(texts, device)
+        # Expand across time
+        features_aligned = features_tensor.unsqueeze(1).expand(-1, temporal_length, -1, -1)  # [B, T, C, feat_dim]
+        masks_aligned = masks_tensor.unsqueeze(1).expand(-1, temporal_length, -1)  # [B, T, C]
+        return features_gen_tensor, features_aligned, masks_aligned
+
+""" Here starts modeling for SSM
+"""
+from einops import rearrange
+from mamba_ssm import Mamba, Mamba2
+from flash_attn.modules.mha import MHA as FlashMHA
+
+class EmbodiedSlotSSMBlock(nn.Module):
+    def __init__(self, d_model, space_attn_num_heads, visual_d_model=None, textual_d_model=None, use_ffn=False,
+                 encoder_attn_num_heads=None, use_cross_attn=True, use_inverted_attention=False,
+                 mamba_version='mamba2', layer_idx=None, attn_impl="flash_attention_2",
+                 mamba_d_state=128, mamba_d_conv=4, mamba_expand=2, mamba_headdim=64,
+                 lookback_only=False):
+        super().__init__()
+        assert mamba_version in ['mamba1', 'mamba2'], "Mamba version must be mamba1 or mamba2"
+        assert attn_impl in ["flash_attention_2", "eager"], \
+            "Attention implementation must be flash_attention_2 or eager"
+        self.attn_impl = attn_impl
+        self.lookback_only = lookback_only
+        if use_cross_attn:
+            self.visual_cross_attn_input_norm = nn.LayerNorm(d_model)
+            self.visual_cross_attn_ref_norm = nn.LayerNorm(d_model)
+            self.visual_proj = nn.Linear(visual_d_model, d_model) if visual_d_model is not None else None
+            if not lookback_only:
+                self.textual_cross_attn_input_norm = nn.LayerNorm(d_model)
+                self.textual_cross_attn_ref_norm = nn.LayerNorm(d_model)
+                self.textual_proj = nn.Linear(textual_d_model, d_model) if textual_d_model is not None else None
+            if use_inverted_attention:
+                if attn_impl == "flash_attention_2":
+                    mprint("Inverted attention with flash attention 2 is not supported, using eager instead")
+                self.visual_cross_attn = MultiHeadAttention(
+                    d_model=d_model,
+                    num_heads=encoder_attn_num_heads,
+                    inverted=True
+                )
+
+                if not lookback_only:
+                    self.textual_cross_attn = MultiHeadAttention(
+                        d_model=d_model,
+                        num_heads=encoder_attn_num_heads,
+                        inverted=True
+                    )
+            else:
+                if attn_impl == "flash_attention_2":
+                    self.visual_cross_attn = FlashMHA(
+                        embed_dim=d_model,
+                        num_heads=encoder_attn_num_heads,
+                        cross_attn=True
+                    )
+                    if not lookback_only:
+                        self.textual_cross_attn = MultiHeadAttention(
+                            d_model=d_model,
+                            num_heads=encoder_attn_num_heads,
+                            inverted=False,
+                            # output_attentions=True
+                        )
+
+                else:
+                    self.visual_cross_attn = MultiHeadAttention(
+                        d_model=d_model,
+                        num_heads=encoder_attn_num_heads,
+                        inverted=False,
+                        # output_attentions=True
+                    )
+                    if not lookback_only:
+                        self.textual_cross_attn = MultiHeadAttention(
+                            d_model=d_model,
+                            num_heads=encoder_attn_num_heads,
+                            inverted=False,
+                            # output_attentions=True
+                        )
+
+        self.time_mixer_norm = nn.LayerNorm(d_model)
+        if mamba_version == 'mamba2':
+            # need to check if d_model * expand / headdim = multiple of 8
+            assert (d_model * mamba_expand / mamba_headdim) % 8 == 0, "d_model * expand must be a multiple of headdim"
+            self.time_mixer = Mamba2(
+                d_model=d_model,
+                d_state=mamba_d_state,
+                d_conv=mamba_d_conv,
+                expand=mamba_expand,
+                headdim=mamba_headdim,
+                layer_idx=layer_idx
+            )
+        else:
+            self.time_mixer = Mamba(
+                d_model=d_model,
+                d_state=mamba_d_state,
+                d_conv=mamba_d_conv,
+                expand=mamba_expand,
+                layer_idx=layer_idx
+            )
+
+        self.space_attn_norm = nn.LayerNorm(d_model)
+        if attn_impl == "flash_attention_2":
+            self.space_attn = FlashMHA(
+                embed_dim=d_model,
+                num_heads=space_attn_num_heads
+            )
+        else:
+            self.space_attn = MultiHeadAttention(
+                d_model=d_model,
+                num_heads=space_attn_num_heads
+            )
+
+        if use_ffn:
+            self.ffn_norm = nn.LayerNorm(d_model)
+            self.ffn = nn.Sequential(
+                nn.Linear(d_model, d_model * 4),
+                nn.GELU(),
+                nn.Linear(d_model * 4, d_model)
+            )
+
+    def single_modality_cross_attn(self, input, ref, ref_attn=None,
+                                   input_proj_fn=None,
+                                   cross_attn_input_norm_fn=None,
+                                   cross_attn_ref_norm_fn=None,
+                                   cross_attn_fn=None,
+                                   output_attentions=False, debug=False):
+        """
+        input: B, T, N, D (slots)
+        ref: B, T, L, D (reference input for cross attention)
+        ref_attn: B, T, L (mask attention)
+        input_proj_fn: projection function for input
+        cross_attn_input_norm: normalization function for input
+        cross_attn_ref_norm: normalization function for reference
+        cross_attn_fn: cross attention function for reference
+        """
+        B, T, N, D = input.shape
+        input_reshape = rearrange(input, 'b t n d -> (b t) n d')
+        x = cross_attn_input_norm_fn(input_reshape)
+        if debug:
+            print('te_x', x.shape, torch.min(x), torch.max(x))
+
+        ref_reshape = rearrange(ref, 'b t n d -> (b t) n d')
+        if input_proj_fn is not None:
+            ref_proj = input_proj_fn(ref_reshape)
+        else:
+            ref_proj = ref_reshape
+        ref_proj = cross_attn_ref_norm_fn(ref_proj)
+        if debug:
+            print('te_ref', ref_proj.shape, torch.min(ref_proj), torch.max(ref_proj))
+
+        if ref_attn is not None:
+            ref_attn = rearrange(ref_attn, 'b t n -> (b t) n', b=B, t=T).unsqueeze(1).unsqueeze(1)
+        
+        if isinstance(cross_attn_fn, FlashMHA):
+            output_attn = cross_attn_fn(x=x, x_kv=ref_proj)
+            input = input + rearrange(output_attn, '(b t) n d -> b t n d', b=B, t=T)
+        else:
+            cross_attn_out = cross_attn_fn(
+                x, ref_proj, ref_proj, attn_mask=ref_attn, output_attentions=output_attentions
+            )
+            if isinstance(cross_attn_out, tuple) and len(cross_attn_out) > 1:
+                output_attn = cross_attn_out[1]
+            else:
+                output_attn = cross_attn_out[0]
+            if debug:
+                print('te_att', output_attn.shape, torch.min(output_attn), torch.max(output_attn))
+            input = input + rearrange(output_attn, '(b t) n d -> b t n d', b=B, t=T)
+        
+        if not output_attentions:
+            return input, None    
+        return input, output_attn
+
+
+    def forward(self, input, visual_ref, textual_ref=None, textual_attn=None, cache_params=None, output_attentions=False):
+        """
+        input: B, T, N, D (slots)
+        visual_ref: B, T, VL, D (reference input for cross attention)
+        textual_ref: B, TL, D (reference input for cross attention)
+        """
+        B, T, N, D = input.shape
+        # Cross attention is enabled and ref is provided
+        output_attn = None
+        if not self.lookback_only:
+            input, tex_output_attn = self.single_modality_cross_attn(input, ref=textual_ref, ref_attn=textual_attn,
+                                                    input_proj_fn=self.textual_proj,
+                                                    cross_attn_input_norm_fn=self.textual_cross_attn_input_norm,
+                                                    cross_attn_ref_norm_fn=self.textual_cross_attn_ref_norm,
+                                                    cross_attn_fn=self.textual_cross_attn,
+                                                    output_attentions=output_attentions, debug=False)
+            # print('te', input.shape, torch.min(input), torch.max(input))
+        input, vis_output_attn = self.single_modality_cross_attn(input, ref=visual_ref,
+                                                input_proj_fn=self.visual_proj,
+                                                cross_attn_input_norm_fn=self.visual_cross_attn_input_norm,
+                                                cross_attn_ref_norm_fn=self.visual_cross_attn_ref_norm,
+                                                cross_attn_fn=self.visual_cross_attn,
+                                                output_attentions=output_attentions)
+        # if not self.lookback_only:
+        #     print('vi', input.shape, torch.min(input), torch.max(input))
+        output_attn = vis_output_attn
+
+        # Time mixing (Mamba)
+        input_reshape = rearrange(input, 'b t n d -> (b n) t d')
+        x = self.time_mixer_norm(input_reshape)
+        time_mixed = self.time_mixer(x, inference_params=cache_params)
+        input = input + rearrange(time_mixed, '(b n) t d -> b t n d', b=B, n=N)
+        # if not self.lookback_only:
+        #     print('tm', input.shape, torch.min(input), torch.max(input))
+
+        # Space attention
+        input_reshape = rearrange(input, 'b t n d -> (b t) n d')
+        x = self.space_attn_norm(input_reshape)
+        if self.attn_impl == "flash_attention_2":
+            space_attn_out = self.space_attn(x)
+        else:
+            space_attn_out = self.space_attn(x, x, x)[0]
+        input = input + rearrange(space_attn_out, '(b t) n d -> b t n d', b=B, t=T)
+        # if not self.lookback_only:
+        #     print('ss', input.shape, torch.min(input), torch.max(input))
+
+        if hasattr(self, 'ffn_norm'):
+            input_norm = self.ffn_norm(input)
+            input = input + self.ffn(input_norm)
+        # if not self.lookback_only:
+        #     print('ff', input.shape, torch.min(input), torch.max(input))
+
+        return input if not output_attentions else (input, output_attn)
+
+class EmbodiedSlotSSM(nn.Module):
+    def __init__(
+        self,
+        num_slots: int = 32,
+        num_blocks: int = 4,
+        d_model: int = 256,
+        d_input: int = 2176,
+        d_output: int = None,
+        visual_d_model: int = None,
+        textual_d_model: int = None,
+        use_cross_attn: bool = True,
+        space_attn_num_heads: int = None,
+        use_inverted_attention: bool = False,
+        encoder_attn_num_heads: int = None,
+        use_ffn: bool = False,
+        mamba_version: str = "mamba2",
+        attn_impl: str = "flash_attention_2",
+        mamba_d_state: int = 128,
+        mamba_d_conv: int = 4,
+        mamba_expand: int = 2,
+        mamba_headdim: int = 64,
+        lookback_only: bool = False,
+        **kwargs
+    ):
+        super().__init__()
+
+        if d_input is not None:
+            self.in_proj = nn.Linear(d_input, d_model)
+        else:
+            self.in_proj = None
+        self.blocks = nn.ModuleList([
+            EmbodiedSlotSSMBlock(
+                d_model=d_model,
+                visual_d_model=visual_d_model,
+                textual_d_model=textual_d_model,
+                space_attn_num_heads=space_attn_num_heads,
+                use_inverted_attention=use_inverted_attention,
+                encoder_attn_num_heads=encoder_attn_num_heads,
+                use_ffn=use_ffn,
+                use_cross_attn=use_cross_attn,
+                mamba_version=mamba_version,
+                attn_impl=attn_impl,
+                mamba_d_state=mamba_d_state,
+                mamba_d_conv=mamba_d_conv,
+                mamba_expand=mamba_expand,
+                mamba_headdim=mamba_headdim,
+                layer_idx=idx,
+                lookback_only=lookback_only
+            )
+            for idx in range(num_blocks)
+        ])
+        self.init_slots = nn.Parameter(torch.randn(1, 1, num_slots, d_model))
+
+        if d_output is not None:
+            self.out_proj = nn.Linear(d_model, d_output)
+        else:
+            self.out_proj = None
+
+
+    def forward(self, slots, visual_ref=None, textual_ref=None, textual_att=None, cache_params=None, output_attentions=False):
+        """
+        slots: B, T, N, D
+        visual_ref: B, T, VL, D
+        textual_ref: B, TL, D
+        textual_att: B, TL
+        """
+        B, T, N, D = slots.shape
+        if self.in_proj is not None:
+            slots = self.in_proj(slots)
+        slot_lists = []
+        attentions = []
+        for block in self.blocks:
+            slots = block(slots, visual_ref, textual_ref, textual_att, cache_params=cache_params, output_attentions=output_attentions)
+            if isinstance(slots, tuple):
+                slots, attn = slots
+                attentions.append(attn)
+            slot_lists.append(slots)          
+
+        if cache_params is not None:
+            cache_params.seqlen_offset += T
+
+        if self.out_proj is not None:
+            slots = self.out_proj(slots)
+        # print('out slots', torch.min(slots), torch.max(slots))
+        return slots, attentions, slot_lists
+
+class OpenVLAForActionPrediction_SlotSSMv3(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None, 
+                 number_of_slots = 16, backward_step = 24) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots
+
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        d_model = 512
+        # self.object_centric_pre_ssm_fusion = SlotFusion(input_dims=[512,512], embed_dims=[512, 512], output_dim=512)
+        self.object_centric_ssm = EmbodiedSlotSSM(
+            num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+            d_input=None, visual_d_model=2176, textual_d_model=None,
+            space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+            encoder_attn_num_heads=d_model // 64, lookback_only=True
+        )
+        # predicting the 16 steps backward and 8 steps head
+        self.backward_steps = backward_step # 16
+        self.forward_steps = 0
+        self.object_centric_latent_decoder = nn.Linear(1024, 512*(self.backward_steps + self.forward_steps)) 
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[list] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        device = pixel_values.device
+        output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens=None
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+
+        return output
+
+    def get_slot_dynamics(
+        self,
+        slots,
+        subgoals,
+        patch_features, 
+        texts, 
+        texts_attn,
+        output,
+        redundant_steps=0
+    ):
+        # print(slots.shape, torch.min(slots), torch.max(slots))
+        # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+        # print(texts.shape, torch.min(texts), torch.max(texts))
+        # print(texts_attn.shape)
+        # slots: [B, T, N, D]
+        if redundant_steps > 0:
+            slots = slots[:,:-redundant_steps]
+            subgoals = subgoals[:,:-redundant_steps]
+            patch_features = patch_features[:,:-redundant_steps]
+            texts       = texts[:,:-redundant_steps]
+            texts_attn  = texts_attn[:,:-redundant_steps]
+
+        # slots = self.object_centric_pre_ssm_fusion([slots, subgoals])
+        latent_slots, attention, slot_lists = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+        slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+        B, T, N, D_x_horizon_steps = slots.shape
+        D = D_x_horizon_steps // (self.backward_steps + self.forward_steps)
+        slots_reshaped = slots.reshape(B*T, N, self.backward_steps + self.forward_steps, D)
+        nxt_bboxes = self.object_centric_bbox_head(slots_reshaped.detach()[:,:,-1,:]).sigmoid() # selecting only the last step for evaluation
+        nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+        nxt_masks = self.object_centric_mask_head(slots_reshaped.detach()[:,:,-1,:]) # selecting only the last step for evaluation
+        nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+        # print(slots.shape, torch.min(slots), torch.max(slots))
+
+        output['latent_slots'] = latent_slots
+        output['nxt_tokens'] = slots.reshape(B, T, N, self.backward_steps + self.forward_steps, D)
+        output['nxt_bboxes'] = nxt_bboxes
+        output['nxt_masks'] = nxt_masks
+
+        # debugging purposes
+        # for key, value in output.items():
+        #     print(key, value.shape, torch.min(value), torch.max(value))
+        # 1/0
+        return output
+
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+class EmbodiedDecodedSlotNaive(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=True, number_of_slots=24) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        d_model = 512
+
+        # predicting the 8 steps head
+        self.forward_steps = 8
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.visual_pool = visual_pool
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+        if callable(name):
+            return name
+
+        name = name.lower()
+        if name == "relu":
+            return nn.ReLU(inplace=inplace)
+        elif name == "leaky_relu":
+            if leaky_relu_slope is None:
+                raise ValueError("Slope of leaky ReLU was not defined")
+            return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+        elif name == "tanh":
+            return nn.Tanh()
+        elif name == "sigmoid":
+            return nn.Sigmoid()
+        elif name == "identity":
+            return nn.Identity()
+        else:
+            raise ValueError(f"Unknown activation function {name}")
+
+    def build_mlp(
+        self,
+        input_dim: int,
+        output_dim: int,
+        features: List[int],
+        activation_fn: Optional[Union[str, Callable]] = "relu",
+        final_activation_fn: Optional[Union[str, Callable]] = None,
+        initial_layer_norm: bool = False,
+        residual: bool = False,
+    ) -> nn.Sequential:
+        layers = []
+        current_dim = input_dim
+        if initial_layer_norm:
+            layers.append(nn.LayerNorm(current_dim))
+
+        for n_features in features:
+            layers.append(nn.Linear(current_dim, n_features))
+            nn.init.xavier_uniform_(layers[-1].weight)
+            nn.init.zeros_(layers[-1].bias)
+            if activation_fn is not None:
+                layers.append(self.get_activation_fn(activation_fn))
+            current_dim = n_features
+
+        layers.append(nn.Linear(current_dim, output_dim))
+        nn.init.xavier_uniform_(layers[-1].weight)
+        nn.init.zeros_(layers[-1].bias)
+        if final_activation_fn is not None:
+            layers.append(self.get_activation_fn(final_activation_fn))
+
+        if residual:
+            return Residual(nn.Sequential(*layers))
+        return nn.Sequential(*layers)
+
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[list] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        device = pixel_values.device
+        output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens=None
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+
+        return output
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+        
+        # patch_features = self.visual_pool(rearrange(patch_features, 'b t v d -> (b t) v d'), reduction=4)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        slotted_features = rearrange(slotted_features, 'b t n d -> b (t n) d')
+        clip_embeddings = clip_embeddings[:, 0]
+        clip_attention_mask = clip_attention_mask[:, 0]
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings
+        llama_attention_mask = llama_attention_mask
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = slotted_features
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (slotted_features.shape[0], slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+from prismatic.models.addons.relational_module import RelationTokensGrounding
+
+#####################################################################################################
+#####################################################################################################
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ResMLP(nn.Module):
+    """
+    Residual MLP with N blocks ⇒ each block:
+        LayerNorm → Linear(d→h) → GELU → Dropout → Linear(h→d) + skip
+    After the last block: LayerNorm → Linear → logits
+    """
+    def __init__(
+        self,
+        in_dim: int,
+        hidden_dims: list | tuple,          # e.g. (256, 128) for two blocks
+        n_classes: int = 1,                 # >1 for soft-max
+        dropout: float = 0.1,
+    ):
+        super().__init__()
+        d = in_dim
+        blocks = []
+
+        for h in hidden_dims:               # build each residual block
+            blocks += [
+                nn.LayerNorm(d),
+                nn.Linear(d, h),
+                nn.GELU(),
+                nn.Dropout(dropout),
+                nn.Linear(h, d),
+            ]
+        self.blocks = nn.ModuleList(blocks)
+
+        self.head = nn.Sequential(
+            nn.LayerNorm(d),
+            nn.Linear(d, n_classes),
+        )
+
+        # optional: Kaiming init for all Linear layers
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.kaiming_uniform_(m.weight, a=F.gelu(torch.tensor(0.0)).item())
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x):
+        # each residual block is five layers; step over them in chunks of 5
+        for i in range(0, len(self.blocks), 5):
+            blk = self.blocks[i : i + 5]
+            x = x + blk[-1](blk[-2](blk[-3](blk[-4](blk[-5](x)))))  # skip-conn
+        return self.head(x)
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+
+def visual_pool(features: torch.Tensor, reduction: int) -> torch.Tensor:
+    """
+    Pools the visual features by reducing the sequence length v by 'reduction' times.
+
+    Args:
+        features (torch.Tensor): Input tensor of shape (batch_size, v, d)
+        reduction (int): Reduction factor (e.g., 4, 16)
+
+    Returns:
+        torch.Tensor: Pooled tensor of shape (batch_size, v // reduction, d)
+    """
+    bz, v, d = features.shape
+    if v % reduction != 0:
+        raise ValueError(f"v ({v}) must be divisible by reduction ({reduction}).")
+
+    # Reshape: group v into reduction groups
+    features = features.view(bz, v // reduction, reduction, d)
+
+    # Average over the reduction dimension
+    pooled = features.mean(dim=2)
+
+    return pooled
+
+class SlotFusion(nn.Module):
+    def __init__(self, input_dims, embed_dims, output_dim=None, normalize=True):
+        """
+        Args:
+            input_dims (list of int): List of input dimensions [d1, d2, d3, ...].
+            embed_dims (list of int): List of embed dimensions [d1, d2, d3, ...] before fusion.
+            normalize (bool): Whether to apply LayerNorm to each input before fusion.
+        """
+        super().__init__()
+        self.normalize = normalize
+        self.projectors = nn.ModuleList()
+        self.norms = nn.ModuleList()
+
+        for i, d in enumerate(input_dims):
+            embed_dim = embed_dims[i]
+            # self.projectors.append(nn.Linear(d, embed_dim))
+            self.projectors.append(nn.Linear(d, embed_dim))
+            if normalize:
+                self.norms.append(nn.LayerNorm(d))
+            else:
+                self.norms.append(nn.Identity())
+
+        self.out_projection = None
+        if output_dim is not None:
+            self.out_projection = nn.Linear(sum(embed_dims), output_dim)
+
+    def forward(self, inputs):
+        """
+        Args:
+            inputs (list of tensors): Each tensor of shape (batch_size, v, d_i)
+
+        Returns:
+            Tensor: Fused tensor of shape (batch_size, v, total_dim)
+        """
+        fused = []
+        for x, proj, norm in zip(inputs, self.projectors, self.norms):
+            x = norm(x)
+            x = proj(x)
+            fused.append(x)
+
+        # Concatenate along the feature dimension
+        out = torch.cat(fused, dim=-1)
+        if self.out_projection is not None:
+            out = self.out_projection(out)
+        return out
+
+class SlotFusionV2(nn.Module):
+    def __init__(self, input_dims=None, embed_dims=None, output_dim=None, normalize=True):
+        """
+        Args:
+            input_dims (list of int): List of input dimensions [d1, d2, d3, ...].
+            embed_dims (list of int): List of embed dimensions [d1, d2, d3, ...] before fusion.
+            normalize (bool): Whether to apply LayerNorm to each input before fusion.
+        """
+        super().__init__()
+        self.output_dim = output_dim
+
+    def forward(self, inputs):
+        """
+        Args:
+            inputs (list of tensors): Each tensor of shape (batch_size, v, d_i)
+
+        Returns:
+            Tensor: Fused tensor of shape (batch_size, v, total_dim)
+        """
+        fused = []
+        for x in inputs:
+            fused.append(x)
+
+        # Concatenate along the feature dimension
+        x = torch.cat(fused, dim=-1)
+        out = torch.nn.functional.interpolate(
+            x.permute(0,1,2,3).reshape(-1, 1, x.shape[-1]), 
+            size=self.output_dim, mode="linear", align_corners=False
+        ).reshape(x.shape[0], x.shape[1], x.shape[2], self.output_dim)
+
+        return out
+
+from prismatic.models.action_heads import DiffusionActionHead, L1RegressionActionHead
+
+# class SubgoalTransitionalPredictor(nn.Module):
+#     def __init__(self, input_dim=512*3, condition_dim=512, model_dim=1024):
+#         super().__init__()
+#         self.input_dim = input_dim
+#         self.condition_dim = condition_dim
+#         self.model_dim = model_dim
+
+#         # projection layers to handle input conditioning
+#         self.inp_proj = nn.Linear(input_dim, model_dim)
+#         self.cond_proj = nn.Linear(condition_dim, model_dim)
+
+#         # FiLM conditioning layers
+#         self.film_gamma = nn.Linear(model_dim, model_dim)
+#         self.film_beta = nn.Linear(model_dim, model_dim)
+
+#         # need an attention model here
+#         self.space_attn = MultiHeadAttention(
+#             d_model=self.model_dim,
+#             num_heads=self.model_dim // 64
+#         )
+
+#         # feed forward neural network to handle event prediction
+#         self.ffn_norm = nn.LayerNorm(self.model_dim)
+#         self.ffn = nn.Sequential(
+#             nn.Linear(self.model_dim, self.model_dim * 4),
+#             nn.GELU(),
+#             nn.Linear(self.model_dim * 4, 1)
+#         )
+
+#     def forward(self, x, cond, patch_features=None):
+#         """
+#         Args:
+#             x: Tensor of shape [B, T, O, input_dim], the features of position, dynamics and subgoals
+#             cond: Tensor of shape [B, 1, 1, condition_dim], the task features
+#             patch_features: Tensor of shape [B, T, V, visual_dim], the visual features for revision
+#         Returns:
+#             pred: Tensor of shape [B, T, 1]
+#         """
+#         b, t, o, _ = x.shape
+
+#         # Project inputs
+#         x_proj = self.inp_proj(x)          # [B, T, O, model_dim]
+#         cond_proj = self.cond_proj(cond)   # [B, 1, 1, model_dim]
+
+#         # FiLM: generate scale and shift from cond_proj
+#         gamma = self.film_gamma(cond_proj)  # [B, 1, 1, model_dim]
+#         beta = self.film_beta(cond_proj)    # [B, 1, 1, model_dim]
+
+#         # Apply FiLM conditioning
+#         h = gamma * x_proj + beta           # [B, T, O, model_dim]
+
+#         # Self-attention
+#         h = rearrange(h, 'b t n d -> (b t) n d') # [B*T, O, model_dim]
+#         h = self.space_attn(h, h, h)[0]
+#         h = rearrange(h, '(b t) n d -> b t n d', b=b, t=t) # [B, T, O, model_dim]
+
+#         # Layer norm and FFN
+#         h = self.ffn_norm(h)
+#         pred = self.ffn(h)                  # [B, T, O, 1]
+
+#         return pred
+
+class SlotEventProcessingBlock(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(dim)  # for FiLM
+        self.ln2 = nn.LayerNorm(dim)  # for cross-attn
+        self.ln3 = nn.LayerNorm(dim)  # for self-attn
+
+        # FiLM modulation
+        self.film_gamma = nn.Linear(dim, dim)
+        self.film_beta = nn.Linear(dim, dim)
+
+        # Cross-attention: slots ← visual
+        self.cross_attn = MultiHeadAttention(
+            d_model=dim,
+            num_heads=dim // 64
+        )
+
+        # Self-attention: slots ↔ slots
+        self.self_attn = MultiHeadAttention(
+            d_model=dim,
+            num_heads=dim // 64
+        )
+
+    def forward(self, x, task_cond, visual_context):
+        """
+        x:              [B, T, O, D]
+        task_cond:      [B, 1, 1, D]
+        visual_context: [B, T, V, D]
+        """
+        B, T, O, D = x.shape
+
+        # --- (a) FiLM conditioning with residual ---
+        x_ln = self.ln1(x)
+        gamma = self.film_gamma(task_cond)  # [B, 1, 1, D]
+        beta = self.film_beta(task_cond)    # [B, 1, 1, D]
+        x = x + gamma * x_ln + beta
+
+        # --- (b) Cross-attention with visual context ---
+        q = rearrange(self.ln2(x), 'b t o d -> (b t) o d')
+        kv = rearrange(visual_context, 'b t v d -> (b t) v d')
+        attn_out = self.cross_attn(q, kv, kv)[0]
+        attn_out = rearrange(attn_out, '(b t) o d -> b t o d', b=B, t=T)
+        x = x + attn_out
+
+        # --- (c) Self-attention across slots ---
+        x_ = rearrange(self.ln3(x), 'b t o d -> (b t) o d')
+        self_out = self.self_attn(x_, x_, x_)[0]
+        x = x + rearrange(self_out, '(b t) o d -> b t o d', b=B, t=T)
+
+        return x
+
+class SubgoalTransitionalPredictor(nn.Module):
+    def __init__(self, input_dim=512*3, condition_dim=512, visual_dim=2176, model_dim=1024, num_blocks=3, heads=4):
+        super().__init__()
+
+        self.inp_proj = nn.Linear(input_dim, model_dim)
+        self.task_proj = nn.Linear(condition_dim, model_dim)
+        self.visual_proj = nn.Linear(visual_dim, model_dim)
+
+        self.blocks = nn.ModuleList([
+            SlotEventProcessingBlock(dim=model_dim)
+            for _ in range(num_blocks)
+        ])
+
+        self.ffn_norm = nn.LayerNorm(model_dim)
+        self.ffn = nn.Sequential(
+            nn.Linear(model_dim, model_dim * 4),
+            nn.GELU(),
+            nn.Linear(model_dim * 4, 1)
+        )
+
+    def forward(self, x, task_cond, visual_context):
+        """
+        x:              [B, T, O, input_dim]     - input slots (e.g., current, delta, goal)
+        task_cond:      [B, 1, 1, condition_dim] - global task embedding
+        visual_context: [B, T, V, visual_dim]     - already projected visual tokens
+        """
+        x = self.inp_proj(x)                    # [B, T, O, model_dim]
+        task = self.task_proj(task_cond)        # [B, 1, 1, model_dim]
+        visual_context = self.visual_proj(visual_context) # [B, T, V, model_dim]
+        # print(visual_context.shape); 1/0
+        for block in self.blocks:
+            x = block(x, task, visual_context)  # include visual context
+
+        # Layer norm and FFN
+        x = self.ffn_norm(x)
+        pred = self.ffn(x)                  # [B, T, O, 1]
+
+        return pred
+
+ACTION_DIM = 7
+NUM_ACTIONS_CHUNK = 5
+
+class EmbodiedObjectSlot(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[str] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+
+class EmbodiedObject_LangSlot(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+
+        # cross attention
+        self.object_centric_afd_interact_head = AfdMultimodalTransformer(d_model=512, num_encoder_layers=3)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[str] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': [], 'interactable_features': [], 'attentions': [], 'bboxes': [], 'masks': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            interactability = self.object_centric_afd_interact_head(suboutput['visual_tokens'].squeeze(1), 
+                                                                    texts.squeeze(1)) #, attention=texts_attn)
+            suboutput['interactable_features'] = torch.unsqueeze(interactability, dim=1)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+        if 'interactable_features' in kwargs:
+            if 'top_k' in kwargs:
+                top_k = kwargs['top_k']
+            else:
+                top_k = 4
+            slotted_features, _ = self.select_top_k_slots(
+                slotted_features, kwargs['interactable_features'], k=top_k)
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+class EmbodiedObject_LangSlotTemporal(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+
+        # cross attention
+        self.object_centric_afd_interact_head = AfdMultimodalTransformer(d_model=512, num_encoder_layers=3)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[str] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': [], 'interactable_features': [], 'attentions': [], 'bboxes': [], 'masks': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            interactability = self.object_centric_afd_interact_head(suboutput['visual_tokens'].squeeze(1), 
+                                                                    texts.squeeze(1)) #, attention=texts_attn)
+            suboutput['interactable_features'] = torch.unsqueeze(interactability, dim=1)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+        if 'interactable_features' in kwargs:
+            if 'top_k' in kwargs:
+                top_k = kwargs['top_k']
+            else:
+                top_k = 4
+            slotted_features, _ = self.select_top_k_slots(
+                slotted_features, kwargs['interactable_features'], k=top_k)
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        # llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        # llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        # projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        # clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+
+        slotted_features = rearrange(slotted_features, '(b t) n d -> b (t n) d', b=bz, t=t)
+        
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (bz, slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+class EmbodiedRelationSlot(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        self.object_centric_relation_encoder = RelationTokensGrounding(dim=512, in_dim=4096, num_relation_tokens=self.object_token_num, n_heads=4, num_layers=3)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[str] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+        projected_patch_embeddings = self.object_centric_relation_encoder(projected_patch_embeddings, slotted_features)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+
+class EmbodiedRelation_LangSlot(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        self.object_centric_relation_encoder = RelationTokensGrounding(dim=512, in_dim=4096, num_relation_tokens=self.object_token_num, n_heads=4, num_layers=3)
+        # cross attention
+        self.object_centric_afd_interact_head = AfdMultimodalTransformer(d_model=512, num_encoder_layers=3)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            output = {}
+            output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+            output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+            return output
+        
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+        
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[Union[str, torch.FloatTensor]] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': [], 'interactable_features': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        if torch.is_tensor(texts):
+            general_texts = None
+            texts = texts
+            texts_attn = None
+        else:
+            general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+        
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            interactability = self.object_centric_afd_interact_head(suboutput['visual_tokens'].squeeze(1), 
+                                                                    texts.squeeze(1)) #, attention=texts_attn)
+            suboutput['interactable_features'] = torch.unsqueeze(interactability, dim=1)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+        if 'interactable_features' in kwargs:
+            if 'top_k' in kwargs:
+                top_k = kwargs['top_k']
+            else:
+                top_k = 4
+            slotted_features, _ = self.select_top_k_slots(
+                slotted_features, kwargs['interactable_features'], k=top_k)
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+        projected_patch_embeddings = self.object_centric_relation_encoder(projected_patch_embeddings, slotted_features)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            # projected_patch_attention_mask = torch.full(
+            #     (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+            #     fill_value=True,
+            #     dtype=llama_attention_mask.dtype,
+            #     device=llama_attention_mask.device,
+            # )
+            projected_patch_attention_mask = torch.ones(
+                (projected_patch_embeddings.shape[0],
+                projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+
+class EmbodiedRelation_LangSlotTemporal(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 use_slotgoals=False, number_of_slots=16) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.object_centric_slot_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+
+        # language tokenizer
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        self.object_centric_relation_encoder = RelationTokensGrounding(dim=512, in_dim=4096, num_relation_tokens=self.object_token_num, n_heads=4, num_layers=3)
+        # cross attention
+        self.object_centric_afd_interact_head = AfdMultimodalTransformer(d_model=512, num_encoder_layers=3)
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[Union[str, torch.FloatTensor]] = None,
+        past_tokens: Optional[torch.FloatTensor] = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        output = {'patch_features': [], 'visual_tokens': [], 'interactable_features': []}
+
+        device = pixel_values.device
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens = past_tokens
+        if torch.is_tensor(texts):
+            general_texts = None
+            texts = texts
+            texts_attn = None
+        else:
+            general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=1, device=device)
+    
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            interactability = self.object_centric_afd_interact_head(suboutput['visual_tokens'].squeeze(1), 
+                                                                    texts.squeeze(1)) #, attention=texts_attn)
+            suboutput['interactable_features'] = torch.unsqueeze(interactability, dim=1)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                if key in output:
+                    output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+        return output
+
+    def select_top_k_slots(
+        self,
+        slot_features,
+        interactable_features,
+        k=4
+    ):
+        """
+        Selects the top-k interactable slots from slot_features based on interactable_features.
+        
+        Args:
+            slot_features: Tensor of shape [bz, T, slot_num, dim]
+            interactable_features: Binary Tensor of shape [bz, T, slot_num] indicating interactability
+            k: Number of top slots to select
+
+        Returns:
+            selected_slots: Tensor of shape [bz, k, dim] containing the top-k interactable slots.
+        """
+        bz, T, slot_num, dim = slot_features.shape
+
+        # Ensure interactable_features is float for sorting
+        interactable_features = interactable_features.float()
+
+        # Get top-k indices along slot_num axis (dim=2), keeping time dimension
+        top_k_indices = torch.topk(
+            interactable_features,
+            k=min(k, slot_num),
+            dim=2,   # <-- slot_num axis
+            largest=True
+        ).indices  # [bz, T, k]
+
+        # Gather slot features corresponding to top-k indices
+        selected_slots = torch.gather(
+            slot_features,
+            2,  # gather along slot_num axis
+            top_k_indices.expand(-1, -1, -1, dim)  # [bz, T, k, dim]
+        )
+
+        return selected_slots, top_k_indices
+
+
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+        if 'interactable_features' in kwargs:
+            if 'top_k' in kwargs:
+                top_k = kwargs['top_k']
+            else:
+                top_k = 4
+            slotted_features, _ = self.select_top_k_slots(
+                slotted_features, kwargs['interactable_features'], k=top_k)
+
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        # llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        # llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        llama_labels = llama_labels
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+        # projected_clip_embeddings = projected_clip_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        # clip_attention_mask = clip_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+
+        # Get Slot encodings
+        # projected_patch_embeddings = self.base_model.projector(patch_features)
+        slotted_features = self.object_centric_slot_projector(slotted_features)
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+        projected_patch_embeddings = self.object_centric_relation_encoder(projected_patch_embeddings, slotted_features)
+
+        slotted_features = rearrange(slotted_features, '(b t) n d -> b (t n) d', b=bz, t=t)
+        projected_patch_embeddings = rearrange(projected_patch_embeddings, '(b t) n d -> b (t n) d', b=bz, t=t)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            # projected_patch_attention_mask = torch.full(
+            #     (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+            #     fill_value=True,
+            #     dtype=llama_attention_mask.dtype,
+            #     device=llama_attention_mask.device,
+            # )
+            projected_patch_attention_mask = torch.ones(
+                (projected_patch_embeddings.shape[0],
+                projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        return continuous_actions_pred
+
+
+#####################################################################################################
+#####################################################################################################
+
+class EmbodiedDecodedSlotSSMv3(nn.Module):
+    config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+    def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+                 latent_decoding=False, use_slotgoals=True, number_of_slots=24) -> None:
+        if config is None and base_model is None:
+            assert(False)
+        if isinstance(config, ObjectCentricVLAConfig):
+            super().__init__(config)
+        elif base_model is not None:
+            super().__init__()
+            self.base_model = base_model
+
+        self.object_token_num = number_of_slots #16 # 24
+        self.use_slotgoals = use_slotgoals
+        # The object-centric tokenizer handles extraction of object-centric features for,
+        # --- capturing object 2D coordinates and object captions
+        # --- capturing object features 
+        # dim 256: 0.7546
+        self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+        self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+        self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+        self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+        self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+        d_model = 512
+        # self.object_centric_pre_ssm_fusion = SlotFusion(input_dims=[512,512], embed_dims=[512, 512], output_dim=512)
+        self.object_centric_ssm = EmbodiedSlotSSM(
+            num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+            d_input=None, visual_d_model=2176, textual_d_model=None,
+            space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+            encoder_attn_num_heads=d_model // 64, lookback_only=True
+        )
+        self.latent_decoding = latent_decoding
+        # predicting the 8 steps head
+        if self.latent_decoding:
+            self.backward_steps = 8
+            self.forward_steps = 6
+            self.object_centric_latent_decoder = nn.Linear(1024, 512*(self.backward_steps + self.forward_steps)) 
+
+        if self.use_slotgoals:
+            self.object_centric_fuser = SlotFusion(input_dims=[512,512,512], embed_dims=[512, 512, 512], output_dim=None)
+        else:
+            self.object_centric_fuser = SlotFusion(input_dims=[512,512], output_dim=None)
+
+        self.object_centric_relation_encoder = EmbodiedSlotSSM(
+            num_slots=self.object_token_num, num_blocks=3, d_model=768,
+            d_input=1536, d_output=4096, visual_d_model=2176, textual_d_model=512,
+            space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+            encoder_attn_num_heads=d_model // 64, lookback_only=False
+        )
+
+        self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+        self.object_centric_action_head = L1RegressionActionHead(
+            input_dim=4096,
+            hidden_dim=4096,
+            action_dim=7,
+        )
+
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+        # print('unnorm_key', unnorm_key); 1/0
+        return self.norm_stats[unnorm_key]["action"]
+
+    def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+        if callable(name):
+            return name
+
+        name = name.lower()
+        if name == "relu":
+            return nn.ReLU(inplace=inplace)
+        elif name == "leaky_relu":
+            if leaky_relu_slope is None:
+                raise ValueError("Slope of leaky ReLU was not defined")
+            return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+        elif name == "tanh":
+            return nn.Tanh()
+        elif name == "sigmoid":
+            return nn.Sigmoid()
+        elif name == "identity":
+            return nn.Identity()
+        else:
+            raise ValueError(f"Unknown activation function {name}")
+
+    def build_mlp(
+        self,
+        input_dim: int,
+        output_dim: int,
+        features: List[int],
+        activation_fn: Optional[Union[str, Callable]] = "relu",
+        final_activation_fn: Optional[Union[str, Callable]] = None,
+        initial_layer_norm: bool = False,
+        residual: bool = False,
+    ) -> nn.Sequential:
+        layers = []
+        current_dim = input_dim
+        if initial_layer_norm:
+            layers.append(nn.LayerNorm(current_dim))
+
+        for n_features in features:
+            layers.append(nn.Linear(current_dim, n_features))
+            nn.init.xavier_uniform_(layers[-1].weight)
+            nn.init.zeros_(layers[-1].bias)
+            if activation_fn is not None:
+                layers.append(self.get_activation_fn(activation_fn))
+            current_dim = n_features
+
+        layers.append(nn.Linear(current_dim, output_dim))
+        nn.init.xavier_uniform_(layers[-1].weight)
+        nn.init.zeros_(layers[-1].bias)
+        if final_activation_fn is not None:
+            layers.append(self.get_activation_fn(final_activation_fn))
+
+        if residual:
+            return Residual(nn.Sequential(*layers))
+        return nn.Sequential(*layers)
+
+
+    def get_obj_slots_per_image(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        past_tokens:  Optional[torch.FloatTensor] = None
+    ):
+        """ This function receives pixel values then produces a set of object-centric slots 
+                using the ObjectCentricTokenizer.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        num_dimensions = len(pixel_values.shape)
+        if num_dimensions == 4: # [bz, cc, h, w]
+            # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+            bz, horizon, num_view = pixel_values.shape[0], 1, 1
+            patch_features = self.base_model.vision_backbone(pixel_values)
+
+        else: # [bz, horizon, cc, h, w]
+            1/0
+
+        patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+        # Extract bboxes
+        visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+                                                                                          past_slots=past_tokens)
+        if not self.training:
+            return visual_tokens, None
+
+        # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+        bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+        masks = self.object_centric_mask_head(visual_tokens)
+
+        bboxes = torch.stack([bboxes], dim=1)
+        masks = torch.stack([masks], dim=1)
+
+        output = {}
+        output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+        output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+        output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+        output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+        output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+        return output
+
+
+    def get_obj_slots(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        texts: Optional[list] = None,
+        in_past_tokens = None,
+    ):
+        """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+            It operates only on each image,
+                if in training mode, outputs the slots and the object-centric predictions.
+                else, outputs only the slots.
+        """
+        device = pixel_values.device
+        output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+        bz, horizon = pixel_values.shape[:2]
+        past_tokens=in_past_tokens
+        for h in range(horizon):
+            suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+            if 'visual_tokens' in suboutput:
+                past_tokens = suboutput['visual_tokens'].detach()
+            for key, value in suboutput.items():
+                output[key].append(value)
+
+        for key, value in output.items():
+            output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+        slots = output['visual_tokens'] # [B, T, N, D]
+        patch_features = output['patch_features'] # [B, T, VL, VD]
+        # texts: [B, T, TL, TD]
+        general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+        output['general_texts'] = general_texts
+        output['texts'] = texts
+        output['texts_attn'] = texts_attn
+
+        return output
+
+    def get_slot_dynamics(
+        self,
+        slots,
+        subgoals,
+        patch_features, 
+        texts, 
+        texts_attn,
+        output,
+        cache_params=None,
+        redundant_steps=0,
+        nohead=True
+    ):
+        # print(slots.shape, torch.min(slots), torch.max(slots))
+        # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+        # print(texts.shape, torch.min(texts), torch.max(texts))
+        # print(texts_attn.shape)
+        # slots: [B, T, N, D]
+        if redundant_steps > 0:
+            slots = slots[:,:-redundant_steps]
+            subgoals = subgoals[:,:-redundant_steps]
+            patch_features = patch_features[:,:-redundant_steps]
+            texts       = texts[:,:-redundant_steps]
+            texts_attn  = texts_attn[:,:-redundant_steps]
+
+        # slots = self.object_centric_pre_ssm_fusion([slots, subgoals])
+        latent_slots, attention, all_dynamic_slots = self.object_centric_ssm(slots, patch_features, texts, texts_attn, 
+                                                                             cache_params=cache_params, output_attentions=False)
+        if not nohead:
+            slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+            B, T, N, D_x_horizon_steps = slots.shape
+            D = D_x_horizon_steps // (self.backward_steps + self.forward_steps)
+            slots_reshaped = slots.reshape(B*T, N, self.backward_steps + self.forward_steps, D)
+            nxt_bboxes = self.object_centric_bbox_head(slots_reshaped.detach()[:,:,-1,:]).sigmoid() # selecting only the last step for evaluation
+            nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+            nxt_masks = self.object_centric_mask_head(slots_reshaped.detach()[:,:,-1,:]) # selecting only the last step for evaluation
+            nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+        else:
+            slots = None
+            nxt_bboxes = None
+            nxt_masks = None
+        # print(slots.shape, torch.min(slots), torch.max(slots))
+
+        output['latent_slots'] = latent_slots
+        output['nxt_tokens'] = slots
+        output['nxt_bboxes'] = nxt_bboxes
+        output['nxt_masks'] = nxt_masks
+
+        # debugging purposes
+        # for key, value in output.items():
+        #     print(key, value.shape, torch.min(value), torch.max(value))
+        # 1/0
+        return output
+
+    def get_slot_fusion(
+        self,
+        crn_slots: Optional[torch.FloatTensor] = None,
+        nxt_slots: Optional[torch.FloatTensor] = None,
+        subgoal_states: Optional[torch.FloatTensor] = None,
+    ):
+        if self.use_slotgoals:
+            return self.object_centric_fuser([crn_slots, nxt_slots, subgoal_states])
+        else:
+            return self.object_centric_fuser([crn_slots, nxt_slots])
+
+    def decode_continuous_actions(
+        self,
+        patch_features: Optional[torch.FloatTensor] = None,
+        slotted_features: Optional[torch.FloatTensor] = None,
+        clip_embeddings:  Optional[torch.FloatTensor] = None,
+        clip_attention_mask: Optional[torch.Tensor] = None,
+        llama_input_ids: Optional[torch.LongTensor] = None,
+        llama_attention_mask: Optional[torch.Tensor] = None,
+        llama_labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+        # Projection Logic =>> Update Attention Mask
+        bz, t, n, d = slotted_features.shape
+
+        slotted_features, _, _ = self.object_centric_relation_encoder(slotted_features, patch_features, clip_embeddings, torch.logical_not(clip_attention_mask), 
+                                                                      cache_params=None, output_attentions=False)
+        patch_features = rearrange(patch_features, 'b t v d -> (b t) v d')
+        slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+        clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+        clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+        # Get Input Embeddings (from Language Model Embeddings)
+        llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+        llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+        llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+        if llama_labels is not None:
+            llama_labels = llama_labels.flatten(0,1)
+        projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+
+        # Get Slot encodings
+        slotted_features = slotted_features
+        projected_patch_embeddings = self.base_model.projector(patch_features)
+
+        projected_patch_attention_mask = None
+        if llama_attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], slotted_features.shape[1]),
+                fill_value=True,
+                dtype=llama_attention_mask.dtype,
+                device=llama_attention_mask.device,
+            )
+
+
+        # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+        multimodal_embeddings = torch.cat(
+            [llama_input_embeddings[:, :1, :], slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+        )
+        multimodal_attention_mask = None
+        if llama_attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+            )
+
+        # Dispatch to Language Model
+        language_model_output = self.base_model.language_model(
+            input_ids=None,
+            attention_mask=multimodal_attention_mask,
+            position_ids=None,
+            past_key_values=None,
+            inputs_embeds=multimodal_embeddings,
+            labels=None,
+            use_cache=None,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        # Extract hidden states for action tokens
+        last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+        actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+        # L1 regression prediction
+        continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+        # print(continuous_actions_pred.shape); 1/0
+        return continuous_actions_pred
+
+
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+# class OpenVLAForActionPrediction_SlotSSM(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif model is not None:
+#             super().__init__()
+#             self.model = model
+
+#         self.object_token_num = 10
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         self.object_centric_latent_decoder = nn.Linear(1024, 512)
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, _, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features)
+#         if not self.training:
+#             return visual_tokens, None
+        
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         bboxes = torch.stack([bboxes], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+
+#         return output
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'bboxes': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h])
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+#         output['general_texts'] = general_texts
+#         output['texts'] = texts
+
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         B, T, N, D = slots.shape
+#         latent_slots, attention, slot_lists = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+#         slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+#         slots_reshaped = slots.reshape(B*T, N, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped.detach()).sigmoid()
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         nxt_masks = self.object_centric_mask_head(slots_reshaped.detach())
+#         nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['latent_slots'] = latent_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+# class OpenVLAForActionPrediction_SlotSSMv2(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and base_model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif base_model is not None:
+#             super().__init__()
+#             self.base_model = base_model
+
+#         self.object_token_num = 24
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_pre_ssm_fusion = SlotFusion(input_dims=[512,512], embed_dims=[512, 512], output_dim=512)
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         # predicting the 8 steps head
+#         self.forward_steps = 8
+#         self.object_centric_latent_decoder = nn.Linear(1024, 512*self.forward_steps) 
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         past_tokens:  Optional[torch.FloatTensor] = None
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.base_model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+#                                                                                           past_slots=past_tokens)
+#         if not self.training:
+#             return visual_tokens, None
+
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         masks = self.object_centric_mask_head(visual_tokens)
+
+#         bboxes = torch.stack([bboxes], dim=1)
+#         masks = torch.stack([masks], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+#         output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+#         return output
+
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         past_tokens=None
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+#             if 'visual_tokens' in suboutput:
+#                 past_tokens = suboutput['visual_tokens'].detach()
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+#         output['general_texts'] = general_texts
+#         output['texts'] = texts
+#         output['texts_attn'] = texts_attn
+
+#         return output
+
+#     def get_slot_dynamics(
+#         self,
+#         slots,
+#         subgoals,
+#         patch_features, 
+#         texts, 
+#         texts_attn,
+#         output,
+#         redundant_steps=0
+#     ):
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         if redundant_steps > 0:
+#             slots = slots[:,:-redundant_steps]
+#             subgoals = subgoals[:,:-redundant_steps]
+#             patch_features = patch_features[:,:-redundant_steps]
+#             texts       = texts[:,:-redundant_steps]
+#             texts_attn  = texts_attn[:,:-redundant_steps]
+
+#         slots = self.object_centric_pre_ssm_fusion([slots, subgoals])
+#         latent_slots, attention, slot_lists = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+#         slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+#         B, T, N, D_x_forward_steps = slots.shape
+#         D = D_x_forward_steps // self.forward_steps
+#         slots_reshaped = slots.reshape(B*T, N, self.forward_steps, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped.detach()[:,:,-1,:]).sigmoid() # selecting only the last step
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         nxt_masks = self.object_centric_mask_head(slots_reshaped.detach()[:,:,-1,:]) # selecting only the last step
+#         nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['latent_slots'] = latent_slots
+#         output['nxt_tokens'] = slots.reshape(B, T, N, self.forward_steps, D)
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+    
+#####################################################################################################
+
+
+# class EmbodiedDecodedSlotSSM(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and base_model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif base_model is not None:
+#             super().__init__()
+#             self.base_model = base_model
+
+#         self.object_token_num = 10
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         self.object_centric_latent_decoder = nn.Linear(1024, 512)
+
+#         # self.object_centric_event_predictor = MLP(input_dim=1024, hidden_dim=2028, output_dim=1, num_layers=4)
+#         self.object_centric_event_predictor = SubgoalTransitionalPredictor(input_dim=512*3, condition_dim=512, model_dim=1024)
+#         self.object_centric_fuser = SlotFusion(input_dims=[512,512,512], embed_dims=[1365, 1365, 1366], output_dim=1365+1365+1366)
+#         self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+#         self.visual_pool = visual_pool
+#         self.object_centric_action_head = L1RegressionActionHead(
+#             input_dim=4096,
+#             hidden_dim=4096,
+#             action_dim=7,
+#         )
+
+#     def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+#         """Get all the logged statistics for the given dataset."""
+#         unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+#         # print('unnorm_key', unnorm_key); 1/0
+#         return self.norm_stats[unnorm_key]["action"]
+
+#     def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+#         if callable(name):
+#             return name
+
+#         name = name.lower()
+#         if name == "relu":
+#             return nn.ReLU(inplace=inplace)
+#         elif name == "leaky_relu":
+#             if leaky_relu_slope is None:
+#                 raise ValueError("Slope of leaky ReLU was not defined")
+#             return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+#         elif name == "tanh":
+#             return nn.Tanh()
+#         elif name == "sigmoid":
+#             return nn.Sigmoid()
+#         elif name == "identity":
+#             return nn.Identity()
+#         else:
+#             raise ValueError(f"Unknown activation function {name}")
+
+#     def build_mlp(
+#         self,
+#         input_dim: int,
+#         output_dim: int,
+#         features: List[int],
+#         activation_fn: Optional[Union[str, Callable]] = "relu",
+#         final_activation_fn: Optional[Union[str, Callable]] = None,
+#         initial_layer_norm: bool = False,
+#         residual: bool = False,
+#     ) -> nn.Sequential:
+#         layers = []
+#         current_dim = input_dim
+#         if initial_layer_norm:
+#             layers.append(nn.LayerNorm(current_dim))
+
+#         for n_features in features:
+#             layers.append(nn.Linear(current_dim, n_features))
+#             nn.init.xavier_uniform_(layers[-1].weight)
+#             nn.init.zeros_(layers[-1].bias)
+#             if activation_fn is not None:
+#                 layers.append(self.get_activation_fn(activation_fn))
+#             current_dim = n_features
+
+#         layers.append(nn.Linear(current_dim, output_dim))
+#         nn.init.xavier_uniform_(layers[-1].weight)
+#         nn.init.zeros_(layers[-1].bias)
+#         if final_activation_fn is not None:
+#             layers.append(self.get_activation_fn(final_activation_fn))
+
+#         if residual:
+#             return Residual(nn.Sequential(*layers))
+#         return nn.Sequential(*layers)
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.base_model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, _, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features)
+#         if not self.training:
+#             return visual_tokens, None
+        
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         bboxes = torch.stack([bboxes], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+
+#         return output
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#         cache_params=None,
+#         segment=True
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'bboxes': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h])
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         B, T, N, D = slots.shape
+#         latent_slots, attention, all_dynamic_slots = self.object_centric_ssm(slots, patch_features, texts, texts_attn, 
+#                                                                              cache_params=cache_params, output_attentions=False)
+#         slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['latent_slots'] = latent_slots
+#         slots_reshaped = slots.reshape(B*T, N, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped).sigmoid()
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         if segment:
+#             nxt_masks = self.object_centric_mask_head(slots_reshaped)
+#             nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         else:
+#             nxt_masks = None
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['general_clip_features'] = general_texts
+#         output['clip_features'] = texts
+#         output['clip_attention'] = texts_attn
+#         output['all_dynamic_slots'] = all_dynamic_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#     def get_event_prediction(self, slot_dynamics, subgoal_states):
+#         """ slot_dynamics: [B, T, N, D]
+#             subgoal_states: [B, T, N, D]
+#         """
+#         B, T, N, D = slot_dynamics.shape
+#         subgoal_dynamics = torch.cat([slot_dynamics, subgoal_states], dim=-1)
+#         event_prediction = self.object_centric_event_predictor(subgoal_dynamics)
+#         return event_prediction[:,:-1].squeeze(-1) # ignore the last state's event and squeeze the last 1-dim of ffn
+
+#     def get_slot_fusion(
+#         self,
+#         crn_slots: Optional[torch.FloatTensor] = None,
+#         nxt_slots: Optional[torch.FloatTensor] = None,
+#         subgoal_states: Optional[torch.FloatTensor] = None,
+#     ):
+#         return self.object_centric_fuser([crn_slots, nxt_slots, subgoal_states])
+
+#     def decode_actions(
+#         self,
+#         patch_features: Optional[torch.FloatTensor] = None,
+#         slotted_features: Optional[torch.FloatTensor] = None,
+#         clip_embeddings:  Optional[torch.FloatTensor] = None,
+#         clip_attention_mask: Optional[torch.Tensor] = None,
+#         llama_input_ids: Optional[torch.LongTensor] = None,
+#         llama_attention_mask: Optional[torch.Tensor] = None,
+#         llama_labels: Optional[torch.LongTensor] = None,
+#         inputs_embeds: Optional[torch.FloatTensor] = None,
+#         output_attentions: Optional[bool] = None,
+#         output_hidden_states: Optional[bool] = None,
+#         output_projector_features: Optional[bool] = None,
+#         return_dict: Optional[bool] = None,
+#         **kwargs
+#     ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+#         """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+#         # Projection Logic =>> Update Attention Mask
+#         bz, t, n, d = slotted_features.shape
+        
+#         patch_features = self.visual_pool(rearrange(patch_features, 'b t v d -> (b t) v d'), reduction=4)
+#         slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+#         clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+#         clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+#         # Get Input Embeddings (from Language Model Embeddings)
+#         llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+#         llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+#         llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+#         llama_labels = llama_labels.flatten(0,1)
+#         projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+
+#         # Get Slot encodings
+#         projected_patch_embeddings = self.base_model.projector(patch_features)
+#         slotted_features = slotted_features
+
+#         projected_patch_attention_mask = None
+#         if llama_attention_mask is not None:
+#             projected_patch_attention_mask = torch.full(
+#                 (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+#                 fill_value=True,
+#                 dtype=llama_attention_mask.dtype,
+#                 device=llama_attention_mask.device,
+#             )
+
+#         # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+#         multimodal_embeddings = torch.cat(
+#             [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+#         )
+#         multimodal_attention_mask = None
+#         if llama_attention_mask is not None:
+#             multimodal_attention_mask = torch.cat(
+#                 [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+#             )
+
+#         # Build Labels (if specified) =>> Ignore Labels for Patch Embeddings
+#         multimodal_labels = None
+#         if llama_labels is not None:
+#             projected_patch_labels = torch.full(
+#                 (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+#                 fill_value=IGNORE_INDEX,
+#                 dtype=llama_labels.dtype,
+#                 device=llama_labels.device,
+#             )
+#             projected_clip_labels = torch.full(
+#                 (projected_clip_embeddings.shape[0], projected_clip_embeddings.shape[1]),
+#                 fill_value=IGNORE_INDEX,
+#                 dtype=llama_labels.dtype,
+#                 device=llama_labels.device,
+#             )
+#             multimodal_labels = torch.cat([llama_labels[:, :1], projected_patch_labels, projected_clip_labels, llama_labels[:, 1:]], dim=1)
+
+#         # Dispatch to Language Model
+#         language_model_output = self.base_model.language_model(
+#             input_ids=None,
+#             attention_mask=multimodal_attention_mask,
+#             position_ids=None,
+#             past_key_values=None,
+#             inputs_embeds=multimodal_embeddings,
+#             labels=None,
+#             use_cache=False,
+#             output_attentions=output_attentions,
+#             output_hidden_states=output_hidden_states,
+#             return_dict=return_dict,
+#         )
+
+#         if self.training:
+#             logits = language_model_output.logits
+#             logits = logits.reshape(bz, t, *logits.shape[1:])
+#             logits = logits[:, 1:].reshape(bz*(t-1), *logits.shape[2:]) # skip the first time step because of intialization
+#             labels = multimodal_labels
+#             labels = labels.reshape(bz, t, *labels.shape[1:])
+#             labels = labels[:, 1:].reshape(bz*(t-1), *labels.shape[2:]) # skip the first time step because of intialization
+
+#             loss = self.base_model.calculate_loss(logits=logits, 
+#                                     labels=labels, 
+#                                     weight=None) # self.token_weights.to(multimodal_labels.device) 
+#         else:
+#             loss = None
+
+#         return PrismaticCausalLMOutputWithPast(
+#             loss=loss,
+#             logits=language_model_output.logits,
+#             past_key_values=language_model_output.past_key_values,
+#             hidden_states=language_model_output.hidden_states,
+#             attentions=language_model_output.attentions,
+#             projector_features=projected_patch_embeddings,
+#         )
+
+
+#     def decode_continuous_actions(
+#         self,
+#         patch_features: Optional[torch.FloatTensor] = None,
+#         slotted_features: Optional[torch.FloatTensor] = None,
+#         clip_embeddings:  Optional[torch.FloatTensor] = None,
+#         clip_attention_mask: Optional[torch.Tensor] = None,
+#         llama_input_ids: Optional[torch.LongTensor] = None,
+#         llama_attention_mask: Optional[torch.Tensor] = None,
+#         llama_labels: Optional[torch.LongTensor] = None,
+#         inputs_embeds: Optional[torch.FloatTensor] = None,
+#         output_attentions: Optional[bool] = None,
+#         output_hidden_states: Optional[bool] = None,
+#         output_projector_features: Optional[bool] = None,
+#         return_dict: Optional[bool] = None,
+#         **kwargs
+#     ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+#         """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+#         # Projection Logic =>> Update Attention Mask
+#         bz, t, n, d = slotted_features.shape
+        
+#         # patch_features = self.visual_pool(rearrange(patch_features, 'b t v d -> (b t) v d'), reduction=4)
+#         slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+#         clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+#         clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+#         # Get Input Embeddings (from Language Model Embeddings)
+#         llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+#         llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+#         llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+#         projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+
+#         # Get Slot encodings
+#         projected_patch_embeddings = self.base_model.projector(patch_features)
+#         slotted_features = slotted_features
+
+#         projected_patch_attention_mask = None
+#         if llama_attention_mask is not None:
+#             projected_patch_attention_mask = torch.full(
+#                 (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+#                 fill_value=True,
+#                 dtype=llama_attention_mask.dtype,
+#                 device=llama_attention_mask.device,
+#             )
+
+
+#         # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+#         multimodal_embeddings = torch.cat(
+#             [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+#         )
+#         multimodal_attention_mask = None
+#         if llama_attention_mask is not None:
+#             multimodal_attention_mask = torch.cat(
+#                 [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+#             )
+
+#         # Dispatch to Language Model
+#         language_model_output = self.base_model.language_model(
+#             input_ids=None,
+#             attention_mask=multimodal_attention_mask,
+#             position_ids=None,
+#             past_key_values=None,
+#             inputs_embeds=multimodal_embeddings,
+#             labels=None,
+#             use_cache=None,
+#             output_attentions=False,
+#             output_hidden_states=True,
+#             return_dict=True,
+#         )
+
+#         # Extract hidden states for action tokens
+#         last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+#         actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+#         # L1 regression prediction
+#         continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+#         # print(continuous_actions_pred.shape); 1/0
+#         return continuous_actions_pred
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+# class RelationalCrossAttention(nn.Module):
+#     def __init__(self, q_dim, kv_dim, heads=8, dim_head=64, dropout=0.0):
+#         super().__init__()
+#         inner_dim = dim_head * heads
+#         self.heads = heads
+#         self.scale = dim_head ** -0.5
+
+#         self.norm_q = nn.LayerNorm(q_dim)
+#         self.norm_kv = nn.LayerNorm(kv_dim)
+
+#         self.to_q = nn.Linear(q_dim, inner_dim, bias=False)
+#         self.to_k = nn.Linear(kv_dim, inner_dim, bias=False)
+#         self.to_v = nn.Linear(kv_dim, inner_dim, bias=False)
+
+#         self.attend = nn.Softmax(dim=-1)
+#         self.dropout = nn.Dropout(dropout)
+
+#         self.to_out = nn.Sequential(
+#             nn.Linear(inner_dim, q_dim),
+#             nn.Dropout(dropout)
+#         )
+
+#     def forward(self, q_input, kv_input):
+#         q_input = self.norm_q(q_input)
+#         kv_input = self.norm_kv(kv_input)
+
+#         q = self.to_q(q_input)
+#         k = self.to_k(kv_input)
+#         v = self.to_v(kv_input)
+
+#         q = rearrange(q, 'b n (h d) -> b h n d', h=self.heads)
+#         k = rearrange(k, 'b n (h d) -> b h n d', h=self.heads)
+#         v = rearrange(v, 'b n (h d) -> b h n d', h=self.heads)
+
+#         attn = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+#         attn = self.attend(attn)
+#         attn = self.dropout(attn)
+
+#         out = torch.matmul(attn, v)
+#         out = rearrange(out, 'b h n d -> b n (h d)')
+#         return self.to_out(out)
+
+# class FeedForward(nn.Module):
+#     def __init__(self, dim, hidden_dim=1024, dropout=0.0):
+#         super().__init__()
+#         self.net = nn.Sequential(
+#             nn.LayerNorm(dim),
+#             nn.Linear(dim, hidden_dim),
+#             nn.GELU(),
+#             nn.Dropout(dropout),
+#             nn.Linear(hidden_dim, dim),
+#             nn.Dropout(dropout),
+#         )
+
+#     def forward(self, x):
+#         return self.net(x)
+
+# class FineSlotAbstractor(nn.Module):
+#     def __init__(self, slot_dim=512, visual_dim=2176, heads=8, dim_head=64, dropout=0.0):
+#         super().__init__()
+#         self.rel_attn1 = RelationalCrossAttention(q_dim=slot_dim, kv_dim=slot_dim, heads=heads, dim_head=dim_head, dropout=dropout)
+#         self.ff1 = FeedForward(slot_dim, dropout=dropout)
+
+#         self.rel_attn2 = RelationalCrossAttention(q_dim=slot_dim, kv_dim=visual_dim, heads=heads, dim_head=dim_head, dropout=dropout)
+#         self.ff2 = FeedForward(slot_dim, dropout=dropout)
+
+#     def forward(self, slots, visual_inputs):
+#         # Step 1: Relational Cross-Attn (slot <-> slot)
+#         slots = slots + self.rel_attn1(slots, slots)
+#         slots = slots + self.ff1(slots)
+
+#         # Step 2: Relational Cross-Attn (slot <-> visual)
+#         slots = slots + self.rel_attn2(slots, visual_inputs)
+#         slots = slots + self.ff2(slots)
+
+#         return slots
+
+#################################################################################################
+# class OpenVLAForActionPrediction_SubgoalSlotSSM(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif model is not None:
+#             super().__init__()
+#             self.model = model
+
+#         self.object_token_num = 10
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         # self.object_centric_event_predictor = MLP(input_dim=1024, hidden_dim=2028, output_dim=1, num_layers=4)
+#         # self.object_centric_event_predictor = self.build_mlp(input_dim=1536, output_dim=1, 
+#         #                                                      features=[2048, 2048, 512],
+#         #                                                      initial_layer_norm=True, residual=False)
+#         self.object_centric_event_predictor = ResMLP(in_dim=1536, hidden_dims=(2048, 512), n_classes=1)
+
+#     def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+#         if callable(name):
+#             return name
+
+#         name = name.lower()
+#         if name == "relu":
+#             return nn.ReLU(inplace=inplace)
+#         elif name == "leaky_relu":
+#             if leaky_relu_slope is None:
+#                 raise ValueError("Slope of leaky ReLU was not defined")
+#             return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+#         elif name == "tanh":
+#             return nn.Tanh()
+#         elif name == "sigmoid":
+#             return nn.Sigmoid()
+#         elif name == "identity":
+#             return nn.Identity()
+#         else:
+#             raise ValueError(f"Unknown activation function {name}")
+
+#     def build_mlp(
+#         self,
+#         input_dim: int,
+#         output_dim: int,
+#         features: List[int],
+#         activation_fn: Optional[Union[str, Callable]] = "relu",
+#         final_activation_fn: Optional[Union[str, Callable]] = None,
+#         initial_layer_norm: bool = False,
+#         residual: bool = False,
+#     ) -> nn.Sequential:
+#         layers = []
+#         current_dim = input_dim
+#         if initial_layer_norm:
+#             layers.append(nn.LayerNorm(current_dim))
+
+#         for n_features in features:
+#             layers.append(nn.Linear(current_dim, n_features))
+#             nn.init.xavier_uniform_(layers[-1].weight)
+#             nn.init.zeros_(layers[-1].bias)
+#             if activation_fn is not None:
+#                 layers.append(self.get_activation_fn(activation_fn))
+#             current_dim = n_features
+
+#         layers.append(nn.Linear(current_dim, output_dim))
+#         nn.init.xavier_uniform_(layers[-1].weight)
+#         nn.init.zeros_(layers[-1].bias)
+#         if final_activation_fn is not None:
+#             layers.append(self.get_activation_fn(final_activation_fn))
+
+#         if residual:
+#             return Residual(nn.Sequential(*layers))
+#         return nn.Sequential(*layers)
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, _, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features)
+#         if not self.training:
+#             return visual_tokens, None
+        
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         bboxes = torch.stack([bboxes], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+
+#         return output
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#         segment=True
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'bboxes': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h])
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         B, T, N, D = slots.shape
+#         slots, attention, all_dynamic_slots = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+#         slots_reshaped = slots.reshape(B*T, N, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped).sigmoid()
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         if segment:
+#             nxt_masks = self.object_centric_mask_head(slots_reshaped)
+#             nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         else:
+#             nxt_masks = None
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['all_dynamic_slots'] = all_dynamic_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#     def get_event_prediction(self, slot_dynamics, subgoal_states):
+#         """ slot_dynamics: [B, T, N, D]
+#             subgoal_states: [B, T, N, D]
+#         """
+#         B, T, N, D = slot_dynamics.shape
+#         subgoal_dynamics = torch.cat([slot_dynamics, subgoal_states], dim=-1)
+#         event_prediction = self.object_centric_event_predictor(subgoal_dynamics)
+#         return event_prediction[:,:-1].squeeze(-1) # ignore the last state's event and squeeze the last 1-dim of ffn
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+# class OpenVLAForActionPrediction_SubgoalSlotSSMv2(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif model is not None:
+#             super().__init__()
+#             self.model = model
+
+#         self.object_token_num = 10
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=1024, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         self.object_centric_subgoal_projector = self.build_mlp(input_dim=512, output_dim=512, 
+#                                                              features=[512], residual=True)
+#         # self.object_centric_event_predictor = MLP(input_dim=1024, hidden_dim=2028, output_dim=1, num_layers=4)
+#         # self.object_centric_event_predictor = self.build_mlp(input_dim=1536, output_dim=1, 
+#         #                                                      features=[2048, 2048, 512],
+#         #                                                      initial_layer_norm=True, residual=False)
+#         self.object_centric_event_predictor = ResMLP(in_dim=512, hidden_dims=(1024, 1024, 512), n_classes=1)
+
+#     def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+#         if callable(name):
+#             return name
+
+#         name = name.lower()
+#         if name == "relu":
+#             return nn.ReLU(inplace=inplace)
+#         elif name == "leaky_relu":
+#             if leaky_relu_slope is None:
+#                 raise ValueError("Slope of leaky ReLU was not defined")
+#             return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+#         elif name == "tanh":
+#             return nn.Tanh()
+#         elif name == "sigmoid":
+#             return nn.Sigmoid()
+#         elif name == "identity":
+#             return nn.Identity()
+#         else:
+#             raise ValueError(f"Unknown activation function {name}")
+
+#     def build_mlp(
+#         self,
+#         input_dim: int,
+#         output_dim: int,
+#         features: List[int],
+#         activation_fn: Optional[Union[str, Callable]] = "relu",
+#         final_activation_fn: Optional[Union[str, Callable]] = None,
+#         initial_layer_norm: bool = False,
+#         residual: bool = False,
+#     ) -> nn.Sequential:
+#         layers = []
+#         current_dim = input_dim
+#         if initial_layer_norm:
+#             layers.append(nn.LayerNorm(current_dim))
+
+#         for n_features in features:
+#             layers.append(nn.Linear(current_dim, n_features))
+#             nn.init.xavier_uniform_(layers[-1].weight)
+#             nn.init.zeros_(layers[-1].bias)
+#             if activation_fn is not None:
+#                 layers.append(self.get_activation_fn(activation_fn))
+#             current_dim = n_features
+
+#         layers.append(nn.Linear(current_dim, output_dim))
+#         nn.init.xavier_uniform_(layers[-1].weight)
+#         nn.init.zeros_(layers[-1].bias)
+#         if final_activation_fn is not None:
+#             layers.append(self.get_activation_fn(final_activation_fn))
+
+#         if residual:
+#             return Residual(nn.Sequential(*layers))
+#         return nn.Sequential(*layers)
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, _, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features)
+#         if not self.training:
+#             return visual_tokens, None
+        
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         bboxes = torch.stack([bboxes], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+
+#         return output
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'bboxes': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h])
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         return output
+
+#     def get_dynamic_slots(self, slots, subgoals, patch_features, texts, segment=True):
+#         """ slots : [B, T, N, D]
+#             patch_features : [B, T, VL, VD]
+#             text : Optional[List
+#         """
+#         device = patch_features.device
+#         bz, horizon = patch_features.shape[:2]
+
+#         # texts: [B, T, TL, TD]
+#         texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         B, T, N, D = slots.shape
+#         subgoals = self.object_centric_subgoal_projector(subgoals)
+#         slots = torch.cat([slots, subgoals], dim=-1)                # 512 x 2
+#         slots, attention, _ = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+#         slots_reshaped = slots.reshape(B*T, N, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped).sigmoid()
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         if segment:
+#             nxt_masks = self.object_centric_mask_head(slots_reshaped)
+#             nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         else:
+#             nxt_masks = None
+#         event_prediction = self.object_centric_event_predictor(slots_reshaped)
+#         event_prediction = event_prediction.reshape([B, T, N])
+
+#         output = {}
+#         # output['all_dynamic_slots'] = all_dynamic_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+#         output['nxt_events'] = event_prediction
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#     def get_event_prediction(self, slot_dynamics, subgoal_states):
+#         """ slot_dynamics: [B, T, N, D]
+#             subgoal_states: [B, T, N, D]
+#         """
+#         B, T, N, D = slot_dynamics.shape
+#         subgoal_dynamics = torch.cat([slot_dynamics, subgoal_states], dim=-1)
+#         event_prediction = self.object_centric_event_predictor(subgoal_dynamics)
+#         return event_prediction[:,:-1].squeeze(-1) # ignore the last state's event and squeeze the last 1-dim of ffn
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+# class OpenVLAForActionPrediction_SubgoalSlotSSMv3(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None) -> None:
+#         if config is None and base_model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif base_model is not None:
+#             super().__init__()
+#             self.base_model = base_model
+
+#         self.object_token_num = 10
+
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         self.object_centric_latent_decoder = nn.Linear(1024, 512)
+#         # self.object_centric_event_predictor = MLP(input_dim=1024, hidden_dim=2028, output_dim=1, num_layers=4)
+#         # self.object_centric_event_predictor = self.build_mlp(input_dim=1536, output_dim=1, 
+#         #                                                      features=[2048, 2048, 512],
+#         #                                                      initial_layer_norm=True, residual=False)
+#         self.object_centric_event_predictor = SubgoalTransitionalPredictor(input_dim=512*3, condition_dim=512, model_dim=1024)
+
+#     def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+#         if callable(name):
+#             return name
+
+#         name = name.lower()
+#         if name == "relu":
+#             return nn.ReLU(inplace=inplace)
+#         elif name == "leaky_relu":
+#             if leaky_relu_slope is None:
+#                 raise ValueError("Slope of leaky ReLU was not defined")
+#             return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+#         elif name == "tanh":
+#             return nn.Tanh()
+#         elif name == "sigmoid":
+#             return nn.Sigmoid()
+#         elif name == "identity":
+#             return nn.Identity()
+#         else:
+#             raise ValueError(f"Unknown activation function {name}")
+
+#     def build_mlp(
+#         self,
+#         input_dim: int,
+#         output_dim: int,
+#         features: List[int],
+#         activation_fn: Optional[Union[str, Callable]] = "relu",
+#         final_activation_fn: Optional[Union[str, Callable]] = None,
+#         initial_layer_norm: bool = False,
+#         residual: bool = False,
+#     ) -> nn.Sequential:
+#         layers = []
+#         current_dim = input_dim
+#         if initial_layer_norm:
+#             layers.append(nn.LayerNorm(current_dim))
+
+#         for n_features in features:
+#             layers.append(nn.Linear(current_dim, n_features))
+#             nn.init.xavier_uniform_(layers[-1].weight)
+#             nn.init.zeros_(layers[-1].bias)
+#             if activation_fn is not None:
+#                 layers.append(self.get_activation_fn(activation_fn))
+#             current_dim = n_features
+
+#         layers.append(nn.Linear(current_dim, output_dim))
+#         nn.init.xavier_uniform_(layers[-1].weight)
+#         nn.init.zeros_(layers[-1].bias)
+#         if final_activation_fn is not None:
+#             layers.append(self.get_activation_fn(final_activation_fn))
+
+#         if residual:
+#             return Residual(nn.Sequential(*layers))
+#         return nn.Sequential(*layers)
+
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.base_model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, _, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features)
+#         if not self.training:
+#             return visual_tokens, None
+        
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         bboxes = torch.stack([bboxes], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+
+#         return output
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#         segment=True
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'bboxes': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h])
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         B, T, N, D = slots.shape
+#         latent_slots, attention, all_dynamic_slots = self.object_centric_ssm(slots, patch_features, texts, texts_attn, output_attentions=False)
+#         slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['latent_slots'] = latent_slots
+#         slots_reshaped = slots.reshape(B*T, N, D)
+#         nxt_bboxes = self.object_centric_bbox_head(slots_reshaped).sigmoid()
+#         nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#         if segment:
+#             nxt_masks = self.object_centric_mask_head(slots_reshaped)
+#             nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         else:
+#             nxt_masks = None
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['general_clip_features'] = general_texts
+#         output['clip_features'] = texts
+#         output['clip_attention'] = texts_attn
+#         output['all_dynamic_slots'] = all_dynamic_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#     def get_event_prediction(self, slot_dynamics, task_embeddings, patch_features=None):
+#         """ slot_dynamics: [B, T, N, D]
+#             task_embeddings: [B, D]
+#         """
+#         B, T, N, D = slot_dynamics.shape
+#         task_embeddings = task_embeddings.reshape(B, 1, 1, task_embeddings.shape[-1])
+#         event_prediction = self.object_centric_event_predictor(slot_dynamics, task_embeddings, visual_context=patch_features)
+#         return event_prediction.squeeze(-1) # squeeze the last 1-dim of ffn
+
+#####################################################################################################
+#####################################################################################################
+#####################################################################################################
+
+
+# class EmbodiedDecodedSlotSSMv2(nn.Module):
+#     config_class: PretrainedConfig = ObjectCentricVLAConfig
+
+#     def __init__(self, config: ObjectCentricVLAConfig = None, base_model: OpenVLAForActionPrediction = None,
+#                  use_slotgoals=True) -> None:
+#         if config is None and base_model is None:
+#             assert(False)
+#         if isinstance(config, ObjectCentricVLAConfig):
+#             super().__init__(config)
+#         elif base_model is not None:
+#             super().__init__()
+#             self.base_model = base_model
+
+#         self.object_token_num = 24
+#         self.use_slotgoals = use_slotgoals
+#         # The object-centric tokenizer handles extraction of object-centric features for,
+#         # --- capturing object 2D coordinates and object captions
+#         # --- capturing object features 
+#         # dim 256: 0.7546
+#         self.object_centric_tokenizer = SlotAttention(n_slots=self.object_token_num, in_dim=2176, feature_dim=512) 
+#         self.object_centric_bbox_head = MLP(input_dim=512, hidden_dim=1024, output_dim=5, num_layers=2) # switch depth to 3 for good results
+#         self.object_centric_mask_head = MaskPredictionHead(slot_dim=512, hidden_dim=1024, mask_size=(64,64))
+#         self.clip_model, self.clip_preprocess = clip.load("ViT-B/32")
+#         self.object_centric_text_encoder = CLIPBasedTextEncoder(self.clip_model, self.clip_preprocess)
+#         d_model = 512
+#         self.object_centric_pre_ssm_fusion = SlotFusion(input_dims=[512,512], embed_dims=[512, 512], output_dim=512)
+#         self.object_centric_ssm = EmbodiedSlotSSM(
+#             num_slots=self.object_token_num, num_blocks=3, d_model=d_model,
+#             d_input=None, visual_d_model=2176, textual_d_model=None,
+#             space_attn_num_heads=d_model // 64, use_inverted_attention=False, 
+#             encoder_attn_num_heads=d_model // 64,
+#         )
+#         # predicting the 8 steps head
+#         self.forward_steps = 8
+#         self.object_centric_latent_decoder = nn.Linear(1024, 512*self.forward_steps) 
+
+#         # self.object_centric_event_predictor = MLP(input_dim=1024, hidden_dim=2028, output_dim=1, num_layers=4)
+#         self.object_centric_relation_encoder = RelationTokensGrounding(dim=512, patch_dim=2176, num_relation_tokens=16, n_heads=4, num_layers=3)
+#         self.object_centric_event_predictor = SubgoalTransitionalPredictor(input_dim=512*3, condition_dim=512, model_dim=1024)
+#         if self.use_slotgoals:
+#             self.object_centric_fuser = SlotFusion(input_dims=[512,512,512], embed_dims=[1365, 1365, 1366])
+#         else:
+#             self.object_centric_fuser = SlotFusion(input_dims=[512,512], embed_dims=[2048, 2048])
+
+#         self.object_centric_clip_text_projector = PrismaticProjector(use_fused_vision_backbone=False, vision_dim=512, llm_dim=4096)
+#         self.object_centric_action_head = L1RegressionActionHead(
+#             input_dim=4096,
+#             hidden_dim=4096,
+#             action_dim=7,
+#         )
+
+
+#     def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+#         """Get all the logged statistics for the given dataset."""
+#         unnorm_key = self.base_model._check_unnorm_key(self.norm_stats, unnorm_key)
+#         # print('unnorm_key', unnorm_key); 1/0
+#         return self.norm_stats[unnorm_key]["action"]
+
+#     def get_activation_fn(self, name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+#         if callable(name):
+#             return name
+
+#         name = name.lower()
+#         if name == "relu":
+#             return nn.ReLU(inplace=inplace)
+#         elif name == "leaky_relu":
+#             if leaky_relu_slope is None:
+#                 raise ValueError("Slope of leaky ReLU was not defined")
+#             return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+#         elif name == "tanh":
+#             return nn.Tanh()
+#         elif name == "sigmoid":
+#             return nn.Sigmoid()
+#         elif name == "identity":
+#             return nn.Identity()
+#         else:
+#             raise ValueError(f"Unknown activation function {name}")
+
+#     def build_mlp(
+#         self,
+#         input_dim: int,
+#         output_dim: int,
+#         features: List[int],
+#         activation_fn: Optional[Union[str, Callable]] = "relu",
+#         final_activation_fn: Optional[Union[str, Callable]] = None,
+#         initial_layer_norm: bool = False,
+#         residual: bool = False,
+#     ) -> nn.Sequential:
+#         layers = []
+#         current_dim = input_dim
+#         if initial_layer_norm:
+#             layers.append(nn.LayerNorm(current_dim))
+
+#         for n_features in features:
+#             layers.append(nn.Linear(current_dim, n_features))
+#             nn.init.xavier_uniform_(layers[-1].weight)
+#             nn.init.zeros_(layers[-1].bias)
+#             if activation_fn is not None:
+#                 layers.append(self.get_activation_fn(activation_fn))
+#             current_dim = n_features
+
+#         layers.append(nn.Linear(current_dim, output_dim))
+#         nn.init.xavier_uniform_(layers[-1].weight)
+#         nn.init.zeros_(layers[-1].bias)
+#         if final_activation_fn is not None:
+#             layers.append(self.get_activation_fn(final_activation_fn))
+
+#         if residual:
+#             return Residual(nn.Sequential(*layers))
+#         return nn.Sequential(*layers)
+
+
+#     def get_obj_slots_per_image(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         past_tokens:  Optional[torch.FloatTensor] = None
+#     ):
+#         """ This function receives pixel values then produces a set of object-centric slots 
+#                 using the ObjectCentricTokenizer.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         num_dimensions = len(pixel_values.shape)
+#         if num_dimensions == 4: # [bz, cc, h, w]
+#             # Assuming Visual Feature Extraction on feature image of horizon 1, single view
+#             bz, horizon, num_view = pixel_values.shape[0], 1, 1
+#             patch_features = self.base_model.vision_backbone(pixel_values)
+
+#         else: # [bz, horizon, cc, h, w]
+#             1/0
+
+#         patch_features = patch_features.reshape(bz*horizon, -1, 2176)
+
+#         # Extract bboxes
+#         visual_tokens, attention, upsampled_visual_tokens = self.object_centric_tokenizer(patch_features, 
+#                                                                                           past_slots=past_tokens)
+#         if not self.training:
+#             return visual_tokens, None
+
+#         # print(patch_features.shape, visual_tokens.shape, torch.min(visual_tokens), torch.max(visual_tokens))
+#         bboxes = self.object_centric_bbox_head(visual_tokens).sigmoid()
+#         masks = self.object_centric_mask_head(visual_tokens)
+
+#         bboxes = torch.stack([bboxes], dim=1)
+#         masks = torch.stack([masks], dim=1)
+
+#         output = {}
+#         output['patch_features'] = patch_features.reshape([bz, horizon, -1, 2176])
+#         output['visual_tokens'] = visual_tokens.reshape([bz, horizon, self.object_token_num, -1])
+#         output['attentions'] = attention.reshape([bz, horizon, self.object_token_num, -1])
+#         output['bboxes'] = bboxes.reshape([bz, horizon, self.object_token_num, 5])
+#         output['masks'] = masks.reshape([bz, horizon, *masks.shape[2:]])
+
+#         return output
+
+
+#     def get_obj_slots(
+#         self,
+#         pixel_values: Optional[torch.FloatTensor] = None,
+#         texts: Optional[list] = None,
+#     ):
+#         """ This function receives pixel values in a horizon to output the corresponding visual representations.
+
+#             It operates only on each image,
+#                 if in training mode, outputs the slots and the object-centric predictions.
+#                 else, outputs only the slots.
+#         """
+#         device = pixel_values.device
+#         output = {'patch_features': [], 'visual_tokens': [], 'attentions': [], 'bboxes': [], 'masks': []}
+#         bz, horizon = pixel_values.shape[:2]
+#         past_tokens=None
+#         for h in range(horizon):
+#             suboutput = self.get_obj_slots_per_image(pixel_values[:,h], past_tokens)
+#             if 'visual_tokens' in suboutput:
+#                 past_tokens = suboutput['visual_tokens'].detach()
+#             for key, value in suboutput.items():
+#                 output[key].append(value)
+
+#         for key, value in output.items():
+#             output[key] = torch.cat(value, axis=1) # concatenate by horizon
+
+#         slots = output['visual_tokens'] # [B, T, N, D]
+#         patch_features = output['patch_features'] # [B, T, VL, VD]
+#         # texts: [B, T, TL, TD]
+#         general_texts, texts, texts_attn = self.object_centric_text_encoder(texts, temporal_length=horizon, device=device)
+#         output['general_texts'] = general_texts
+#         output['texts'] = texts
+#         output['texts_attn'] = texts_attn
+
+#         return output
+
+#     def get_slot_dynamics(
+#         self,
+#         slots,
+#         subgoals,
+#         patch_features, 
+#         texts, 
+#         texts_attn,
+#         output,
+#         cache_params=None,
+#         redundant_steps=0,
+#         nohead=False
+#     ):
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+#         # print(patch_features.shape, torch.min(patch_features), torch.max(patch_features))
+#         # print(texts.shape, torch.min(texts), torch.max(texts))
+#         # print(texts_attn.shape)
+#         # slots: [B, T, N, D]
+#         if redundant_steps > 0:
+#             slots = slots[:,:-redundant_steps]
+#             subgoals = subgoals[:,:-redundant_steps]
+#             patch_features = patch_features[:,:-redundant_steps]
+#             texts       = texts[:,:-redundant_steps]
+#             texts_attn  = texts_attn[:,:-redundant_steps]
+
+#         slots = self.object_centric_pre_ssm_fusion([slots, subgoals])
+#         latent_slots, attention, all_dynamic_slots = self.object_centric_ssm(slots, patch_features, texts, texts_attn, 
+#                                                                              cache_params=cache_params, output_attentions=False)
+#         if not nohead:
+#             slots = self.object_centric_latent_decoder(torch.cat([latent_slots, slots], dim=-1))
+#             B, T, N, D_x_forward_steps = slots.shape
+#             D = D_x_forward_steps // self.forward_steps
+#             slots_reshaped = slots.reshape(B*T, N, self.forward_steps, D)
+#             slots = slots.reshape(B, T, N, self.forward_steps, D)
+#             nxt_bboxes = self.object_centric_bbox_head(slots_reshaped.detach()[:,:,-1,:]).sigmoid() # selecting only the last step
+#             nxt_bboxes = nxt_bboxes.reshape(B, T, N, 5)
+#             nxt_masks = self.object_centric_mask_head(slots_reshaped.detach()[:,:,-1,:]) # selecting only the last step
+#             nxt_masks = nxt_masks.reshape([B, T, N, *nxt_masks.shape[2:]])
+#         else:
+#             slots = None
+#             nxt_bboxes = None
+#             nxt_masks = None
+#         # print(slots.shape, torch.min(slots), torch.max(slots))
+
+#         output['latent_slots'] = latent_slots
+#         output['nxt_tokens'] = slots
+#         output['nxt_bboxes'] = nxt_bboxes
+#         output['nxt_masks'] = nxt_masks
+
+#         # debugging purposes
+#         # for key, value in output.items():
+#         #     print(key, value.shape, torch.min(value), torch.max(value))
+#         # 1/0
+#         return output
+
+#     def get_slot_fusion(
+#         self,
+#         crn_slots: Optional[torch.FloatTensor] = None,
+#         nxt_slots: Optional[torch.FloatTensor] = None,
+#         subgoal_states: Optional[torch.FloatTensor] = None,
+#     ):
+#         if self.use_slotgoals:
+#             return self.object_centric_fuser([crn_slots, nxt_slots, subgoal_states])
+#         else:
+#             return self.object_centric_fuser([crn_slots, nxt_slots])
+
+#     def decode_continuous_actions(
+#         self,
+#         patch_features: Optional[torch.FloatTensor] = None,
+#         slotted_features: Optional[torch.FloatTensor] = None,
+#         clip_embeddings:  Optional[torch.FloatTensor] = None,
+#         clip_attention_mask: Optional[torch.Tensor] = None,
+#         llama_input_ids: Optional[torch.LongTensor] = None,
+#         llama_attention_mask: Optional[torch.Tensor] = None,
+#         llama_labels: Optional[torch.LongTensor] = None,
+#         inputs_embeds: Optional[torch.FloatTensor] = None,
+#         output_attentions: Optional[bool] = None,
+#         output_hidden_states: Optional[bool] = None,
+#         output_projector_features: Optional[bool] = None,
+#         return_dict: Optional[bool] = None,
+#         **kwargs
+#     ) -> Union[Tuple, PrismaticCausalLMOutputWithPast]:
+#         """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        
+#         # Projection Logic =>> Update Attention Mask
+#         bz, t, n, d = slotted_features.shape
+        
+#         patch_features = self.visual_pool(rearrange(patch_features, 'b t v d -> (b t) v d'), reduction=4)
+#         slotted_features = rearrange(slotted_features, 'b t n d -> (b t) n d')
+#         clip_embeddings = rearrange(clip_embeddings, 'b t n d -> (b t) n d')
+#         clip_attention_mask = rearrange(clip_attention_mask, 'b t n -> (b t) n')
+
+#         # Get Input Embeddings (from Language Model Embeddings)
+#         llama_input_embeddings = self.base_model.get_input_embeddings()(llama_input_ids)
+#         llama_input_embeddings = llama_input_embeddings.unsqueeze(1).repeat(1, t, 1, 1).flatten(0,1)
+#         llama_attention_mask = llama_attention_mask.unsqueeze(1).repeat(1, t, 1).flatten(0,1)
+#         projected_clip_embeddings = self.object_centric_clip_text_projector(clip_embeddings)
+
+#         # Get Slot encodings
+#         projected_patch_embeddings = self.base_model.projector(patch_features)
+#         slotted_features = slotted_features
+
+#         projected_patch_attention_mask = None
+#         if llama_attention_mask is not None:
+#             projected_patch_attention_mask = torch.full(
+#                 (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1] + slotted_features.shape[1]),
+#                 fill_value=True,
+#                 dtype=llama_attention_mask.dtype,
+#                 device=llama_attention_mask.device,
+#             )
+
+
+#         # Build Multimodal Embeddings & Attention Mask =>> Prismatic defaults to inserting after <BOS> token (1:)
+#         multimodal_embeddings = torch.cat(
+#             [llama_input_embeddings[:, :1, :], projected_patch_embeddings, slotted_features, projected_clip_embeddings, llama_input_embeddings[:, 1:, :]], dim=1
+#         )
+#         multimodal_attention_mask = None
+#         if llama_attention_mask is not None:
+#             multimodal_attention_mask = torch.cat(
+#                 [llama_attention_mask[:, :1], projected_patch_attention_mask, clip_attention_mask, llama_attention_mask[:, 1:]], dim=1
+#             )
+
+#         # Dispatch to Language Model
+#         language_model_output = self.base_model.language_model(
+#             input_ids=None,
+#             attention_mask=multimodal_attention_mask,
+#             position_ids=None,
+#             past_key_values=None,
+#             inputs_embeds=multimodal_embeddings,
+#             labels=None,
+#             use_cache=None,
+#             output_attentions=False,
+#             output_hidden_states=True,
+#             return_dict=True,
+#         )
+
+#         # Extract hidden states for action tokens
+#         last_hidden_states = language_model_output.hidden_states[-1]  # (B, seq_len, D)
+#         actions_hidden_states = last_hidden_states[:,-1-(NUM_ACTIONS_CHUNK*ACTION_DIM):-1,:]  # (B, act_chunk_len, D)
+
+#         # L1 regression prediction
+#         continuous_actions_pred = self.object_centric_action_head.predict_action(actions_hidden_states)
+#         # print(continuous_actions_pred.shape); 1/0
+#         return continuous_actions_pred
+
+#####################################################################################################
+#####################################################################################################
diff --git a/prismatic/extern/hf/processing_prismatic.py b/prismatic/extern/hf/processing_prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7ae121b87a8aa76ee63ea2cde9a033d264f4d06
--- /dev/null
+++ b/prismatic/extern/hf/processing_prismatic.py
@@ -0,0 +1,252 @@
+"""
+processing_prismatic.py
+
+HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
+specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
+import timm.data
+import torch
+import torchvision.transforms.functional as TVF
+from PIL import Image
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from transformers import PreTrainedTokenizerBase
+from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+# === Image Processing ===
+def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
+    """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+    (w, h), max_wh = image.size, max(image.size)
+    horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+    padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+
+    return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
+
+
+class PrismaticImageProcessor(ImageProcessingMixin):
+    model_input_names: ClassVar[List[str]] = ["pixel_values"]
+
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool = False,
+        image_resize_strategy: str = "letterbox",
+        input_sizes: Optional[List[Tuple[int, int, int]]] = None,
+        interpolations: Optional[List[str]] = None,
+        means: Optional[List[Tuple[float, float, float]]] = None,
+        stds: Optional[List[Tuple[float, float, float]]] = None,
+        **kwargs: str,
+    ) -> None:
+        """
+        Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
+        created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
+        @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
+        @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
+        @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
+        @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
+        @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
+        @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
+        """
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.image_resize_strategy = image_resize_strategy
+
+        # Handle `None` default values
+        input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
+        means = [(0.5, 0.5, 0.5)] if means is None else means
+        stds = [(0.5, 0.5, 0.5)] if stds is None else stds
+
+        # TIMM `data_cfg` Parameters
+        self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
+
+        # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
+        self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
+        self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+        for idx in range(len(input_sizes)):
+            transform = timm.data.create_transform(
+                input_size=self.input_sizes[idx],
+                interpolation=self.interpolations[idx],
+                mean=self.means[idx],
+                std=self.stds[idx],
+                crop_pct=1.0,  # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
+                crop_mode="center",  # Default crop mode -- no-op when `crop_pct == 1.0`
+                is_training=False,  # No image augmentations when loading the transform!
+            )
+
+            # [Validation] Ensure appropriate transform structure, expected sizes
+            if not (
+                isinstance(transform, Compose)
+                and (len(transform.transforms) == 4)
+                and isinstance(transform.transforms[0], Resize)
+                and isinstance(transform.transforms[1], CenterCrop)
+                and isinstance(transform.transforms[2], ToTensor)
+                and isinstance(transform.transforms[3], Normalize)
+                and (transform.transforms[0].size == self.input_sizes[idx][-1])
+                and (transform.transforms[1].size == self.input_sizes[idx][-2:])
+            ):
+                raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
+
+            # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
+            #   => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
+            resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
+            self.tvf_resize_params.append(
+                {
+                    "size": resize_t.size,
+                    "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
+                    "max_size": None,
+                    "antialias": True,
+                }
+            )
+            self.tvf_crop_params.append({"output_size": crop_t.size})
+            self.tvf_normalize_params.append(
+                {
+                    "mean": norm_t.mean.float().numpy().tolist(),
+                    "std": norm_t.std.float().numpy().tolist(),
+                    "inplace": False,
+                }
+            )
+            self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+            # Handle Prismatic `image_resize_strategy`
+            if self.image_resize_strategy == "resize-naive":
+                self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
+            elif self.image_resize_strategy == "letterbox":
+                self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
+            elif self.image_resize_strategy == "resize-crop":
+                pass
+            else:
+                raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
+
+        # Dispatch **kwargs to super()
+        super().__init__(**kwargs)
+
+    def apply_transform(self, img: Image.Image) -> torch.Tensor:
+        """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
+        if self.tvf_do_letterbox:
+            img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
+
+        # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
+        imgs_t = []
+        for idx in range(len(self.input_sizes)):
+            img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
+            img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
+            img_idx_t = TVF.to_tensor(img_idx)
+            img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
+            imgs_t.append(img_idx_t)
+
+        # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
+        img_t = torch.vstack(imgs_t)
+
+        return img_t
+
+    def preprocess(
+        self,
+        images: Union[Image.Image, List[Image.Image]],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **_: str,
+    ) -> BatchFeature:
+        """
+        Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
+        explicitly only handle PIL.Image.Image instances for simplicity.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
+        @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
+        pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
+
+        # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
+        return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
+
+    def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
+        return self.preprocess(images, **kwargs)
+
+
+# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
+#   =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
+class PrismaticProcessor(ProcessorMixin):
+    attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
+    image_processor_class: str = "AutoImageProcessor"
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[ImageProcessingMixin] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ) -> None:
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: Union[Image.Image, List[Image.Image]],
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
+        forwards images to PrismaticImageProcessor.
+        @param text: The (batch) of text to encode; must be a string or list of strings.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
+        @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
+        @param max_length: Maximum length (in tokens) to truncate
+        @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
+        @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
+        """
+        pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+        text_inputs = self.tokenizer(
+            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+        )
+
+        # [Validate] Need same number of images and text inputs!
+        if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
+            raise ValueError("Batch is malformed; expected same number of images and text inputs!")
+
+        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+
+    # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
+    def batch_decode(
+        self,
+        sequences: Union[List[int], List[List[int]], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> List[str]:
+        return self.tokenizer.batch_decode(
+            sequences=sequences,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> str:
+        return self.tokenizer.decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self) -> List[str]:
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
diff --git a/prismatic/losses/__init__.py b/prismatic/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/losses/training_losses.py b/prismatic/losses/training_losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ce2e83ff3b96aab3fdbf16080b65506ac5abb50
--- /dev/null
+++ b/prismatic/losses/training_losses.py
@@ -0,0 +1,912 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import torch
+from scipy.optimize import linear_sum_assignment
+from torch import nn
+import pdb
+
+import torch
+from torchvision.ops.boxes import box_area
+
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
+         (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2,
+         (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+def box_iou(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+    eps = 1e-6
+    iou = inter / (union + eps)  # avoid division by zero
+
+    return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+    (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    (boxes2[:, 2:] >= boxes2[:, :2]).all()
+
+    iou, union = box_iou(boxes1, boxes2)
+
+    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    area = wh[:, :, 0] * wh[:, :, 1]  # enclosing area
+
+    eps = 1e-6
+    return iou - (area - union) / (area + eps)  # avoid division by zero
+
+
+class HungarianMatcherbboxDSGG(nn.Module):
+    def __init__(self, cost_bbox: float = 1, cost_giou: float = 1):
+        super().__init__()
+        self.cost_bbox = cost_bbox
+        self.cost_giou = cost_giou
+        assert cost_bbox != 0 or cost_giou != 0, 'all costs cant be 0'
+
+    @torch.no_grad()
+    def forward(self, outputs, targets):
+        bs, num_queries = outputs.shape[:2]
+        indices = []
+
+        for b in range(bs):
+            out_bbox = outputs[b].float()
+            if targets[b] is None:
+                continue
+            tgt_boxes = targets[b].float().to(out_bbox.device)
+
+            cost_bbox = torch.cdist(out_bbox, tgt_boxes, p=1)
+            cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_boxes))
+
+            C = self.cost_bbox * cost_bbox + self.cost_giou * cost_giou
+
+            C = C.view(num_queries, -1).cpu()
+            indices.append(linear_sum_assignment(C))
+
+        return [
+            (
+                torch.as_tensor(i, dtype=torch.int64),
+                torch.as_tensor(j, dtype=torch.int64),
+            )
+            for i, j in indices
+        ]
+        
+
+class HungarianMatcherbboxTRACK(nn.Module):
+    def __init__(self, cost_bbox: float = 1, cost_giou: float = 1):
+        super().__init__()
+        self.cost_bbox = cost_bbox
+        self.cost_giou = cost_giou
+        assert cost_bbox != 0 or cost_giou != 0, 'all costs cant be 0'
+
+    @torch.no_grad()
+    def forward(self, outputs, targets):
+        bs, num_queries, horizon = outputs.shape[:3]
+        indices = []
+
+        for b in range(bs):
+            out_bbox = outputs[b].float()
+            if targets[b] is None:
+                continue
+            tgt_boxes = targets[b].float().to(out_bbox.device)
+
+            out_bbox, tgt_boxes = out_bbox.permute(1, 0, 2), tgt_boxes.permute(1, 0, 2)
+            cost_bbox = torch.cdist(out_bbox, tgt_boxes, p=1).sum(0)
+
+            cost_giou = 0
+            for h in range(horizon):
+                # print(b, h, box_cxcywh_to_xyxy(out_bbox[h,:,:4]), box_cxcywh_to_xyxy(tgt_boxes[h,:,:4]))
+                # print(-generalized_box_iou(box_cxcywh_to_xyxy(out_bbox[h,:,:4]), box_cxcywh_to_xyxy(tgt_boxes[h,:,:4])))
+                cost_giou += -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox[h,:,:4]), box_cxcywh_to_xyxy(tgt_boxes[h,:,:4]))
+
+            C = self.cost_bbox * cost_bbox + self.cost_giou * cost_giou
+
+            C = C.view(num_queries, -1).cpu()
+            try:
+                indices.append(linear_sum_assignment(C))
+            except:
+                print("WHAT")
+                print(out_bbox[:,:,:4])
+                print(tgt_boxes[:,:,:4])
+                print(C)
+        return [
+            (
+                torch.as_tensor(i, dtype=torch.int64),
+                torch.as_tensor(j, dtype=torch.int64),
+            )
+            for i, j in indices
+        ]
+
+
+def build_bbox_matcher(cost_bbox=2.5, cost_giou=2, with_tracking=False):
+    if not with_tracking:
+        return HungarianMatcherbboxDSGG(cost_bbox=cost_bbox, cost_giou=cost_giou)
+    else:
+        return HungarianMatcherbboxTRACK(cost_bbox=cost_bbox, cost_giou=cost_giou)
+
+
+def _get_src_permutation_idx(indices):
+    batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+    src_idx = torch.cat([src for (src, _) in indices])
+    return batch_idx, src_idx
+
+
+def _get_matched_pairs(src_data, target_data, indices):
+    idx = _get_src_permutation_idx(indices)
+    src_data = src_data[idx]
+    target_data = torch.cat([t[i] for t, (_, i) in zip(target_data, indices)], dim=0)
+    return src_data, target_data
+
+def _get_unmatched_src_permutation_idx(src_data, indices):
+    """
+    Return (batch_idx, src_idx) for all UNMATCHED predictions.
+    
+    Args:
+        indices: list of (src_indices, tgt_indices) for each image in the batch
+        src_data: the model outputs, shape [batch_size, num_preds, ...]
+    """
+    batch_size, num_preds = src_data.shape[:2]
+    device = src_data.device
+
+    unmatched_batch_idx = []
+    unmatched_src_idx = []
+
+    # Loop over each element in the batch
+    for i, (src, _) in enumerate(indices):
+        # src contains the matched prediction indices for image i
+        # We'll compute the "unmatched" indices by difference
+        all_indices = torch.arange(num_preds, device=device)
+        matched_mask = torch.zeros(num_preds, dtype=torch.bool, device=device)
+        matched_mask[src] = True  # Mark matched predictions
+        unmatched_mask = ~matched_mask
+        unmatched = all_indices[unmatched_mask]  # Indices that are NOT matched
+
+        # Keep track of the batch index for each unmatched prediction
+        unmatched_batch_idx.append(torch.full_like(unmatched, i, dtype=torch.long))
+        unmatched_src_idx.append(unmatched)
+
+    # Concatenate everything into two 1D tensors
+    unmatched_batch_idx = torch.cat(unmatched_batch_idx)
+    unmatched_src_idx = torch.cat(unmatched_src_idx)
+
+    return unmatched_batch_idx, unmatched_src_idx
+
+
+def get_unmatched_src_data(src_data, indices):
+    """
+    Return just the UNMATCHED portion of src_data, flattened across the batch.
+    
+    Args:
+        src_data: model outputs, shape [batch_size, num_preds, ...]
+        indices: list of (src_indices, tgt_indices), matched indices per image
+    """
+    unmatched_idx = _get_unmatched_src_permutation_idx(src_data, indices)
+    unmatched_idx = (unmatched_idx[0].cpu(), unmatched_idx[1].cpu())
+    # Advanced indexing using (batch_idx, src_idx)
+    unmatched_src_data = src_data[unmatched_idx]
+    return unmatched_src_data
+
+
+def loss_boxes(outputs, targets, indices, num_interactions):
+    # idx = _get_src_permutation_idx(indices)
+    # src_boxes = outputs[idx]
+    # target_boxes = torch.cat([t[i] for t, (_, i) in zip(targets, indices)], dim=0)
+    src_boxes, target_boxes = _get_matched_pairs(outputs, targets, indices)
+    losses = {}
+    if src_boxes.shape[0] == 0:
+        losses['loss_bbox'] = 0
+        losses['loss_giou'] = 0
+        losses['loss_objectness'] = 0
+    else:
+        loss_bbox = F.l1_loss(src_boxes[:,:4], target_boxes, reduction='none')
+        losses['loss_bbox'] = loss_bbox.sum() / num_interactions
+        loss_giou = 1 - torch.diag(generalized_box_iou(box_cxcywh_to_xyxy(src_boxes[:,:4]),
+                                                            box_cxcywh_to_xyxy(target_boxes)))
+    
+        losses['loss_giou'] = loss_giou.sum() / num_interactions
+
+        pred_ones = src_boxes[:,4] # needs to be aligned with 1
+        pred_zeros = get_unmatched_src_data(outputs, indices)[:,4] # needs to be aligned with 0
+
+        assert(pred_ones.shape[0]+pred_zeros.shape[0] == outputs.shape[0]*outputs.shape[1])
+        
+        preds = torch.cat([pred_ones, pred_zeros], dim=0)
+        labels = torch.cat([torch.ones_like(pred_ones), 
+                            torch.zeros_like(pred_zeros)], dim=0).to(outputs.device)
+        
+        losses['loss_objectness'] = torch.nn.functional.binary_cross_entropy(preds, labels)
+                                                 
+    return losses
+
+
+
+class BoxLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.bbox_matcher = build_bbox_matcher(with_tracking=True)
+
+    def forward(self, bboxes, gt_bboxes):
+        num_interactions = sum(t.shape[0] for t in gt_bboxes)
+        num_interactions = torch.as_tensor([num_interactions], dtype=torch.float, device=bboxes.device)
+        num_interactions = torch.clamp(num_interactions, min=1).item()
+        indices = self.bbox_matcher(bboxes[:,:,:4], gt_bboxes)
+        return loss_boxes(bboxes, gt_bboxes, indices, num_interactions), indices
+
+
+class CaptionLoss(nn.Module):
+    def __init__(self, ignore_index=32000):
+        super().__init__()
+        self.ignore_index = ignore_index
+
+    def forward(self, captions, gt_captions, indices):
+        # idx = _get_src_permutation_idx(indices)
+        # src_captions = captions[idx]
+        # target_captions = torch.cat([t[i] for t, (_, i) in zip(gt_captions, indices)], dim=0)
+        src_captions, target_captions = _get_matched_pairs(captions, gt_captions, indices)
+        B, S, dim = src_captions.shape
+        src_captions = src_captions.reshape(B*S, -1)
+        target_captions = target_captions.reshape(B*S)
+        caption_loss = F.cross_entropy(src_captions, target_captions, ignore_index=self.ignore_index)
+        return caption_loss
+
+class MSEFeatureLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.criterion = torch.nn.MSELoss()  # By default, reduction='mean'
+
+    def forward(self, features, gt_features, indices):
+        src_features, target_features = _get_matched_pairs(features, gt_features, indices)
+        return self.criterion(src_features, target_features)
+    
+
+class L1ImputeLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.criterion = torch.nn.L1Loss()  # By default, reduction='mean'
+
+    def forward(self, impute_learning_data):
+        text_pred, text_gt = impute_learning_data['text_pred'], impute_learning_data['text_gt']
+        visual_pred, visual_gt = impute_learning_data['visual_pred'], impute_learning_data['visual_gt']
+
+        text_impute_losses = []
+        visual_impute_losses = []
+        for ind in range(3):
+            text_impute_losses.append(self.criterion(text_pred[:,ind], 
+                                                     text_gt[:,ind].detach()))
+            visual_impute_losses.append(self.criterion(visual_pred[:,ind], 
+                                                       visual_gt[:,ind].detach()))
+
+        return text_impute_losses, visual_impute_losses
+
+
+
+class BoxLossWithTrack(nn.Module):
+    def __init__(self, objectness_required=False, eval_mode=False):
+        super().__init__()
+        self.bbox_coef = 1 # 2.5
+        self.giou_coef = 1 # 2
+        self.obj_coef = 1
+        self.objectness_required = objectness_required
+        self.eval_mode = eval_mode
+
+    def loss_boxes(self, outputs, targets, indices, num_interactions):
+        # idx = _get_src_permutation_idx(indices)
+        # src_boxes = outputs[idx]
+        # target_boxes = torch.cat([t[i] for t, (_, i) in zip(targets, indices)], dim=0)
+        src_boxes, target_boxes = _get_matched_pairs(outputs, targets, indices) # works well
+        # print(src_boxes.shape)
+        # print(target_boxes.shape); 
+        # print(target_boxes); 1/0
+        losses = {}
+        if src_boxes.shape[0] == 0:
+            losses['loss_bbox'] = 0
+            losses['loss_giou'] = 0
+            losses['loss_objectness'] = 0
+        else:
+            bn, horizon, dim = src_boxes.shape
+            loss_bbox = F.l1_loss(src_boxes[:,:,:4], target_boxes[:,:,:4], reduction='none')
+            losses['loss_bbox'] = loss_bbox.sum() / (num_interactions) * self.bbox_coef
+
+            loss_giou = 0
+            for h in range(horizon):
+                loss_giou += 1 - torch.diag(generalized_box_iou(box_cxcywh_to_xyxy(src_boxes[:,h,:4]), 
+                                                            box_cxcywh_to_xyxy(target_boxes[:,h,:4])))
+            losses['loss_giou'] = loss_giou.sum() / (num_interactions) * self.giou_coef
+
+            pred_ones = src_boxes[:,:,4] # needs to be aligned with 1
+            pred_zeros = get_unmatched_src_data(outputs, indices)[:,:,4] # needs to be aligned with 0
+
+            assert(pred_ones.shape[0]+pred_zeros.shape[0] == outputs.shape[0]*outputs.shape[1])
+            
+            preds = torch.cat([pred_ones, pred_zeros], dim=0).flatten()
+            labels = torch.cat([torch.ones_like(pred_ones), 
+                                torch.zeros_like(pred_zeros)], dim=0).flatten()
+
+            if self.objectness_required:
+                with torch.cuda.amp.autocast(enabled=False):
+                    losses['loss_objectness'] = torch.nn.functional.binary_cross_entropy(preds, labels, reduction='sum') / num_interactions * self.obj_coef
+
+            if self.eval_mode:
+                # Threshold the predictions at 0.5
+                binary_preds = (preds >= 0.5).int()
+
+                # Compare with ground truth
+                correct = (binary_preds == labels).sum().item()
+                total = labels.numel()
+
+                # Compute accuracy
+                accuracy = correct / total
+                losses['accuracy_objectness'] = accuracy
+
+        return losses
+
+
+    def forward(self, bboxes, gt_bboxes, indices):
+        num_interactions = sum(t.shape[0] for t in gt_bboxes)
+        num_interactions = torch.as_tensor([num_interactions], dtype=torch.float, device=bboxes.device)
+        num_interactions = torch.clamp(num_interactions, min=1).item()
+        return self.loss_boxes(bboxes, gt_bboxes, indices, num_interactions)
+
+
+class MSEFeatureLossWithTrack(nn.Module):
+    def __init__(self, eval_mode=False):
+        super().__init__()
+        self.depth_coef = 0.02
+        self.eval_mode = eval_mode
+        if self.eval_mode:
+            self.criterion = torch.nn.MSELoss(reduction='mean')  # By default, reduction='mean'
+        else:
+            self.criterion = torch.nn.MSELoss(reduction='sum')  # By default, reduction='mean'
+
+    def forward(self, features, gt_features, indices):
+        num_interactions = sum(t.shape[0] for t in gt_features)
+        num_interactions = torch.as_tensor([num_interactions], dtype=torch.float, device=features.device)
+        num_interactions = torch.clamp(num_interactions, min=1).item()
+
+        src_features, target_features = _get_matched_pairs(features, gt_features, indices)
+        src_features = src_features.flatten(0,1)
+        target_features = target_features.flatten(0,1)
+        
+        
+        # Create a mask: True if the batch contains at least one value not equal to -1
+        mask = (target_features != -1).any(dim=(1, 2, 3))
+
+        # Use the mask to index the valid batch indices
+        a_filtered = src_features[mask]
+        b_filtered = target_features[mask]
+
+        # # Print the shape to verify
+        # print(a_filtered.shape, b_filtered.shape)
+        # 1/0
+        if self.eval_mode:
+            return {
+                'loss_obj_depth': self.criterion(a_filtered, b_filtered)
+            }
+        
+        return {
+            'loss_obj_depth': self.criterion(a_filtered, b_filtered) / num_interactions * self.depth_coef
+        }
+
+import numpy as np
+def all_to_one(outputs, targets):
+    bs, num_queries, horizon = outputs.shape[:3]
+    indices = []
+    for b in range(bs):
+        indices.append((np.arange(num_queries), np.zeros(num_queries)))
+    # print(indices); 1/0
+    return [
+        (
+            torch.as_tensor(i, dtype=torch.int64),
+            torch.as_tensor(j, dtype=torch.int64),
+        )
+        for i, j in indices
+    ]
+
+class ObjectCentricLoss(nn.Module):
+    # object centric losses is designed for slots to learn
+    # # # 2D bounding box and objectness prediction
+    # # # depth estimation
+    # # # consistently through time
+    #
+    # It uses bipartite matching between predictions and gts
+    # in order to select the best pairs of correspondence.
+    #
+    def __init__(self, bbox_matching='standard', objectness_required=True, eval_mode=False):
+        super().__init__()
+        if bbox_matching == 'standard':
+            self.bbox_matcher = build_bbox_matcher(with_tracking=True)
+        else:
+            self.bbox_matcher = all_to_one
+        self.bbox_loss = BoxLossWithTrack(objectness_required=objectness_required, eval_mode=eval_mode)
+        self.recon_loss = MSEFeatureLossWithTrack(eval_mode=eval_mode)
+
+    def forward(self, object_preds, object_gts, view='main'):
+        assert(view in ['main', 'wrist'])
+        if view == 'main':
+            bz, horizon, num_view, num_boxes, box_dim = object_preds["bboxes"].shape
+            batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"][:,:,0].permute(0, 2, 1, 3)
+            batched_gt_depths = []; batched_pd_depths = object_preds["depths"][:,:,0].permute(0, 2, 1, 3, 4, 5)
+            for i in range(bz):
+                batched_gt_bboxes.append(torch.stack(list(object_gts["bboxes"]["main"][i].values())))
+                batched_gt_depths.append(torch.stack(list(object_gts["depths"]["main"][i].values())))
+        
+        elif view == 'wrist':
+            bz, horizon, num_view, num_boxes, box_dim = object_preds["bboxes"].shape
+            batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"][:,:,1].permute(0, 2, 1, 3)
+            batched_gt_depths = []; batched_pd_depths = object_preds["depths"][:,:,1].permute(0, 2, 1, 3, 4, 5)
+            for i in range(bz):
+                batched_gt_bboxes.append(torch.stack(list(object_gts["bboxes"]["wrist"][i].values())))
+                batched_gt_depths.append(torch.stack(list(object_gts["depths"]["wrist"][i].values())))
+
+        # find optimal assignment based on bboxes
+        indices = self.bbox_matcher(batched_pd_bboxes, batched_gt_bboxes)
+        # print(self.bbox_matcher)
+        # print(indices); 1/0
+        losses = {}
+        losses.update(self.bbox_loss(batched_pd_bboxes, batched_gt_bboxes, indices))
+        losses.update(self.recon_loss(batched_pd_depths, batched_gt_depths, indices))
+        return losses
+
+
+def jaccard_loss(pred, true, smooth=100):
+    """Computes the Jaccard loss, a.k.a the IoU loss.
+    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
+            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
+    Note that PyTorch optimizers minimize a loss. In this
+    case, we would like to maximize the jaccard loss so we
+    return the negated jaccard loss.
+    Args:
+        true (tensor): 1D ground truth tensor.
+        preds (tensor): 1D prediction truth tensor.
+        eps (int): Smoothing factor
+    Returns:
+        jacc_loss: the Jaccard loss.
+    """
+    intersection = torch.sum(true*pred)
+    jac = (intersection + smooth) / (torch.sum(true) + torch.sum(pred) - intersection + smooth)
+    return (1 - jac) * smooth
+
+def recall(true, pred):
+    """
+    Computes Recall (Sensitivity) between ground truth and predictions.
+    
+    Args:
+        true (tensor): 1D ground truth tensor (binary labels: 0 or 1).
+        pred (tensor): 1D prediction tensor (probabilities or logits).
+    
+    Returns:
+        recall (float): The recall score.
+    """
+    pred = pred.round()  # Convert probabilities/logits to binary (0 or 1)
+    
+    tp = torch.sum((true == 1) & (pred == 1)).float()  # True Positives
+    fn = torch.sum((true == 1) & (pred == 0)).float()  # False Negatives
+
+    recall = tp / (tp + fn + 1e-6)  # Avoid division by zero
+
+    return recall
+
+class BoxLossWithTrackV2(nn.Module):
+    def __init__(self, objectness_required=False, interaction_required=False, eval_mode=False):
+        super().__init__()
+        self.bbox_coef = 1 # 2.5
+        self.giou_coef = 1 # 2
+        self.obj_coef = 1
+        self.objectness_required = objectness_required
+        self.interaction_required = interaction_required
+        self.eval_mode = eval_mode
+
+    def loss_boxes(self, outputs, targets, indices, num_interactions):
+        # idx = _get_src_permutation_idx(indices)
+        # src_boxes = outputs[idx]
+        # target_boxes = torch.cat([t[i] for t, (_, i) in zip(targets, indices)], dim=0)
+        src_boxes, target_boxes = _get_matched_pairs(outputs, targets, indices) # works well
+        # print(src_boxes.shape)
+        # print(target_boxes.shape); 
+        # print(target_boxes); 1/0
+        losses = {}
+        if src_boxes.shape[0] == 0:
+            losses['loss_bbox'] = 0
+            losses['loss_giou'] = 0
+            losses['loss_objectness'] = 0
+        else:
+            bn, horizon, dim = src_boxes.shape
+            loss_bbox = F.l1_loss(src_boxes[:,:,:4], target_boxes[:,:,:4], reduction='none')
+            losses['loss_bbox'] = loss_bbox.sum() / (num_interactions) * self.bbox_coef
+
+            loss_giou = 0
+            for h in range(horizon):
+                loss_giou += 1 - torch.diag(generalized_box_iou(box_cxcywh_to_xyxy(src_boxes[:,h,:4]), 
+                                                            box_cxcywh_to_xyxy(target_boxes[:,h,:4])))
+            losses['loss_giou'] = loss_giou.sum() / (num_interactions) * self.giou_coef
+
+            pred_ones = src_boxes[:,:,4] # needs to be aligned with 1
+            unmatched_preds = get_unmatched_src_data(outputs, indices)
+            pred_zeros = unmatched_preds[:,:,4] # needs to be aligned with 0
+
+            assert(pred_ones.shape[0]+pred_zeros.shape[0] == outputs.shape[0]*outputs.shape[1])
+            
+            preds = torch.cat([pred_ones, pred_zeros], dim=0).flatten()
+            labels = torch.cat([torch.ones_like(pred_ones), 
+                                torch.zeros_like(pred_zeros)], dim=0).flatten()
+
+            if self.objectness_required:
+                with torch.cuda.amp.autocast(enabled=False):
+                    losses['loss_objectness'] = torch.nn.functional.binary_cross_entropy(preds, labels, reduction='sum') / num_interactions * self.obj_coef
+
+            # print(src_boxes[:,:,5].shape)
+            # print(src_boxes[:,:,5])
+
+            # print(unmatched_preds[:,:,5].shape)
+            # print(unmatched_preds[:,:,5])
+
+            # print(target_boxes[:,:,5].shape)
+            # print(target_boxes[:,:,5])
+
+            # print(torch.zeros_like(unmatched_preds[:,:,5]).shape)
+            # print(torch.zeros_like(unmatched_preds[:,:,5]))
+
+            if self.eval_mode:
+                # Threshold the predictions at 0.5
+                binary_preds = (preds >= 0.5).int()
+
+                # Compare with ground truth
+                correct = (binary_preds == labels).sum().item()
+                total = labels.numel()
+
+                # Compute accuracy
+                accuracy = correct / total
+                losses['accuracy_objectness'] = accuracy
+
+            if self.interaction_required:
+                preds = torch.cat([src_boxes[:,:,5], unmatched_preds[:,:,5]], dim=0).float()
+                labels = torch.cat([target_boxes[:,:,5], torch.zeros_like(unmatched_preds[:,:,5])], dim=0).float()
+                # 1/0
+                with torch.cuda.amp.autocast(enabled=False):
+                    weights = torch.where(labels == 1, 2.0, 1.0)  # Higher weight for positives
+                    losses['loss_interactable'] = torch.nn.functional.binary_cross_entropy(preds, labels, reduction='sum', weight=weights) / num_interactions * self.obj_coef
+                losses['recall_interactable'] = recall(labels, preds.detach())
+
+        return losses
+
+    def forward(self, bboxes, gt_bboxes, indices):
+        num_interactions = sum(t.shape[0] for t in gt_bboxes)
+        num_interactions = torch.as_tensor([num_interactions], dtype=torch.float, device=bboxes.device)
+        num_interactions = torch.clamp(num_interactions, min=1).item()
+        return self.loss_boxes(bboxes, gt_bboxes, indices, num_interactions)
+
+class SegmentationLossWithTrack(nn.Module):
+    def __init__(self, foreground_weight=100.0, eval_mode=False):
+        super().__init__()
+        self.eval_mode = eval_mode
+        # self.criterion = self.jaccard_loss
+
+        self.pos_weight = torch.tensor([foreground_weight])
+        self.criterion = F.binary_cross_entropy_with_logits
+
+    def jaccard_loss(self, preds, targets, eps=1e-7):
+        # preds: shape [B, D], values in [0,1]
+        # targets: shape [B, D], binary (0 or 1)
+        intersection = (preds * targets).sum(dim=1)
+        union = (preds + targets - preds * targets).sum(dim=1)
+        jaccard = (intersection + eps) / (union + eps)
+        loss = 1.0 - jaccard
+        return loss
+
+    def get_jaccard_loss(self, features, gt_features, indices):
+        device = features.device
+        src_features, target_features = _get_matched_pairs(features, gt_features, indices)
+        # print(torch.min(src_features), torch.max(src_features), torch.mean(src_features))
+        src_features = src_features.detach().flatten(2,3).flatten(0,1).sigmoid()
+        target_features = target_features.detach().flatten(2,3).flatten(0,1).float()
+        return self.jaccard_loss(src_features, target_features).mean()
+
+    def forward(self, features, gt_features, indices):
+        # num_interactions = sum(t.shape[0] for t in gt_features)
+        # num_interactions = torch.as_tensor([num_interactions], dtype=torch.float, device=features.device)
+        # num_interactions = torch.clamp(num_interactions, min=1).item()
+        device = features.device
+        src_features, target_features = _get_matched_pairs(features, gt_features, indices)
+        # print(torch.min(src_features), torch.max(src_features), torch.mean(src_features))
+        src_features = src_features.flatten()
+        target_features = target_features.flatten().float()
+        if self.eval_mode:
+            return {
+                'loss_obj_seg': self.criterion(src_features, target_features, pos_weight=self.pos_weight.to(device), reduction='sum')
+            }
+        
+        return {
+            'loss_obj_seg': self.criterion(src_features, target_features, pos_weight=self.pos_weight.to(device))
+        }
+
+class ObjectCentricLossV2(nn.Module):
+    # object centric losses is designed for slots to learn
+    # # # 2D bounding box and objectness prediction
+    # # # depth estimation
+    # # # consistently through time
+    # # # with active object filtering
+    #
+    # It uses bipartite matching between predictions and gts
+    # in order to select the best pairs of correspondence.
+    #
+    def __init__(self, bbox_matching='standard', objectness_required=True, eval_mode=False):
+        super().__init__()
+        if bbox_matching == 'standard':
+            self.bbox_matcher = build_bbox_matcher(with_tracking=True)
+        else:
+            self.bbox_matcher = all_to_one
+        self.bbox_loss = BoxLossWithTrackV2(objectness_required=objectness_required, eval_mode=eval_mode)
+        self.recon_loss = MSEFeatureLossWithTrack(eval_mode=eval_mode)
+        self.interactable_loss = torch.nn.functional.binary_cross_entropy
+
+    def forward(self, object_preds, object_gts, view='main'):
+        assert(view in ['main', 'wrist'])
+        if view == 'main':
+            bz, horizon, num_view, num_boxes, box_dim = object_preds["bboxes"].shape
+            batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"][:,:,0].permute(0, 2, 1, 3)
+            batched_gt_depths = []; batched_pd_depths = object_preds["depths"][:,:,0].permute(0, 2, 1, 3, 4, 5)
+            for i in range(bz):
+                batched_gt_bboxes.append(torch.stack(list(object_gts["bboxes"]["main"][i].values())))
+                batched_gt_depths.append(torch.stack(list(object_gts["depths"]["main"][i].values())))
+        
+        elif view == 'wrist':
+            bz, horizon, num_view, num_boxes, box_dim = object_preds["bboxes"].shape
+            batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"][:,:,1].permute(0, 2, 1, 3)
+            batched_gt_depths = []; batched_pd_depths = object_preds["depths"][:,:,1].permute(0, 2, 1, 3, 4, 5)
+            for i in range(bz):
+                batched_gt_bboxes.append(torch.stack(list(object_gts["bboxes"]["wrist"][i].values())))
+                batched_gt_depths.append(torch.stack(list(object_gts["depths"]["wrist"][i].values())))
+
+        # find optimal assignment based on bboxes
+        indices = self.bbox_matcher(batched_pd_bboxes, batched_gt_bboxes)
+        # print(self.bbox_matcher)
+        # print(indices); 1/0
+        losses = {}
+        losses.update(self.bbox_loss(batched_pd_bboxes, batched_gt_bboxes, indices))
+        losses.update(self.recon_loss(batched_pd_depths, batched_gt_depths, indices))
+        return losses
+
+class FocalLoss(nn.Module):
+    def __init__(self, alpha=1, gamma=2):
+        super().__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, inputs, targets):
+        bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets.float(), reduction='none')
+        probs = torch.sigmoid(inputs)
+        pt = torch.where(targets == 1, probs, 1 - probs)
+        focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss
+        return focal_loss.mean()
+
+def jaccard_loss(logits, targets, smooth=1e-6):
+    """
+    logits: raw output from the model (before sigmoid)
+    targets: binary labels (0 or 1)
+    """
+    probs = torch.sigmoid(logits)
+    intersection = (probs * targets).sum()
+    union = (probs + targets - probs * targets).sum()
+    loss = 1 - (intersection + smooth) / (union + smooth)
+    return loss
+
+class RobotSSMObjectLossWithTrack(nn.Module):
+    # object centric losses is designed for slots to learn
+    # # # 2D bounding box and objectness prediction
+    # # # depth estimation
+    # # # consistently through time
+    # # # with active object filtering
+    #
+    # It uses bipartite matching between predictions and gts
+    # in order to select the best pairs of correspondence.
+    #
+    def __init__(self, bbox_matching='standard', objectness_required=True, seg_required=True, event_required=True, 
+                       interact_required=False, eval_mode=False):
+        super().__init__()
+        if bbox_matching == 'standard':
+            self.bbox_matcher = build_bbox_matcher(with_tracking=True)
+        else:
+            self.bbox_matcher = all_to_one
+        self.seg_required = seg_required
+
+        self.bbox_loss = BoxLossWithTrackV2(objectness_required=objectness_required, eval_mode=eval_mode)
+        if seg_required:
+            self.seg_loss = SegmentationLossWithTrack(eval_mode=eval_mode)
+        else:
+            self.seg_loss  = None
+        if interact_required:
+            self.interactable_loss = torch.nn.functional.binary_cross_entropy
+        else:
+            self.interactable_loss = None
+        if event_required:
+            self.event_pos_weight = torch.tensor([5.0])
+            self.event_loss = F.binary_cross_entropy_with_logits
+            self.event_loss_focal = FocalLoss()
+            # self.event_loss = jaccard_loss
+
+    def preprocess(self, all_obj_bboxes, all_obj_segids, all_pixel_seg_values, all_interaction_cnts):
+        object_gts = {}
+
+        # Get obj boxes
+        object_gts["bboxes"] = [] # [b x o x h x 5]
+        for b, obj_bboxes in enumerate(all_obj_bboxes):
+            temp_obj_bboxes = []
+            for obj_data in obj_bboxes:
+                temp_obj_bboxes.append(np.stack(obj_data, axis=0))
+            temp_obj_bboxes = torch.tensor(
+                np.stack(temp_obj_bboxes, axis=0)
+            )
+            object_gts["bboxes"].append(temp_obj_bboxes)
+
+        # Get obj seg maps
+        object_gts["segs"] = []  # [b x o x h x img_dim]
+        for b, obj_segids in enumerate(all_obj_segids):
+            obj_masks = []
+            for s, segid in enumerate(obj_segids):
+                obj_masks.append(all_pixel_seg_values[b] == segid)
+            obj_masks = torch.stack(obj_masks, dim=0)
+            object_gts["segs"].append(obj_masks)
+
+        # Get interaction events
+        object_gts["itrn_cnt"] = [] # [b x o x (h-1)]
+        # for b, obj_itrn_cnts in enumerate(all_interaction_cnts):
+        #     temp_obj_cnts = []
+        #     for obj_data in obj_itrn_cnts:
+        #         temp_obj_cnts.append(torch.tensor(obj_data)[1:] != obj_data[0])
+        #     temp_obj_cnts = torch.stack(temp_obj_cnts, dim=0).float()
+        #     object_gts["itrn_cnt"].append(temp_obj_cnts)
+        return object_gts
+
+    def update_weights(self, losses, weights):
+        updated_losses = {}
+        for key, value in losses.items():
+            updated_losses[key] = value * weights[key]
+        return updated_losses
+
+    def get_match_indices(self, object_preds, object_gts):
+        device = object_preds["bboxes"].device
+        bz, num_boxes, horizon, box_dim = object_preds["bboxes"].shape
+        batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"]
+        for i in range(bz):
+            batched_gt_bboxes.append(object_gts["bboxes"][i].to(device))
+
+        # find optimal assignment based on bboxes
+        indices = self.bbox_matcher(batched_pd_bboxes, batched_gt_bboxes)
+        return indices
+
+    def get_interactable_loss(self, interact_predictions, interactables, indices, device):
+        src_interactables, target_interactables = _get_matched_pairs(interact_predictions, interactables, indices) # works well
+        target_interactables = target_interactables.to(device)
+        pred_ones = src_interactables[:,:] # needs to be aligned with 1
+        unmatched_preds = get_unmatched_src_data(interact_predictions, indices)
+        pred_zeros = unmatched_preds[:,:] # needs to be aligned with 0
+        
+        loss = {}
+        preds = torch.cat([pred_ones, pred_zeros], dim=0).flatten()
+        labels = torch.cat([torch.ones_like(pred_ones), 
+                            torch.zeros_like(pred_zeros)], dim=0).flatten().to(device)
+        with torch.cuda.amp.autocast(enabled=False):
+            weights = torch.where(labels == 1, 2.0, 1.0)  # Higher weight for positives
+            loss = torch.nn.functional.binary_cross_entropy(preds, labels, reduction='mean', weight=weights)
+
+        return loss
+
+    def get_event_prediction_loss(self, event_predictions, events, indices, device, include_event_metrics=True, event_pos_weight=None):
+        src_events, target_events = _get_matched_pairs(event_predictions, events, indices) # works well
+        target_events = target_events.to(device)
+        if event_pos_weight is None:
+            event_pos_weight = self.event_pos_weight.to(device)
+        else:
+            event_pos_weight = torch.tensor(event_pos_weight).to(device)
+        loss = self.event_loss(src_events, target_events, pos_weight=event_pos_weight, reduction='mean')
+        loss += self.event_loss_focal(src_events, target_events)
+
+        if include_event_metrics:
+            probs = torch.sigmoid(src_events.detach())
+            preds = (probs > 0.8).long()  # you can tune the threshold later
+
+            preds = preds.long()
+            targets = target_events.long()
+            
+            true_positives = ((preds == 1) & (targets == 1)).sum().item()
+            false_positives = ((preds == 1) & (targets == 0)).sum().item()
+            false_negatives = ((preds == 0) & (targets == 1)).sum().item()
+            
+            precision = true_positives / (true_positives + false_positives + 1e-8)
+            recall = true_positives / (true_positives + false_negatives + 1e-8)
+            f1 = 2 * precision * recall / (precision + recall + 1e-8)
+            
+            metrics = {
+                'precision': precision,
+                'recall': recall,
+                'f1': f1
+            }
+            return loss, metrics
+
+        return loss, None
+
+    def forward(self, object_preds, object_gts, indices=None, implicit_jaccard=True):
+        device = object_preds["bboxes"].device
+        bz, num_boxes, horizon, box_dim = object_preds["bboxes"].shape
+        batched_gt_bboxes = []; batched_pd_bboxes = object_preds["bboxes"]
+        batched_gt_segs = []; batched_pd_segs = object_preds["segs"]
+        for i in range(bz):
+            batched_gt_bboxes.append(object_gts["bboxes"][i].to(device))
+            batched_gt_segs.append(object_gts["segs"][i].to(device))
+        # find optimal assignment based on bboxes
+        if indices is None:
+            indices = self.bbox_matcher(batched_pd_bboxes, batched_gt_bboxes)
+        # print(self.bbox_matcher)
+        # print(indices); 1/0
+        losses = {}
+        losses.update(self.bbox_loss(batched_pd_bboxes, batched_gt_bboxes, indices))
+        losses.update(self.seg_loss(batched_pd_segs, batched_gt_segs, indices))
+        if implicit_jaccard:
+            self.jaccard_loss = self.seg_loss.get_jaccard_loss(batched_pd_segs, batched_gt_segs, indices)
+        return losses, indices
+
+    def get_jaccard_evaluations(self):
+        return self.jaccard_loss
+
+def info_nce_for_one_list_pair(list1, list2, temperature=0.07):
+    """
+    list1, list2: lists of (key, feature) pairs (already aligned or not).
+    temperature: float
+
+    Returns: scalar InfoNCE loss
+    """
+    # 1) Convert to dict and align
+    dict1 = dict(list1)
+    dict2 = dict(list2)
+    common_keys = list(set(dict1.keys()) & set(dict2.keys()))
+
+    if len(common_keys) == 0:
+        return 0
+    # 2) Build feature tensors
+    x = torch.stack([dict1[k] for k in common_keys])  # shape: (N, D)
+    y = torch.stack([dict2[k] for k in common_keys])  # shape: (N, D)
+    # 3) Optionally normalize
+    x = torch.nn.functional.normalize(x, dim=1)
+    y = torch.nn.functional.normalize(y, dim=1)
+
+    # 4) Compute similarity (logits)
+    logits = (x @ y.T) / temperature  # shape: (N, N)
+
+    # 5) Create labels (i -> i) and compute cross-entropy
+    labels = torch.arange(x.size(0)).to(logits.device)
+    loss = torch.nn.functional.cross_entropy(logits, labels)
+
+    return loss
+
+
+def batched_info_nce_loss(batched_list1, batched_list2, temperature=0.07):
+    """
+    Computes the average InfoNCE loss over a batch of list-pairs.
+    """
+    total_loss = 0.0
+    n_samples = len(batched_list1)
+    
+    for i in range(n_samples):
+        loss_i = info_nce_for_one_list_pair(batched_list1[i], batched_list2[i], temperature)
+        total_loss += loss_i
+    
+    return total_loss / n_samples
diff --git a/prismatic/models/__init__.py b/prismatic/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..073c620e0e23c09e05ac0a4877458777fc981f22
--- /dev/null
+++ b/prismatic/models/__init__.py
@@ -0,0 +1,2 @@
+from .load import available_model_names, available_models, get_model_description, load, load_vla
+from .materialize import get_llm_backbone_and_tokenizer, get_vision_backbone_and_transform, get_vlm
diff --git a/prismatic/models/action_heads.py b/prismatic/models/action_heads.py
new file mode 100644
index 0000000000000000000000000000000000000000..54ec19845f31fe08a8e7ff9bfa7c57f6adaf7673
--- /dev/null
+++ b/prismatic/models/action_heads.py
@@ -0,0 +1,212 @@
+"""Implementations of various action heads, which serve as alternatives to VLM sequential token prediction."""
+
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+# from diffusers.schedulers.scheduling_ddim import DDIMScheduler
+
+ACTION_DIM = 7
+NUM_ACTIONS_CHUNK = 5
+
+class SinusoidalPositionalEncoding(nn.Module):
+    """
+    Sine- and cosine-based positional encoding that produces embeddings of a batch of timesteps.
+
+    For example, at train time, the input might be a batch of 32 randomly sampled diffusion timesteps -> shape (32,)
+    Then the output would be a batch of 32 timestep embeddings -> shape (32, D)
+
+    Adapted from: https://github.com/real-stanford/diffusion_policy/blob/main/diffusion_policy/model/diffusion/positional_embedding.py
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim  # dimensionality of the positional encoding
+
+    def forward(self, x):
+        # x: (batch_size,)
+        device = x.device
+        assert self.dim % 2 == 0, f"# dimensions must be even but got {self.dim}"
+        half_dim = self.dim // 2
+        exponent = torch.arange(half_dim, device=device) * -math.log(10000) / (half_dim - 1)  # shape: (D/2,)
+        emb = torch.exp(exponent)  # shape: (D/2,)
+        emb = x[:, None] * emb[None, :]  # shape: (batch_size, 1) * (1, D/2) -> (batch_size, D/2)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)  # shape: (batch_size, D)
+        return emb
+
+
+class MLPResNetBlock(nn.Module):
+    """One MLP ResNet block with a residual connection."""
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+        self.ffn = nn.Sequential(  # feedforward network, similar to the ones in Transformers
+            nn.LayerNorm(dim),
+            nn.Linear(dim, dim),
+            nn.ReLU(),
+        )
+
+    def forward(self, x):
+        # x: (batch_size, hidden_dim)
+        # We follow the module ordering of "Pre-Layer Normalization" feedforward networks in Transformers as
+        # described here: https://arxiv.org/pdf/2002.04745.pdf
+        identity = x
+        x = self.ffn(x)
+        x = x + identity
+        return x
+
+
+class MLPResNet(nn.Module):
+    """MLP with residual connection blocks."""
+    def __init__(self, num_blocks, input_dim, hidden_dim, output_dim):
+        super().__init__()
+        self.layer_norm1 = nn.LayerNorm(input_dim)
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.relu = nn.ReLU()
+        self.mlp_resnet_blocks = nn.ModuleList()
+        for _ in range(num_blocks):
+            self.mlp_resnet_blocks.append(MLPResNetBlock(dim=hidden_dim))
+        self.layer_norm2 = nn.LayerNorm(hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        # x: (batch_size, input_dim)
+        x = self.layer_norm1(x)  # shape: (batch_size, input_dim)
+        x = self.fc1(x)  # shape: (batch_size, hidden_dim)
+        x = self.relu(x)  # shape: (batch_size, hidden_dim)
+        for block in self.mlp_resnet_blocks:
+            x = block(x)  # shape: (batch_size, hidden_dim)
+        x = self.layer_norm2(x)  # shape: (batch_size, hidden_dim)
+        x = self.fc2(x)  # shape: (batch_size, output_dim)
+        return x
+
+
+class L1RegressionActionHead(nn.Module):
+    """Simple MLP-based action head that generates continuous actions via L1 regression."""
+    def __init__(
+        self,
+        input_dim=4096,
+        hidden_dim=4096,
+        action_dim=7,
+    ):
+        super().__init__()
+        self.action_dim = action_dim
+        self.model = MLPResNet(
+            num_blocks=2, input_dim=input_dim*ACTION_DIM, hidden_dim=hidden_dim, output_dim=action_dim
+        )
+
+    def predict_action(self, actions_hidden_states):
+        # actions_hidden_states: last hidden states of Transformer corresponding to action tokens in sequence
+        # - shape: (batch_size, chunk_len * action_dim, hidden_dim)
+        # ground_truth_actions: ground-truth actions
+        # - shape: (batch_size, chunk_len, action_dim)
+        batch_size = actions_hidden_states.shape[0]
+        device = actions_hidden_states.device
+        rearranged_actions_hidden_states = actions_hidden_states.reshape(batch_size, NUM_ACTIONS_CHUNK, -1)
+        action = self.model(rearranged_actions_hidden_states)
+        return action
+
+
+class NoisePredictionModel(nn.Module):
+    """
+    Diffusion noise prediction model that takes an observation embedding (which fuses the
+    noisy action, diffusion timestep, and image-language observation embeddings) and
+    outputs a noise prediction.
+    """
+
+    def __init__(
+        self,
+        transformer_hidden_dim,  # Transformer hidden embedding size
+        hidden_dim,  # MLP hidden size
+        action_dim=7,  # action dimensionality
+    ):
+        super().__init__()
+        self.mlp_resnet = MLPResNet(
+            num_blocks=2,
+            input_dim=transformer_hidden_dim,
+            hidden_dim=hidden_dim,
+            output_dim=action_dim,
+        )
+
+    def forward(
+        self,
+        obs,
+    ):
+        # obs: observation embeddings to condition the generation on
+        # - shape: (batch_size, chunk_len, rearranged_hidden_dim=action_dim*hidden_dim)
+        #
+        # output: predicted noise
+        # - shape: (batch_size, action_dim)
+        output = self.mlp_resnet(obs)
+        return output
+
+
+class DiffusionActionHead(nn.Module):
+    """
+    Simple MLP-based action head that generates continuous actions via conditional denoising diffusion process.
+
+    Loosely inspired by: https://github.com/real-stanford/diffusion_policy/blob/main/diffusion_policy/model/diffusion/transformer_for_diffusion.py
+    """
+
+    def __init__(
+        self,
+        input_dim=4096,
+        hidden_dim=4096,
+        action_dim=7,
+        num_diffusion_steps_train=50,
+    ):
+        super().__init__()
+        self.action_dim = action_dim
+        self.noise_predictor = NoisePredictionModel(
+            transformer_hidden_dim=hidden_dim*ACTION_DIM, hidden_dim=hidden_dim, action_dim=action_dim
+        )
+        self.num_diffusion_steps_train = num_diffusion_steps_train
+        self.noise_scheduler = DDIMScheduler(num_train_timesteps=num_diffusion_steps_train, beta_schedule="squaredcos_cap_v2")
+        self.time_encoder = SinusoidalPositionalEncoding(dim=hidden_dim)
+
+    def sample_noisy_actions(self, ground_truth_actions):
+        """
+        Samples noise and applies noise to ground-truth actions to produce noisy actions, which are
+        used as input in the noise prediction network. Returns noise, noisy actions, and the
+        corresponding diffusion timestep embeddings.
+        """
+        # ground_truth_actions: ground-truth actions
+        # - shape: (batch_size, chunk_len, action_dim)
+        batch_size = ground_truth_actions.shape[0]
+        device = ground_truth_actions.device
+        # Sample random noise with shape equal to actions, used for closed-form forward diffusion.
+        noise = torch.randn(size=(batch_size, NUM_ACTIONS_CHUNK, ACTION_DIM), device=device, dtype=ground_truth_actions.dtype)  # (B, chunk_len, action_dim)
+        # Sample random diffusion timesteps (one for each action in batch).
+        timesteps = torch.randint(
+            low=0, high=self.noise_scheduler.config.num_train_timesteps, size=(batch_size,), device=device
+        )
+        # Add noise to clean actions according to the magnitude at each diffusion timestep via
+        # closed-form forward diffusion.
+        noisy_actions = self.noise_scheduler.add_noise(ground_truth_actions, noise, timesteps)  # (B, chunk_len, action_dim)
+
+        # Get diffusion timestep embeddings as well
+        diffusion_timestep_embeddings = self.time_encoder(timesteps).to(noisy_actions.dtype).to(noisy_actions.device)  # (B, llm_dim)
+        diffusion_timestep_embeddings = diffusion_timestep_embeddings.unsqueeze(1)  # (B, 1, llm_dim)
+
+        return_dict = dict(
+            noise=noise,
+            noisy_actions=noisy_actions,
+            diffusion_timestep_embeddings=diffusion_timestep_embeddings,
+        )
+
+        return return_dict
+
+    def predict_noise(self, actions_hidden_states):
+        """
+        Given a batch of last hidden Transformer layer embeddings (which fuse the vision-language observation embeddings,
+        noisy action embeddings, and diffusion timestep embedding), predicts the noise applied to the actions.
+        """
+        # actions_hidden_states: last hidden states of Transformer corresponding to action tokens in sequence
+        # - shape: (batch_size, chunk_len * action_dim, hidden_dim)
+        batch_size = actions_hidden_states.shape[0]
+        device = actions_hidden_states.device
+        rearranged_actions_hidden_states = actions_hidden_states.reshape(batch_size, NUM_ACTIONS_CHUNK, -1)  # (batch_size, chunk_len, action_dim * hidden_dim)
+        # Get diffusion model's noise prediction.
+        noise_pred = self.noise_predictor(rearranged_actions_hidden_states)
+        return noise_pred
\ No newline at end of file
diff --git a/prismatic/models/addons/__init__.py b/prismatic/models/addons/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ba694a0a9fd716b4a14efc6b3c9cfb4ddbc704b
--- /dev/null
+++ b/prismatic/models/addons/__init__.py
@@ -0,0 +1 @@
+from .slot_attention import SlotAttention
\ No newline at end of file
diff --git a/prismatic/models/addons/attention.py b/prismatic/models/addons/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..950b5621f43590952f133ad3b569731382dda643
--- /dev/null
+++ b/prismatic/models/addons/attention.py
@@ -0,0 +1,533 @@
+import torch
+import torch.nn as nn
+import einops
+import torch.nn.functional as F
+
+class BasicConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, **kwargs):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.relu(x)
+        return x
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop)
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop2 = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, attn
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim, bias=qkv_bias)
+        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, f):
+        B, N, C = x.shape
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(f).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+class InstanceNorm1d(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.norm1 = nn.InstanceNorm1d(dim, affine=True)
+    def forward(self, x):
+        ######input has shape: [b n c]
+        x = einops.rearrange(x, 'b n c -> b c n')
+        x = self.norm1(x)
+        x = einops.rearrange(x, 'b c n -> b n c')
+        return x
+
+class Block(nn.Module):
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+            act_layer=nn.GELU, norm_layer=nn.LayerNorm, n_token=5*16*28):
+        super().__init__()
+        self.encoder_pos = nn.Parameter(torch.randn(1, n_token, dim) * .02)
+        self.norm1 = norm_layer(dim)
+        # self.norm1 = InstanceNorm1d(dim)
+        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        # self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        # self.norm2 = InstanceNorm1d(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        # self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        encoder_pos = self.encoder_pos.unsqueeze(2).repeat([1, 1, x.shape[1]//self.encoder_pos.shape[1], 1])
+        encoder_pos = encoder_pos.view(1, x.shape[1], x.shape[2])
+        x = x + encoder_pos
+        inter, attn = self.attn(self.norm1(x))
+        x = x + inter
+        x = x + self.mlp(self.norm2(x))
+        return x, attn
+
+class CrossBlock(nn.Module):
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+            act_layer=nn.GELU, norm_layer=nn.LayerNorm, n_token=5*16*28):
+        super().__init__()
+        self.encoder_pos = nn.Parameter(torch.randn(1, n_token, dim) * .02)
+        self.norm1 = norm_layer(dim)
+#         self.norm1 = InstanceNorm1d(dim)
+        self.attn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+#         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+#         self.norm2 = InstanceNorm1d(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+#         self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x, f):
+        x = x + self.encoder_pos
+        f = f + self.encoder_pos
+        x = x + self.attn(self.norm1(x), self.norm1(f))
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+class Conv2Plus1D(nn.Sequential):
+
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 midplanes,
+                 stride=1,
+                 padding=1):
+        super(Conv2Plus1D, self).__init__(
+            nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
+                      stride=(1, stride, stride), padding=(0, padding, padding),
+                      bias=False, padding_mode='replicate'),
+            nn.BatchNorm3d(midplanes),
+            nn.ReLU(inplace=True),
+            nn.Conv3d(midplanes, out_planes, kernel_size=(3, 1, 1),
+                      stride=(stride, 1, 1), padding=(padding, 0, 0),
+                      bias=False, padding_mode='replicate'))
+
+    @staticmethod
+    def get_downsample_stride(stride):
+        return stride, stride, stride
+
+
+class BasicBlock(nn.Module):
+
+    expansion = 1
+
+    def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
+        midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
+
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Sequential(
+            conv_builder(inplanes, planes, midplanes, stride),
+            nn.BatchNorm3d(planes),
+            nn.ReLU(inplace=True)
+        )
+        self.conv2 = nn.Sequential(
+            conv_builder(planes, planes, midplanes),
+            nn.BatchNorm3d(planes)
+        )
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.conv2(out)
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+class STBlock(nn.Module):
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+            act_layer=nn.GELU, norm_layer=nn.LayerNorm, window=False, num_frames=3, end_size=None):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
+                              window=window, num_frames=num_frames, end_size=end_size)
+        self.time_norm1 = norm_layer(dim)
+        self.time_attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
+                              window=window, num_frames=num_frames, end_size=end_size)
+        self.time_fc = nn.Linear(dim, dim)
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.drop_path = DropPath(0.1)
+
+    def forward(self, x, S, T):
+        # x B 1+ST C
+        xt = x[:, 1:]
+        xt = einops.rearrange(xt, 'b (t s) c -> (b s) t c', t=T, s=S)
+        res_time = self.drop_path(self.time_attn(self.time_norm1(xt)))
+        res_time = einops.rearrange(res_time, '(b s) t c -> b (t s) c', s=S)
+        res_time = self.time_fc(res_time)
+        xt = x[:, 1:] + res_time
+
+        init_st_token = x[:, 0].unsqueeze(1)
+        st_token = init_st_token.repeat(1, T, 1)
+        st_token = einops.rearrange(st_token, 'b t c -> (b t) c').unsqueeze(1)
+        xs = xt
+        xs = einops.rearrange(xs, 'b (t s) c -> (b t) s c', t=T, s=S)
+        xs = torch.cat([st_token, xs], dim=1)
+        res_slot = self.drop_path(self.attn(self.norm1(xs)))
+
+        st_token = res_slot[:, 0]
+        st_token = einops.rearrange(st_token, '(b t) c -> b t c', t=T)
+        st_token = torch.mean(st_token, dim=1, keepdim=True)
+        res_slot = res_slot[:, 1:]
+        res_slot = einops.rearrange(res_slot, '(b t) s c -> b (t s) c', t=T)
+        x = xt
+
+        x = torch.cat((init_st_token, x), 1) + torch.cat((st_token, res_slot), 1)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+class SlotBlock(nn.Module):
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+            act_layer=nn.GELU, norm_layer=nn.LayerNorm, window=False, num_frames=3, end_size=None):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
+                              window=window, num_frames=num_frames, end_size=end_size)
+        self.norm2 = norm_layer(dim)
+        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+        self.drop_path = DropPath(0.1)
+
+    def forward(self, x):
+        # x B T S C
+        x = einops.rearrange(x, 'b t s c -> (b t) s c')
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+class MEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(MEBlock, self).__init__()
+        self.channel = channel
+        self.reduction = reduction
+        self.conv1 = nn.Conv2d(in_channels=self.channel, 
+                               out_channels=self.channel//self.reduction, 
+                               kernel_size=1, 
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(num_features=self.channel//self.reduction)
+        
+        self.conv2 = nn.Conv2d(in_channels=self.channel//self.reduction,
+                               out_channels=self.channel//self.reduction,
+                               kernel_size=3,
+                               padding=1,
+                               groups=self.channel//self.reduction,
+                               bias=False)
+        
+        self.conv3 = nn.Conv2d(in_channels=self.channel//self.reduction,
+                               out_channels=self.channel,
+                               kernel_size=1,
+                               bias=False)
+        self.bn3 = nn.BatchNorm2d(num_features=self.channel)
+        
+        self.pad = (0, 0, 0, 0, 0, 0, 0, 1)
+    
+    def forward(self, x):
+        n, c, t, h, w = x.shape
+        x = x.permute(0, 2, 1, 3, 4).contiguous()
+        x = x.view(n*t, c, h, w)
+        xr = self.conv1(x)
+        xr = self.bn1(xr)
+        xrp = self.conv2(xr)
+        xr = xr.view(n, t, c//self.reduction, h, w)[:, :t-1]
+        xrp = xrp.view(n, t, c//self.reduction, h, w)[:, 1:]
+        m = xrp - xr
+        m = F.pad(m, self.pad, mode='constant', value=0)
+        m = m.view(n*t, c//self.reduction, h, w)
+        m = F.adaptive_avg_pool2d(m, (1, 1))
+        m = self.conv3(m)
+        m = self.bn3(m)
+        m = torch.sigmoid(m) - 0.5
+        out = m * x
+        out = out.view(n, t, c, h, w).permute(0, 2, 1, 3, 4).contiguous()
+        return out
+    
+
+
+# The following snippet is taken from GroundingDINO
+# https://github.com/IDEA-Research/GroundingDINO/blob/main/groundingdino/models/GroundingDINO/fuse_modules.py#L99
+
+from timm.models.layers import DropPath
+
+class BiMultiHeadAttention(nn.Module):
+    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+        super(BiMultiHeadAttention, self).__init__()
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.v_dim = v_dim
+        self.l_dim = l_dim
+
+        assert (
+            self.head_dim * self.num_heads == self.embed_dim
+        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+        self.scale = self.head_dim ** (-0.5)
+        self.dropout = dropout
+
+        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+        self.stable_softmax_2d = True
+        self.clamp_min_for_underflow = True
+        self.clamp_max_for_overflow = True
+
+        self._reset_parameters()
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _reset_parameters(self):
+        nn.init.xavier_uniform_(self.v_proj.weight)
+        self.v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.l_proj.weight)
+        self.l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_v_proj.weight)
+        self.values_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_l_proj.weight)
+        self.values_l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_v_proj.weight)
+        self.out_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_l_proj.weight)
+        self.out_l_proj.bias.data.fill_(0)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        """_summary_
+
+        Args:
+            v (_type_): bs, n_img, dim
+            l (_type_): bs, n_text, dim
+            attention_mask_v (_type_, optional): _description_. bs, n_img
+            attention_mask_l (_type_, optional): _description_. bs, n_text
+
+        Returns:
+            _type_: _description_
+        """
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        bsz, tgt_len, _ = v.size()
+
+        query_states = self.v_proj(v) * self.scale
+        key_states = self._shape(self.l_proj(l), -1, bsz)
+        # value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        # value_v_states = value_v_states.view(*proj_shape)
+        value_l_states = value_l_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
+
+        if self.stable_softmax_2d:
+            attn_weights = attn_weights - attn_weights.max()
+
+        if self.clamp_min_for_underflow:
+            attn_weights = torch.clamp(
+                attn_weights, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights = torch.clamp(
+                attn_weights, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        # attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        # if self.clamp_min_for_underflow:
+        #     attn_weights_l = torch.clamp(
+        #         attn_weights_l, min=-50000
+        #     )  # Do not increase -50000, data type half has quite limited range
+        # if self.clamp_max_for_overflow:
+        #     attn_weights_l = torch.clamp(
+        #         attn_weights_l, max=50000
+        #     )  # Do not increase 50000, data type half has quite limited range
+
+        # mask vison for language
+        if attention_mask_v is not None:
+            attention_mask_v = (
+                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            # attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+        # attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+        # mask language for vision
+        if attention_mask_l is not None:
+            attention_mask_l = (
+                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+        attn_weights_v = attn_weights.softmax(dim=-1)
+
+        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+        # attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+        # attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+            )
+
+        # if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+        #     raise ValueError(
+        #         f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+        #     )
+
+        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output_v = attn_output_v.transpose(1, 2)
+        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+        # attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+        # attn_output_l = attn_output_l.transpose(1, 2)
+        # attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+        attn_output_v = self.out_v_proj(attn_output_v)
+        # attn_output_l = self.out_l_proj(attn_output_l)
+
+        return attn_output_v # , attn_output_l
+
+
+class CrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        l_dim,
+        embed_dim,
+        num_heads,
+        dropout=0.1,
+        drop_path=0.0,
+        init_values=1e-4,
+        cfg=None,
+    ):
+        """
+        Inputs:
+            embed_dim - Dimensionality of input and attention feature vectors
+            hidden_dim - Dimensionality of hidden layer in feed-forward network
+                         (usually 2-4x larger than embed_dim)
+            num_heads - Number of heads to use in the Multi-Head Attention block
+            dropout - Amount of dropout to apply in the feed-forward network
+        """
+        super(CrossAttentionBlock, self).__init__()
+
+        # pre layer norm
+        self.layer_norm_v = nn.LayerNorm(v_dim)
+        self.layer_norm_l = nn.LayerNorm(l_dim)
+        self.attn = BiMultiHeadAttention(
+            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+        )
+
+        # add layer scale for training stability
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.gamma_v = init_values # nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
+        # self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        v = self.layer_norm_v(v)
+        l = self.layer_norm_l(l)
+        delta_v = self.attn(
+            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+        )
+
+        # # v, l = v + delta_v, l + delta_l
+        # v = v + self.drop_path(self.gamma_v * delta_v)
+        # l = l + self.drop_path(self.gamma_l * delta_l)
+        # return v, l
+
+        # v = v + delta_v
+        v = v + self.drop_path(self.gamma_v * delta_v)
+        return v
+
+    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)
\ No newline at end of file
diff --git a/prismatic/models/addons/helping_gripper.py b/prismatic/models/addons/helping_gripper.py
new file mode 100644
index 0000000000000000000000000000000000000000..431c5da00f62ea785805505285c20d4c7faf72eb
--- /dev/null
+++ b/prismatic/models/addons/helping_gripper.py
@@ -0,0 +1,286 @@
+import math
+import torch
+from torch import nn, einsum, Tensor
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Callable, Optional, List, Union, Tuple
+from torchvision import transforms
+from timm.models import create_model
+from einops import rearrange, repeat
+import copy
+from einops import rearrange
+from typing import Optional, List
+from timm.models.layers import  trunc_normal_
+
+
+
+class Cross_Attention(nn.Module):
+    def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
+                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", normalize_before=False, hidden_dim=768,
+                 return_intermediate_dec=False, sa_first=True,):
+        super().__init__()
+
+        self.pre_norm = nn.LayerNorm(d_model) if normalize_before else None
+        decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,
+                                                dropout, activation, normalize_before,
+                                                sa_first = sa_first)
+        decoder_norm = nn.LayerNorm(d_model)
+        self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,
+                                          return_intermediate=return_intermediate_dec)
+        self._reset_parameters()
+        self.d_model = d_model
+        self.nhead = nhead
+        self.dec_layers = num_decoder_layers
+        self.enc_layers = num_encoder_layers
+
+            
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+    
+    def forward(self, src, mask, query_embed):
+        # # flatten NxCxHxW to HWxNxC
+        # bs, c, h, w = src.shape
+        # src = src.flatten(2).permute(2, 0, 1)
+        # pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+        # query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
+        # mask = mask.flatten(1)
+
+        # flatten NxTxC to TxNxC
+        bs, t, c = src.shape
+        src = src.permute(1, 0, 2)
+        query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
+        mask = mask
+
+        tgt = torch.zeros_like(query_embed)
+        if self.pre_norm:
+            memory = self.pre_norm(src)
+        else:
+            memory = src
+
+        hs, attn_rollout, self_attn = self.decoder(tgt, memory, memory_key_padding_mask=mask,
+                          query_pos=query_embed, num_frames=1, seq_len=t)
+        return hs[0].transpose(0, 1), memory.permute(0, 1, 2), attn_rollout, self_attn
+
+
+
+class TransformerDecoder(nn.Module):
+
+    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+
+    def forward(self, tgt, memory,
+                tgt_mask: Optional[Tensor] = None,
+                memory_mask: Optional[Tensor] = None,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                memory_key_padding_mask: Optional[Tensor] = None,
+                pos: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None,
+                num_frames: Optional[int] = 4,
+                seq_len: Optional[int] = 196):
+        output = tgt
+
+        intermediate = []
+        Q, B = output.shape[:2]
+        # attn_rollout = torch.ones(B,Q,memory.shape[0]).to(output.device)
+        attns, self_attns= [],[]
+        for layer_i,layer in enumerate(self.layers):
+            output, attn, self_attn = layer(output, memory, tgt_mask=tgt_mask,
+                           memory_mask=memory_mask,
+                           tgt_key_padding_mask=tgt_key_padding_mask,
+                           memory_key_padding_mask=memory_key_padding_mask,
+                           pos=pos, query_pos=query_pos, counter=layer_i,
+                           num_frames = num_frames, seq_len=seq_len)
+            # attns.append(attn.view(B,Q,4,16,16))
+            # self_attns.append(self_attn[28][0])
+            # attn_rollout  = attn_rollout*attn
+            # plot_attn_map(attn.view(B,Q,4,16,16)[27][0].detach().cpu(),name=str(layer_i) )
+            if self.return_intermediate:
+                intermediate.append(self.norm(output))
+        # attn_rollout = attn_rollout.view(B,Q,4,16,16)
+        # attns = torch.stack(attns).sum(0)
+        # self_attns = torch.stack(self_attns)
+
+        if self.norm is not None:
+            output = self.norm(output)
+            if self.return_intermediate:
+                intermediate.pop()
+                intermediate.append(output)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate), attns, self_attns
+        return output.unsqueeze(0), attns, self_attns
+
+
+class TransformerDecoderLayer(nn.Module):
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", normalize_before=False, sa_first=True):
+        super().__init__()
+        self.sa_first = sa_first
+
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+    
+        
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, tgt, memory,
+                     tgt_mask: Optional[Tensor] = None,
+                     memory_mask: Optional[Tensor] = None,
+                     tgt_key_padding_mask: Optional[Tensor] = None,
+                     memory_key_padding_mask: Optional[Tensor] = None,
+                     pos: Optional[Tensor] = None,
+                     query_pos: Optional[Tensor] = None,
+                    counter: Optional[Tensor] = None,
+                    num_frames: Optional[int] = 4,
+                    seq_len: Optional[int] = 196):
+        
+        q = k = self.with_pos_embed(tgt, query_pos)
+        tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+                                key=self.with_pos_embed(memory, pos),
+                                value=memory, attn_mask=memory_mask,
+                                key_padding_mask=memory_key_padding_mask)[0]
+        tgt2 = tgt2.transpose(0,1)
+
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+
+
+    def forward_pre(self, tgt, memory,
+                    tgt_mask: Optional[Tensor] = None,
+                    memory_mask: Optional[Tensor] = None,
+                    tgt_key_padding_mask: Optional[Tensor] = None,
+                    memory_key_padding_mask: Optional[Tensor] = None,
+                    pos: Optional[Tensor] = None,
+                    query_pos: Optional[Tensor] = None,
+                    counter: Optional[Tensor] = None,
+                    num_frames: Optional[int] = 4,
+                    seq_len: Optional[int] = 196):
+        if self.sa_first:
+            tgt2 = self.norm1(tgt)
+            q = k = self.with_pos_embed(tgt2, query_pos)
+            tgt2, self_attn = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+                                key_padding_mask=tgt_key_padding_mask)
+            tgt = tgt + self.dropout1(tgt2)
+            tgt2 = self.norm2(tgt)
+
+            tgt2, attn = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+                        key=self.with_pos_embed(memory, pos),
+                        value=memory, attn_mask=memory_mask,
+                        key_padding_mask=memory_key_padding_mask)
+
+        else:
+            tgt2 = self.norm1(tgt)
+            tgt2, attn = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+                                    key=self.with_pos_embed(memory, pos),
+                                    value=memory, attn_mask=memory_mask,
+                                    key_padding_mask=memory_key_padding_mask)
+            tgt = tgt + self.dropout1(tgt2)
+            tgt2 = self.norm2(tgt)
+
+            q = k = self.with_pos_embed(tgt2, query_pos)
+            tgt2, self_attn = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+                                key_padding_mask=tgt_key_padding_mask)
+            
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+
+        return tgt, attn, self_attn
+
+    def forward(self, tgt, memory,
+                tgt_mask: Optional[Tensor] = None,
+                memory_mask: Optional[Tensor] = None,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                memory_key_padding_mask: Optional[Tensor] = None,
+                pos: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None,
+                counter: Optional[Tensor] = None,
+                num_frames: Optional[int] = 4,
+                seq_len: Optional[int] = 196):
+        if self.normalize_before:
+            return self.forward_pre(tgt, memory, tgt_mask, memory_mask,
+                                    tgt_key_padding_mask, memory_key_padding_mask, pos, 
+                                    query_pos, counter, num_frames, seq_len)
+        return self.forward_post(tgt, memory, tgt_mask, memory_mask,
+                                 tgt_key_padding_mask, memory_key_padding_mask, pos, 
+                                 query_pos, counter, num_frames, seq_len)
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class ObjDecoder(nn.Module):
+    def __init__(self, 
+                 num_queries, 
+                 feature_dim=768, 
+                 aux_loss=False,
+                 pred_traj=True,
+                 num_frames=4,
+                 patches_per_frame=256,
+                 self_attn=False):
+        super().__init__()
+
+        self.num_queries = num_queries
+        self.transformer = Cross_Attention(d_model=feature_dim, nhead=8, num_encoder_layers=6,
+                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", normalize_before=True, hidden_dim=768)
+
+        hidden_dim = self.transformer.d_model
+        self.hidden_dim = hidden_dim
+        self.query_embed = nn.Embedding(num_queries, hidden_dim)
+        self.n_decode = 1
+
+    def forward(self, features):
+        features = features
+        B,T,D = features.shape
+        mask = features.new_zeros(B,T).bool()
+
+        hs,_,attn, self_attn = self.transformer(features, mask, self.query_embed.weight)
+        return hs, attn, self_attn
diff --git a/prismatic/models/addons/multimodal_transformer.py b/prismatic/models/addons/multimodal_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b04e3ebe48cb028164f1dfc65c231ac1ff8c4a7
--- /dev/null
+++ b/prismatic/models/addons/multimodal_transformer.py
@@ -0,0 +1,1332 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+from typing import Optional
+from torch import Tensor, nn
+import copy
+import math
+
+def _get_activation_fn(activation, d_model=256, batch_dim=0):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    if activation == "prelu":
+        return nn.PReLU()
+    if activation == "selu":
+        return F.selu
+
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+def _get_clones(module, N, layer_share=False):
+    # import ipdb; ipdb.set_trace()
+    if layer_share:
+        return nn.ModuleList([module for i in range(N)])
+    else:
+        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+def get_sine_pos_embed(
+    pos_tensor: torch.Tensor,
+    num_pos_feats: int = 128,
+    temperature: int = 10000,
+    exchange_xy: bool = True,
+):
+    """generate sine position embedding from a position tensor
+    Args:
+        pos_tensor (torch.Tensor): shape: [..., n].
+        num_pos_feats (int): projected shape for each float in the tensor.
+        temperature (int): temperature in the sine/cosine function.
+        exchange_xy (bool, optional): exchange pos x and pos y. \
+            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
+    Returns:
+        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
+    """
+    scale = 2 * math.pi
+    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+
+    def sine_func(x: torch.Tensor):
+        sin_x = x * scale / dim_t
+        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+        return sin_x
+
+    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
+    if exchange_xy:
+        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
+    pos_res = torch.cat(pos_res, dim=-1)
+    return pos_res
+
+class FeatureResizer(nn.Module):
+    """
+    This class takes as input a set of embeddings of dimension C1 and outputs a set of
+    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
+    """
+
+    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
+        super().__init__()
+        self.do_ln = do_ln
+        # Object feature encoding
+        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
+        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, encoder_features):
+        x = self.fc(encoder_features)
+        if self.do_ln:
+            x = self.layer_norm(x)
+        output = self.dropout(x)
+        return output
+
+
+def l1norm(X, dim, eps=1e-8):
+    """L1-normalize columns of X"""
+    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X"""
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
+    """
+    query: (n_context, queryL, d)
+    context: (n_context, sourceL, d)
+    """
+    batch_size_q, queryL = query.size(0), query.size(1)
+    batch_size, sourceL = context.size(0), context.size(1)
+
+    # Get attention
+    # --> (batch, d, queryL)
+    queryT = torch.transpose(query, 1, 2)
+
+    # (batch, sourceL, d)(batch, d, queryL)
+    # --> (batch, sourceL, queryL)
+    attn = torch.bmm(context, queryT)
+    if raw_feature_norm == "softmax":
+        # --> (batch*sourceL, queryL)
+        attn = attn.view(batch_size * sourceL, queryL)
+        attn = nn.Softmax()(attn)
+        # --> (batch, sourceL, queryL)
+        attn = attn.view(batch_size, sourceL, queryL)
+    elif raw_feature_norm == "l2norm":
+        attn = l2norm(attn, 2)
+    elif raw_feature_norm == "clipped_l2norm":
+        attn = nn.LeakyReLU(0.1)(attn)
+        attn = l2norm(attn, 2)
+    else:
+        raise ValueError("unknown first norm type:", raw_feature_norm)
+    # --> (batch, queryL, sourceL)
+    attn = torch.transpose(attn, 1, 2).contiguous()
+    # --> (batch*queryL, sourceL)
+    attn = attn.view(batch_size * queryL, sourceL)
+    attn = nn.Softmax()(attn * smooth)
+    # --> (batch, queryL, sourceL)
+    attn = attn.view(batch_size, queryL, sourceL)
+    # --> (batch, sourceL, queryL)
+    attnT = torch.transpose(attn, 1, 2).contiguous()
+
+    # --> (batch, d, sourceL)
+    contextT = torch.transpose(context, 1, 2)
+    # (batch x d x sourceL)(batch x sourceL x queryL)
+    # --> (batch, d, queryL)
+    weightedContext = torch.bmm(contextT, attnT)
+    # --> (batch, queryL, d)
+    weightedContext = torch.transpose(weightedContext, 1, 2)
+
+    return weightedContext, attnT
+
+
+class BiMultiHeadAttention(nn.Module):
+    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+        super(BiMultiHeadAttention, self).__init__()
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.v_dim = v_dim
+        self.l_dim = l_dim
+
+        assert (
+            self.head_dim * self.num_heads == self.embed_dim
+        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+        self.scale = self.head_dim ** (-0.5)
+        self.dropout = dropout
+
+        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+        self.stable_softmax_2d = True
+        self.clamp_min_for_underflow = True
+        self.clamp_max_for_overflow = True
+
+        self._reset_parameters()
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _reset_parameters(self):
+        nn.init.xavier_uniform_(self.v_proj.weight)
+        self.v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.l_proj.weight)
+        self.l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_v_proj.weight)
+        self.values_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_l_proj.weight)
+        self.values_l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_v_proj.weight)
+        self.out_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_l_proj.weight)
+        self.out_l_proj.bias.data.fill_(0)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        """_summary_
+
+        Args:
+            v (_type_): bs, n_img, dim
+            l (_type_): bs, n_text, dim
+            attention_mask_v (_type_, optional): _description_. bs, n_img
+            attention_mask_l (_type_, optional): _description_. bs, n_text
+
+        Returns:
+            _type_: _description_
+        """
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        bsz, tgt_len, _ = v.size()
+
+        query_states = self.v_proj(v) * self.scale
+        key_states = self._shape(self.l_proj(l), -1, bsz)
+        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_v_states = value_v_states.view(*proj_shape)
+        value_l_states = value_l_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
+
+        if self.stable_softmax_2d:
+            attn_weights = attn_weights - attn_weights.max()
+
+        if self.clamp_min_for_underflow:
+            attn_weights = torch.clamp(
+                attn_weights, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights = torch.clamp(
+                attn_weights, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        attn_weights_T = attn_weights.transpose(1, 2)
+        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        if self.clamp_min_for_underflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        # mask vison for language
+        if attention_mask_v is not None:
+            attention_mask_v = (
+                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+        attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+        # mask language for vision
+        if attention_mask_l is not None:
+            attention_mask_l = (
+                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+        attn_weights_v = attn_weights.softmax(dim=-1)
+
+        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+            )
+
+        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+            )
+
+        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output_v = attn_output_v.transpose(1, 2)
+        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+        attn_output_l = attn_output_l.transpose(1, 2)
+        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+        attn_output_v = self.out_v_proj(attn_output_v)
+        attn_output_l = self.out_l_proj(attn_output_l)
+
+        return attn_output_v, attn_output_l
+
+import numpy as np
+# Bi-Direction MHA (text->image, image->text)
+class BiAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        l_dim,
+        embed_dim,
+        num_heads,
+        dropout=0.1,
+        drop_path=0.0,
+        init_values=1e-4,
+        cfg=None,
+    ):
+        """
+        Inputs:
+            embed_dim - Dimensionality of input and attention feature vectors
+            hidden_dim - Dimensionality of hidden layer in feed-forward network
+                         (usually 2-4x larger than embed_dim)
+            num_heads - Number of heads to use in the Multi-Head Attention block
+            dropout - Amount of dropout to apply in the feed-forward network
+        """
+        super(BiAttentionBlock, self).__init__()
+
+        # pre layer norm
+        self.layer_norm_v = nn.LayerNorm(v_dim)
+        self.layer_norm_l = nn.LayerNorm(l_dim)
+        self.attn = BiMultiHeadAttention(
+            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+        )
+
+        # add layer scale for training stability
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        gamma_v = init_values * np.ones((v_dim))
+        gamma_l = init_values * np.ones((l_dim))
+        self.gamma_v = nn.Parameter(torch.tensor(gamma_v))
+        self.gamma_l = nn.Parameter(torch.tensor(gamma_l))
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        v = self.layer_norm_v(v)
+        l = self.layer_norm_l(l)
+        delta_v, delta_l = self.attn(
+            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+        )
+        # v, l = v + delta_v, l + delta_l
+        v = v + self.drop_path(self.gamma_v * delta_v)
+        l = l + self.drop_path(self.gamma_l * delta_l)
+        return v.to(torch.bfloat16), l.to(torch.bfloat16)
+
+    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+        self.nhead = nhead
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(
+        self,
+        src,
+        pos: Optional[Tensor] = None,
+        src_mask: Optional[Tensor] = None,
+        # src_key_padding_mask: Optional[Tensor] = None,
+    ):
+        # # repeat attn mask
+        # if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
+        #     # bs, num_q, num_k
+        #     src_mask = src_mask.repeat(self.nhead, 1, 1)
+
+        q = k = self.with_pos_embed(src, pos)
+
+        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
+
+        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src
+
+class MLP(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+class AfdMultimodalTransformer(nn.Module):
+    def __init__(
+        self,
+        d_model=2176, 
+        num_encoder_layers=3
+    ):
+        super().__init__()
+        self.d_model = d_model*2
+        self.num_encoder_layers = 3
+        self.language_projector = MLP(input_dim=d_model, hidden_dim=self.d_model, output_dim=d_model, num_layers=1)
+        self.multimodal_transformer = MultimodalTransformer(
+            d_model=d_model, num_encoder_layers=num_encoder_layers
+        )
+        self.afd_predictor = MLP(input_dim=d_model, hidden_dim=self.d_model, output_dim=1, num_layers=2)
+
+    def forward(
+        self,
+        visual_features,
+        textual_features
+    ):
+        # print(visual_features.shape, textual_features.shape)
+        textual_features = self.language_projector(textual_features)
+        visual_features, textual_features = self.multimodal_transformer(visual_features, 
+                                                                        textual_features)
+        interactability = self.afd_predictor(visual_features).sigmoid()
+        return interactability
+
+class MultimodalTransformer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        nhead=8,
+        num_queries=300,
+        num_encoder_layers=3,
+        dim_feedforward=2048,
+        normalize_before=False,
+        # for deformable encoder
+        num_feature_levels=1,
+        text_dropout=0.1,
+        image_dropout=0.1,
+        fusion_dropout=0.1,
+        fusion_droppath=0.0,
+    ):
+        super().__init__()
+        self.num_feature_levels = num_feature_levels
+        self.num_encoder_layers = num_encoder_layers
+        self.num_queries = num_queries
+
+        # choose encoder layer type
+        encoder_layer = TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead // 2,
+            dim_feedforward=dim_feedforward // 2,
+            dropout=image_dropout,
+        )
+
+        text_enhance_layer = TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead // 2,
+            dim_feedforward=dim_feedforward // 2,
+            dropout=text_dropout,
+        )
+
+        feature_fusion_layer = BiAttentionBlock(
+            v_dim=d_model,
+            l_dim=d_model,
+            embed_dim=dim_feedforward // 2,
+            num_heads=nhead // 2,
+            dropout=fusion_dropout,
+            drop_path=fusion_droppath,
+        )
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        assert encoder_norm is None
+        self.encoder = TransformerEncoder(
+            encoder_layer,
+            num_encoder_layers,
+            d_model=d_model,
+            num_queries=num_queries,
+            text_enhance_layer=text_enhance_layer,
+            feature_fusion_layer=feature_fusion_layer,
+        )
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.num_queries = num_queries  # useful for single stage model only
+        if num_feature_levels > 1:
+            if self.num_encoder_layers > 0:
+                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+            else:
+                self.level_embed = None
+
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        if self.num_feature_levels > 1 and self.level_embed is not None:
+            nn.init.normal_(self.level_embed)
+
+    def forward(self, slot_features, text_features):
+        """
+        Input:
+            - srcs: List of multi features [bs, \sum{hxw}, c]
+
+        """
+        #########################################################
+        # Begin Encoder
+        #########################################################
+        memory, memory_text = self.encoder(
+            visual_features=slot_features,
+            textual_features=text_features,
+        )
+        return memory, memory_text
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_layer,
+        num_layers,
+        d_model=256,
+        num_queries=300,
+        enc_layer_share=False,
+        text_enhance_layer=None,
+        feature_fusion_layer=None,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+    ):
+        """_summary_
+
+        Args:
+            encoder_layer (_type_): _description_
+            num_layers (_type_): _description_
+            norm (_type_, optional): _description_. Defaults to None.
+            d_model (int, optional): _description_. Defaults to 256.
+            num_queries (int, optional): _description_. Defaults to 300.
+            enc_layer_share (bool, optional): _description_. Defaults to False.
+
+        """
+        super().__init__()
+        # prepare layers
+        self.layers = []
+        self.text_layers = []
+        self.fusion_layers = []
+        if num_layers > 0:
+            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+            if text_enhance_layer is not None:
+                self.text_layers = _get_clones(
+                    text_enhance_layer, num_layers, layer_share=enc_layer_share
+                )
+            if feature_fusion_layer is not None:
+                self.fusion_layers = _get_clones(
+                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
+                )
+        else:
+            self.layers = []
+            del encoder_layer
+
+            if text_enhance_layer is not None:
+                self.text_layers = []
+                del text_enhance_layer
+            if feature_fusion_layer is not None:
+                self.fusion_layers = []
+                del feature_fusion_layer
+
+        self.query_scale = None
+        self.num_queries = num_queries
+        self.num_layers = num_layers
+        self.d_model = d_model
+
+        self.use_checkpoint = use_checkpoint
+        self.use_transformer_ckpt = use_transformer_ckpt
+        # self.project_in_features  = nn.Linear(4096, 2176, bias=True)
+        # self.project_out_features = nn.Linear(2176, 4096, bias=True)
+
+    def forward(
+        self,
+        # for images
+        visual_features: Tensor,
+        # for texts
+        textual_features: Tensor = None,
+    ):
+        """
+        Input:
+            - visual_features: [bs, sum(hi*wi), cc]
+            - textual_features: bs, n_text, cc
+        Outpus:
+            - output: [bs, sum(hi*wi), 256]
+        """
+        output = visual_features # self.project_in_features(visual_features)
+
+        if self.text_layers:
+            # generate pos_text
+            bs, n_text, text_dim = textual_features.shape
+            pos_text = (
+                torch.arange(n_text, device=textual_features.device)
+                .float()
+                .unsqueeze(0)
+                .unsqueeze(-1)
+                .repeat(bs, 1, 1)
+            )
+            pos_text = get_sine_pos_embed(pos_text, num_pos_feats=visual_features.shape[-1], exchange_xy=False)
+
+        # main process
+        for layer_id, visual_layer in enumerate(self.layers):
+            # if output.isnan().any() or memory_text.isnan().any():
+            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #         import ipdb; ipdb.set_trace()
+            if self.fusion_layers:
+                output, textual_features = self.fusion_layers[layer_id](
+                    v=output,
+                    l=textual_features,
+                    # attention_mask_v=key_padding_mask,
+                    # attention_mask_l=text_attention_mask,
+                )
+
+            if self.text_layers:
+                textual_features = self.text_layers[layer_id](
+                    src=textual_features.transpose(0, 1),
+                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
+                    # src_mask=~text_self_attention_masks,  # note we use ~ for mask here
+                    # src_key_padding_mask=text_attention_mask,
+                ).transpose(0, 1)
+
+            # main process
+            output = visual_layer(
+                src=output,
+                # pos=pos,
+                # reference_points=reference_points,
+                # spatial_shapes=spatial_shapes,
+                # level_start_index=level_start_index,
+                # key_padding_mask=key_padding_mask,
+            )
+        
+        # output = self.project_out_features(output)
+
+        return output, textual_features
+
+
+
+class EgoExoTransformer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        nhead=8,
+        num_queries=300,
+        num_encoder_layers=3,
+        dim_feedforward=2048,
+        normalize_before=False,
+        # for deformable encoder
+        num_feature_levels=1,
+        text_dropout=0.1,
+        image_dropout=0.1,
+        fusion_dropout=0.1,
+        fusion_droppath=0.0,
+    ):
+        super().__init__()
+        self.num_feature_levels = num_feature_levels
+        self.num_encoder_layers = num_encoder_layers
+        self.num_queries = num_queries
+
+        # choose encoder layer type
+        encoder_layer = TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead // 2,
+            dim_feedforward=dim_feedforward // 2,
+            dropout=image_dropout,
+        )
+
+        ego_enhance_layer = TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead // 2,
+            dim_feedforward=dim_feedforward // 2,
+            dropout=image_dropout,
+        )
+
+        feature_fusion_layer = BiAttentionBlock(
+            v_dim=d_model,
+            l_dim=d_model,
+            embed_dim=dim_feedforward // 2,
+            num_heads=nhead // 2,
+            dropout=fusion_dropout,
+            drop_path=fusion_droppath,
+        )
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        assert encoder_norm is None
+        self.encoder = EgoExoTransformerEncoder(
+            encoder_layer,
+            num_encoder_layers,
+            d_model=d_model,
+            num_queries=num_queries,
+            ego_enhance_layer=ego_enhance_layer,
+            feature_fusion_layer=feature_fusion_layer,
+        )
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.num_queries = num_queries  # useful for single stage model only
+        if num_feature_levels > 1:
+            if self.num_encoder_layers > 0:
+                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+            else:
+                self.level_embed = None
+
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        if self.num_feature_levels > 1 and self.level_embed is not None:
+            nn.init.normal_(self.level_embed)
+
+    def forward(self, exo_features, ego_features):
+        """
+        Input:
+            - srcs: List of multi features [bs, \sum{hxw}, c]
+
+        """
+        #########################################################
+        # Begin Encoder
+        #########################################################
+        memory_exo, memory_ego = self.encoder(
+            exo_features=exo_features,
+            ego_features=ego_features,
+        )
+        return memory_exo, memory_ego
+
+
+class EgoExoTransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_layer,
+        num_layers,
+        d_model=256,
+        num_queries=300,
+        enc_layer_share=False,
+        ego_enhance_layer=None,
+        feature_fusion_layer=None,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+    ):
+        """_summary_
+
+        Args:
+            encoder_layer (_type_): _description_
+            num_layers (_type_): _description_
+            norm (_type_, optional): _description_. Defaults to None.
+            d_model (int, optional): _description_. Defaults to 256.
+            num_queries (int, optional): _description_. Defaults to 300.
+            enc_layer_share (bool, optional): _description_. Defaults to False.
+
+        """
+        super().__init__()
+        # prepare layers
+        self.layers = []
+        self.ego_layers = []
+        self.fusion_layers = []
+        if num_layers > 0:
+            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+            if ego_enhance_layer is not None:
+                self.ego_layers = _get_clones(
+                    ego_enhance_layer, num_layers, layer_share=enc_layer_share
+                )
+            if feature_fusion_layer is not None:
+                self.fusion_layers = _get_clones(
+                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
+                )
+        else:
+            self.layers = []
+            del encoder_layer
+
+            if ego_enhance_layer is not None:
+                self.ego_layers = []
+                del ego_enhance_layer
+            if feature_fusion_layer is not None:
+                self.fusion_layers = []
+                del feature_fusion_layer
+
+        self.query_scale = None
+        self.num_queries = num_queries
+        self.num_layers = num_layers
+        self.d_model = d_model
+
+        self.use_checkpoint = use_checkpoint
+        self.use_transformer_ckpt = use_transformer_ckpt
+        # self.project_in_features  = nn.Linear(4096, 2176, bias=True)
+        # self.project_out_features = nn.Linear(2176, 4096, bias=True)
+
+    def forward(
+        self,
+        # for exo images
+        exo_features: Tensor,
+        # for ego images
+        ego_features: Tensor = None,
+    ):
+        """
+        Input:
+            - exo_features: [bs, sum(hi*wi), cc]
+            - ego_features: [bs, sum(hi*wi), cc]
+        Outpus:
+            - output: [bs, sum(hi*wi), 256]
+        """
+        # exo_features = exo_features # self.project_in_features(visual_features)
+
+        # main process
+        for layer_id, exo_layer in enumerate(self.layers):
+            # if output.isnan().any() or memory_text.isnan().any():
+            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #         import ipdb; ipdb.set_trace()
+            if self.fusion_layers:
+                exo_features, ego_features = self.fusion_layers[layer_id](
+                    v=exo_features,
+                    l=ego_features,
+                    # attention_mask_v=key_padding_mask,
+                    # attention_mask_l=text_attention_mask,
+                )
+
+            if self.ego_layers:
+                ego_features = self.ego_layers[layer_id](
+                    src=ego_features,
+                    # pos=pos,
+                    # reference_points=reference_points,
+                    # spatial_shapes=spatial_shapes,
+                    # level_start_index=level_start_index,
+                    # key_padding_mask=key_padding_mask,
+                )
+
+            # main process
+            exo_features = exo_layer(
+                src=exo_features,
+                # pos=pos,
+                # reference_points=reference_points,
+                # spatial_shapes=spatial_shapes,
+                # level_start_index=level_start_index,
+                # key_padding_mask=key_padding_mask,
+            )
+        
+        # output = self.project_out_features(output)
+
+        return exo_features, ego_features
+
+
+class AfdTransformer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        nhead=8,
+        num_queries=300,
+        num_encoder_layers=3,
+        dim_feedforward=2048,
+        normalize_before=False,
+        # for deformable encoder
+        num_feature_levels=1,
+        text_dropout=0.1,
+        image_dropout=0.1,
+        fusion_dropout=0.1,
+        fusion_droppath=0.0,
+    ):
+        super().__init__()
+        self.num_feature_levels = num_feature_levels
+        self.num_encoder_layers = num_encoder_layers
+        self.num_queries = num_queries
+
+        # choose encoder layer type
+        encoder_layer = TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead // 2,
+            dim_feedforward=dim_feedforward // 2,
+            dropout=image_dropout,
+        )
+
+        feature_fusion_layer = BiAttentionBlock(
+            v_dim=d_model,
+            l_dim=d_model,
+            embed_dim=dim_feedforward // 2,
+            num_heads=nhead // 2,
+            dropout=fusion_dropout,
+            drop_path=fusion_droppath,
+        )
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        assert encoder_norm is None
+        self.encoder = AfdTransformerEncoder(
+            encoder_layer,
+            num_encoder_layers,
+            d_model=d_model,
+            num_queries=num_queries,
+            ctx_enhance_layer=None,
+            feature_fusion_layer=feature_fusion_layer,
+        )
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.num_queries = num_queries  # useful for single stage model only
+        if num_feature_levels > 1:
+            if self.num_encoder_layers > 0:
+                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+            else:
+                self.level_embed = None
+
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        if self.num_feature_levels > 1 and self.level_embed is not None:
+            nn.init.normal_(self.level_embed)
+
+    def forward(self, context_tokens, object_tokens):
+        """
+        Input:
+            - srcs: List of multi features [bs, \sum{hxw}, c]
+
+        """
+        #########################################################
+        # Begin Encoder
+        #########################################################
+        context_tokens = torch.concat([context_tokens, object_tokens], dim=1)
+        memory_ctx, memory_afd = self.encoder(
+            context_features=context_tokens,
+            object_features=object_tokens,
+        )
+        return memory_afd
+
+
+class AfdTransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_layer,
+        num_layers,
+        d_model=256,
+        num_queries=300,
+        enc_layer_share=False,
+        ctx_enhance_layer=None,
+        feature_fusion_layer=None,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+    ):
+        """_summary_
+
+        Args:
+            encoder_layer (_type_): _description_
+            num_layers (_type_): _description_
+            norm (_type_, optional): _description_. Defaults to None.
+            d_model (int, optional): _description_. Defaults to 256.
+            num_queries (int, optional): _description_. Defaults to 300.
+            enc_layer_share (bool, optional): _description_. Defaults to False.
+
+        """
+        super().__init__()
+        # prepare layers
+        self.layers = []
+        self.ctx_layers = []
+        self.fusion_layers = []
+        if num_layers > 0:
+            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+            if ctx_enhance_layer is not None:
+                self.ctx_layers = _get_clones(
+                    ctx_enhance_layer, num_layers, layer_share=enc_layer_share
+                )
+            if feature_fusion_layer is not None:
+                self.fusion_layers = _get_clones(
+                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
+                )
+        else:
+            self.layers = []
+            del encoder_layer
+
+            if ctx_enhance_layer is not None:
+                self.ctx_layers = []
+                del ctx_enhance_layer
+            if feature_fusion_layer is not None:
+                self.fusion_layers = []
+                del feature_fusion_layer
+
+        self.query_scale = None
+        self.num_queries = num_queries
+        self.num_layers = num_layers
+        self.d_model = d_model
+
+        self.use_checkpoint = use_checkpoint
+        self.use_transformer_ckpt = use_transformer_ckpt
+        # self.project_in_features  = nn.Linear(4096, 2176, bias=True)
+        # self.project_out_features = nn.Linear(2176, 4096, bias=True)
+
+    def forward(
+        self,
+        # for exo images
+        context_features: Tensor,
+        # for ego images
+        object_features: Tensor = None,
+    ):
+        """
+        Input:
+            - exo_features: [bs, sum(hi*wi), cc]
+            - ego_features: [bs, sum(hi*wi), cc]
+        Outpus:
+            - output: [bs, sum(hi*wi), 256]
+        """
+        # exo_features = exo_features # self.project_in_features(visual_features)
+
+        # main process
+        for layer_id, obj_layer in enumerate(self.layers):
+            # if output.isnan().any() or memory_text.isnan().any():
+            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #         import ipdb; ipdb.set_trace()
+            if self.fusion_layers:
+                object_features, context_features = self.fusion_layers[layer_id]( # to be changed
+                    v=object_features,
+                    l=context_features,
+                    # attention_mask_v=key_padding_mask,
+                    # attention_mask_l=text_attention_mask,
+                )
+                # context_features, object_features = self.fusion_layers[layer_id]( # current version
+                #     v=context_features,
+                #     l=object_features,
+                #     # attention_mask_v=key_padding_mask,
+                #     # attention_mask_l=text_attention_mask,
+                # )
+
+            if self.ctx_layers:
+                context_features = self.ctx_layers[layer_id](
+                    src=context_features,
+                    # pos=pos,
+                    # reference_points=reference_points,
+                    # spatial_shapes=spatial_shapes,
+                    # level_start_index=level_start_index,
+                    # key_padding_mask=key_padding_mask,
+                )
+
+            # main process
+            object_features = obj_layer(
+                src=object_features,
+                # pos=pos,
+                # reference_points=reference_points,
+                # spatial_shapes=spatial_shapes,
+                # level_start_index=level_start_index,
+                # key_padding_mask=key_padding_mask,
+            )
+        
+        # output = self.project_out_features(output)
+
+        return context_features, object_features
+
+
+
+
+# class CrossAttentionBlock(nn.Module):
+#     def __init__(
+#         self,
+#         dim,
+#         *,
+#         context_dim=None,
+#         dim_head=64,
+#         heads=8,
+#         parallel_ff=False,
+#         ff_mult=4,
+#         norm_context=False
+#     ):
+#         super().__init__()
+#         self.heads = heads
+#         self.scale = dim_head ** -0.5
+#         inner_dim = heads * dim_head
+#         context_dim = default(context_dim, dim)
+
+#         # self.norm = LayerNorm(dim)
+#         # self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
+
+#         self.to_q = nn.Linear(dim, inner_dim, bias=False)
+#         self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
+#         self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+#         # whether to have parallel feedforward
+
+#         ff_inner_dim = ff_mult * dim
+
+#         self.ff = nn.Sequential(
+#             nn.Linear(dim, ff_inner_dim * 2, bias=False),
+#             SwiGLU(),
+#             nn.Linear(ff_inner_dim, dim, bias=False)
+#         ) if parallel_ff else None
+
+#     def forward(self, x, context):
+#         """
+#         einstein notation
+#         b - batch
+#         h - heads
+#         n, i, j - sequence length (base sequence length, source, target)
+#         d - feature dimension
+#         """
+
+#         # pre-layernorm, for queries and context
+
+#         x = x # self.norm(x)
+#         context = context # self.context_norm(context)
+
+#         # get queries
+
+#         q = self.to_q(x)
+#         q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
+
+#         # scale
+
+#         q = q * self.scale
+
+#         # get key / values
+
+#         k, v = self.to_kv(context).chunk(2, dim=-1)
+
+#         # query / key similarity
+
+#         sim = torch.einsum('b h i d, b j d -> b h i j', q, k)
+
+#         # attention
+
+#         sim = sim - sim.amax(dim=-1, keepdim=True)
+#         attn = sim.softmax(dim=-1)
+
+#         # aggregate
+
+#         out = torch.einsum('b h i j, b j d -> b h i d', attn, v)
+
+#         # merge and combine heads
+
+#         out = rearrange(out, 'b h n d -> b n (h d)')
+#         out = self.to_out(out)
+
+#         # add parallel feedforward (for multimodal layers)
+
+#         if exists(self.ff):
+#             out = out + self.ff(x)
+
+#         return out
+
+
+# class TransformerBlock(nn.Module):
+#     def __init__(self, d_model, n_heads=8, dim_feedforward=2048, dropout=0.1):
+#         """
+#         Args:
+#             d_model: Dimension of the model (embedding size).
+#             n_heads: Number of attention heads.
+#             dim_feedforward: Hidden layer size in the feedforward network.
+#             dropout: Dropout probability.
+#         """
+#         super().__init__()
+#         self.attention_layer = nn.MultiheadAttention(
+#             embed_dim=d_model, 
+#             num_heads=n_heads, 
+#             dropout=dropout, 
+#             batch_first=True
+#         )
+#         self.linear1 = nn.Linear(d_model, dim_feedforward)
+#         self.linear2 = nn.Linear(dim_feedforward, d_model)
+#         self.drop_out_1 = nn.Dropout(dropout)
+#         self.drop_out_2 = nn.Dropout(dropout)
+
+#     def forward(self, x, mask=None, key_padding_mask=None):
+#         attn_out, _ = self.attention_layer(
+#             query=x, key=x, value=x,
+#             attn_mask=mask,
+#             key_padding_mask=key_padding_mask
+#         )
+#         x = x + self.drop_out_1(attn_out)
+#         x = x + self.linear2(self.drop_out_2(F.relu(self.linear1(x))))
+#         return x
+
+# class ParallelTransformerBlock(nn.Module):
+#     def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
+#         super().__init__()
+#         # self.norm = LayerNorm(dim)
+
+#         attn_inner_dim = dim_head * heads
+#         ff_inner_dim = dim * ff_mult
+#         self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
+
+#         self.heads = heads
+#         self.scale = dim_head**-0.5
+
+#         self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
+#         self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)
+
+#         self.ff_out = nn.Sequential(
+#             SwiGLU(),
+#             nn.Linear(ff_inner_dim, dim, bias=False)
+#         )
+
+#         # for caching causal mask and rotary embeddings
+
+#         self.mask = None
+#         self.pos_emb = None
+
+#     def get_mask(self, n, device):
+#         if self.mask is not None and self.mask.shape[-1] >= n:
+#             return self.mask[:n, :n].to(device)
+
+#         mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1)
+#         self.mask = mask
+#         return mask
+
+#     def forward(self, x, attn_mask=None):
+#         """
+#         einstein notation
+#         b - batch
+#         h - heads
+#         n, i, j - sequence length (base sequence length, source, target)
+#         d - feature dimension
+#         """
+
+#         n, device, h = x.shape[1], x.device, self.heads
+
+#         # pre layernorm
+
+#         # x = self.norm(x)
+
+#         # attention queries, keys, values, and feedforward inner
+
+#         q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
+
+#         # split heads
+#         # they use multi-query single-key-value attention, yet another Noam Shazeer paper
+#         # they found no performance loss past a certain scale, and more efficient decoding obviously
+#         # https://arxiv.org/abs/1911.02150
+
+#         q = rearrange(q, "b n (h d) -> b h n d", h=h)
+
+#         # rotary embeddings
+
+#         # positions = self.get_rotary_embedding(n, device)
+#         # q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
+
+#         # scale
+
+#         q = q * self.scale
+
+#         # similarity
+
+#         sim = torch.einsum("b h i d, b j d -> b h i j", q, k)
+
+#         # causal mask
+
+#         causal_mask = self.get_mask(n, device)
+#         sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max)
+
+#         # extra attention mask - for masking out attention from text CLS token to padding
+
+#         if exists(attn_mask):
+#             attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
+#             sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)
+
+#         # attention
+
+#         sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+#         attn = sim.softmax(dim=-1)
+
+#         # aggregate values
+
+#         out = torch.einsum("b h i j, b j d -> b h i d", attn, v)
+
+#         # merge heads
+
+#         out = rearrange(out, "b h n d -> b n (h d)")
+#         return self.attn_out(out) + self.ff_out(ff)
+
+# class Residual(nn.Module):
+#     def __init__(self, fn):
+#         super().__init__()
+#         self.fn = fn
+
+#     def forward(self, x, *args, **kwargs):
+#         return self.fn(x, *args, **kwargs) + x
+
+# class AfdTransformer(nn.Module):
+#     def __init__(
+#         self,
+#         dim=2176,
+#         dim_head=256,
+#         n_heads=8,
+#         levels=3,
+#         ff_mult=4
+#     ):
+#         super().__init__()
+#         self.attention_layers = nn.ModuleList([])
+#         for ind in range(levels):
+#             self.attention_layers.append(nn.ModuleList([
+#                 Residual(CrossAttentionBlock(dim=dim, dim_head=dim_head, heads=n_heads, parallel_ff=True, ff_mult=ff_mult)),
+#                 Residual(TransformerBlock(d_model=dim, n_heads=n_heads, dim_feedforward=dim, dropout=0.1))
+#             ]))
+
+
+#     def forward(self, context_tokens, object_tokens):
+#         """
+#         Input:
+#             - srcs: List of multi features [bs, \sum{hxw}, c]
+
+#         """
+#         #########################################################
+#         # Begin Encoder
+#         #########################################################
+#         context_tokens = torch.concat([context_tokens, object_tokens], dim=1)
+
+#         # main process
+#         for cross_attn, attn_ff in self.attention_layers:
+#             object_tokens = cross_attn(object_tokens, context_tokens)
+#             object_tokens = attn_ff(object_tokens)
+#         # output = self.project_out_features(output)
+
+#         return object_tokens
diff --git a/prismatic/models/addons/relational_module.py b/prismatic/models/addons/relational_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..71e66adbad6077b338ae2e3ae11c73bc1c46bba2
--- /dev/null
+++ b/prismatic/models/addons/relational_module.py
@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+
+class CrossAttentionBlock(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+        self.attn = nn.MultiheadAttention(embed_dim=dim, num_heads=n_heads, batch_first=True)
+        self.norm = nn.LayerNorm(dim)
+        self.ff = nn.Sequential(
+            nn.Linear(dim, dim * 4),
+            nn.ReLU(),
+            nn.Linear(dim * 4, dim)
+        )
+        self.norm2 = nn.LayerNorm(dim)
+
+    def forward(self, query, context):
+        # Cross-attention: query attends to context
+        attn_out, _ = self.attn(query, context, context)
+        query = self.norm(query + attn_out)
+
+        # Feedforward
+        ff_out = self.ff(query)
+        query = self.norm2(query + ff_out)
+        return query
+
+class DualCrossAttentionLayer(nn.Module):
+    def __init__(self, dim, n_heads):
+        super().__init__()
+        self.visual_attn = CrossAttentionBlock(dim, n_heads)
+        self.slot_attn = CrossAttentionBlock(dim, n_heads)
+
+    def forward(self, relation_tokens, patch_tokens, slot_tokens):
+        relation_tokens = self.visual_attn(relation_tokens, patch_tokens)
+        relation_tokens = self.slot_attn(relation_tokens, slot_tokens)
+        return relation_tokens
+
+class RelationTokensGrounding(nn.Module):
+    def __init__(self, dim, in_dim=None, num_relation_tokens=16, n_heads=4, num_layers=3):
+        super().__init__()
+        self.num_relation_tokens = num_relation_tokens
+        self.in_dim = in_dim
+        self.dim = dim
+
+        # Projection: patch tokens → internal dim
+        if self.in_dim != dim:
+            self.patch_proj_in = nn.Linear(self.in_dim, dim)
+            self.slot_proj_in = nn.Linear(self.in_dim, dim)
+            self.relation_proj_out = nn.Linear(dim, self.in_dim)
+        else:
+            self.patch_proj_in = nn.Identity()
+            self.slot_proj_in = nn.Identity()
+            self.relation_proj_out = nn.Identity()
+
+        # Learnable relation tokens (shared across batch)
+        self.relation_tokens = nn.Parameter(torch.randn(1, num_relation_tokens, dim))
+
+        # Cross-attention processing layers
+        self.layers = nn.ModuleList([
+            DualCrossAttentionLayer(dim, n_heads)
+            for _ in range(num_layers)
+        ])
+
+    def forward(self, patch_tokens, slot_tokens):
+        B = patch_tokens.size(0)
+
+        # Project patch tokens in
+        patch_tokens = self.patch_proj_in(patch_tokens)  # [B, N_patch, dim]
+        slot_tokens = self.slot_proj_in(slot_tokens)
+
+        # Expand learnable relation tokens
+        relation_tokens = self.relation_tokens.expand(B, -1, -1)  # [B, N_rel, dim]
+
+        # Process through stacked dual-attention
+        for layer in self.layers:
+            relation_tokens = layer(relation_tokens, patch_tokens, slot_tokens)
+
+        # Project relation tokens out
+        relation_tokens = self.relation_proj_out(relation_tokens)  # [B, N_rel, patch_dim]
+        return relation_tokens  # [B, N_rel, D]
diff --git a/prismatic/models/addons/slot_attention.py b/prismatic/models/addons/slot_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..a641748aba0e730a9c1bc1ab72476b04eb67ad3d
--- /dev/null
+++ b/prismatic/models/addons/slot_attention.py
@@ -0,0 +1,507 @@
+import torch
+import einops
+import random
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from .attention import Block, InstanceNorm1d, BasicConv2d, CrossBlock, BasicBlock, Conv2Plus1D, Attention, STBlock, SlotBlock
+from .vision_transformer import vit_small
+
+def build_grid(resolution):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    ranges = [np.linspace(0., 1., num=res) for res in resolution]
+    grid = np.meshgrid(*ranges, sparse=False, indexing="ij")
+    grid = np.stack(grid, axis=-1)
+    grid = np.reshape(grid, [res for res in resolution] + [-1])
+    grid = np.expand_dims(grid, axis=0)
+    grid = grid.astype(np.float32)
+    return torch.tensor(np.concatenate([grid, 1.0 - grid], axis=-1)).to(device)
+
+
+class SoftPositionEmbed(nn.Module):
+  """Adds soft (include spatio-temporal) positional embedding with learnable projection."""
+  def __init__(self, hidden_size, resolution):
+        """Builds the soft position embedding layer.
+
+        Args:
+          hidden_size: Size of input feature dimension.
+          resolution: Tuple of integers specifying width and height of grid.
+        """
+        super(SoftPositionEmbed, self).__init__()
+        self.proj = nn.Linear(len(resolution)*2, hidden_size)
+        self.grid = build_grid(resolution)
+  def forward(self, inputs):
+        return inputs + self.proj(self.grid)
+
+
+def spatial_broadcast(slots, resolution):
+    """Broadcast slot features to a 2D grid and collapse slot dimension."""
+    # `slots` has shape: [batch_size, num_slots, slot_size].
+    slots = torch.reshape(slots, [-1, slots.shape[-1]])[:, None, None, :]
+    grid = einops.repeat(slots, 'b_n i j d -> b_n (tilei i) (tilej j) d', tilei=resolution[0], tilej=resolution[1])
+    # `grid` has shape: [batch_size*num_slots, height, width, slot_size].
+    return grid
+
+
+def unstack_and_split(x, batch_size, num_channels=3):
+    """Unstack batch dimension and split into channels and alpha mask."""
+    unstacked = einops.rearrange(x, '(b s) c h w -> b s c h w', b=batch_size)
+    channels, masks = torch.split(unstacked, [num_channels, 1], dim=2)
+    return channels, masks
+
+
+class SlotAttention(nn.Module):
+    """Slot Attention module."""
+
+    def __init__(self, num_slots, encoder_dims, iters=3, hidden_dim=128, eps=1e-8, background=False):
+        """Builds the Slot Attention module.
+        Args:
+            iters: Number of iterations.
+            num_slots: Number of slots.
+            encoder_dims: Dimensionality of slot feature vectors.
+            hidden_dim: Hidden layer size of MLP.
+            eps: Offset for attention coefficients before normalization.
+        """
+        super(SlotAttention, self).__init__()
+        
+        self.eps = eps
+        self.iters = iters
+        self.num_slots = num_slots
+        self.scale = encoder_dims ** -0.5
+        self.background = background
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.norm_input = nn.LayerNorm(encoder_dims)
+        self.norm_slots = nn.LayerNorm(encoder_dims)
+        self.norm_pre_ff = nn.LayerNorm(encoder_dims)
+        self.slots_embedding = nn.Embedding(num_slots, encoder_dims)
+        # Linear maps for the attention module.
+        self.project_q = nn.Linear(encoder_dims, hidden_dim)
+        self.project_k = nn.Linear(encoder_dims, hidden_dim)
+        self.project_v = nn.Linear(encoder_dims, encoder_dims)
+        # Slot update functions.
+        self.gru = nn.GRUCell(encoder_dims, encoder_dims)
+        self.mlp_1 = nn.Linear(encoder_dims, hidden_dim)
+        self.mlp_2 = nn.Linear(hidden_dim, encoder_dims)
+
+    def forward(self, inputs, bs=None, weight=None, init_slots=None, num_slots=None):
+        # inputs has shape [batch_size, num_inputs, inputs_size].
+        inputs = self.norm_input(inputs)  # Apply layer norm to the input.
+        k = self.project_k(inputs)  # Shape: [batch_size, num_inputs, slot_size].
+        v = self.project_v(inputs)  # Shape: [batch_size, num_inputs, slot_size].
+
+        # Initialize the slots. Shape: [batch_size, num_slots, slot_size].
+        b, n, d = inputs.shape
+        n_s = num_slots if num_slots is not None else self.num_slots
+
+        # learnable slots initializations
+        if init_slots == None:
+            init_slots = self.slots_embedding(torch.arange(0, n_s).expand(b, n_s).to(self.device))
+            if self.background:
+                init_slots = torch.cat([init_slots, torch.zeros_like(init_slots[:, -1:, :])], dim=1)
+            slots = init_slots
+        else:
+            slots = init_slots
+
+        # Slot update.
+        with torch.autocast(device_type='cuda', dtype=torch.float32):
+            # Multiple rounds of attention.
+            for t in range(self.iters):
+                slots_prev = slots
+                slots = self.norm_slots(slots)
+
+                # Attention.
+                q = self.project_q(slots)  # Shape: [batch_size, num_slots, slot_size].
+                dots = torch.einsum('bid,bjd->bij', q, k) * self.scale
+                attn = dots.softmax(dim=1) + self.eps
+                attn = attn / attn.sum(dim=-1, keepdim=True)  # weighted mean.
+
+                updates = torch.einsum('bjd,bij->bid', v, attn)
+
+                # `updates` has shape: [batch_size, num_slots, slot_size].
+                slots = self.gru(
+                    updates.reshape(-1, d),
+                    slots_prev.reshape(-1, d)
+                )
+                slots = slots.reshape(b, -1, d)
+                slots = slots + self.mlp_2(F.relu(self.mlp_1(self.norm_pre_ff(slots))))
+                if t == self.iters-2:
+                    slots = slots.detach() - init_slots.detach() + init_slots
+                
+        return slots.to(torch.bfloat16), dots.to(torch.bfloat16)
+
+
+class LinearPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super(LinearPositionEmbedding, self).__init__()
+        self.freq_bands = torch.linspace(0, dim//4-1, dim//4)
+
+    def forward(self, correlation, NORMALIZE_FACOR=1/200, fusion='cat'):
+        # correlation b h w m
+        b, h, w, m = correlation.shape
+        coord = torch.meshgrid(torch.arange(h), torch.arange(w))
+        x = torch.stack(coord[::-1], dim=0).float().to(correlation.device)
+        x = x.permute(1, 2, 0).contiguous()
+        freq_bands = self.freq_bands.to(correlation.device)
+        position_embed = torch.cat([torch.sin(3.14*x[..., -2:-1]*freq_bands*NORMALIZE_FACOR), 
+                                    torch.cos(3.14*x[..., -2:-1]*freq_bands*NORMALIZE_FACOR), 
+                                    torch.sin(3.14*x[..., -1:]*freq_bands*NORMALIZE_FACOR), 
+                                    torch.cos(3.14*x[..., -1:]*freq_bands*NORMALIZE_FACOR)], dim=-1)
+        position_embed = position_embed.unsqueeze(0).repeat(b, 1, 1, 1)
+        return torch.cat([correlation, position_embed], dim=-1) if fusion=='cat' else correlation + position_embed
+
+
+class SpaceTimeAttention(nn.Module):
+    def __init__(self, num_frames, num_slots, encoder_dims, output_dim, attn_drop_t, path_drop, task):
+        super(SpaceTimeAttention, self).__init__()
+        self.T = num_frames
+        self.num_slots = num_slots
+        self.encoder_dims = encoder_dims
+        self.task = task
+        self.st_token = nn.Parameter(torch.rand(1, 1, self.encoder_dims) * .02)
+        self.time_embed = nn.Parameter(torch.randn(1, num_frames, self.encoder_dims) * .02)
+        self.slots_embed = nn.Parameter(torch.randn(1, self.num_slots+1, self.encoder_dims) * .02)
+        self.pos_drop = nn.Dropout(path_drop)
+        self.st_transformer = nn.ModuleList([STBlock(dim=self.encoder_dims, num_heads=8, drop=path_drop, attn_drop=attn_drop_t)
+                                             for _ in range(3)])
+        if task == 'object':
+            self.mlp_token = nn.Sequential(
+                nn.Linear(self.encoder_dims, 2*self.encoder_dims), ##TODO: test mlp_ratio
+                nn.ReLU(inplace=True),
+                nn.Linear(2*self.encoder_dims, output_dim)
+            )
+        elif task == 'action':
+            self.cls_layer = nn.Linear(self.encoder_dims, 174)
+        elif task == 'joint':
+            self.mlp_token = nn.Sequential(
+                nn.Linear(self.encoder_dims, 2*self.encoder_dims), ##TODO: test mlp_ratio
+                nn.ReLU(inplace=True),
+                nn.Linear(2*self.encoder_dims, output_dim)
+            )
+            self.cls_layer = nn.Linear(self.encoder_dims, 174)
+
+    def forward(self, slots):
+        slots_input = einops.rearrange(slots, 'b t s c -> (b t) s c')
+        slots_input = torch.cat([self.st_token.expand(slots_input.shape[0], -1, -1), slots_input], dim=1)
+        slots_input = slots_input + self.slots_embed
+        slots_input = self.pos_drop(slots_input)
+        st_token = slots_input[:slots.shape[0], 0].unsqueeze(1)
+        slots_input = slots_input[:, 1:]
+        slots_input = einops.rearrange(slots_input, '(b t) s c -> (b s) t c', t=self.T)
+        slots_input = slots_input + self.time_embed
+        slots_input = self.pos_drop(slots_input)
+        slots_input = einops.rearrange(slots_input, '(b s) t c -> b (t s) c', s=self.num_slots)
+        slots_input = torch.cat([st_token, slots_input], dim=1)
+        for block in self.st_transformer:
+            slots_input = block(slots_input, self.num_slots, self.T)
+        st_token = slots_input[:, 0]
+        if self.task == 'object':
+            st_token = self.mlp_token(st_token)
+            return st_token
+        elif self.task == 'action':
+            logit = self.cls_layer(st_token)
+            return logit
+        elif self.task == 'joint':
+            logit = self.cls_layer(st_token)
+            st_token = self.mlp_token(st_token)
+            return logit, st_token
+
+
+class ObjectStateChange(nn.Module):
+    def __init__(self, num_frames, num_slots, encoder_dims, output_dim, attn_drop_t, path_drop, task):
+        super(ObjectStateChange, self).__init__()
+        self.T = num_frames
+        self.num_slots = num_slots
+        self.encoder_dims = encoder_dims
+        self.task = task
+        self.pos_drop = nn.Dropout(path_drop)
+        self.st_transformer = SlotBlock(dim=self.encoder_dims, num_heads=8, drop=path_drop, attn_drop=attn_drop_t)
+
+        if task == 'object':
+            self.mlp_token = nn.Sequential(
+                nn.Linear(self.encoder_dims, 2*self.encoder_dims), ##TODO: test mlp_ratio
+                nn.ReLU(inplace=True),
+                nn.Linear(2*self.encoder_dims, output_dim)
+            )
+        elif task == 'action':
+            self.cls_layer = nn.Linear(self.encoder_dims, 174)
+        elif task == 'joint':
+            self.mlp_token = nn.Sequential(
+                nn.Linear(2*self.encoder_dims, 2*self.encoder_dims), ##TODO: test mlp_ratio
+                nn.ReLU(inplace=True),
+                nn.Linear(2*self.encoder_dims, output_dim)
+            )
+            self.mlp_state = nn.Sequential(
+                nn.Linear(output_dim, output_dim), ##TODO: test mlp_ratio
+                nn.ReLU(inplace=True),
+                nn.Linear(output_dim, output_dim)
+            )
+            self.cls_layer = nn.Linear(output_dim, 174)
+
+    def forward(self, slots_input):
+        slots = self.st_transformer(slots_input)
+        slots = einops.rearrange(slots, '(b t) s c -> b t s c', t=self.T)
+        states_changes = []
+        for i in range(1, self.T):
+            states_change = torch.cat([slots[:, :-i], slots[:, i:]], dim=-1)
+            states_changes.append(states_change)
+        states_change = torch.cat(states_changes, dim=1)
+        states_change = self.mlp_token(states_change.contiguous())
+        states_all = states_change.max(dim=1)[0].mean(dim=1)
+        logit = self.cls_layer(states_all)
+        states_change = self.mlp_state(states_change)
+        return logit, states_change
+
+
+class SlotAttentionAutoEncoder(nn.Module):
+    """Slot Attention-based auto-encoder for object discovery."""
+    def __init__(self, resolution, 
+                       num_slots, 
+                       num_o=3, 
+                       num_t=3,
+                       in_channels=3, 
+                       out_channels=3, 
+                       hid_dim=32,
+                       iters=5, 
+                       path_drop=0.1,
+                       attn_drop_t=0.2,
+                       attn_drop_f=0.2,
+                       num_frames=7,
+                       background=False,
+                       teacher=False,
+                       slot_tune=False,
+                       task='object',
+                       student_model='dino',
+                       teacher_model='dino',
+                       correlation='none',
+                       dino_path='/home/ma-user/work/shuangrui/01_feature_warp/dino_deitsmall16_pretrain.pth'
+                ):
+        """Builds the Slot Attention-based Auto-encoder.
+        Args:
+            resolution: Tuple of integers specifying width and height of input image
+            num_slots: Number of slots in Slot Attention.
+            iters: Number of iterations in Slot Attention.
+        """
+        super(SlotAttentionAutoEncoder, self).__init__()
+
+        self.iters = iters
+        self.num_slots = num_slots + 1 if background else num_slots
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.T = num_frames
+        
+        self.student = student_model
+        if self.student == 'dino':
+            self.encoder_dims = 384
+            self.encoder = vit_small(16, slot_tune=slot_tune, background=background, num_slots=num_slots)
+            self.encoder.load_state_dict(torch.load(dino_path), strict=False)
+            self.down_time = 16
+        elif self.student == 'dinov2':
+            self.encoder_dims = 384
+            # self.encoder = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+            self.encoder = vit_small(14, block_chunks=0)
+            self.encoder.load_state_dict(torch.load('dinov2_small_14.pth'))
+            self.down_time = 14
+        elif self.student == 'clip':
+            self.encoder_dims = 768
+            self.encoder, _ = clip.load('ViT-B/16')
+            self.down_time = 16
+        self.end_size = (resolution[0] // self.down_time, resolution[1] // self.down_time)
+        
+        self.num_t = num_t
+        self.teacher = teacher
+        self.correlation = correlation
+        self.slot_tune = slot_tune
+        self.task = task
+        self.temporal_transformer = nn.ModuleList([Block(
+                                                        dim=self.encoder_dims, 
+                                                        num_heads=8,
+                                                        n_token=num_frames, 
+                                                        drop=path_drop,
+                                                        attn_drop=attn_drop_t,
+                                                        window=False,
+                                                        num_frames=self.T,
+                                                        end_size=self.end_size)
+                                                        for j in range(self.num_t)])
+        output_dim = self.encoder_dims if teacher_model == 'dino' else 512
+        self.mlp_slot = nn.Sequential(
+            nn.Linear(self.encoder_dims, 2*self.encoder_dims), ##TODO: test mlp_ratio
+            nn.ReLU(inplace=True),
+            nn.Linear(2*self.encoder_dims, output_dim)
+        )
+        self.slot_attention = SlotAttention(
+            iters=self.iters,
+            num_slots=self.num_slots,
+            encoder_dims=self.encoder_dims,
+            hidden_dim=hid_dim,
+            background=background)
+        
+        if self.correlation == 'global':
+            self.transport_proj = nn.Linear(self.end_size[0]*self.end_size[1], self.encoder_dims)
+        elif self.correlation == 'cats':
+            self.position_embed_x = nn.Parameter(
+                torch.zeros(1, self.end_size[0], 1, (self.end_size[0]*self.end_size[1]+self.encoder_dims)//2))
+            self.position_embed_y = nn.Parameter(
+                torch.zeros(1, 1, self.end_size[1], (self.end_size[0]*self.end_size[1]+self.encoder_dims)//2))
+            self.transport_proj = nn.Linear(self.end_size[0]*self.end_size[1]+self.encoder_dims, self.encoder_dims)
+            self.attention = Attention(self.encoder_dims, attn_drop=attn_drop_t, proj_drop=path_drop)
+        elif self.correlation == 'pos':
+            self.position_embed = LinearPositionEmbedding(dim=128)
+            self.transport_proj = nn.Linear(self.end_size[0]*self.end_size[1]+128, self.encoder_dims)
+        elif self.correlation == 'window':
+            self.window_sz = 5
+            self.unfold = nn.Unfold(kernel_size=(self.window_sz, self.window_sz), padding=self.window_sz//2)
+            self.position_embed = LinearPositionEmbedding(dim=128)
+            self.transport_proj = nn.Linear(self.window_sz**2+128, self.encoder_dims)
+        elif self.correlation == 'flow':
+            self.patch_proj = nn.Sequential(
+                nn.Conv2d(1, 16, kernel_size=6, stride=2, padding=2),
+                nn.ReLU(),
+                nn.Conv2d(16, 64, kernel_size=6, stride=2, padding=2)
+            )
+            self.position_embed = LinearPositionEmbedding(dim=64)
+            self.transport_proj = nn.Linear(self.end_size[0]//4*self.end_size[1]//4*64, self.encoder_dims)
+        elif self.correlation == 'adapt':
+            self.adapter = nn.Sequential(
+                nn.Conv3d(self.encoder_dims, self.encoder_dims//4, kernel_size=1),
+                nn.ReLU(),
+                nn.Conv3d(self.encoder_dims//4, self.encoder_dims//4, kernel_size=3, padding=1),
+                nn.ReLU(),
+                nn.Conv3d(self.encoder_dims//4, self.encoder_dims, kernel_size=1)
+            )
+        
+        self.st_block = ObjectStateChange(num_frames=num_frames, num_slots=num_slots, encoder_dims=self.encoder_dims, 
+                                           output_dim=output_dim, attn_drop_t=attn_drop_t, path_drop=path_drop, task=task)
+
+    def forward(self, image, weight=0.01, p_s=None):
+        # Convolutional encoder with position embedding.
+        bs = image.shape[0]
+        image_t = einops.rearrange(image, 'b t c h w -> (b t) c h w')
+        if self.student == 'dino':
+            x, cls_token, attn, k = self.encoder(image_t)  # CNN Backbone/ DINO backbone
+            x = einops.rearrange(x, '(b t) c h w -> b (t h w) c', t=self.T) ##spatial_flatten
+            k = einops.rearrange(k, '(b t) c h w -> b (t h w) c', t=self.T)
+            if self.num_t > 0:
+                for block in self.temporal_transformer:
+                    k = block(k)
+
+            x = einops.rearrange(x, 'b (t hw) c -> b t hw c', t=self.T) ##spatial-temporal_map
+            k = einops.rearrange(k, 'b (t hw) c -> b t hw c', t=self.T)
+            if self.teacher:
+                cls_token = einops.rearrange(cls_token, '(b t) c -> b t c', t=self.T)
+                return x, cls_token
+
+            init_slot = attn if self.slot_tune else None
+            slots, motion_mask = self.decode(x, k, weight, ts=self.T, init_slot=init_slot)
+        elif self.student == 'dinov2':
+            x = self.encoder(image_t, is_training=True)
+            k = x['k'][:, :, 1:].contiguous()
+            k = einops.rearrange(k, '(b t) k hw c -> b t hw (k c)', t=self.T)
+            x = x['x_norm_patchtokens']
+            x = einops.rearrange(x, '(b t) hw c -> b t hw c', t=self.T)
+            slots, motion_mask = self.decode(x, k, weight, ts=self.T)
+        elif self.student == 'clip':
+            cls_token, x = self.encoder.encode_image(image_t)
+            cls_token = cls_token.float()
+            x = x.float()
+            x = einops.rearrange(x, '(b t) c h w -> b t (h w) c', t=self.T)
+            slots, motion_mask = self.decode(x, x, weight, ts=self.T)
+        
+        st_token = self.st_block(slots)
+
+        if p_s is None:
+            motion_mask = F.softmax(motion_mask, dim=-2) + 1e-8
+            return x, motion_mask, self.end_size[0]
+        else:
+            slots = self.mlp_slot(slots)
+            slots = F.normalize(slots, dim=-1, p=2)
+            return x, motion_mask, slots, st_token
+    
+    def decode(self, x, k, weight, ts=7, init_slot=None):
+        bs = x.shape[0]
+        if self.correlation == 'global':
+            k_roll = torch.roll(k, shifts=1, dims=(1,))
+            if self.end_size[0] * self.end_size[1] != k_roll.shape[-2]:
+                k_roll = einops.rearrange(k_roll, 'b t (h w) c -> (b t) c h w', h=self.end_size[0])
+                k_roll = F.interpolate(k_roll, (self.end_size[0], self.end_size[1]), mode='bilinear')
+                k_roll = einops.rearrange(k_roll, '(b t) c h w -> b t (h w) c', t=self.T)
+            correlation = torch.einsum('btnc,btmc->btnm', k, k_roll)
+            correlation = einops.rearrange(correlation, 'b t n m -> (b t) n m')
+            correlation = self.transport_proj(correlation)
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            slots, motion_mask = self.slot_attention(correlation+k, bs, weight, init_slot)
+        elif self.correlation == 'cats':
+            k_roll = torch.roll(k, shifts=1, dims=(1,))
+            k_roll[:, 0] = k[:, 0]
+            correlation = torch.einsum('btnc,btmc->btnm', k, k_roll)
+            correlation = einops.rearrange(correlation, 'b t n m -> (b t) n m')
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            feat = torch.cat([correlation, k], dim=-1)
+            position = torch.cat([self.position_embed_x.repeat(1, 1, self.end_size[1], 1), self.position_embed_y.repeat(1, self.end_size[0], 1, 1)], dim=-1)
+            position = einops.rearrange(position, 'b h w c -> b (h w) c')
+            feat = self.transport_proj(feat+position)
+            feat = self.attention(feat) + feat
+            slots, motion_mask = self.slot_attention(feat, bs, weight, init_slot)
+        elif self.correlation == 'pos':
+            k_roll = torch.roll(k, shifts=1, dims=(1,))
+            correlation = torch.einsum('btnc,btmc->btnm', k, k_roll)
+            correlation = einops.rearrange(correlation, 'b t (h w) m -> (b t) h w m', h=self.end_size[0])
+            correlation = self.position_embed(correlation)
+            correlation = einops.rearrange(correlation, 'b h w d -> b (h w) d')
+            correlation = self.transport_proj(correlation)
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            slots, motion_mask = self.slot_attention(correlation+k, bs, weight, init_slot)
+        elif self.correlation == 'window':
+            k_roll = torch.roll(k, shifts=1, dims=(1,))
+            k_roll[:, 0] = k[:, 0]
+            k_roll = einops.rearrange(k_roll, 'b t (h w) c -> (b t) c h w', h=self.end_size[0])
+            k_roll = self.unfold(k_roll).contiguous()
+            k_roll = einops.rearrange(k_roll, '(b t) (ks c) hw -> b t ks c hw', t=self.T, ks=self.window_sz**2)
+            correlation = torch.einsum('btnc,btkcn->btnk', k, k_roll)
+            correlation = einops.rearrange(correlation, 'b t (h w) k -> (b t) h w k', h=self.end_size[0])
+            correlation = self.position_embed(correlation)
+            correlation = einops.rearrange(correlation, 'b h w d -> b (h w) d')
+            correlation = self.transport_proj(correlation)
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            slots, motion_mask = self.slot_attention(correlation+k, bs, weight, init_slot)
+        elif self.correlation == 'flow':
+            k_roll = torch.roll(k, shifts=1, dims=(1,))
+            k_roll[:, 0] = k[:, 0]
+            correlation = torch.einsum('btnc,btmc->btnm', k, k_roll)
+            correlation = einops.rearrange(correlation, 'b t n (h w) -> (b t n) h w', h=self.end_size[0])
+            correlation = self.patch_proj(correlation.unsqueeze(1))
+            correlation = einops.rearrange(correlation, 'b c h w -> b h w c')
+            correlation = self.position_embed(correlation, fusion='sum')
+            correlation = einops.rearrange(correlation, '(b n) h w c -> b n (h w c)', n=self.end_size[0]*self.end_size[1])
+            correlation = self.transport_proj(correlation)
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            slots, motion_mask = self.slot_attention(correlation+k, bs, weight, init_slot)
+        elif self.correlation == 'adapt':
+            k = einops.rearrange(k, 'b t (h w) c -> b c t h w', h=self.end_size[0])
+            k = k + self.adapter(k)
+            k = einops.rearrange(k, 'b c t h w -> (b t) (h w) c')
+            slots, motion_mask = self.slot_attention(k, bs, weight, init_slot)
+        else:
+            x = einops.rearrange(x, 'b t hw c -> (b t) hw c')
+            k = einops.rearrange(k, 'b t hw c -> (b t) hw c')
+            slots, motion_mask = self.slot_attention(k, bs, weight, init_slot) 
+        slots = einops.rearrange(slots, '(b t) s c -> b t s c', b=bs)
+        motion_mask = einops.rearrange(motion_mask, '(b t) s hw -> b t s hw', b=bs)
+        return slots, motion_mask
+
+
+if __name__ == "__main__":
+    model = SlotAttentionAutoEncoder(resolution=(192, 384), 
+                       num_slots=2, 
+                       num_o=3, 
+                       num_t=3,
+                       in_channels=3, 
+                       out_channels=3, 
+                       hid_dim=32,
+                       iters=5, 
+                       path_drop=0.1,
+                       attn_drop_t=0.4,
+                       num_frames=7)
+    model.to('cuda')
+    input = torch.randn(2, 7, 3, 192, 384)
+    model(input.to('cuda'))
\ No newline at end of file
diff --git a/prismatic/models/addons/slot_attention_v2.py b/prismatic/models/addons/slot_attention_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d81967a8cf04aa87834a4f6d7f6f4aa2747a367a
--- /dev/null
+++ b/prismatic/models/addons/slot_attention_v2.py
@@ -0,0 +1,385 @@
+import functools
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from scipy.optimize import linear_sum_assignment
+
+
+# Kernel Normalized Kernel
+class WNConv(nn.Conv2d):
+    def __init__(self, in_channels, out_channels, kernel_size, padding, bias=False, norm_type="softmax", use_normed_logits=False):
+        super(WNConv, self).__init__(in_channels, out_channels, kernel_size, padding=padding, bias=bias)
+        self.norm_type = norm_type
+
+    def forward(self, input):
+        o_c, i_c, k1, k2 = self.weight.shape
+        weight = torch.reshape(self.weight.data, (o_c, i_c, k1 * k2))
+        weight = weight / torch.linalg.norm(weight, dim=-1, keepdim=True) # norm logits to 1.
+
+        if 'linear' in self.norm_type:
+            weight = weight / torch.sum(weight, dim=-1, keepdim=True)
+        elif 'softmax' in self.norm_type:
+            weight = F.softmax(weight, dim=-1)
+
+        self.weight = nn.Parameter(torch.reshape(weight, (o_c, i_c, k1, k2)))
+
+        # we don't recommend ver lower than 1.7
+        if '1.7' in torch.__version__:
+            return self._conv_forward(input, self.weight)
+        else:
+            return self._conv_forward(input, self.weight, self.bias)
+        
+
+class SlotAttention(nn.Module):
+    """Slot Attention module."""
+
+    def __init__(self, num_slots, encoder_dims, iters=3, hidden_dim=128, eps=1e-8, background=True):
+        """Builds the Slot Attention module.
+        Args:
+            iters: Number of iterations.
+            num_slots: Number of slots.
+            encoder_dims: Dimensionality of slot feature vectors.
+            hidden_dim: Hidden layer size of MLP.
+            eps: Offset for attention coefficients before normalization.
+        """
+        super(SlotAttention, self).__init__()
+        
+        self.eps = eps
+        self.iters = iters
+        self.num_slots = num_slots
+        self.scale = encoder_dims ** -0.5
+        self.background = background
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.norm_input = nn.LayerNorm(encoder_dims)
+        self.norm_slots = nn.LayerNorm(encoder_dims)
+        self.norm_pre_ff = nn.LayerNorm(encoder_dims)
+        self.slots_embedding = nn.Embedding(num_slots, encoder_dims)
+        # Linear maps for the attention module.
+        self.project_q = nn.Linear(encoder_dims, hidden_dim)
+        self.project_k = nn.Linear(encoder_dims, hidden_dim)
+        self.project_v = nn.Linear(encoder_dims, encoder_dims)
+        # Slot update functions.
+        self.gru = nn.GRUCell(encoder_dims, encoder_dims)
+        self.mlp_1 = nn.Linear(encoder_dims, hidden_dim)
+        self.mlp_2 = nn.Linear(hidden_dim, encoder_dims)
+
+    def forward(self, inputs, bs=None, weight=None, init_slots=None, num_slots=None):
+        # inputs has shape [batch_size, num_inputs, inputs_size].
+        inputs = self.norm_input(inputs)  # Apply layer norm to the input.
+        k = self.project_k(inputs)  # Shape: [batch_size, num_inputs, slot_size].
+        v = self.project_v(inputs)  # Shape: [batch_size, num_inputs, slot_size].
+
+        # Initialize the slots. Shape: [batch_size, num_slots, slot_size].
+        b, n, d = inputs.shape
+        n_s = num_slots if num_slots is not None else self.num_slots
+
+        # learnable slots initializations
+        if init_slots == None:
+            init_slots = self.slots_embedding(torch.arange(0, n_s).expand(b, n_s).to(self.device))
+            if self.background:
+                init_slots = torch.cat([init_slots, torch.zeros_like(init_slots[:, -1:, :])], dim=1)
+            slots = init_slots
+        else:
+            slots = init_slots
+
+        # Slot update.
+        with torch.autocast(device_type='cuda', dtype=torch.float32):
+            # Multiple rounds of attention.
+            for t in range(self.iters):
+                slots_prev = slots
+                slots = self.norm_slots(slots)
+
+                # Attention.
+                q = self.project_q(slots)  # Shape: [batch_size, num_slots, slot_size].
+                dots = torch.einsum('bid,bjd->bij', q, k) * self.scale
+                attn = dots.softmax(dim=1) + self.eps
+                attn = attn / attn.sum(dim=-1, keepdim=True)  # weighted mean.
+
+                updates = torch.einsum('bjd,bij->bid', v, attn)
+
+                # `updates` has shape: [batch_size, num_slots, slot_size].
+                slots = self.gru(
+                    updates.reshape(-1, d),
+                    slots_prev.reshape(-1, d)
+                )
+                slots = slots.reshape(b, -1, d)
+                slots = slots + self.mlp_2(F.relu(self.mlp_1(self.norm_pre_ff(slots))))
+                # if t == self.iters-2:
+                #     slots = slots.detach() - init_slots.detach() + init_slots
+                
+        return slots.to(torch.bfloat16), dots.to(torch.bfloat16)
+
+def build_grid(resolution):
+    ranges = [np.linspace(0.0, 1.0, num=res) for res in resolution]
+    grid = np.meshgrid(*ranges, sparse=False, indexing="ij")
+    grid = np.stack(grid, axis=-1)
+    grid = np.reshape(grid, [resolution[0], resolution[1], -1])
+    grid = np.expand_dims(grid, axis=0)
+    grid = grid.astype(np.float32)
+    return torch.from_numpy(np.concatenate([grid, 1.0 - grid], axis=-1))
+
+
+"""Adds soft positional embedding with learnable projection."""
+class SoftPositionEmbed(nn.Module):
+    def __init__(self, hidden_size, resolution):
+        """Builds the soft position embedding layer.
+        Args:
+        hidden_size: Size of input feature dimension.
+        resolution: Tuple of integers specifying width and height of grid.
+        """
+        super().__init__()
+        self.embedding = nn.Linear(4, hidden_size, bias=True)
+        self.grid = build_grid(resolution)
+
+    def forward(self, inputs):
+        self.grid = self.grid.to(inputs.device)
+        with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
+            grid = self.embedding(self.grid)
+        return inputs + grid
+
+    def get_pos_emb(self, pos):
+        """ 
+        pos (*, 2)
+        """
+        return self.embedding(torch.cat([pos, 1.0 - pos], dim=-1))
+
+
+class Encoder(nn.Module):
+    def __init__(self, resolution=[16,16], hid_dim=2176):
+        super().__init__()
+
+        self.resolution = resolution
+        self.hid_dim = hid_dim
+        self.encoder_pos = SoftPositionEmbed(hid_dim, resolution)
+        # self.layer_norm = nn.LayerNorm([resolution[0] * resolution[1], hid_dim])
+        # self.mlp = nn.Linear(hid_dim, hid_dim)
+
+    def forward(self, x):
+        B, HW, D = x.shape
+        assert(HW == self.resolution[0]*self.resolution[1])
+        x = x.reshape(B, self.resolution[0], self.resolution[1], D) # [B, H, W ,D]
+        x = self.encoder_pos(x)
+        x = torch.flatten(x, 1, 2)
+        # x = self.layer_norm(x)
+        # x = self.mlp(x)
+        return x
+
+
+class Decoder(nn.Module):
+
+    def __init__(self, num_slots, init_dim, hid_dim, input_size, target_size):
+        super().__init__()
+        
+        self.num_slots = num_slots
+        self.init_dim = init_dim
+        self.hid_dim = hid_dim
+        self.target_size = target_size
+
+        self.resolution = (target_size, target_size)
+        self.dec_init_size = 1
+        # dec_init_resolution = (self.dec_init_size, self.dec_init_size)
+        # self.decoder_pos = SoftPositionEmbed(hid_dim, dec_init_resolution)
+        upsample_step = int(np.log2(target_size // self.dec_init_size)) + int((target_size % self.dec_init_size) == 0)
+        DEC_DEPTH = 7
+        count_layer = 0 
+
+        deconvs = nn.ModuleList()
+        for _ in range(upsample_step):
+            if count_layer == 0: 
+                deconvs.extend([
+                    nn.ConvTranspose2d(self.init_dim, self.hid_dim, 5, stride=(2, 2), padding=2, output_padding=1),
+                    nn.ReLU(),
+                ])
+            else: 
+                deconvs.extend([
+                    nn.ConvTranspose2d(self.hid_dim, self.hid_dim, 5, stride=(2, 2), padding=2, output_padding=1),
+                    nn.ReLU(),
+                ])
+            
+            count_layer += 1
+
+        for _ in range(DEC_DEPTH - upsample_step - 1):
+            
+            if count_layer == 0: 
+                deconvs.extend([nn.ConvTranspose2d(self.hid_dim, self.hid_dim, 5, stride=(1, 1), padding=2), nn.ReLU()])
+            else: 
+                deconvs.extend([nn.ConvTranspose2d(self.hid_dim, self.hid_dim, 5, stride=(1, 1), padding=2), nn.ReLU()])
+
+            count_layer += 1
+
+        deconvs.append(nn.ConvTranspose2d(self.hid_dim, self.init_dim+1, 3, stride=(1, 1), padding=1))
+        count_layer += 1
+
+        assert DEC_DEPTH == count_layer, "The number of layers of decoder differs from the configuration"
+        self.deconvs = nn.Sequential(*deconvs)
+
+    def forward(self, x):
+        # """Broadcast slot features to a 2D grid and collapse slot dimension.""".
+        # x: [batch_size, 16, 2176]
+        x = x.reshape(-1, x.shape[-1]).unsqueeze(1).unsqueeze(2)       # x: [batch_size*16, 1, 1, 2176]
+        # x = x.repeat((1, self.dec_init_size, self.dec_init_size, 1))   # x: [batch_size*16, 1, 1, 2176]
+        # x = self.decoder_pos(x)
+        x = x.permute(0, 3, 1, 2)                                      # x: [batch_size*16, 2176, 1, 1]
+        x = self.deconvs(x)
+        x = x[:, :, : self.resolution[0], : self.resolution[1]]
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+class PositionPredictor(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        assert cfg.WEAK_SUP.TYPE != ""
+
+        slot_dim = cfg.MODEL.SLOT.DIM
+        pos_num = 4 if cfg.WEAK_SUP.TYPE == 'bbox' else 2
+        self.layer_norm = nn.LayerNorm(slot_dim)
+        if cfg.POS_PRED.PP_SIZE == 'base':
+            self.mlp = nn.Sequential(
+                nn.Linear(slot_dim, slot_dim // 2), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim // 2, slot_dim // 4), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim // 4, pos_num)
+            )
+        elif cfg.POS_PRED.PP_SIZE == 'small':
+            self.mlp = nn.Sequential(
+                nn.Linear(slot_dim, slot_dim // 4), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim // 4, pos_num)
+            )
+        elif cfg.POS_PRED.PP_SIZE == 'big':
+            self.mlp = nn.Sequential(
+                nn.Linear(slot_dim, slot_dim), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim, slot_dim // 4), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim // 4, slot_dim // 8), 
+                nn.ReLU(), 
+                nn.Linear(slot_dim // 8, pos_num)
+            )
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        return self.sigmoid(self.mlp(self.layer_norm(x))) - 0.5
+
+
+class PositionEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        slot_dim = cfg.MODEL.SLOT.DIM
+        pos_num = 4 if cfg.WEAK_SUP.TYPE == 'bbox' else 2
+        self.encoder = nn.Sequential(
+            nn.Linear(pos_num, slot_dim // 4), 
+            nn.ReLU(), 
+            nn.Linear(slot_dim // 4, slot_dim // 2), 
+            nn.ReLU(), 
+            nn.Linear(slot_dim // 2, slot_dim)
+        )
+            
+    def forward(self, x):
+        return self.encoder(x)
+        
+
+"""Slot Attention-based auto-encoder for object discovery."""
+class SlotAttentionAutoEncoder(nn.Module):
+    def __init__(self, num_slots=16, res=16, encoder_dims=2176, iters=3, hidden_dim=724, eps=1e-8, background=True):
+        """Builds the Slot Attention-based auto-encoder."""
+        super().__init__()
+        self.obj_slots = num_slots
+        self.background = background
+        self.encoder_nn = Encoder(resolution=[res,res], hid_dim=encoder_dims)
+        self.decoder_cnn = Decoder(num_slots=num_slots, init_dim=encoder_dims, hid_dim=hidden_dim, input_size=res, target_size=32)
+
+        self.slot_attention = SlotAttention(num_slots=num_slots, encoder_dims=encoder_dims, 
+                                            iters=iters, hidden_dim=hidden_dim, eps=eps, 
+                                            background=background)
+        self.criterion = nn.MSELoss()
+
+    @property
+    def num_slots(self):
+        return self.obj_slots + int(self.background)
+
+    def encode(self, patch_features):
+        # `patch_features` has shape: [batch_size, feature_h*feature_w, feature_dim].
+        
+        # Convolutional encoder with position embedding.
+        x = self.encoder_nn(patch_features)  # CNN Backbone.
+        # `x` has shape: [B, height*width, hid_dim].
+
+        # Slot Attention module.
+        sa_outputs = self.slot_attention(x)
+        return sa_outputs
+    
+    def decode(self, slotted_features, reference=None, recon_loss_only=True):
+        # Interpolate the image tensor to the new size
+        reference = F.interpolate(reference, size=self.decoder_cnn.resolution, mode='bilinear', align_corners=False)
+        # print('slotted_features', slotted_features.shape)
+        # print('reference', reference.shape)
+        x = self.decoder_cnn(slotted_features)
+        # `x` has shape: [B*K, height, width, num_channels+1].
+
+        # Undo combination of slot and batch dimension; split alpha masks.
+        recons, masks = x.reshape(reference.shape[0], -1, x.shape[1], x.shape[2], x.shape[3]
+                        ).split([x.shape[3]-1, 1], dim=-1)
+        # `recons` has shape: [B, K, height, width, num_channels].
+        # `masks` has shape: [B, K, height, width, 1].
+
+        # Normalize alpha masks over slots.
+        masks = nn.Softmax(dim=1)(masks)
+        # print('recons', recons.shape)
+        # print('masks', masks.shape)
+
+        recon_combined = torch.sum(recons * masks, dim=1)  # Recombine image.
+        recon_combined = recon_combined.permute(0, 3, 1, 2)
+        # `recon_combined` has shape: [batch_size, num_channels, height, width].
+        # print('recon_combined', recon_combined.shape)
+        recon_loss = self.criterion(recon_combined, reference) 
+        if recon_loss_only:
+            return recon_loss, None
+        return recon_loss, recon_combined
+
+    def forward(self, image, pos=None, train=True):
+        # `image` has shape: [batch_size, num_channels, height, width].
+        
+        # Convolutional encoder with position embedding.
+        x = self.encoder_cnn(image)  # CNN Backbone.
+        # `x` has shape: [B, height*width, hid_dim].
+
+        # Slot Attention module.
+        sa_outputs = self.slot_attention(x, pos=pos, train=train)
+        slots = sa_outputs["slots"]
+        # `slots` has shape: [N, K, slot_dim].
+
+        x = self.decoder_cnn(slots)
+        # `x` has shape: [B*K, height, width, num_channels+1].
+
+        # Undo combination of slot and batch dimension; split alpha masks.
+        recons, masks = x.reshape(image.shape[0], -1, x.shape[1], x.shape[2], x.shape[3]
+                        ).split([3, 1], dim=-1)
+        # `recons` has shape: [B, K, height, width, num_channels].
+        # `masks` has shape: [B, K, height, width, 1].
+
+        # Normalize alpha masks over slots.
+        masks = nn.Softmax(dim=1)(masks)
+
+        recon_combined = torch.sum(recons * masks, dim=1)  # Recombine image.
+        recon_combined = recon_combined.permute(0, 3, 1, 2)
+        # `recon_combined` has shape: [batch_size, num_channels, height, width].
+
+        outputs = dict()
+        outputs['recon_combined'] = recon_combined
+        outputs['recons'] = recons
+        outputs['masks'] = masks
+        outputs['slots'] = slots
+        outputs['attn'] = sa_outputs["attn"]
+        outputs['pos_pred'] = sa_outputs["pos_pred"]
+        outputs['pos_gt_aranged'] = sa_outputs['pos_gt_aranged']
+        if not train: 
+            outputs['attns'] = sa_outputs['attns']
+            outputs['attns_origin'] = sa_outputs['attns_origin']
+            # `attns`: list of (B, N_heads, N_in, K) x T
+
+        return outputs
\ No newline at end of file
diff --git a/prismatic/models/addons/slot_attention_v3.py b/prismatic/models/addons/slot_attention_v3.py
new file mode 100644
index 0000000000000000000000000000000000000000..23e71851b4627e68f1fbab18b6bd0e9f4fd943a7
--- /dev/null
+++ b/prismatic/models/addons/slot_attention_v3.py
@@ -0,0 +1,773 @@
+import math
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from einops import rearrange
+from typing import Callable, Optional, List, Union, Tuple
+from torchvision import transforms
+from timm.models import create_model
+from einops import rearrange, repeat
+from timm.models.layers import DropPath
+from .multimodal_transformer import MultimodalTransformer
+
+class LearnableQueries(nn.Module):
+    def __init__(self, token_num, object_dim, init_const):
+        super().__init__()
+        self.tokens = nn.Parameter(
+            torch.randn(1, token_num, object_dim) * init_const
+        )
+    
+    def forward(self, x):
+        object_tokens = self.tokens.repeat(x.shape[0], 1, 1)  # (b, 16, d)
+        x = x + object_tokens
+        return x
+
+class CrossAttention(nn.Module):
+    """
+    CrossAttention module used in the Perceiver IO model.
+
+    Args:
+        query_dim (int): The dimension of the query input.
+        heads (int, optional): The number of attention heads. Defaults to 8.
+        dim_head (int, optional): The dimension of each attention head. Defaults to 64.
+        dropout (float, optional): The dropout probability. Defaults to 0.0.
+    """
+
+    def __init__(self, query_dim: int, heads: int = 8, dim_head: int = 64, dropout: float = 0.0):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = query_dim
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, query_dim)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor, context: torch.Tensor, 
+                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """
+        Forward pass of the CrossAttention module.
+
+        Args:
+            x (torch.Tensor): The query input tensor.
+            context (torch.Tensor): The context input tensor.
+            mask (torch.Tensor, optional): The attention mask tensor. Defaults to None.
+
+        Returns:
+            torch.Tensor: The output tensor.
+        """
+        h = self.heads
+        q = self.to_q(x)
+        k, v = self.to_kv(context).chunk(2, dim=-1)
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
+        sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
+
+        if mask is not None:
+            # fill in the masks with negative values
+            mask = rearrange(mask, "b ... -> b (...)")
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, "b j -> (b h) () j", h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        attn = sim.softmax(dim=-1)
+
+        # dropout
+        attn = self.dropout(attn)
+        out = einsum("b i j, b j d -> b i d", attn, v)
+        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
+        return self.to_out(out)
+
+class RandomConditioning(nn.Module):
+    """Random conditioning with potentially learnt mean and stddev."""
+
+    def __init__(
+        self,
+        object_dim: int,
+        n_slots: int,
+        learn_mean: bool = True,
+        learn_std: bool = True,
+        mean_init: Optional[Callable[[torch.Tensor], None]] = None,
+        logsigma_init: Optional[Callable[[torch.Tensor], None]] = None
+    ):
+        super().__init__()
+        self.n_slots = n_slots
+        self.object_dim = object_dim
+
+        if learn_mean:
+            self.slots_mu = nn.Parameter(torch.zeros(1, 1, object_dim))
+        else:
+            self.register_buffer("slots_mu", torch.zeros(1, 1, object_dim))
+
+        if learn_std:
+            self.slots_logsigma = nn.Parameter(torch.zeros(1, 1, object_dim))
+        else:
+            self.register_buffer("slots_logsigma", torch.zeros(1, 1, object_dim))
+
+        if mean_init is None:
+            mean_init = nn.init.xavier_uniform_
+        if logsigma_init is None:
+            logsigma_init = nn.init.xavier_uniform_
+
+        with torch.no_grad():
+            mean_init(self.slots_mu)
+            logsigma_init(self.slots_logsigma)
+
+    def forward(self, batch_size: int):
+        mu = self.slots_mu.expand(batch_size, self.n_slots, -1)
+        sigma = self.slots_logsigma.exp().expand(batch_size, self.n_slots, -1)
+        return mu + sigma * torch.randn_like(mu)
+
+
+class LearnableConditioning(nn.Module):
+    def __init__(
+        self,
+        object_dim: int,
+        n_slots: int,
+        init_const: float
+    ):
+        super().__init__()
+        self.n_slots = n_slots
+        self.object_dim = object_dim
+        self.init_const = init_const
+        self.tokens = nn.Parameter(
+            torch.randn(1, n_slots, object_dim) * init_const
+        )
+    
+    def forward(self, batch_size: int):
+        condition = self.tokens.expand(batch_size, -1, -1)
+        return condition
+
+
+def get_activation_fn(name: str, inplace: bool = True, leaky_relu_slope: Optional[float] = 0.1):
+    if callable(name):
+        return name
+
+    name = name.lower()
+    if name == "relu":
+        return nn.ReLU(inplace=inplace)
+    elif name == "leaky_relu":
+        if leaky_relu_slope is None:
+            raise ValueError("Slope of leaky ReLU was not defined")
+        return nn.LeakyReLU(leaky_relu_slope, inplace=inplace)
+    elif name == "tanh":
+        return nn.Tanh()
+    elif name == "sigmoid":
+        return nn.Sigmoid()
+    elif name == "identity":
+        return nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation function {name}")
+
+
+class Residual(nn.Module):
+    def __init__(self, module: nn.Module):
+        super().__init__()
+        self.module = module
+
+    def forward(self, inputs):
+        return inputs + self.module(inputs)
+
+
+def build_mlp(
+    input_dim: int,
+    output_dim: int,
+    features: List[int],
+    activation_fn: Optional[Union[str, Callable]] = "relu",
+    final_activation_fn: Optional[Union[str, Callable]] = None,
+    initial_layer_norm: bool = False,
+    residual: bool = False,
+) -> nn.Sequential:
+    layers = []
+    current_dim = input_dim
+    if initial_layer_norm:
+        layers.append(nn.LayerNorm(current_dim))
+
+    for n_features in features:
+        layers.append(nn.Linear(current_dim, n_features))
+        nn.init.xavier_uniform_(layers[-1].weight)
+        nn.init.zeros_(layers[-1].bias)
+        if activation_fn is not None:
+            layers.append(get_activation_fn(activation_fn))
+        current_dim = n_features
+
+    layers.append(nn.Linear(current_dim, output_dim))
+    nn.init.xavier_uniform_(layers[-1].weight)
+    nn.init.zeros_(layers[-1].bias)
+    if final_activation_fn is not None:
+        layers.append(get_activation_fn(final_activation_fn))
+
+    if residual:
+        return Residual(nn.Sequential(*layers))
+    return nn.Sequential(*layers)
+
+def build_mlp_v2(
+    input_dim: int,
+    output_dim: int,
+    features: List[int],
+    activation_fn: Optional[Union[str, Callable]] = "relu",
+    final_activation_fn: Optional[Union[str, Callable]] = None,
+    initial_layer_norm: bool = False,
+    residual: bool = False,
+) -> nn.Sequential:
+    layers = []
+    current_dim = input_dim
+    if initial_layer_norm:
+        layers.append(nn.LayerNorm(current_dim))
+
+    for n_features in features:
+        layers.append(nn.Linear(current_dim, n_features))
+        # nn.init.xavier_uniform_(layers[-1].weight)
+        # nn.init.zeros_(layers[-1].bias)
+        if activation_fn is not None:
+            layers.append(get_activation_fn(activation_fn))
+        current_dim = n_features
+
+    feature_layers = nn.Sequential(*layers)
+    if residual:
+        feature_layers = Residual(feature_layers)
+
+    all_layers = [feature_layers]
+    all_layers.append(nn.Linear(current_dim, output_dim))
+    # nn.init.xavier_uniform_(all_layers[-1].weight)
+    # nn.init.zeros_(all_layers[-1].bias)
+    if final_activation_fn is not None:
+        all_layers.append(get_activation_fn(final_activation_fn))
+    return nn.Sequential(*all_layers)
+
+def build_two_layer_mlp(
+    input_dim, output_dim, hidden_dim, initial_layer_norm: bool = False, residual: bool = False
+):
+    """Build a two layer MLP, with optional initial layer norm.
+
+    Separate class as this type of construction is used very often for slot attention and
+    transformers.
+    """
+    return build_mlp(
+        input_dim, output_dim, [hidden_dim], initial_layer_norm=initial_layer_norm, residual=residual
+    )
+
+
+class SlotAttention(nn.Module):
+    """Implementation of SlotAttention.
+
+    Based on the slot attention implementation of Phil Wang available at:
+    https://github.com/lucidrains/slot-attention
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        feature_dim: int,
+        kvq_dim: Optional[int] = None,
+        n_heads: int = 1,
+        iters: int = 3,
+        eps: float = 1e-8,
+        ff_mlp: Optional[nn.Module] = None,
+        use_projection_bias: bool = False,
+        use_implicit_differentiation: bool = False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.n_heads = n_heads
+        self.iters = iters
+        self.eps = eps
+        self.use_implicit_differentiation = use_implicit_differentiation
+
+        if kvq_dim is None:
+            self.kvq_dim = dim
+        else:
+            self.kvq_dim = kvq_dim
+
+        if self.kvq_dim % self.n_heads != 0:
+            raise ValueError("Key, value, query dimensions must be divisible by number of heads.")
+        self.dims_per_head = self.kvq_dim // self.n_heads
+        self.scale = self.dims_per_head**-0.5
+
+        self.to_q = nn.Linear(dim, self.kvq_dim, bias=use_projection_bias)
+        self.to_k = nn.Linear(feature_dim, self.kvq_dim, bias=use_projection_bias)
+        self.to_v = nn.Linear(feature_dim, self.kvq_dim, bias=use_projection_bias)
+
+        self.gru = nn.GRUCell(self.kvq_dim, dim)
+
+        self.norm_input = nn.LayerNorm(feature_dim)
+        self.norm_slots = nn.LayerNorm(dim)
+        self.ff_mlp = ff_mlp
+
+    def step(self, slots, k, v, masks=None):
+        bs, n_slots, _ = slots.shape
+        slots_prev = slots
+
+        slots = self.norm_slots(slots)
+        q = self.to_q(slots).view(bs, n_slots, self.n_heads, self.dims_per_head)
+
+        dots = torch.einsum("bihd,bjhd->bihj", q, k) * self.scale
+        if masks is not None:
+            # Masked slots should not take part in the competition for features. By replacing their
+            # dot-products with -inf, their attention values will become zero within the softmax.
+            dots.masked_fill_(masks.to(torch.bool).view(bs, n_slots, 1, 1), float("-inf"))
+
+        attn = dots.flatten(1, 2).softmax(dim=1)  # Take softmax over slots and heads
+        attn = attn.view(bs, n_slots, self.n_heads, -1)
+        attn_before_reweighting = attn
+        attn = attn + self.eps
+        attn = attn / attn.sum(dim=-1, keepdim=True)
+
+        updates = torch.einsum("bjhd,bihj->bihd", v, attn)
+
+        slots = self.gru(updates.reshape(-1, self.kvq_dim), slots_prev.reshape(-1, self.dim))
+
+        slots = slots.reshape(bs, -1, self.dim)
+
+        if self.ff_mlp:
+            slots = self.ff_mlp(slots)
+
+        return slots, attn_before_reweighting.mean(dim=2)
+
+    def iterate(self, slots, k, v, masks=None):
+        # Slot update.
+        with torch.autocast(device_type='cuda', dtype=torch.float32):
+            for _ in range(self.iters):
+                slots, attn = self.step(slots, k, v, masks)
+        return slots.to(torch.bfloat16), attn.to(torch.bfloat16)
+
+    def forward(
+        self, inputs: torch.Tensor, conditioning: torch.Tensor, masks: Optional[torch.Tensor] = None
+    ):
+        b, n, d = inputs.shape
+        slots = conditioning
+
+        inputs = self.norm_input(inputs)
+        k = self.to_k(inputs).view(b, n, self.n_heads, self.dims_per_head)
+        v = self.to_v(inputs).view(b, n, self.n_heads, self.dims_per_head)
+
+        if self.use_implicit_differentiation:
+            slots, attn = self.iterate(slots, k, v, masks)
+            slots, attn = self.step(slots.detach(), k, v, masks)
+        else:
+            slots, attn = self.iterate(slots, k, v, masks)
+
+        return slots, attn
+
+
+class SlotAttentionGrouping(nn.Module):
+    """Implementation of SlotAttention for perceptual grouping.
+
+    Args:
+        feature_dim: Dimensionality of features to slot attention (after positional encoding).
+        object_dim: Dimensionality of slots.
+        kvq_dim: Dimensionality after projecting to keys, values, and queries. If `None`,
+            `object_dim` is used.
+        n_heads: Number of heads slot attention uses.
+        iters: Number of slot attention iterations.
+        eps: Epsilon in slot attention.
+        ff_mlp: Optional module applied slot-wise after GRU update.
+        positional_embedding: Optional module applied to the features before slot attention, adding
+            positional encoding.
+        use_projection_bias: Whether to use biases in key, value, query projections.
+        use_implicit_differentiation: Whether to use implicit differentiation trick. If true,
+            performs one more iteration of slot attention that is used for the gradient step after
+            `iters` iterations of slot attention without gradients. Faster and more memory efficient
+            than the standard version, but can not backpropagate gradients to the conditioning input.
+        input_dim: Dimensionality of features before positional encoding is applied. Specifying this
+            is optional but can be convenient to structure configurations.
+    """
+
+    def __init__(
+        self,
+        feature_dim: int,
+        object_dim: int,
+        kvq_dim: Optional[int] = None,
+        n_heads: int = 1,
+        iters: int = 3,
+        eps: float = 1e-8,
+        ff_mlp: Optional[nn.Module] = None,
+        use_projection_bias: bool = False,
+        use_implicit_differentiation: bool = False,
+        use_empty_slot_for_masked_slots: bool = False,
+        slot_mask_path: Optional[str] = None,
+    ):
+        super().__init__()
+
+        self._object_dim = object_dim
+        self.slot_attention = SlotAttention(
+            dim=object_dim,
+            feature_dim=object_dim,
+            kvq_dim=kvq_dim,
+            n_heads=n_heads,
+            iters=iters,
+            eps=eps,
+            ff_mlp=ff_mlp,
+            use_projection_bias=use_projection_bias,
+            use_implicit_differentiation=use_implicit_differentiation,
+        )
+
+        self.positional_embedding = build_two_layer_mlp(input_dim=feature_dim,
+                                                        output_dim=object_dim,
+                                                        hidden_dim=feature_dim,
+                                                        initial_layer_norm=True)
+
+        if use_empty_slot_for_masked_slots:
+            if slot_mask_path is None:
+                raise ValueError("Need `slot_mask_path` for `use_empty_slot_for_masked_slots`")
+            self.empty_slot = nn.Parameter(torch.randn(object_dim) * object_dim**-0.5)
+        else:
+            self.empty_slot = None
+
+    def forward(
+        self,
+        extracted_features,
+        conditioning,
+        slot_masks=None,
+    ):
+        features = self.positional_embedding(extracted_features)
+        slots, attn = self.slot_attention(features, conditioning, slot_masks)
+        if slot_masks is not None and self.empty_slot is not None:
+            slots[slot_masks] = self.empty_slot.to(dtype=slots.dtype)
+
+        return slots, attn
+
+class PatchDecoderVideo(nn.Module):
+    def __init__(
+        self,
+        object_dim: int,
+        output_dim: int,
+        num_patches_per_frame: int,
+        num_frames: int,
+        decoder_input_dim: Optional[int] = None,
+        resize: int = 256
+    ):
+        nn.Module.__init__(self)
+        self.output_dim = output_dim
+        self.num_patches_per_frame = num_patches_per_frame
+        self.num_frames = num_frames
+        self.resize = resize
+
+        if decoder_input_dim is not None:
+            self.inp_transform = nn.Linear(object_dim, decoder_input_dim, bias=True)
+            nn.init.xavier_uniform_(self.inp_transform.weight)
+            nn.init.zeros_(self.inp_transform.bias)
+        else:
+            self.inp_transform = None
+            decoder_input_dim = object_dim
+
+        self.decoder = build_mlp_v2(input_dim=decoder_input_dim, output_dim=3+1, features=[512, 256, 128],
+                                    activation_fn='relu', initial_layer_norm=True, residual=False)
+        self.pos_embed = nn.Parameter(torch.randn(self.num_frames, self.num_patches_per_frame, decoder_input_dim) * 0.02)
+
+    def forward(self, object_features: torch.Tensor, nh, nw):
+        assert object_features.dim() >= 3   # Image or video data.  (b, s, d)
+
+        initial_shape = object_features.shape[:-1]              # (b, s)
+        object_features = object_features.flatten(0, -2)        # (b*s, d)
+        
+        if self.inp_transform is not None:
+            object_features = self.inp_transform(object_features)
+
+        # duplicate the slot representation into each patch, (b*s, t*n, d)
+        object_features = object_features.unsqueeze(1).expand(-1, self.num_frames*nw*nh, -1)
+
+        # Simple learned additive embedding as in ViT
+        N = self.num_patches_per_frame
+        dim = self.pos_embed.shape[-1]
+        patch_pos_embed = nn.functional.interpolate(
+            self.pos_embed.reshape(self.num_frames, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
+            size=(nh, nw),
+            mode='bicubic',
+            align_corners=False
+        )
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).reshape(1, -1, dim)
+        object_features = object_features + patch_pos_embed
+        # print('decoder before', object_features.shape)
+        # print('decoder before', torch.min(object_features), torch.max(object_features))
+        output = self.decoder(object_features)              # (b*s, t*n, d+1)
+        output = output.unflatten(0, initial_shape)         # (b, s, t*n, d+1)
+
+        # Split out alpha channel and normalize over slots.
+        decoded_patches, alpha = output.split([self.output_dim, 1], dim=-1)     # (b, s, t*n, d), (b, s, t*n, 1)
+        decoded_patches = decoded_patches
+        alpha = alpha.softmax(dim=-3)       # (b, s, t*n, 1)
+        # print('decoded_patches', decoded_patches.shape)
+        # print('decoded_patches', torch.min(decoded_patches), torch.max(decoded_patches))
+        # print('alpha', torch.min(alpha), torch.max(alpha))
+
+        reconstruction = torch.sum(decoded_patches * alpha, dim=-3)     # (b, t*n, d)
+        reconstruction = rearrange(reconstruction, "b (t nh nw) c -> (b t) c nh nw", t=self.num_frames, nh=nh, nw=nw)  # (b*t, s n)
+        masks = alpha.squeeze(-1)           # (b, s, t*n)
+
+        masks = rearrange(masks, "b s (t n) -> (b t) s n", t=self.num_frames)       # (b*t, s n)
+        masks = masks.reshape(masks.shape[0], masks.shape[1], nh, nw)
+
+        masks_as_image = masks
+
+        # masks_as_image = resize_patches_to_image_non_square(
+        #     masks,
+        #     size=(nh, nw),
+        #     resize_mode="bilinear"
+        # )
+
+        return reconstruction, masks, masks_as_image
+
+def resize_patches_to_image_non_square(patches, size, resize_mode):
+    H, W = size
+    n_channels = patches.shape[-2]
+    image = torch.nn.functional.interpolate(
+        patches.view(-1, n_channels, H, W),
+        scale_factor=(16.0, 16.0),
+        mode=resize_mode,
+    )
+    return image.view(*patches.shape[:-1], image.shape[-2], image.shape[-1])
+
+# class GroupingVideoMAE(nn.Module):
+#     """
+#     Spatiotemporal Grouping for non-square multi-resolution input video.
+#     """
+#     def __init__(
+#         self,
+#         checkpoint_path,
+#         object_dim=128,
+#         n_slots=24,
+#         feat_dim=768,
+#         num_patches=256,
+#         num_frames=4,
+#         img_size=224
+#     ):
+#         super().__init__()
+#         self.num_patches = num_patches
+#         self.num_frames = num_frames
+#         self.img_size = img_size
+
+#         # conditioning
+#         self.conditioning = RandomConditioning(object_dim=object_dim, n_slots=n_slots)
+
+#         # feature extractor
+#         self.model = create_model(
+#             "videomae_vit_base_patch16_224",
+#             pretrained=False,
+#             num_classes=174,
+#             all_frames=16 * 1,
+#             drop_rate=0.0,
+#             drop_path_rate=0.1,
+#             attn_drop_rate=0.0,
+#             drop_block_rate=None,
+#             use_mean_pooling=False,
+#             init_scale=0.001,
+#         )
+
+#         ckpt = torch.load(checkpoint_path,
+#                           map_location='cpu')
+#         new_ckpt = {}
+#         for k, v in ckpt['model'].items():
+#             if 'encoder.' in k:
+#                 new_ckpt[k.replace('encoder.', '')] = v
+#         self.model.load_state_dict(new_ckpt, strict=False)
+
+#         self.model.requires_grad_(False)
+#         self.model.eval()
+
+#         # perceptual grouping
+#         ff_mlp = build_two_layer_mlp(input_dim=object_dim, output_dim=object_dim, hidden_dim=object_dim*4,
+#                                      initial_layer_norm=True, residual=True)
+#         self.grouping = SlotAttentionGrouping(feature_dim=feat_dim,
+#                                               object_dim=object_dim,
+#                                               use_projection_bias=False,
+#                                               ff_mlp=ff_mlp)
+
+#         # object decoder
+#         dec_mlp = build_mlp(input_dim=object_dim, output_dim=feat_dim+1, features=[1024, 1024, 1024],
+#                             activation_fn=None)
+#         self.decoder = PatchDecoderVideo(object_dim=object_dim,
+#                                                  output_dim=feat_dim,
+#                                                  num_patches_per_frame=num_patches,
+#                                                  num_frames=num_frames,
+#                                                  decoder=dec_mlp,
+#                                                  decoder_input_dim=256,
+#                                                  resize=img_size)
+
+#     def forward(self, images):
+#         _, _, H, W, _ = images.shape            # (b, t, h, w, c)
+#         assert self.num_frames == images.shape[1]
+
+#         conditioning = self.conditioning(images.shape[0])     # (b, s, d)
+
+#         images = rearrange(images, 'b t h w c -> b c t h w')
+#         self.model.eval()
+#         features = self.model(images)  # (b, t*n, d)
+
+#         slots, attn = self.grouping(features, conditioning)         # (b, s, d), (b, s, t*n)
+
+#         reconstruction, masks, masks_as_image = self.decoder(slots, H, W) # H//16, W//16)
+#         # (b, t*n, d),  (b, s, t*n),  (b*t, s, img_size, img_size)
+
+#         masks_as_image = rearrange(masks_as_image, '(n t) s h w -> n t s h w', t=self.num_frames)
+
+#         feat_recon_loss = F.mse_loss(reconstruction, features)
+
+#         return feat_recon_loss, masks_as_image
+
+#     def inference(self, images):
+#         _, _, H, W, _ = images.shape
+#         assert self.num_frames == images.shape[1]           # (b, t, h, w, c)
+#         conditioning = self.conditioning(images.shape[0])     # (b, s, d)
+#         images = rearrange(images, 'b t h w c -> b c t h w')
+#         self.model.eval()
+#         features = self.model(images)  # (b, t*n ,d)
+#         slots, attn = self.grouping(features, conditioning)         # (b, s, d), (b, s, t*n)
+#         reconstruction, masks, masks_as_image = self.decoder(slots, H//16, W//16)
+#         # (b, t*n, d),  (b*t, s, n),  (b, t, s, img_size, img_size)
+#         masks = rearrange(masks, '(n t) s l -> n t s l', t=self.num_frames)
+#         masks_as_image = rearrange(masks_as_image, '(n t) s h w -> n t s h w', t=self.num_frames)
+
+#         return masks, masks_as_image
+
+def denormalize(tensor, mean=[0.484375, 0.455078125, 0.40625], std=[0.228515625, 0.2236328125, 0.224609375]):
+    """
+    Denormalizes a torch tensor using the given mean and std.
+    
+    Args:
+        tensor (torch.Tensor): The normalized tensor with shape (C, H, W) or (N, C, H, W).
+        mean (list): The mean values for each channel (list of floats).
+        std (list): The standard deviation values for each channel (list of floats).
+    
+    Returns:
+        torch.Tensor: The denormalized tensor with the same shape as input.
+    """
+    mean = torch.tensor(mean, device=tensor.device).view(1, -1, 1, 1)  # Reshape to match tensor shape
+    std = torch.tensor(std, device=tensor.device).view(1, -1, 1, 1)
+    
+    return tensor * std + mean
+
+INIT_CONST = 0.02
+class ObjectCentricDynamics(nn.Module):
+    def __init__(self, n_slots=24, feat_dim=2176, object_dim=2176, object_decode_dim=256, num_patches=256, num_frames=1, img_size=224, transformer_dim=4096,
+                 conditioning='learnable'):
+        super().__init__()
+        # conditioning
+        if conditioning == 'learnable':
+            self.conditioning = LearnableConditioning(object_dim=object_dim, n_slots=n_slots, init_const=INIT_CONST)
+        else:
+            self.conditioning = RandomConditioning(object_dim=object_dim, n_slots=n_slots)
+        # perceptual grouping
+        ff_mlp = build_two_layer_mlp(input_dim=object_dim, output_dim=object_dim, hidden_dim=object_dim*4,
+                                     initial_layer_norm=True, residual=True)
+        self.grouping = SlotAttentionGrouping(feature_dim=feat_dim,
+                                              object_dim=object_dim,
+                                              use_projection_bias=False,
+                                              ff_mlp=ff_mlp)
+
+        # # object decoder
+        # self.decoder = PatchDecoderVideo(object_dim=object_dim,
+        #                                          output_dim=3,
+        #                                          num_patches_per_frame=num_patches,
+        #                                          num_frames=num_frames,
+        #                                          decoder_input_dim=object_decode_dim,
+        #                                          resize=img_size)
+
+        token_num = n_slots
+        self.token_num = token_num
+        
+        # self.tokens = LearnableQueries(
+        #     token_num, 
+        #     object_dim, 
+        #     INIT_CONST
+        # ) 
+        # self.cross_attention = CrossAttention(
+        #     object_dim,
+        #     heads=16,
+        #     dim_head=object_dim,
+        #     dropout=0.0,
+        # )
+        #
+        # self.transformer_encoder = MultimodalTransformer(
+        #     d_model=transformer_dim, 
+        #     nhead=8, 
+        #     num_encoder_layers=3, 
+        #     dim_feedforward=transformer_dim,
+        # )
+        # self.action_latent = build_mlp(input_dim=object_dim, output_dim=feat_dim, features=[1024])
+
+    def forward(self, input_embeddings, patch_features, cur_pixel_values=None, nxt_pixel_values=None):
+        b, s, d = patch_features.shape
+        # Slot Encoding
+        conditioning = self.conditioning(patch_features.shape[0])     # (b, s, d)
+        # print('conditioning', conditioning.shape)
+        slotted_features, attn = self.grouping(patch_features, conditioning)         # (b, s, d), (b, s, t*n)
+        # print('slotted_features', slotted_features.shape)
+
+        # if self.training:
+        #     if s == 256:
+        #         reconstruction, masks, masks_as_image = self.decoder(slotted_features, 224, 224) # the numbers correspond to size for expanding slots
+        #         # (b, t*n, d),  (b, s, t*n),  (b*t, s, img_size, img_size)
+        #     else:
+        #         reconstruction, masks, masks_as_image = self.decoder(slotted_features, 16, 32)
+        #         # (b, t*n, d),  (b, s, t*n),  (b*t, s, img_size, img_size)
+        #     # print('reconstruction', reconstruction.shape)
+        #     # masks_as_image = rearrange(masks_as_image, '(n t) s h w -> n t s h w', t=1)
+        #     # print('reconstruction', torch.min(reconstruction), torch.max(reconstruction))
+        #     # print('masks', torch.min(masks), torch.max(masks))
+        #     cur_pixel_values = denormalize(cur_pixel_values[:,:3,:,:])
+        #     # print('cur_pixel_values', torch.min(cur_pixel_values[:,:3,:,:]), torch.max(cur_pixel_values[:,:3,:,:]))
+        #     cur_recon_loss = F.mse_loss(reconstruction, cur_pixel_values[:,:3,:,:])*10
+        #     # print('recon_loss', cur_recon_loss)
+        #     # 1/0
+        # else:
+        #     cur_recon_loss = 
+
+        # ###########################################################################
+        # # count at __embeddings
+        # import os
+        # import pickle
+        # embedding_dir = '__embeddings_dino_slots_revised'
+        # embeddings = os.listdir(embedding_dir)
+        # data = {
+        #     'reconstruction': reconstruction,
+        #     'masks': masks,
+        #     'cur_pixel_values': cur_pixel_values
+        # }
+        # # save at __embeddings
+        # # Open a file and use dump() 
+        # with open(os.path.join(embedding_dir, 'embed_' + str(len(embeddings)//2).zfill(5) + '.pkl'), 'wb') as file:
+        #     # A new file will be created 
+        #     pickle.dump(data, file) 
+        # ###########################################################################
+
+        # combine slots with learnable tokens, also cross attention them
+        # patch_features = patch_features                             # (b, t*n, d)
+        # Replicating tokens for each item in the batch and computing cross-attention
+        # slotted_features = self.tokens(slotted_features)
+        # slotted_features = self.cross_attention(slotted_features, patch_features)  # (b, 16, d)
+
+        # aligned_slots, input_embeddings = self.transformer_encoder(slot_features=slotted_features, 
+        #                                                            text_features=input_embeddings)
+
+        # nxt_recon_loss = 0
+        # if next_patch_features is not None:
+        #     if s == 256:
+        #         reconstruction, _, _ = self.decoder(aligned_slots, 16, 16)
+        #         # (b, t*n, d),  (b, s, t*n),  (b*t, s, img_size, img_size)
+        #     else:
+        #         reconstruction, _, _ = self.decoder(aligned_slots, 16, 32)
+        #     nxt_recon_loss = F.mse_loss(reconstruction, next_patch_features)
+
+        #     aligned_slots = torch.concat([slotted_features, aligned_slots], dim=-1)
+        #     aligned_slots = self.action_latent(slotted_features)
+        # else:
+        #     nxt_recon_loss = cur_recon_loss
+            
+        #     aligned_slots = torch.concat([slotted_features, aligned_slots], dim=-1)
+        #     aligned_slots = self.action_latent(slotted_features)
+
+        return slotted_features, input_embeddings
+
+
+
+
+
+
+
+
+
+
diff --git a/prismatic/models/addons/vision_transformer.py b/prismatic/models/addons/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..874289cbfb190a6b930fc7e9b76ce69dbd834238
--- /dev/null
+++ b/prismatic/models/addons/vision_transformer.py
@@ -0,0 +1,461 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# 
+# 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.
+"""
+Mostly copy-paste from timm library.
+https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+"""
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, attn, k
+
+
+class Block(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, return_attention=False):
+        y, attn, k = self.attn(self.norm1(x))
+        # if return_attention:
+        #     return attn
+        x = x + self.drop_path(y)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        if return_attention:
+            return x, attn, k
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        num_patches = (img_size // patch_size) * (img_size // patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        x = self.proj(x)
+        pw, ph = x.shape[-2:]
+        x = x.flatten(2).transpose(1, 2)
+        return x, pw, ph
+
+
+class VisionTransformer(nn.Module):
+    """ Vision Transformer """
+    def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0.1, norm_layer=nn.LayerNorm, **kwargs):
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((8, 8))
+
+        # Classifier head
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        class_pos_embed = self.pos_embed[:, 0]
+        patch_pos_embed = self.pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_embed.patch_size
+        h0 = h // self.patch_embed.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        w0, h0 = w0 + 0.1, h0 + 0.1
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
+            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
+            mode='bicubic',
+        )
+        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
+
+    def prepare_tokens(self, x):
+        B, nc, h, w = x.shape
+        x, ph, pw = self.patch_embed(x)  # patch linear embedding
+
+        # add the [CLS] token to the embed patch tokens
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        # add positional encoding to each token
+        x = x + self.interpolate_pos_encoding(x, h, w)
+
+        return self.pos_drop(x), ph, pw
+
+    def forward(self, x):
+        x, h, w = self.prepare_tokens(x)
+        for i, blk in enumerate(self.blocks):
+            if i < len(self.blocks) - 1:
+                x = blk(x)
+            else:
+                # return attention of the last block
+                x, attn, k = blk(x, return_attention=True)
+        cls_token = x[:, 0]
+        x = x[:, 1:].view(x.shape[0], h, w, x.shape[-1])
+        x = x.permute(0, 3, 1, 2)
+        k = k.permute(0, 2, 1, 3).contiguous()
+        k = k[:, 1:].view(k.shape[0], h, w, k.shape[-1]*k.shape[-2])
+        k = k.permute(0, 3, 1, 2)
+        # x = self.avgpool(x)
+        return x, cls_token, attn, k
+
+    def get_last_selfattention(self, x):
+        x, h, w = self.prepare_tokens(x)
+        for i, blk in enumerate(self.blocks):
+            if i < len(self.blocks) - 1:
+                x = blk(x)
+            else:
+                # return attention of the last block
+                return blk(x, return_attention=True)
+
+    def get_intermediate_layers(self, x, n=1):
+        x, h, w = self.prepare_tokens(x)
+        # we return the output tokens from the `n` last blocks
+        output = []
+        for i, blk in enumerate(self.blocks):
+            if i == len(self.blocks) - 1:
+                x, _, k = blk(x, return_attention=True)
+            else:
+                x = blk(x)
+            if len(self.blocks) - i <= n:
+                output.append(self.norm(x))
+                # output.append(x)
+        cls_token = x[:, 0]
+        k = k.permute(0, 2, 1, 3).contiguous()
+        k = k[:, 1:].view(k.shape[0], h, w, k.shape[-1]*k.shape[-2])
+        k = k.permute(0, 3, 1, 2)        
+        return output, cls_token, k
+
+
+class VisionTransformerNoCls(nn.Module):
+    """ Vision Transformer """
+    def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0.1, norm_layer=nn.LayerNorm, **kwargs):
+        super().__init__()
+        self.num_features = self.embed_dim = embed_dim
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        # Classifier head
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.pos_embed, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        npatch = x.shape[1]
+        N = self.pos_embed.shape[1]
+        if npatch == N and w == h:
+            return self.pos_embed
+        patch_pos_embed = self.pos_embed
+        dim = x.shape[-1]
+        w0 = w // self.patch_embed.patch_size
+        h0 = h // self.patch_embed.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        w0, h0 = w0 + 0.1, h0 + 0.1
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
+            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
+            mode='bicubic',
+        )
+        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def prepare_tokens(self, x):
+        B, nc, h, w = x.shape
+        x, ph, pw = self.patch_embed(x)  # patch linear embedding
+
+        # add positional encoding to each token
+        x = x + self.interpolate_pos_encoding(x, h, w)
+
+        return self.pos_drop(x), ph, pw
+
+    def forward(self, x):
+        x, h, w = self.prepare_tokens(x)
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)
+        x = x.view(x.shape[0], h, w, x.shape[-1])
+        x = x.permute(0, 3, 1, 2)
+        avg = self.avgpool(x).flatten(1)
+        return x, avg
+
+    def get_last_selfattention(self, x):
+        x, h, w = self.prepare_tokens(x)
+        for i, blk in enumerate(self.blocks):
+            if i < len(self.blocks) - 1:
+                x = blk(x)
+            else:
+                # return attention of the last block
+                return blk(x, return_attention=True)
+
+    def get_intermediate_layers(self, x, n=1):
+        x = self.prepare_tokens(x)
+        # we return the output tokens from the `n` last blocks
+        output = []
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if len(self.blocks) - i <= n:
+                output.append(self.norm(x))
+        return output
+
+
+def vit_tiny(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
+        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    return model
+
+
+def vit_small(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
+        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    return model
+
+
+def vit_base(patch_size=16, **kwargs):
+    model = VisionTransformer(
+        patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
+        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    return model
+
+
+def vit_small_no_cls(patch_size=16, **kwargs):
+    model = VisionTransformerNoCls(
+        patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
+        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    return model
+
+
+class DINOHead(nn.Module):
+    def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048, bottleneck_dim=256):
+        super().__init__()
+        nlayers = max(nlayers, 1)
+        if nlayers == 1:
+            self.mlp = nn.Linear(in_dim, bottleneck_dim)
+        else:
+            layers = [nn.Linear(in_dim, hidden_dim)]
+            if use_bn:
+                layers.append(nn.BatchNorm1d(hidden_dim))
+            layers.append(nn.GELU())
+            for _ in range(nlayers - 2):
+                layers.append(nn.Linear(hidden_dim, hidden_dim))
+                if use_bn:
+                    layers.append(nn.BatchNorm1d(hidden_dim))
+                layers.append(nn.GELU())
+            layers.append(nn.Linear(hidden_dim, bottleneck_dim))
+            self.mlp = nn.Sequential(*layers)
+        self.apply(self._init_weights)
+        self.last_layer = nn.utils.weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
+        self.last_layer.weight_g.data.fill_(1)
+        if norm_last_layer:
+            self.last_layer.weight_g.requires_grad = False
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.mlp(x)
+        x = nn.functional.normalize(x, dim=-1, p=2)
+        x = self.last_layer(x)
+        return x
\ No newline at end of file
diff --git a/prismatic/models/backbones/__init__.py b/prismatic/models/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/models/backbones/llm/__init__.py b/prismatic/models/backbones/llm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cdb19953b6529cb4015544c9d0cf8c43a22c2e9
--- /dev/null
+++ b/prismatic/models/backbones/llm/__init__.py
@@ -0,0 +1,4 @@
+from .base_llm import LLMBackbone
+from .llama2 import LLaMa2LLMBackbone
+from .mistral import MistralLLMBackbone
+from .phi import PhiLLMBackbone
diff --git a/prismatic/models/backbones/llm/base_llm.py b/prismatic/models/backbones/llm/base_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d5a91913dc851b16684a4b4729f031ee48fce50
--- /dev/null
+++ b/prismatic/models/backbones/llm/base_llm.py
@@ -0,0 +1,223 @@
+"""
+base_llm.py
+
+Abstract class definition of a large (autoregressive) language model backbone (LLM), with full annotations of class
+methods, utility functions, and initialization logic.
+
+We also define the generic HFLLMBackbone class here, providing a default interface for loading any HF
+AutoModelForCausalLM (e.g., LLamaForCausalLM). In general, we make the assumption that any given LLM backbone implements
+the AutoModelForCausalLM API (though we may add Seq2Seq models in the future).
+
+We make this assumption to keep the LLM handling in this codebase relatively lightweight, and to inherit all the nice HF
+utilities around different types of decoding/generation strategies.
+"""
+
+import warnings
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import Callable, List, Optional, Sequence, Type
+
+import torch
+import torch.nn as nn
+from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
+from transformers import AutoConfig, AutoTokenizer, PreTrainedModel, PreTrainedTokenizerBase
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.overwatch import initialize_overwatch
+
+# Suppress HF Deprecation Warnings
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# === Abstract Base Class for arbitrary HF LLM Backbones ===
+class LLMBackbone(nn.Module, ABC):
+    def __init__(self, llm_backbone_id: str) -> None:
+        super().__init__()
+        self.identifier = llm_backbone_id
+
+        # Instance attributes for an LLM Backbone
+        self.llm: PreTrainedModel = None
+        self.tokenizer: PreTrainedTokenizerBase = None
+
+    def get_tokenizer(self) -> PreTrainedTokenizerBase:
+        return self.tokenizer
+
+    @abstractmethod
+    def get_fsdp_wrapping_policy(self) -> Callable: ...
+
+    @abstractmethod
+    def enable_gradient_checkpointing(self) -> None: ...
+
+    @abstractmethod
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> CausalLMOutputWithPast:
+        """Run a forward pass through the LLM given targets (labels), returning the scalar Cross-Entropy Loss"""
+        raise NotImplementedError
+
+    @abstractmethod
+    def embed_input_ids(self, input_ids: torch.LongTensor) -> torch.Tensor: ...
+
+    @property
+    @abstractmethod
+    def prompt_builder_fn(self) -> Type[PromptBuilder]: ...
+
+    @property
+    @abstractmethod
+    def transformer_layer_cls(self) -> Type[nn.Module]: ...
+
+    @property
+    @abstractmethod
+    def half_precision_dtype(self) -> torch.dtype: ...
+
+    @property
+    @abstractmethod
+    def last_layer_finetune_modules(self) -> Sequence[nn.Module]: ...
+
+    @property
+    def embed_dim(self) -> int:
+        return self.llm.config.hidden_size
+
+    @property
+    def pad_token_id(self) -> int:
+        return self.tokenizer.pad_token_id
+
+
+# === Abstract Base Class for Arbitrary HF Causal LLMs ===
+class HFCausalLLMBackbone(LLMBackbone, ABC):
+    def __init__(
+        self,
+        llm_backbone_id: str,
+        llm_family: str,
+        llm_cls: Type[PreTrainedModel],
+        hf_hub_path: str,
+        llm_max_length: int = 2048,
+        hf_token: Optional[str] = None,
+        inference_mode: bool = False,
+        use_flash_attention_2: bool = False,
+    ) -> None:
+        super().__init__(llm_backbone_id)
+        self.llm_family = llm_family
+        self.llm_max_length = llm_max_length
+        self.inference_mode = inference_mode
+
+        # Initialize LLM (downloading from HF Hub if necessary) --> `llm_cls` is the actual {Model}ForCausalLM class!
+        #   => Note: We're eschewing use of the AutoModel API so that we can be more explicit about LLM-specific details
+        if not self.inference_mode:
+            overwatch.info(f"Loading [bold]{llm_family}[/] LLM from [underline]`{hf_hub_path}`[/]", ctx_level=1)
+            self.llm = llm_cls.from_pretrained(
+                hf_hub_path,
+                token=hf_token,
+                use_flash_attention_2=use_flash_attention_2 if not self.inference_mode else False,
+                # The following parameters are set to prevent `UserWarnings` from HF; we want greedy decoding!
+                do_sample=False,
+                temperature=1.0,
+                top_p=1.0,
+            )
+
+        # [Contract] `inference_mode` means we're loading from a pretrained checkpoint; no need to load base weights!
+        else:
+            overwatch.info(f"Building empty [bold]{llm_family}[/] LLM from [underline]`{hf_hub_path}`[/]", ctx_level=1)
+            llm_config = AutoConfig.from_pretrained(hf_hub_path, token=hf_token)
+            self.llm = llm_cls._from_config(llm_config)
+
+        # Lightweight Handling (with extended explanation) for setting some LLM Parameters
+        #   => Set `decoder.use_cache = False` --> incompatible with gradient checkpointing (+ training in general)
+        #
+        #      Reference: https://discuss.huggingface.co/t/what-is-the-purpose-of-use-cache-in-decoder/958
+        self.llm.config.use_cache = False if not self.inference_mode else True
+
+        #   => Turns out that when gradient checkpointing is on and the underlying LLM has no "trainable" parameters
+        #      (requires_grad is False), backprop will fail; setting `enable_input_requires_grad()` registers a new
+        #      forward hook that fixes this =>> also totally safe for the "full finetuning" setting!
+        if not self.inference_mode:
+            self.llm.enable_input_require_grads()
+
+        # Load (Fast) Tokenizer
+        overwatch.info(f"Loading [bold]{llm_family}[/] (Fast) Tokenizer via the AutoTokenizer API", ctx_level=1)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            hf_hub_path, model_max_length=self.llm_max_length, token=hf_token, padding_side="right"
+        )
+
+        # Validation =>> Our VLM logic currently operates under the assumption that the tokenization of a new input
+        #                starts with a <BOS> token unless `add_special_tokens = False`; for these models, we empirically
+        #                find that adding image patches *after* the BOS leads to much better performance.
+        #
+        # As a result we explicitly validate that a tokenizer conforms to the expected behavior; if you're reading this
+        # line, it's probably because you're adding a new LLM with a different tokenizer behavior. If so, feel free to
+        # override the `SPECIAL_CASES` set below, but make sure to make the appropriate changes in the `datasets.py`
+        # and VLM `forward()` logic!
+        SPECIAL_CASES = {
+            # Phi-2 Tokenizer doesn't add any BOS tokens by default, and sets BOS == EOS == "<|endoftext|>"
+            #   =>> We'll prepend BOS to first input (to play nicely with image token insertion logic; verified that
+            #       this works well with base LLM generation.
+            #   =>> Like Llama-2 Tokenizers -- we'll add a special PAD token for training purposes.
+            "phi-2-3b",
+        }
+        if self.identifier in SPECIAL_CASES:
+            return
+
+        # Note =>> this assert should hold for all Llama-derived tokenizers (`LlamaTokenizerFast` ==> includes Mistral!
+        assert (self.tokenizer("Test 123", add_special_tokens=True).input_ids[0] == self.tokenizer.bos_token_id) and (
+            self.tokenizer("Test 123", add_special_tokens=False).input_ids[0] != self.tokenizer.bos_token_id
+        ), (
+            f"Default Tokenizer of type `{type(self.tokenizer)}` does not automatically prefix inputs with BOS token!\n"
+            "Please read the comment in `base_llm.py` for more information!"
+        )
+
+    def get_fsdp_wrapping_policy(self) -> Callable:
+        """Return a `transformer_auto_wrap_policy` where we wrap each instance of `self.transformer_layer_cls`"""
+        transformer_block_policy = partial(
+            transformer_auto_wrap_policy, transformer_layer_cls={self.transformer_layer_cls}
+        )
+
+        return transformer_block_policy
+
+    def enable_gradient_checkpointing(self) -> None:
+        """Dispatch to underlying LLM instance's `gradient_checkpointing_enable`; defined for all `PretrainedModel`."""
+        self.llm.gradient_checkpointing_enable()
+
+    def embed_input_ids(self, input_ids: torch.LongTensor) -> torch.Tensor:
+        return self.llm.get_input_embeddings()(input_ids)
+
+    # [Contract] Should match the `forward` call of the underlying `llm` instance!
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> CausalLMOutputWithPast:
+        output: CausalLMOutputWithPast = self.llm(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        return output
diff --git a/prismatic/models/backbones/llm/llama2.py b/prismatic/models/backbones/llm/llama2.py
new file mode 100644
index 0000000000000000000000000000000000000000..60f1f5756b564a67693870399d18d574eedbfbd9
--- /dev/null
+++ b/prismatic/models/backbones/llm/llama2.py
@@ -0,0 +1,102 @@
+"""
+llama2.py
+
+Class definition for all LLMs derived from LlamaForCausalLM.
+"""
+
+from typing import Optional, Sequence, Type
+
+import torch
+from torch import nn as nn
+from transformers import LlamaForCausalLM
+from transformers.models.llama.modeling_llama import LlamaDecoderLayer
+
+from prismatic.models.backbones.llm.base_llm import HFCausalLLMBackbone
+from prismatic.models.backbones.llm.prompting import (
+    LLaMa2ChatPromptBuilder,
+    PromptBuilder,
+    PurePromptBuilder,
+    VicunaV15ChatPromptBuilder,
+)
+
+# Registry =>> Support LLaMa-2 Models (from HF Transformers)
+# fmt: off
+LLAMA2_MODELS = {
+    # === Pure Meta LLaMa-2 (non-instruct/chat-tuned) Models ===
+    "llama2-7b-pure": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "meta-llama/Llama-2-7b-hf"
+    },
+
+    "llama2-13b-pure": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "meta-llama/Llama-2-13b-hf"
+    },
+
+    # === Meta LLaMa-2 Chat Models ===
+    "llama2-7b-chat": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "meta-llama/Llama-2-7b-chat-hf"
+    },
+
+    "llama2-13b-chat": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "meta-llama/Llama-2-13b-chat-hf"
+    },
+
+    # === Vicuna v1.5 Chat Models ===
+    "vicuna-v15-7b": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "lmsys/vicuna-7b-v1.5"
+    },
+
+    "vicuna-v15-13b": {
+        "llm_family": "llama2", "llm_cls": LlamaForCausalLM, "hf_hub_path": "lmsys/vicuna-13b-v1.5"
+    },
+}
+# fmt: on
+
+
+class LLaMa2LLMBackbone(HFCausalLLMBackbone):
+    def __init__(
+        self,
+        llm_backbone_id: str,
+        llm_max_length: int = 2048,
+        hf_token: Optional[str] = None,
+        inference_mode: bool = False,
+        use_flash_attention_2: bool = True,
+    ) -> None:
+        super().__init__(
+            llm_backbone_id,
+            llm_max_length=llm_max_length,
+            hf_token=hf_token,
+            inference_mode=inference_mode,
+            use_flash_attention_2=use_flash_attention_2,
+            **LLAMA2_MODELS[llm_backbone_id],
+        )
+
+        # [Special Case] LLaMa-2 PAD Token Handling --> for clarity, we add an extra token (and resize)
+        self.tokenizer.add_special_tokens({"pad_token": "<PAD>"})
+        self.llm.config.pad_token_id = self.tokenizer.pad_token_id
+        self.llm.resize_token_embeddings(len(self.tokenizer), pad_to_multiple_of=64)
+
+    @property
+    def prompt_builder_fn(self) -> Type[PromptBuilder]:
+        if self.identifier.startswith("llama2-") and self.identifier.endswith("-pure"):
+            return PurePromptBuilder
+
+        elif self.identifier.startswith("llama2-") and self.identifier.endswith("-chat"):
+            return LLaMa2ChatPromptBuilder
+
+        elif self.identifier.startswith("vicuna"):
+            return VicunaV15ChatPromptBuilder
+
+        raise ValueError(f"No PromptBuilder defined for LLM Backbone `{self.identifier}`")
+
+    @property
+    def transformer_layer_cls(self) -> Type[nn.Module]:
+        return LlamaDecoderLayer
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        """LLaMa-2 was trained in BF16; see https://huggingface.co/docs/transformers/main/model_doc/llama2."""
+        return torch.bfloat16
+
+    @property
+    def last_layer_finetune_modules(self) -> Sequence[nn.Module]:
+        return (self.llm.model.embed_tokens, self.llm.model.layers[-1], self.llm.lm_head)
diff --git a/prismatic/models/backbones/llm/mistral.py b/prismatic/models/backbones/llm/mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc25553db6f9d2cbd292c98615b67c2105345f4
--- /dev/null
+++ b/prismatic/models/backbones/llm/mistral.py
@@ -0,0 +1,72 @@
+"""
+mistral.py
+
+Class definition for all LLMs derived from MistralForCausalLM.
+"""
+
+from typing import Optional, Type
+
+import torch
+from torch import nn as nn
+from transformers import MistralForCausalLM
+from transformers.models.mistral.modeling_mistral import MistralDecoderLayer
+
+from prismatic.models.backbones.llm.base_llm import HFCausalLLMBackbone
+from prismatic.models.backbones.llm.prompting import MistralInstructPromptBuilder, PromptBuilder, PurePromptBuilder
+
+# Registry =>> Support Mistral Models (from HF Transformers)
+# fmt: off
+MISTRAL_MODELS = {
+    # === Base Mistral v0.1 ===
+    "mistral-v0.1-7b-pure": {
+        "llm_family": "mistral", "llm_cls": MistralForCausalLM, "hf_hub_path": "mistralai/Mistral-7B-v0.1"
+    },
+
+    # === Mistral Instruct v0.1 ===
+    "mistral-v0.1-7b-instruct": {
+        "llm_family": "mistral", "llm_cls": MistralForCausalLM, "hf_hub_path": "mistralai/Mistral-7B-Instruct-v0.1"
+    }
+}
+# fmt: on
+
+
+class MistralLLMBackbone(HFCausalLLMBackbone):
+    def __init__(
+        self,
+        llm_backbone_id: str,
+        llm_max_length: int = 2048,
+        hf_token: Optional[str] = None,
+        inference_mode: bool = False,
+        use_flash_attention_2: bool = True,
+    ) -> None:
+        super().__init__(
+            llm_backbone_id,
+            llm_max_length=llm_max_length,
+            hf_token=hf_token,
+            inference_mode=inference_mode,
+            use_flash_attention_2=use_flash_attention_2,
+            **MISTRAL_MODELS[llm_backbone_id],
+        )
+
+        # [Special Case] Mistral PAD Token Handling --> for clarity, we add an extra token (and resize)
+        self.tokenizer.add_special_tokens({"pad_token": "<PAD>"})
+        self.llm.config.pad_token_id = self.tokenizer.pad_token_id
+        self.llm.resize_token_embeddings(len(self.tokenizer), pad_to_multiple_of=64)
+
+    @property
+    def prompt_builder_fn(self) -> Type[PromptBuilder]:
+        if self.identifier.endswith("-pure"):
+            return PurePromptBuilder
+
+        elif self.identifier.endswith("-instruct"):
+            return MistralInstructPromptBuilder
+
+        raise ValueError(f"No PromptBuilder defined for LLM Backbone `{self.identifier}`")
+
+    @property
+    def transformer_layer_cls(self) -> Type[nn.Module]:
+        return MistralDecoderLayer
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        return torch.bfloat16
diff --git a/prismatic/models/backbones/llm/phi.py b/prismatic/models/backbones/llm/phi.py
new file mode 100644
index 0000000000000000000000000000000000000000..27bb8b79b0e384af02071a21589050e41aebb4ae
--- /dev/null
+++ b/prismatic/models/backbones/llm/phi.py
@@ -0,0 +1,64 @@
+"""
+phi.py
+
+Class definition for all LLMs derived from PhiForCausalLM.
+"""
+
+from typing import Optional, Type
+
+import torch
+from torch import nn as nn
+from transformers import PhiForCausalLM
+from transformers.models.phi.modeling_phi import PhiDecoderLayer
+
+from prismatic.models.backbones.llm.base_llm import HFCausalLLMBackbone
+from prismatic.models.backbones.llm.prompting import PhiPromptBuilder, PromptBuilder
+
+# Registry ==> Support Phi Models (from HF Transformers)
+# fmt: off
+PHI_MODELS = {
+    # === Phi-2 ===
+    "phi-2-3b": {
+        "llm_family": "phi", "llm_cls": PhiForCausalLM, "hf_hub_path": "microsoft/phi-2"
+    }
+}
+# fmt: on
+
+
+class PhiLLMBackbone(HFCausalLLMBackbone):
+    def __init__(
+        self,
+        llm_backbone_id: str,
+        llm_max_length: int = 2048,
+        hf_token: Optional[str] = None,
+        inference_mode: bool = False,
+        use_flash_attention_2: bool = True,
+    ) -> None:
+        super().__init__(
+            llm_backbone_id,
+            llm_max_length=llm_max_length,
+            hf_token=hf_token,
+            inference_mode=inference_mode,
+            use_flash_attention_2=use_flash_attention_2,
+            **PHI_MODELS[llm_backbone_id],
+        )
+
+        # [Special Case] Phi PAD Token Handling --> for clarity, we add an extra token (and resize)
+        self.tokenizer.add_special_tokens({"pad_token": "<|pad|>"})
+        self.llm.config.pad_token_id = self.tokenizer.pad_token_id
+        self.llm.resize_token_embeddings(len(self.tokenizer), pad_to_multiple_of=64)
+
+    @property
+    def prompt_builder_fn(self) -> Type[PromptBuilder]:
+        if self.identifier.startswith("phi-2"):
+            return PhiPromptBuilder
+
+        raise ValueError(f"No PromptBuilder defined for LLM Backbone `{self.identifier}`")
+
+    @property
+    def transformer_layer_cls(self) -> Type[nn.Module]:
+        return PhiDecoderLayer
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        return torch.bfloat16
diff --git a/prismatic/models/backbones/llm/prompting/__init__.py b/prismatic/models/backbones/llm/prompting/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8292789c29f76344bf400f844cb946e8a4081a98
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/__init__.py
@@ -0,0 +1,5 @@
+from .base_prompter import PromptBuilder, PurePromptBuilder
+from .llama2_chat_prompter import LLaMa2ChatPromptBuilder
+from .mistral_instruct_prompter import MistralInstructPromptBuilder
+from .phi_prompter import PhiPromptBuilder
+from .vicuna_v15_prompter import VicunaV15ChatPromptBuilder
diff --git a/prismatic/models/backbones/llm/prompting/base_prompter.py b/prismatic/models/backbones/llm/prompting/base_prompter.py
new file mode 100644
index 0000000000000000000000000000000000000000..3816b1831177d128bbe872c041ed5b0ffaf5b5ed
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/base_prompter.py
@@ -0,0 +1,73 @@
+"""
+base_prompter.py
+
+Abstract class definition of a multi-turn prompt builder for ensuring consistent formatting for chat-based LLMs.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class PromptBuilder(ABC):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        self.model_family = model_family
+
+        # Only some models define a system prompt => let subclasses handle this logic!
+        self.system_prompt = system_prompt
+
+    @abstractmethod
+    def add_turn(self, role: str, message: str) -> str: ...
+
+    @abstractmethod
+    def get_potential_prompt(self, user_msg: str) -> None: ...
+
+    @abstractmethod
+    def get_prompt(self) -> str: ...
+
+
+class PurePromptBuilder(PromptBuilder):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        super().__init__(model_family, system_prompt)
+
+        # TODO (siddk) =>> Can't always assume LlamaTokenizer --> FIX ME!
+        self.bos, self.eos = "<s>", "</s>"
+
+        # Get role-specific "wrap" functions
+        self.wrap_human = lambda msg: f"In: {msg}\nOut: "
+        self.wrap_gpt = lambda msg: f"{msg if msg != '' else ' '}{self.eos}"
+
+        # === `self.prompt` gets built up over multiple turns ===
+        self.prompt, self.turn_count = "", 0
+
+    def add_turn(self, role: str, message: str) -> str:
+        assert (role == "human") if (self.turn_count % 2 == 0) else (role == "gpt")
+        message = message.replace("<image>", "").strip()
+
+        if (self.turn_count % 2) == 0:
+            human_message = self.wrap_human(message)
+            wrapped_message = human_message
+        else:
+            gpt_message = self.wrap_gpt(message)
+            wrapped_message = gpt_message
+
+        # Update Prompt
+        self.prompt += wrapped_message
+
+        # Bump Turn Counter
+        self.turn_count += 1
+
+        # Return "wrapped_message" (effective string added to context)
+        return wrapped_message
+
+    def get_potential_prompt(self, message: str) -> None:
+        # Assumes that it's always the user's (human's) turn!
+        prompt_copy = str(self.prompt)
+
+        human_message = self.wrap_human(message)
+        prompt_copy += human_message
+
+        return prompt_copy.removeprefix(self.bos).rstrip()
+
+    def get_prompt(self) -> str:
+        # Remove prefix <bos> (if exists) because it gets auto-inserted by tokenizer!
+        return self.prompt.removeprefix(self.bos).rstrip()
diff --git a/prismatic/models/backbones/llm/prompting/llama2_chat_prompter.py b/prismatic/models/backbones/llm/prompting/llama2_chat_prompter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b094675a6ee550a8d2d99ed35be2d3c4a71e861d
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/llama2_chat_prompter.py
@@ -0,0 +1,91 @@
+"""
+llama2_prompter.py
+
+Defines a PromptBuilder for building LLaMa-2 Chat Prompts --> not sure if this is "optimal", but this is the pattern
+that's used by HF and other online tutorials.
+
+Reference: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
+"""
+
+from typing import Optional
+
+from prismatic.models.backbones.llm.prompting.base_prompter import PromptBuilder
+
+# Default System Prompt for Prismatic Models
+SYS_PROMPTS = {
+    "prismatic": (
+        "You are a helpful language and vision assistant. "
+        "You are able to understand the visual content that the user provides, "
+        "and assist the user with a variety of tasks using natural language."
+    ),
+    "openvla": (
+        "You are a helpful language and vision assistant. "
+        "You are able to understand the visual content that the user provides, "
+        "and assist the user with a variety of tasks using natural language."
+    ),
+}
+
+
+def format_system_prompt(system_prompt: str) -> str:
+    return f"<<SYS>\n{system_prompt.strip()}\n<</SYS>>\n\n"
+
+
+class LLaMa2ChatPromptBuilder(PromptBuilder):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        super().__init__(model_family, system_prompt)
+        self.system_prompt = format_system_prompt(
+            SYS_PROMPTS[self.model_family] if system_prompt is None else system_prompt
+        )
+
+        # LLaMa-2 Specific
+        self.bos, self.eos = "<s>", "</s>"
+
+        # Get role-specific "wrap" functions
+        self.wrap_human = lambda msg: f"[INST] {msg} [/INST] "
+        self.wrap_gpt = lambda msg: f"{msg if msg != '' else ' '}{self.eos}"
+
+        # === `self.prompt` gets built up over multiple turns ===
+        self.prompt, self.turn_count = "", 0
+
+    def add_turn(self, role: str, message: str) -> str:
+        assert (role == "human") if (self.turn_count % 2 == 0) else (role == "gpt")
+        message = message.replace("<image>", "").strip()
+
+        # Special Handling for "system" prompt (turn_count == 0)
+        if self.turn_count == 0:
+            sys_message = self.wrap_human(self.system_prompt + message)
+            wrapped_message = sys_message
+        elif (self.turn_count % 2) == 0:
+            human_message = self.wrap_human(message)
+            wrapped_message = human_message
+        else:
+            gpt_message = self.wrap_gpt(message)
+            wrapped_message = gpt_message
+
+        # Update Prompt
+        self.prompt += wrapped_message
+
+        # Bump Turn Counter
+        self.turn_count += 1
+
+        # Return "wrapped_message" (effective string added to context)
+        return wrapped_message
+
+    def get_potential_prompt(self, message: str) -> None:
+        # Assumes that it's always the user's (human's) turn!
+        prompt_copy = str(self.prompt)
+
+        # Special Handling for "system" prompt (turn_count == 0)
+        if self.turn_count == 0:
+            sys_message = self.wrap_human(self.system_prompt + message)
+            prompt_copy += sys_message
+
+        else:
+            human_message = self.wrap_human(message)
+            prompt_copy += human_message
+
+        return prompt_copy.removeprefix(self.bos).rstrip()
+
+    def get_prompt(self) -> str:
+        # Remove prefix <bos> because it gets auto-inserted by tokenizer!
+        return self.prompt.removeprefix(self.bos).rstrip()
diff --git a/prismatic/models/backbones/llm/prompting/mistral_instruct_prompter.py b/prismatic/models/backbones/llm/prompting/mistral_instruct_prompter.py
new file mode 100644
index 0000000000000000000000000000000000000000..35a5eab8b3caf31ea671727230d1aed29903dde3
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/mistral_instruct_prompter.py
@@ -0,0 +1,60 @@
+"""
+mistral_instruct_prompter.py
+
+Defines a PromptBuilder for building Mistral Instruct Chat Prompts --> recommended pattern used by HF / online tutorial.s
+
+Reference: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
+"""
+
+from typing import Optional
+
+from prismatic.models.backbones.llm.prompting.base_prompter import PromptBuilder
+
+
+class MistralInstructPromptBuilder(PromptBuilder):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        super().__init__(model_family, system_prompt)
+
+        # Note =>> Mistral Tokenizer is an instance of `LlamaTokenizer(Fast)`
+        #      =>> Mistral Instruct *does not* use a System Prompt
+        self.bos, self.eos = "<s>", "</s>"
+
+        # Get role-specific "wrap" functions
+        self.wrap_human = lambda msg: f"[INST] {msg} [/INST] "
+        self.wrap_gpt = lambda msg: f"{msg if msg != '' else ' '}{self.eos}"
+
+        # === `self.prompt` gets built up over multiple turns ===
+        self.prompt, self.turn_count = "", 0
+
+    def add_turn(self, role: str, message: str) -> str:
+        assert (role == "human") if (self.turn_count % 2 == 0) else (role == "gpt")
+        message = message.replace("<image>", "").strip()
+
+        if (self.turn_count % 2) == 0:
+            human_message = self.wrap_human(message)
+            wrapped_message = human_message
+        else:
+            gpt_message = self.wrap_gpt(message)
+            wrapped_message = gpt_message
+
+        # Update Prompt
+        self.prompt += wrapped_message
+
+        # Bump Turn Counter
+        self.turn_count += 1
+
+        # Return "wrapped_message" (effective string added to context)
+        return wrapped_message
+
+    def get_potential_prompt(self, message: str) -> None:
+        # Assumes that it's always the user's (human's) turn!
+        prompt_copy = str(self.prompt)
+
+        human_message = self.wrap_human(message)
+        prompt_copy += human_message
+
+        return prompt_copy.removeprefix(self.bos).rstrip()
+
+    def get_prompt(self) -> str:
+        # Remove prefix <bos> because it gets auto-inserted by tokenizer!
+        return self.prompt.removeprefix(self.bos).rstrip()
diff --git a/prismatic/models/backbones/llm/prompting/phi_prompter.py b/prismatic/models/backbones/llm/prompting/phi_prompter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b350ea3a6b6c448cc65caa6fa72db3c253dd0b7b
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/phi_prompter.py
@@ -0,0 +1,65 @@
+"""
+phi_prompter.py
+
+Defines a PromptBuilder for building Phi-2 Input/Output Prompts --> recommended pattern used by HF / Microsoft.
+Also handles Phi special case BOS token additions.
+
+Reference: https://huggingface.co/microsoft/phi-2#qa-format
+"""
+
+from typing import Optional
+
+from prismatic.models.backbones.llm.prompting.base_prompter import PromptBuilder
+
+
+class PhiPromptBuilder(PromptBuilder):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        super().__init__(model_family, system_prompt)
+
+        # Note =>> Phi Tokenizer is an instance of `CodeGenTokenizer(Fast)`
+        #      =>> By default, does *not* append <BOS> / <EOS> tokens --> we handle that here (IMPORTANT)!
+        self.bos, self.eos = "<|endoftext|>", "<|endoftext|>"
+
+        # Get role-specific "wrap" functions
+        #   =>> Note that placement of <bos>/<eos> were based on experiments generating from Phi-2 in Input/Output mode
+        self.wrap_human = lambda msg: f"Input: {msg}\nOutput: "
+        self.wrap_gpt = lambda msg: f"{msg if msg != '' else ' '}\n{self.eos}"
+
+        # === `self.prompt` gets built up over multiple turns ===
+        self.prompt, self.turn_count = "", 0
+
+    def add_turn(self, role: str, message: str) -> str:
+        assert (role == "human") if (self.turn_count % 2 == 0) else (role == "gpt")
+        message = message.replace("<image>", "").strip()
+
+        # Special Handling for "first" input --> prepend a <BOS> token (expected by Prismatic)
+        if self.turn_count == 0:
+            bos_human_message = f"{self.bos}{self.wrap_human(message)}"
+            wrapped_message = bos_human_message
+        elif (self.turn_count % 2) == 0:
+            human_message = self.wrap_human(message)
+            wrapped_message = human_message
+        else:
+            gpt_message = self.wrap_gpt(message)
+            wrapped_message = gpt_message
+
+        # Update Prompt
+        self.prompt += wrapped_message
+
+        # Bump Turn Counter
+        self.turn_count += 1
+
+        # Return "wrapped_message" (effective string added to context)
+        return wrapped_message
+
+    def get_potential_prompt(self, message: str) -> None:
+        # Assumes that it's always the user's (human's) turn!
+        prompt_copy = str(self.prompt)
+
+        human_message = self.wrap_human(message)
+        prompt_copy += human_message
+
+        return prompt_copy.rstrip()
+
+    def get_prompt(self) -> str:
+        return self.prompt.rstrip()
diff --git a/prismatic/models/backbones/llm/prompting/vicuna_v15_prompter.py b/prismatic/models/backbones/llm/prompting/vicuna_v15_prompter.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5e79e443a791a149cec09a81d55b1a3788eb9de
--- /dev/null
+++ b/prismatic/models/backbones/llm/prompting/vicuna_v15_prompter.py
@@ -0,0 +1,82 @@
+"""
+vicuna_v15_prompter.py
+
+Defines a PromptBuilder for building Vicuna-v1.5 Chat Prompts.
+
+Reference: https://huggingface.co/lmsys/vicuna-13b-v1.5
+"""
+
+from typing import Optional
+
+from prismatic.models.backbones.llm.prompting.base_prompter import PromptBuilder
+
+# Default System Prompt for LLaVa Models
+SYS_PROMPTS = {
+    "prismatic": (
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    "openvla": (
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+}
+
+
+class VicunaV15ChatPromptBuilder(PromptBuilder):
+    def __init__(self, model_family: str, system_prompt: Optional[str] = None) -> None:
+        super().__init__(model_family, system_prompt)
+        self.system_prompt = (SYS_PROMPTS[self.model_family] if system_prompt is None else system_prompt).strip() + " "
+
+        # LLaMa-2 Specific
+        self.bos, self.eos = "<s>", "</s>"
+
+        # Get role-specific "wrap" functions
+        self.wrap_human = lambda msg: f"USER: {msg} ASSISTANT: "
+        self.wrap_gpt = lambda msg: f"{msg if msg != '' else ' '}{self.eos}"
+
+        # === `self.prompt` gets built up over multiple turns ===
+        self.prompt, self.turn_count = "", 0
+
+    def add_turn(self, role: str, message: str) -> str:
+        assert (role == "human") if (self.turn_count % 2 == 0) else (role == "gpt")
+        message = message.replace("<image>", "").strip()
+
+        # Special Handling for "system" prompt (turn_count == 0)
+        if self.turn_count == 0:
+            sys_message = self.system_prompt + self.wrap_human(message)
+            wrapped_message = sys_message
+        elif (self.turn_count % 2) == 0:
+            human_message = self.wrap_human(message)
+            wrapped_message = human_message
+        else:
+            gpt_message = self.wrap_gpt(message)
+            wrapped_message = gpt_message
+
+        # Update Prompt
+        self.prompt += wrapped_message
+
+        # Bump Turn Counter
+        self.turn_count += 1
+
+        # Return "wrapped_message" (effective string added to context)
+        return wrapped_message
+
+    def get_potential_prompt(self, message: str) -> None:
+        # Assumes that it's always the user's (human's) turn!
+        prompt_copy = str(self.prompt)
+
+        # Special Handling for "system" prompt (turn_count == 0)
+        if self.turn_count == 0:
+            sys_message = self.system_prompt + self.wrap_human(message)
+            prompt_copy += sys_message
+
+        else:
+            human_message = self.wrap_human(message)
+            prompt_copy += human_message
+
+        return prompt_copy.removeprefix(self.bos).rstrip()
+
+    def get_prompt(self) -> str:
+        # Remove prefix <bos> (if exists) because it gets auto-inserted by tokenizer!
+        return self.prompt.removeprefix(self.bos).rstrip()
diff --git a/prismatic/models/backbones/vision/__init__.py b/prismatic/models/backbones/vision/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9f33f8aa95245bdeb84f16b78ebb97b0e2bcd13
--- /dev/null
+++ b/prismatic/models/backbones/vision/__init__.py
@@ -0,0 +1,7 @@
+from .base_vision import ImageTransform, VisionBackbone
+from .clip_vit import CLIPViTBackbone
+from .dinoclip_vit import DinoCLIPViTBackbone
+from .dinosiglip_vit import DinoSigLIPViTBackbone
+from .dinov2_vit import DinoV2ViTBackbone
+from .in1k_vit import IN1KViTBackbone
+from .siglip_vit import SigLIPViTBackbone
diff --git a/prismatic/models/backbones/vision/base_vision.py b/prismatic/models/backbones/vision/base_vision.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9ccadee35f08bf81e370824dcab5feffdfffd0c
--- /dev/null
+++ b/prismatic/models/backbones/vision/base_vision.py
@@ -0,0 +1,207 @@
+"""
+base_vision.py
+
+Abstract class definition of a Vision Backbone (Visual Featurizer), with full annotations of class methods, utility
+functions, and initialization logic.
+
+We also define the generic TimmViTBackbone class here, providing a default interface for loading any TIMM Vision
+Transformer model for feature extraction.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Callable, Dict, Optional, Protocol, Tuple, Union
+
+import timm
+import torch
+import torch.nn as nn
+import torchvision.transforms.functional as TVF
+from PIL.Image import Image
+from timm.models.vision_transformer import Block, VisionTransformer
+from torch.distributed.fsdp.wrap import _module_wrap_policy, _or_policy, transformer_auto_wrap_policy
+from torchvision.transforms import Compose, Resize
+
+
+# === Utility Functions for Monkey-Patching ===
+def unpack_tuple(fn: Callable[[Any], Tuple[Any]]) -> Callable[[Any], Any]:
+    def wrapper(*args: Any, **kwargs: Any) -> Any:
+        result = fn(*args, **kwargs)
+        return result[0] if isinstance(result, tuple) else result
+
+    return wrapper
+
+
+# === Interface for an Image Transform ===
+class ImageTransform(Protocol):
+    def __call__(self, img: Image, **kwargs: str) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: ...
+
+
+# === Custom Torchvision Image Transforms ===
+@dataclass
+class LetterboxPad:
+    padding_fill_value: Tuple[int, int, int]
+
+    def __call__(self, image: Image) -> Image:
+        """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+        (w, h), max_wh = image.size, max(image.size)
+        horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+        padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+        return TVF.pad(image, padding, fill=self.padding_fill_value, padding_mode="constant")
+
+
+# === Abstract Base Class for arbitrary Vision Backbones ===
+class VisionBackbone(nn.Module, ABC):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__()
+        self.identifier: str = vision_backbone_id
+        self.image_resize_strategy: str = image_resize_strategy
+        self.default_image_size: int = default_image_size
+
+        # Instance attributes for a Vision Backbone
+        self.featurizer: nn.Module = None
+        self.image_transform: ImageTransform = None
+
+    def get_image_transform(self) -> ImageTransform:
+        return self.image_transform
+
+    @abstractmethod
+    def get_fsdp_wrapping_policy(self) -> Callable: ...
+
+    @abstractmethod
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """Run a forward pass through the featurizer given a set of processed images, returning patch/grid features."""
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def default_image_resolution(self) -> Tuple[int, int, int]: ...
+
+    @property
+    @abstractmethod
+    def embed_dim(self) -> int: ...
+
+    @property
+    @abstractmethod
+    def num_patches(self) -> int: ...
+
+    @property
+    @abstractmethod
+    def half_precision_dtype(self) -> torch.dtype: ...
+
+
+# === Abstract Base Class for Arbitrary TIMM Vision Transformer Backbones ===
+class TimmViTBackbone(VisionBackbone, ABC):
+    def __init__(
+        self,
+        vision_backbone_id: str,
+        timm_path_or_url: str,
+        image_resize_strategy: str,
+        default_image_size: int = 224,
+        override_act_layer: Optional[str] = None,
+    ) -> None:
+        super().__init__(vision_backbone_id, image_resize_strategy, default_image_size=default_image_size)
+        self.timm_path_or_url = timm_path_or_url
+        self.override_act_layer = override_act_layer
+        self.dtype = torch.bfloat16
+
+        # Initialize Featurizer (ViT) by downloading from HF / TIMM Hub if necessary
+        if self.override_act_layer is None:
+            self.featurizer: VisionTransformer = timm.create_model(
+                self.timm_path_or_url, pretrained=True, num_classes=0, img_size=self.default_image_size
+            )
+        else:
+            self.featurizer: VisionTransformer = timm.create_model(
+                self.timm_path_or_url,
+                pretrained=True,
+                num_classes=0,
+                img_size=self.default_image_size,
+                act_layer=self.override_act_layer,
+            )
+        self.featurizer.eval()
+
+        # Monkey-Patch the `forward()` function of the featurizer to ensure FSDP-compatibility
+        #   => Note: By default set `get_intermediate_layers` to return the *SECOND-TO-LAST* layer patches!
+        #   => TODO (siddk) Remove after resolution of https://github.com/pytorch/pytorch/issues/109385
+        self.featurizer.forward = unpack_tuple(
+            partial(self.featurizer.get_intermediate_layers, n={len(self.featurizer.blocks) - 2})
+        )
+
+        # Validation =>> for now, this class *only* supports TIMM Vision Transformers (but can be extended!)
+        assert isinstance(self.featurizer, VisionTransformer), (
+            "Featurizer is not a TIMM VisionTransformer; if you would like to support a new visual representation, "
+            "file an issue or implement the requisite logic (see `prismatic/models/backbones/vision/base_vision.py`)!"
+        )
+
+        # Get Config =>> Note :: Override default image size to ensure correct image transform
+        self.data_cfg = timm.data.resolve_model_data_config(self.featurizer)
+        self.data_cfg["input_size"] = (3, self.default_image_size, self.default_image_size)
+
+        # Initialize Default Image Transform --> Modified by `self.image_resize_strategy`
+        default_image_transform = timm.data.create_transform(**self.data_cfg, is_training=False)
+
+        # Fix =>> SigLIP & IN1K default transforms resize to *larger* than `self.default_image_size` (crops image)!
+        if "siglip" in self.timm_path_or_url or "in1k" in self.timm_path_or_url:
+            assert isinstance(default_image_transform, Compose), "Unexpected `default_image_transform`!"
+            assert isinstance(default_image_transform.transforms[0], Resize)
+            default_image_transform = Compose(
+                [
+                    Resize(self.default_image_size, interpolation=default_image_transform.transforms[0].interpolation),
+                    *default_image_transform.transforms[1:],
+                ]
+            )
+
+        # Switch on `image_resize_strategy`
+        if self.image_resize_strategy == "resize-naive":
+            assert isinstance(default_image_transform, Compose), "Unexpected `default_image_transform`!"
+            assert isinstance(default_image_transform.transforms[0], Resize)
+
+            target_size = (self.default_image_size, self.default_image_size)
+            self.image_transform = Compose(
+                [
+                    Resize(target_size, interpolation=default_image_transform.transforms[0].interpolation),
+                    *default_image_transform.transforms[1:],
+                ]
+            )
+
+        elif self.image_resize_strategy == "resize-crop":
+            self.image_transform = default_image_transform
+
+        elif self.image_resize_strategy == "letterbox":
+            assert isinstance(default_image_transform, Compose), "Unexpected `default_image_transform`!"
+            assert "mean" in self.data_cfg, "TIMM `data_cfg` missing image normalization mean!"
+
+            # Compute Padding Fill Value (rescaled normalization mean if applicable)
+            fill = tuple([int(x * 255) for x in self.data_cfg["mean"]])
+
+            # Build New Transform
+            self.image_transform = Compose([LetterboxPad(fill), *default_image_transform.transforms])
+
+        else:
+            raise ValueError(f"Image Resize Strategy `{self.image_resize_strategy}` is not supported!")
+
+    def get_fsdp_wrapping_policy(self) -> Callable:
+        """Return a simple FSDP policy that wraps each ViT block and then the _entire_ featurizer."""
+        vit_wrap_policy = partial(_module_wrap_policy, module_classes={VisionTransformer})
+        transformer_block_policy = partial(transformer_auto_wrap_policy, transformer_layer_cls={Block})
+        return partial(_or_policy, policies=[vit_wrap_policy, transformer_block_policy])
+
+    def forward(self, pixel_values: Union[torch.Tensor, Dict[str, torch.Tensor]]) -> torch.Tensor:
+        """Runs transformed image/pixel tensor through vision backbone, returning _all_ patch features."""
+        return self.featurizer(pixel_values)
+
+    @property
+    def default_image_resolution(self) -> Tuple[int, int, int]:
+        return self.data_cfg["input_size"]
+
+    @property
+    def embed_dim(self) -> int:
+        return self.featurizer.embed_dim
+
+    @property
+    def num_patches(self) -> int:
+        return self.featurizer.patch_embed.num_patches
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        return self.dtype
diff --git a/prismatic/models/backbones/vision/clip_vit.py b/prismatic/models/backbones/vision/clip_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5d162ec9c7d22a475a71917ea4458c2351835c6
--- /dev/null
+++ b/prismatic/models/backbones/vision/clip_vit.py
@@ -0,0 +1,27 @@
+"""
+clip_vit.py
+"""
+
+from prismatic.models.backbones.vision.base_vision import TimmViTBackbone
+
+# Registry =>> Supported CLIP Vision Backbones (from TIMM)
+CLIP_VISION_BACKBONES = {
+    "clip-vit-b": "vit_base_patch16_clip_224.openai",
+    "clip-vit-l": "vit_large_patch14_clip_224.openai",
+    "clip-vit-l-336px": "vit_large_patch14_clip_336.openai",
+}
+
+
+# [IMPORTANT] By Default, TIMM initialized OpenAI CLIP models with the standard GELU activation from PyTorch.
+#             HOWEVER =>> Original OpenAI models were trained with the quick_gelu *approximation* -- while it's
+#                         a decent approximation, the resulting features are *worse*; this was a super tricky bug
+#                         to identify, but luckily there's an easy fix (`override_act_layer`)
+class CLIPViTBackbone(TimmViTBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(
+            vision_backbone_id,
+            CLIP_VISION_BACKBONES[vision_backbone_id],
+            image_resize_strategy,
+            default_image_size=default_image_size,
+            override_act_layer="quick_gelu" if CLIP_VISION_BACKBONES[vision_backbone_id].endswith(".openai") else None,
+        )
diff --git a/prismatic/models/backbones/vision/dinoclip_vit.py b/prismatic/models/backbones/vision/dinoclip_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..6653d69242104fecfcad8605223508edb96a7357
--- /dev/null
+++ b/prismatic/models/backbones/vision/dinoclip_vit.py
@@ -0,0 +1,147 @@
+"""
+dinoclip_vit.py
+
+Vision backbone that returns concatenated features from both DINOv2 and CLIP.
+"""
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Callable, Dict, Tuple
+
+import timm
+import torch
+from PIL import Image
+from timm.models.vision_transformer import Block, VisionTransformer
+from torch.distributed.fsdp.wrap import _module_wrap_policy, _or_policy, transformer_auto_wrap_policy
+from torchvision.transforms import Compose, Resize
+
+from prismatic.models.backbones.vision.base_vision import ImageTransform, LetterboxPad, VisionBackbone, unpack_tuple
+
+# Registry =>> Supported DinoCLIP Pairs (as TIMM identifiers)
+DINOCLIP_VISION_BACKBONES = {
+    "dinoclip-vit-l-336px": {
+        "dino": "vit_large_patch14_reg4_dinov2.lvd142m",
+        "clip": "vit_large_patch14_clip_336.openai",
+    },
+}
+
+
+@dataclass
+class DinoCLIPImageTransform:
+    dino_image_transform: ImageTransform
+    clip_image_transform: ImageTransform
+    is_prismatic: bool = True
+
+    def __call__(self, img: Image, **kwargs: str) -> Dict[str, torch.Tensor]:
+        return {"dino": self.dino_image_transform(img, **kwargs), "clip": self.clip_image_transform(img, **kwargs)}
+
+
+class DinoCLIPViTBackbone(VisionBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(vision_backbone_id, image_resize_strategy, default_image_size=default_image_size)
+        self.dino_timm_path_or_url = DINOCLIP_VISION_BACKBONES[vision_backbone_id]["dino"]
+        self.clip_timm_path_or_url = DINOCLIP_VISION_BACKBONES[vision_backbone_id]["clip"]
+
+        # Initialize both Featurizers (ViTs) by downloading from HF / TIMM Hub if necessary
+        self.dino_featurizer: VisionTransformer = timm.create_model(
+            self.dino_timm_path_or_url, pretrained=True, num_classes=0, img_size=self.default_image_size
+        )
+        self.dino_featurizer.eval()
+
+        self.clip_featurizer: VisionTransformer = timm.create_model(
+            self.clip_timm_path_or_url, pretrained=True, num_classes=0, img_size=self.default_image_size
+        )
+        self.clip_featurizer.eval()
+
+        # Monkey-Patch the `forward()` function of the featurizers to ensure FSDP-compatibility
+        #   => Note: By default set `get_intermediate_layers` to return the *SECOND-TO-LAST* layer patches!
+        #   => TODO (siddk) Remove after resolution of https://github.com/pytorch/pytorch/issues/109385
+        self.dino_featurizer.forward = unpack_tuple(
+            partial(self.dino_featurizer.get_intermediate_layers, n={len(self.dino_featurizer.blocks) - 2})
+        )
+        self.clip_featurizer.forward = unpack_tuple(
+            partial(self.clip_featurizer.get_intermediate_layers, n={len(self.clip_featurizer.blocks) - 2})
+        )
+
+        # Get Configs for _both_ Featurizers =>> Note :: Override default image size for larger resolution models
+        self.dino_data_cfg = timm.data.resolve_model_data_config(self.dino_featurizer)
+        self.dino_data_cfg["input_size"] = (3, self.default_image_size, self.default_image_size)
+
+        self.clip_data_cfg = timm.data.resolve_model_data_config(self.clip_featurizer)
+        self.clip_data_cfg["input_size"] = (3, self.default_image_size, self.default_image_size)
+
+        # Initialize *both* Transforms
+        default_dino_transform = timm.data.create_transform(**self.dino_data_cfg, is_training=False)
+        default_clip_transform = timm.data.create_transform(**self.clip_data_cfg, is_training=False)
+        if self.image_resize_strategy == "resize-naive":
+            assert isinstance(default_dino_transform, Compose), "Unexpected `default_dino_image_transform`!"
+            assert isinstance(default_clip_transform, Compose), "Unexpected `default_clip_image_transform`!"
+            assert isinstance(default_dino_transform.transforms[0], Resize)
+            assert isinstance(default_clip_transform.transforms[0], Resize)
+
+            target_size = (self.default_image_size, self.default_image_size)
+            dino_transform = Compose(
+                [
+                    Resize(target_size, interpolation=default_dino_transform.transforms[0].interpolation),
+                    *default_dino_transform.transforms[1:],
+                ]
+            )
+            clip_transform = Compose(
+                [
+                    Resize(target_size, interpolation=default_clip_transform.transforms[0].interpolation),
+                    *default_clip_transform.transforms[1:],
+                ]
+            )
+
+            self.image_transform = DinoCLIPImageTransform(dino_transform, clip_transform)
+
+        elif self.image_resize_strategy == "resize-crop":
+            self.image_transform = DinoCLIPImageTransform(default_dino_transform, default_clip_transform)
+
+        elif self.image_resize_strategy == "letterbox":
+            assert isinstance(default_dino_transform, Compose), "Unexpected `default_dino_transform`!"
+            assert isinstance(default_clip_transform, Compose), "Unexpected `default_clip_transform`!"
+            assert "mean" in self.dino_data_cfg and "mean" in self.clip_data_cfg, "DinoCLIP `data_cfg` missing `mean`!"
+
+            # Compute Padding Fill Value(s) (rescaled normalization mean if applicable)
+            dino_fill = tuple([int(x * 255) for x in self.dino_data_cfg["mean"]])
+            clip_fill = tuple([int(x * 255) for x in self.clip_data_cfg["mean"]])
+
+            # Build New Transform
+            self.image_transform = DinoCLIPImageTransform(
+                Compose([LetterboxPad(dino_fill), *default_dino_transform.transforms]),
+                Compose([LetterboxPad(clip_fill), *default_clip_transform.transforms]),
+            )
+
+        else:
+            raise ValueError(f"Image Resize Strategy `{self.image_resize_strategy}` is not supported!")
+
+    def get_fsdp_wrapping_policy(self) -> Callable:
+        """Return a simple FSDP policy that wraps each ViT block and then both of the _entire_ featurizers."""
+        vit_wrap_policy = partial(_module_wrap_policy, module_classes={VisionTransformer})
+        transformer_block_policy = partial(transformer_auto_wrap_policy, transformer_layer_cls={Block})
+        return partial(_or_policy, policies=[vit_wrap_policy, transformer_block_policy])
+
+    def forward(self, pixel_values: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """Runs the transformed image/pixel tensors through each vision backbone, returning concatenated patches."""
+        dino_patches = self.dino_featurizer(pixel_values["dino"])
+        clip_patches = self.clip_featurizer(pixel_values["clip"])
+
+        return torch.cat([dino_patches, clip_patches], dim=2)
+
+    @property
+    def default_image_resolution(self) -> Tuple[int, int, int]:
+        return self.dino_data_cfg["input_size"]
+
+    @property
+    def embed_dim(self) -> int:
+        return self.dino_featurizer.embed_dim + self.clip_featurizer.embed_dim
+
+    @property
+    def num_patches(self) -> int:
+        assert self.dino_featurizer.patch_embed.num_patches == self.clip_featurizer.patch_embed.num_patches
+        return self.dino_featurizer.patch_embed.num_patches
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        return torch.bfloat16
diff --git a/prismatic/models/backbones/vision/dinosiglip_vit.py b/prismatic/models/backbones/vision/dinosiglip_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..bced8d179db3bf3ee488bbec36b0af7b4a1001cb
--- /dev/null
+++ b/prismatic/models/backbones/vision/dinosiglip_vit.py
@@ -0,0 +1,164 @@
+"""
+dinosiglip_vit.py
+
+Vision backbone that returns concatenated features from both DINOv2 and SigLIP.
+"""
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Callable, Dict, Tuple
+
+import timm
+import torch
+from PIL import Image
+from timm.models.vision_transformer import Block, VisionTransformer
+from torch.distributed.fsdp.wrap import _module_wrap_policy, _or_policy, transformer_auto_wrap_policy
+from torchvision.transforms import Compose, Resize
+
+from prismatic.models.backbones.vision.base_vision import ImageTransform, LetterboxPad, VisionBackbone, unpack_tuple
+
+# Registry =>> Supported DinoSigLIP Pairs (as TIMM identifiers)
+DINOSigLIP_VISION_BACKBONES = {
+    "dinosiglip-vit-so-224px": {
+        "dino": "vit_large_patch14_reg4_dinov2.lvd142m",
+        "siglip": "vit_so400m_patch14_siglip_224",
+    },
+    "dinosiglip-vit-so-384px": {
+        "dino": "vit_large_patch14_reg4_dinov2.lvd142m",
+        "siglip": "vit_so400m_patch14_siglip_384",
+    },
+}
+
+
+@dataclass
+class DinoSigLIPImageTransform:
+    dino_image_transform: ImageTransform
+    siglip_image_transform: ImageTransform
+    is_prismatic: bool = True
+
+    def __call__(self, img: Image, **kwargs: str) -> Dict[str, torch.Tensor]:
+        return {"dino": self.dino_image_transform(img, **kwargs), "siglip": self.siglip_image_transform(img, **kwargs)}
+
+
+class DinoSigLIPViTBackbone(VisionBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(vision_backbone_id, image_resize_strategy, default_image_size=default_image_size)
+        self.dino_timm_path_or_url = DINOSigLIP_VISION_BACKBONES[vision_backbone_id]["dino"]
+        self.siglip_timm_path_or_url = DINOSigLIP_VISION_BACKBONES[vision_backbone_id]["siglip"]
+
+        # Initialize both Featurizers (ViTs) by downloading from HF / TIMM Hub if necessary
+        self.dino_featurizer: VisionTransformer = timm.create_model(
+            self.dino_timm_path_or_url, pretrained=True, num_classes=0, img_size=self.default_image_size
+        )
+        self.dino_featurizer.eval()
+
+        self.siglip_featurizer: VisionTransformer = timm.create_model(
+            self.siglip_timm_path_or_url, pretrained=True, num_classes=0, img_size=self.default_image_size
+        )
+        self.siglip_featurizer.eval()
+
+        # Monkey-Patch the `forward()` function of the featurizers to ensure FSDP-compatibility
+        #   => Note: By default set `get_intermediate_layers` to return the *SECOND-TO-LAST* layer patches!
+        #   => TODO (siddk) Remove after resolution of https://github.com/pytorch/pytorch/issues/109385
+        self.dino_featurizer.forward = unpack_tuple(
+            partial(self.dino_featurizer.get_intermediate_layers, n={len(self.dino_featurizer.blocks) - 2})
+        )
+        self.siglip_featurizer.forward = unpack_tuple(
+            partial(self.siglip_featurizer.get_intermediate_layers, n={len(self.siglip_featurizer.blocks) - 2})
+        )
+
+        # Get Configs for _both_ Featurizers =>> Note :: Override default image size for larger resolution models
+        self.dino_data_cfg = timm.data.resolve_model_data_config(self.dino_featurizer)
+        self.dino_data_cfg["input_size"] = (3, self.default_image_size, self.default_image_size)
+
+        self.siglip_data_cfg = timm.data.resolve_model_data_config(self.siglip_featurizer)
+        self.siglip_data_cfg["input_size"] = (3, self.default_image_size, self.default_image_size)
+
+        # Initialize *both* Transforms
+        default_dino_transform = timm.data.create_transform(**self.dino_data_cfg, is_training=False)
+        default_siglip_transform = timm.data.create_transform(**self.siglip_data_cfg, is_training=False)
+
+        # Fix =>> SigLIP default transform resizes to *larger* than `self.default_image_size` (crops image)!!
+        assert isinstance(default_siglip_transform, Compose), "Unexpected `default_image_transform`!"
+        assert isinstance(default_siglip_transform.transforms[0], Resize)
+        default_siglip_transform = Compose(
+            [
+                Resize(self.default_image_size, interpolation=default_siglip_transform.transforms[0].interpolation),
+                *default_siglip_transform.transforms[1:],
+            ]
+        )
+
+        if self.image_resize_strategy == "resize-naive":
+            assert isinstance(default_dino_transform, Compose), "Unexpected `default_dino_image_transform`!"
+            assert isinstance(default_siglip_transform, Compose), "Unexpected `default_siglip_image_transform`!"
+            assert isinstance(default_dino_transform.transforms[0], Resize)
+            assert isinstance(default_siglip_transform.transforms[0], Resize)
+
+            target_size = (self.default_image_size, self.default_image_size)
+            dino_transform = Compose(
+                [
+                    Resize(target_size, interpolation=default_dino_transform.transforms[0].interpolation),
+                    *default_dino_transform.transforms[1:],
+                ]
+            )
+            siglip_transform = Compose(
+                [
+                    Resize(target_size, interpolation=default_siglip_transform.transforms[0].interpolation),
+                    *default_siglip_transform.transforms[1:],
+                ]
+            )
+
+            self.image_transform = DinoSigLIPImageTransform(dino_transform, siglip_transform)
+
+        elif self.image_resize_strategy == "resize-crop":
+            self.image_transform = DinoSigLIPImageTransform(default_dino_transform, default_siglip_transform)
+
+        elif self.image_resize_strategy == "letterbox":
+            assert isinstance(default_dino_transform, Compose), "Unexpected `default_dino_transform`!"
+            assert isinstance(default_siglip_transform, Compose), "Unexpected `default_siglip_transform`!"
+            assert (
+                "mean" in self.dino_data_cfg and "mean" in self.siglip_data_cfg
+            ), "DinoSigLIP `data_cfg` missing `mean`!"
+
+            # Compute Padding Fill Value(s) (rescaled normalization mean if applicable)
+            dino_fill = tuple([int(x * 255) for x in self.dino_data_cfg["mean"]])
+            siglip_fill = tuple([int(x * 255) for x in self.siglip_data_cfg["mean"]])
+
+            # Build New Transform
+            self.image_transform = DinoSigLIPImageTransform(
+                Compose([LetterboxPad(dino_fill), *default_dino_transform.transforms]),
+                Compose([LetterboxPad(siglip_fill), *default_siglip_transform.transforms]),
+            )
+
+        else:
+            raise ValueError(f"Image Resize Strategy `{self.image_resize_strategy}` is not supported!")
+
+    def get_fsdp_wrapping_policy(self) -> Callable:
+        """Return a simple FSDP policy that wraps each ViT block and then both of the _entire_ featurizers."""
+        vit_wrap_policy = partial(_module_wrap_policy, module_classes={VisionTransformer})
+        transformer_block_policy = partial(transformer_auto_wrap_policy, transformer_layer_cls={Block})
+        return partial(_or_policy, policies=[vit_wrap_policy, transformer_block_policy])
+
+    def forward(self, pixel_values: Dict[str, torch.Tensor]) -> torch.Tensor:
+        """Runs the transformed image/pixel tensors through each vision backbone, returning concatenated patches."""
+        dino_patches = self.dino_featurizer(pixel_values["dino"])
+        siglip_patches = self.siglip_featurizer(pixel_values["siglip"])
+
+        return torch.cat([dino_patches, siglip_patches], dim=2)
+
+    @property
+    def default_image_resolution(self) -> Tuple[int, int, int]:
+        return self.dino_data_cfg["input_size"]
+
+    @property
+    def embed_dim(self) -> int:
+        return self.dino_featurizer.embed_dim + self.siglip_featurizer.embed_dim
+
+    @property
+    def num_patches(self) -> int:
+        assert self.dino_featurizer.patch_embed.num_patches == self.siglip_featurizer.patch_embed.num_patches
+        return self.dino_featurizer.patch_embed.num_patches
+
+    @property
+    def half_precision_dtype(self) -> torch.dtype:
+        return torch.bfloat16
diff --git a/prismatic/models/backbones/vision/dinov2_vit.py b/prismatic/models/backbones/vision/dinov2_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1e858827100833764394f062693b1dd38c6015a
--- /dev/null
+++ b/prismatic/models/backbones/vision/dinov2_vit.py
@@ -0,0 +1,19 @@
+"""
+dinov2_vit.py
+"""
+
+from prismatic.models.backbones.vision.base_vision import TimmViTBackbone
+
+# Registry =>> Supported DINOv2 Vision Backbones (from TIMM) =>> Note:: Using DINOv2 w/ Registers!
+#   => Reference: https://arxiv.org/abs/2309.16588
+DINOv2_VISION_BACKBONES = {"dinov2-vit-l": "vit_large_patch14_reg4_dinov2.lvd142m"}
+
+
+class DinoV2ViTBackbone(TimmViTBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(
+            vision_backbone_id,
+            DINOv2_VISION_BACKBONES[vision_backbone_id],
+            image_resize_strategy,
+            default_image_size=default_image_size,
+        )
diff --git a/prismatic/models/backbones/vision/in1k_vit.py b/prismatic/models/backbones/vision/in1k_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0f6d3e0082e7e15f963b4123cdabe1deceb55d
--- /dev/null
+++ b/prismatic/models/backbones/vision/in1k_vit.py
@@ -0,0 +1,22 @@
+"""
+in1k_vit.py
+
+Vision Transformers trained / finetuned on ImageNet (ImageNet-21K =>> ImageNet-1K)
+"""
+
+from prismatic.models.backbones.vision.base_vision import TimmViTBackbone
+
+# Registry =>> Supported Vision Backbones (from TIMM)
+IN1K_VISION_BACKBONES = {
+    "in1k-vit-l": "vit_large_patch16_224.augreg_in21k_ft_in1k",
+}
+
+
+class IN1KViTBackbone(TimmViTBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(
+            vision_backbone_id,
+            IN1K_VISION_BACKBONES[vision_backbone_id],
+            image_resize_strategy,
+            default_image_size=default_image_size,
+        )
diff --git a/prismatic/models/backbones/vision/siglip_vit.py b/prismatic/models/backbones/vision/siglip_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ffdbbe8d343524d0914e40742dcfa2f11e7cd4b
--- /dev/null
+++ b/prismatic/models/backbones/vision/siglip_vit.py
@@ -0,0 +1,24 @@
+"""
+siglip_vit.py
+"""
+
+from prismatic.models.backbones.vision.base_vision import TimmViTBackbone
+
+# Registry =>> Supported SigLIP Vision Backbones (from TIMM) =>> Note:: Using SigLIP w/ Patch = 14 (but SO400M Arch)
+SIGLIP_VISION_BACKBONES = {
+    "siglip-vit-b16-224px": "vit_base_patch16_siglip_224",
+    "siglip-vit-b16-256px": "vit_base_patch16_siglip_256",
+    "siglip-vit-b16-384px": "vit_base_patch16_siglip_384",
+    "siglip-vit-so400m": "vit_so400m_patch14_siglip_224",
+    "siglip-vit-so400m-384px": "vit_so400m_patch14_siglip_384",
+}
+
+
+class SigLIPViTBackbone(TimmViTBackbone):
+    def __init__(self, vision_backbone_id: str, image_resize_strategy: str, default_image_size: int = 224) -> None:
+        super().__init__(
+            vision_backbone_id,
+            SIGLIP_VISION_BACKBONES[vision_backbone_id],
+            image_resize_strategy,
+            default_image_size=default_image_size,
+        )
diff --git a/prismatic/models/load.py b/prismatic/models/load.py
new file mode 100644
index 0000000000000000000000000000000000000000..322cbaaa4635ce3ee06df194f6efbabd65057503
--- /dev/null
+++ b/prismatic/models/load.py
@@ -0,0 +1,240 @@
+"""
+load.py
+
+Entry point for loading pretrained VLMs for inference; exposes functions for listing available models (with canonical
+IDs, mappings to paper experiments, and short descriptions), as well as for loading models (from disk or HF Hub).
+"""
+
+import json
+import os
+from pathlib import Path
+from typing import List, Optional, Union
+
+from huggingface_hub import HfFileSystem, hf_hub_download
+
+from prismatic.conf import ModelConfig
+from prismatic.models.materialize import get_llm_backbone_and_tokenizer, get_vision_backbone_and_transform
+from prismatic.models.registry import GLOBAL_REGISTRY, MODEL_REGISTRY
+from prismatic.models.vlas import OpenVLA
+from prismatic.models.vlas import SlotVLA
+from prismatic.models.vlms import PrismaticVLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.action_tokenizer import ActionTokenizer
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# === HF Hub Repository ===
+HF_HUB_REPO = "TRI-ML/prismatic-vlms"
+VLA_HF_HUB_REPO = "openvla/openvla-dev"
+
+
+# === Available Models ===
+def available_models() -> List[str]:
+    return list(MODEL_REGISTRY.keys())
+
+
+def available_model_names() -> List[str]:
+    return list(GLOBAL_REGISTRY.items())
+
+
+def get_model_description(model_id_or_name: str) -> str:
+    if model_id_or_name not in GLOBAL_REGISTRY:
+        raise ValueError(f"Couldn't find `{model_id_or_name = }; check `prismatic.available_model_names()`")
+
+    # Print Description & Return
+    print(json.dumps(description := GLOBAL_REGISTRY[model_id_or_name]["description"], indent=2))
+
+    return description
+
+
+# === Load Pretrained Model ===
+def load(
+    model_id_or_path: Union[str, Path],
+    hf_token: Optional[str] = None,
+    cache_dir: Optional[Union[str, Path]] = None,
+    load_for_training: bool = False,
+) -> PrismaticVLM:
+    """Loads a pretrained PrismaticVLM from either local disk or the HuggingFace Hub."""
+    if os.path.isdir(model_id_or_path):
+        overwatch.info(f"Loading from local path `{(run_dir := Path(model_id_or_path))}`")
+
+        # Get paths for `config.json` and pretrained checkpoint
+        config_json, checkpoint_pt = run_dir / "config.json", run_dir / "checkpoints" / "latest-checkpoint.pt"
+        assert config_json.exists(), f"Missing `config.json` for `{run_dir = }`"
+        assert checkpoint_pt.exists(), f"Missing checkpoint for `{run_dir = }`"
+    else:
+        if model_id_or_path not in GLOBAL_REGISTRY:
+            raise ValueError(f"Couldn't find `{model_id_or_path = }; check `prismatic.available_model_names()`")
+
+        overwatch.info(f"Downloading `{(model_id := GLOBAL_REGISTRY[model_id_or_path]['model_id'])} from HF Hub")
+        with overwatch.local_zero_first():
+            config_json = hf_hub_download(repo_id=HF_HUB_REPO, filename=f"{model_id}/config.json", cache_dir=cache_dir)
+            checkpoint_pt = hf_hub_download(
+                repo_id=HF_HUB_REPO, filename=f"{model_id}/checkpoints/latest-checkpoint.pt", cache_dir=cache_dir
+            )
+
+    # Load Model Config from `config.json`
+    with open(config_json, "r") as f:
+        model_cfg = json.load(f)["model"]
+
+    # = Load Individual Components necessary for Instantiating a VLM =
+    #   =>> Print Minimal Config
+    overwatch.info(
+        f"Found Config =>> Loading & Freezing [bold blue]{model_cfg['model_id']}[/] with:\n"
+        f"             Vision Backbone =>> [bold]{model_cfg['vision_backbone_id']}[/]\n"
+        f"             LLM Backbone    =>> [bold]{model_cfg['llm_backbone_id']}[/]\n"
+        f"             Arch Specifier  =>> [bold]{model_cfg['arch_specifier']}[/]\n"
+        f"             Checkpoint Path =>> [underline]`{checkpoint_pt}`[/]"
+    )
+
+    # Load Vision Backbone
+    overwatch.info(f"Loading Vision Backbone [bold]{model_cfg['vision_backbone_id']}[/]")
+    vision_backbone, image_transform = get_vision_backbone_and_transform(
+        model_cfg["vision_backbone_id"],
+        model_cfg["image_resize_strategy"],
+    )
+
+    # Load LLM Backbone --> note `inference_mode = True` by default when calling `load()`
+    overwatch.info(f"Loading Pretrained LLM [bold]{model_cfg['llm_backbone_id']}[/] via HF Transformers")
+    llm_backbone, tokenizer = get_llm_backbone_and_tokenizer(
+        model_cfg["llm_backbone_id"],
+        llm_max_length=model_cfg.get("llm_max_length", 2048),
+        hf_token=hf_token,
+        inference_mode=not load_for_training,
+    )
+
+    # Load VLM using `from_pretrained` (clobbers HF syntax... eventually should reconcile)
+    overwatch.info(f"Loading VLM [bold blue]{model_cfg['model_id']}[/] from Checkpoint")
+    vlm = PrismaticVLM.from_pretrained(
+        checkpoint_pt,
+        model_cfg["model_id"],
+        vision_backbone,
+        llm_backbone,
+        arch_specifier=model_cfg["arch_specifier"],
+        freeze_weights=not load_for_training,
+    )
+
+    return vlm
+
+
+# === Load Pretrained VLA Model ===
+def load_vla(
+    model_id_or_path: Union[str, Path],
+    hf_token: Optional[str] = None,
+    cache_dir: Optional[Union[str, Path]] = None,
+    load_for_training: bool = False,
+    step_to_load: Optional[int] = None,
+    model_type: str = "pretrained",
+    base_vlm: str = "",
+) -> PrismaticVLM:
+    """Loads a pretrained OpenVLA from either local disk or the HuggingFace Hub."""
+
+    # TODO (siddk, moojink) :: Unify semantics with `load()` above; right now, `load_vla()` assumes path points to
+    #   checkpoint `.pt` file, rather than the top-level run directory!
+    if os.path.isfile(model_id_or_path):
+        overwatch.info(f"Loading from local checkpoint path `{(checkpoint_pt := Path(model_id_or_path))}`")
+
+        # [Validate] Checkpoint Path should look like `.../<RUN_ID>/checkpoints/<CHECKPOINT_PATH>.pt`
+        assert (checkpoint_pt.suffix == ".pt") and (checkpoint_pt.parent.name == "checkpoints"), "Invalid checkpoint!"
+        run_dir = checkpoint_pt.parents[1]
+
+        # Get paths for `config.json`, `dataset_statistics.json` and pretrained checkpoint
+        config_json, dataset_statistics_json = run_dir / "config.json", run_dir / "dataset_statistics.json"
+        assert config_json.exists(), f"Missing `config.json` for `{run_dir = }`"
+        assert dataset_statistics_json.exists(), f"Missing `dataset_statistics.json` for `{run_dir = }`"
+
+    # Otherwise =>> try looking for a match on `model_id_or_path` on the HF Hub (`VLA_HF_HUB_REPO`)
+    else:
+        # Search HF Hub Repo via fsspec API
+        overwatch.info(f"Checking HF for `{(hf_path := str(Path(VLA_HF_HUB_REPO) / model_type / model_id_or_path))}`")
+        if not (tmpfs := HfFileSystem()).exists(hf_path):
+            raise ValueError(f"Couldn't find valid HF Hub Path `{hf_path = }`")
+
+        # Identify Checkpoint to Load (via `step_to_load`)
+        step_to_load = f"{step_to_load:06d}" if step_to_load is not None else None
+        valid_ckpts = tmpfs.glob(f"{hf_path}/checkpoints/step-{step_to_load if step_to_load is not None else ''}*.pt")
+        if (len(valid_ckpts) == 0) or (step_to_load is not None and len(valid_ckpts) != 1):
+            raise ValueError(f"Couldn't find a valid checkpoint to load from HF Hub Path `{hf_path}/checkpoints/")
+
+        # Call to `glob` will sort steps in ascending order (if `step_to_load` is None); just grab last element
+        target_ckpt = Path(valid_ckpts[-1]).name
+
+        overwatch.info(f"Downloading Model `{model_id_or_path}` Config & Checkpoint `{target_ckpt}`")
+        with overwatch.local_zero_first():
+            relpath = Path(model_type) / model_id_or_path
+            config_json = hf_hub_download(
+                repo_id=VLA_HF_HUB_REPO, filename=f"{(relpath / 'config.json')!s}", cache_dir=cache_dir
+            )
+            dataset_statistics_json = hf_hub_download(
+                repo_id=VLA_HF_HUB_REPO, filename=f"{(relpath / 'dataset_statistics.json')!s}", cache_dir=cache_dir
+            )
+            checkpoint_pt = hf_hub_download(
+                repo_id=VLA_HF_HUB_REPO, filename=f"{(relpath / 'checkpoints' / target_ckpt)!s}", cache_dir=cache_dir
+            )
+
+    # Load VLA Config (and corresponding base VLM `ModelConfig`) from `config.json`
+    with open(config_json, "r") as f:
+        vla_cfg = json.load(f)["vla"]
+        model_cfg = ModelConfig.get_choice_class(vla_cfg["base_vlm"])()
+
+    # Load Dataset Statistics for Action Denormalization
+    with open(dataset_statistics_json, "r") as f:
+        norm_stats = json.load(f)
+
+    # = Load Individual Components necessary for Instantiating a VLA (via base VLM components) =
+    #   =>> Print Minimal Config
+    overwatch.info(
+        f"Found Config =>> Loading & Freezing [bold blue]{model_cfg.model_id}[/] with:\n"
+        f"             Vision Backbone =>> [bold]{model_cfg.vision_backbone_id}[/]\n"
+        f"             LLM Backbone    =>> [bold]{model_cfg.llm_backbone_id}[/]\n"
+        f"             Arch Specifier  =>> [bold]{model_cfg.arch_specifier}[/]\n"
+        f"             Checkpoint Path =>> [underline]`{checkpoint_pt}`[/]"
+    )
+
+    # Load Vision Backbone
+    overwatch.info(f"Loading Vision Backbone [bold]{model_cfg.vision_backbone_id}[/]")
+    vision_backbone, image_transform = get_vision_backbone_and_transform(
+        model_cfg.vision_backbone_id,
+        model_cfg.image_resize_strategy,
+    )
+
+    # Load LLM Backbone --> note `inference_mode = True` by default when calling `load()`
+    overwatch.info(f"Loading Pretrained LLM [bold]{model_cfg.llm_backbone_id}[/] via HF Transformers")
+    llm_backbone, tokenizer = get_llm_backbone_and_tokenizer(
+        model_cfg.llm_backbone_id,
+        llm_max_length=model_cfg.llm_max_length,
+        hf_token=hf_token,
+        inference_mode=not load_for_training,
+    )
+
+    # Create Action Tokenizer
+    action_tokenizer = ActionTokenizer(llm_backbone.get_tokenizer())
+
+    # Load VLM using `from_pretrained` (clobbers HF syntax... eventually should reconcile)
+    overwatch.info(f"Loading VLA [bold blue]{model_cfg.model_id}[/] from Checkpoint")
+    if base_vlm == vla_cfg["base_vlm"]:
+        vla = OpenVLA.from_pretrained(
+            checkpoint_pt,
+            model_cfg.model_id,
+            vision_backbone,
+            llm_backbone,
+            arch_specifier=model_cfg.arch_specifier,
+            freeze_weights=not load_for_training,
+            norm_stats=norm_stats,
+            action_tokenizer=action_tokenizer,
+        )
+    else:
+        vla = SlotVLA.from_pretrained(
+            checkpoint_pt,
+            model_cfg.model_id,
+            vision_backbone,
+            llm_backbone,
+            arch_specifier=model_cfg.arch_specifier,
+            freeze_weights=not load_for_training,
+            norm_stats=norm_stats,
+            action_tokenizer=action_tokenizer,
+        )
+
+    return vla
diff --git a/prismatic/models/materialize.py b/prismatic/models/materialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..df1ad612eceeaf1af2c8a58dc8242bceac778af5
--- /dev/null
+++ b/prismatic/models/materialize.py
@@ -0,0 +1,130 @@
+"""
+materialize.py
+
+Factory class for initializing Vision Backbones, LLM Backbones, and VLMs from a set registry; provides and exports
+individual functions for clear control flow.
+"""
+
+from typing import Optional, Tuple
+
+from transformers import PreTrainedTokenizerBase
+
+from prismatic.models.backbones.llm import LLaMa2LLMBackbone, LLMBackbone, MistralLLMBackbone, PhiLLMBackbone
+from prismatic.models.backbones.vision import (
+    CLIPViTBackbone,
+    DinoCLIPViTBackbone,
+    DinoSigLIPViTBackbone,
+    DinoV2ViTBackbone,
+    ImageTransform,
+    IN1KViTBackbone,
+    SigLIPViTBackbone,
+    VisionBackbone,
+)
+from prismatic.models.vlms import PrismaticVLM
+
+# === Registries =>> Maps ID --> {cls(), kwargs} :: Different Registries for Vision Backbones, LLM Backbones, VLMs ===
+# fmt: off
+
+# === Vision Backbone Registry ===
+VISION_BACKBONES = {
+    # === 224px Backbones ===
+    "clip-vit-l": {"cls": CLIPViTBackbone, "kwargs": {"default_image_size": 224}},
+    "siglip-vit-so400m": {"cls": SigLIPViTBackbone, "kwargs": {"default_image_size": 224}},
+    "dinov2-vit-l": {"cls": DinoV2ViTBackbone, "kwargs": {"default_image_size": 224}},
+    "in1k-vit-l": {"cls": IN1KViTBackbone, "kwargs": {"default_image_size": 224}},
+    "dinosiglip-vit-so-224px": {"cls": DinoSigLIPViTBackbone, "kwargs": {"default_image_size": 224}},
+
+    # === Assorted CLIP Backbones ===
+    "clip-vit-b": {"cls": CLIPViTBackbone, "kwargs": {"default_image_size": 224}},
+    "clip-vit-l-336px": {"cls": CLIPViTBackbone, "kwargs": {"default_image_size": 336}},
+
+    # === Assorted SigLIP Backbones ===
+    "siglip-vit-b16-224px": {"cls": SigLIPViTBackbone, "kwargs": {"default_image_size": 224}},
+    "siglip-vit-b16-256px": {"cls": SigLIPViTBackbone, "kwargs": {"default_image_size": 256}},
+    "siglip-vit-b16-384px": {"cls": SigLIPViTBackbone, "kwargs": {"default_image_size": 384}},
+    "siglip-vit-so400m-384px": {"cls": SigLIPViTBackbone, "kwargs": {"default_image_size": 384}},
+
+    # === Fused Backbones ===
+    "dinoclip-vit-l-336px": {"cls": DinoCLIPViTBackbone, "kwargs": {"default_image_size": 336}},
+    "dinosiglip-vit-so-384px": {"cls": DinoSigLIPViTBackbone, "kwargs": {"default_image_size": 384}},
+}
+
+
+# === Language Model Registry ===
+LLM_BACKBONES = {
+    # === LLaMa-2 Pure (Non-Chat) Backbones ===
+    "llama2-7b-pure": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+    "llama2-13b-pure": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+
+    # === LLaMa-2 Chat Backbones ===
+    "llama2-7b-chat": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+    "llama2-13b-chat": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+
+    # === Vicuna-v1.5 Backbones ===
+    "vicuna-v15-7b": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+    "vicuna-v15-13b": {"cls": LLaMa2LLMBackbone, "kwargs": {}},
+
+    # === Mistral v0.1 Backbones ===
+    "mistral-v0.1-7b-pure": {"cls": MistralLLMBackbone, "kwargs": {}},
+    "mistral-v0.1-7b-instruct": {"cls": MistralLLMBackbone, "kwargs": {}},
+
+    # === Phi-2 Backbone ===
+    "phi-2-3b": {"cls": PhiLLMBackbone, "kwargs": {}},
+}
+
+# fmt: on
+
+
+def get_vision_backbone_and_transform(
+    vision_backbone_id: str, image_resize_strategy: str
+) -> Tuple[VisionBackbone, ImageTransform]:
+    """Instantiate a Vision Backbone, returning both the nn.Module wrapper class and default Image Transform."""
+    if vision_backbone_id in VISION_BACKBONES:
+        vision_cfg = VISION_BACKBONES[vision_backbone_id]
+        vision_backbone: VisionBackbone = vision_cfg["cls"](
+            vision_backbone_id, image_resize_strategy, **vision_cfg["kwargs"]
+        )
+        image_transform = vision_backbone.get_image_transform()
+        return vision_backbone, image_transform
+
+    else:
+        raise ValueError(f"Vision Backbone `{vision_backbone_id}` is not supported!")
+
+
+def get_llm_backbone_and_tokenizer(
+    llm_backbone_id: str,
+    llm_max_length: int = 2048,
+    hf_token: Optional[str] = None,
+    inference_mode: bool = False,
+) -> Tuple[LLMBackbone, PreTrainedTokenizerBase]:
+    if llm_backbone_id in LLM_BACKBONES:
+        llm_cfg = LLM_BACKBONES[llm_backbone_id]
+        llm_backbone: LLMBackbone = llm_cfg["cls"](
+            llm_backbone_id,
+            llm_max_length=llm_max_length,
+            hf_token=hf_token,
+            inference_mode=inference_mode,
+            **llm_cfg["kwargs"],
+        )
+        tokenizer = llm_backbone.get_tokenizer()
+        return llm_backbone, tokenizer
+
+    else:
+        raise ValueError(f"LLM Backbone `{llm_backbone_id}` is not supported!")
+
+
+def get_vlm(
+    model_id: str,
+    arch_specifier: str,
+    vision_backbone: VisionBackbone,
+    llm_backbone: LLMBackbone,
+    enable_mixed_precision_training: bool = True,
+) -> PrismaticVLM:
+    """Lightweight wrapper around initializing a VLM, mostly for future-proofing (if one wants to add a new VLM)."""
+    return PrismaticVLM(
+        model_id,
+        vision_backbone,
+        llm_backbone,
+        enable_mixed_precision_training=enable_mixed_precision_training,
+        arch_specifier=arch_specifier,
+    )
diff --git a/prismatic/models/registry.py b/prismatic/models/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..eabe6acc5fd2d567ddfa6d4e5a3894c5e8ad19a5
--- /dev/null
+++ b/prismatic/models/registry.py
@@ -0,0 +1,691 @@
+"""
+registry.py
+
+Exhaustive list of pretrained VLMs (with full descriptions / links to corresponding names and sections of paper).
+"""
+
+# === Pretrained Model Registry ===
+# fmt: off
+MODEL_REGISTRY = {
+    # === LLaVa v1.5 Reproductions ===
+    "reproduction-llava-v15+7b": {
+        "model_id": "reproduction-llava-v15+7b",
+        "names": ["LLaVa v1.5 7B (Reproduction)"],
+        "description": {
+            "name": "LLaVa v1.5 7B (Reproduction)",
+            "optimization_procedure": "multi-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "reproduction-llava-v15+13b": {
+        "model_id": "reproduction-llava-v15+13b",
+        "names": ["LLaVa v1.5 13B (Reproduction)"],
+        "description": {
+            "name": "LLaVa v1.5 13B (Reproduction)",
+            "optimization_procedure": "multi-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    # === Section 4.1 :: Optimization Procedure ===
+    "one-stage+7b": {
+        "model_id": "one-stage+7b",
+        "names": [
+            "One-Stage 7B",
+            "Single-Stage 7B",
+            "Frozen ViT (Single-Stage)",
+            "CLIP ViT-L 336px (Letterbox)",
+            "CLIP ViT-L 336px",
+            "Vicuña v1.5 7B",
+            "1 Epoch",
+            "Base",
+        ],
+        "description": {
+            "name": "Single-Stage 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "one-stage+13b": {
+        "model_id": "one-stage+13b",
+        "names": [
+            "One-Stage 13B",
+            "Single-Stage 13B",
+            "Vicuña v1.5 13B",
+        ],
+        "description": {
+            "name": "Single-Stage 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    "full-ft-multi-stage+7b": {
+        "model_id": "full-ft-multi-stage+7b",
+        "names": ["Finetune ViT (Multi-Stage)"],
+        "description": {
+            "name": "Finetune ViT (Multi-Stage)",
+            "optimization_procedure": "multi-stage-full-finetune",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "full-ft-one-stage+7b": {
+        "model_id": "full-ft-one-stage+7b",
+        "names": ["Finetune ViT (Single-Stage)"],
+        "description": {
+            "name": "Finetune ViT (Single-Stage)",
+            "optimization_procedure": "single-stage-full-finetune",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    # === Section 4.2 :: Image Processing and Visual Representations ===
+    "in1k-224px+7b": {
+        "model_id": "in1k-224px+7b",
+        "names": ["IN1K ViT-L 224px"],
+        "description": {
+            "name": "IN1K ViT-L 224px",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "ImageNet-21K+1K ViT-L/16 @ 224px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+    "dinov2-224px+7b": {
+        "model_id": "dinov2-224px+7b",
+        "names": ["DINOv2 ViT-L 224px"],
+        "description": {
+            "name": "DINOv2 ViT-L 224px",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 @ 224px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+    "clip-224px+7b": {
+        "model_id": "clip-224px+7b",
+        "names": ["CLIP ViT-L 224px"],
+        "description": {
+            "name": "CLIP ViT-L 224px",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 224px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+    "siglip-224px+7b": {
+        "model_id": "siglip-224px+7b",
+        "names": ["SigLIP ViT-SO 224px"],
+        "description": {
+            "name": "SigLIP ViT-SO 224px",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 224px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+
+    "clip-336px-resize-crop+7b": {
+        "model_id": "clip-336px-resize-crop+7b",
+        "names": ["CLIP ViT-L 336px (Resize Crop)"],
+        "description": {
+            "name": "CLIP ViT-L 336px (Resize Crop)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Resize Crop",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "clip-336px-resize-naive+7b": {
+        "model_id": "clip-336px-resize-naive+7b",
+        "names": ["CLIP ViT-L 336px (Naive Resize)", "CLIP 336px (Naive Resize)"],
+        "description": {
+            "name": "CLIP ViT-L 336px (Naive Resize)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "siglip-384px-letterbox+7b": {
+        "model_id": "siglip-384px-letterbox+7b",
+        "names": ["SigLIP ViT-SO 384px (Letterbox)", "SigLIP ViT-SO 384px"],
+        "description": {
+            "name": "SigLIP ViT-SO 384px (Letterbox)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "siglip-384px-resize-crop+7b": {
+        "model_id": "siglip-384px-resize-crop+7b",
+        "names": ["SigLIP ViT-SO 384px (Resize Crop)"],
+        "description": {
+            "name": "SigLIP ViT-SO 384px (Resize Crop)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Resize Crop",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "siglip-384px-resize-naive+7b": {
+        "model_id": "siglip-384px-resize-naive+7b",
+        "names": ["SigLIP ViT-SO 384px (Naive Resize)", "SigLIP 384px (Naive Resize)"],
+        "description": {
+            "name": "SigLIP ViT-SO 384px (Naive Resize)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    "dinoclip-336px-letterbox+7b": {
+        "model_id": "dinoclip-336px-letterbox+7b",
+        "names": ["DINOv2 + CLIP 336px (Letterbox)"],
+        "description": {
+            "name": "DINOv2 + CLIP 336px (Letterbox)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "dinoclip-336px-resize-naive+7b": {
+        "model_id": "dinoclip-336px-resize-naive+7b",
+        "names": ["DINOv2 + CLIP 336px (Naive Resize)"],
+        "description": {
+            "name": "DINOv2 + CLIP 336px (Naive Resize)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "dinosiglip-384px-letterbox+7b": {
+        "model_id": "dinosiglip-384px-letterbox+7b",
+        "names": ["DINOv2 + SigLIP 384px (Letterbox)"],
+        "description": {
+            "name": "DINOv2 + SigLIP 384px (Letterbox)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-L/14 @ 384px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "dinosiglip-384px-resize-naive+7b": {
+        "model_id": "dinosiglip-384px-resize-naive+7b",
+        "names": ["DINOv2 + SigLIP 384px (Naive Resize)"],
+        "description": {
+            "name": "DINOv2 + SigLIP 384px (Naive Resize)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-L/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    # === Section 4.3 :: Language Models ===
+    "llama2+7b": {
+        "model_id": "llama2+7b",
+        "names": ["Llama-2 7B"],
+        "description": {
+            "name": "Llama-2 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+    "llama2+13b": {
+        "model_id": "llama2+13b",
+        "names": ["Llama-2 13B"],
+        "description": {
+            "name": "Llama-2 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        },
+    },
+
+    "vicuna-no-cotraining+7b": {
+        "model_id": "vicuna-no-cotraining+7b",
+        "names": ["Vicuña v1.5 7B (No Co-training)"],
+        "description": {
+            "name": "Vicuña v1.5 7B (No Co-training)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Multimodal-Only"],
+            "train_epochs": 1,
+        },
+    },
+    "llama2-no-cotraining+7b": {
+        "model_id": "llama2-no-cotraining+7b",
+        "names": ["Llama-2 7B (No Co-training)"],
+        "description": {
+            "name": "Llama-2 7B (No Co-training)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Multimodal-Only"],
+            "train_epochs": 1,
+        },
+    },
+
+    # === Section 4.4 :: Scaling Properties ===
+    "train-1.25-epochs+7b": {
+        "model_id": "train-1.25-epochs+7b",
+        "names": ["1.25 Epochs"],
+        "description": {
+            "name": "1.25 Epochs",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1.25,
+        }
+    },
+    "train-1.5-epochs+7b": {
+        "model_id": "train-1.5-epochs+7b",
+        "names": ["1.5 Epochs"],
+        "description": {
+            "name": "1.5 Epochs",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1.5,
+        }
+    },
+    "train-2-epochs+7b": {
+        "model_id": "train-2-epochs+7b",
+        "names": ["2 Epochs"],
+        "description": {
+            "name": "2 Epochs",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 2,
+        }
+    },
+    "train-3-epochs+7b": {
+        "model_id": "train-3-epochs+7b",
+        "names": ["3 Epochs"],
+        "description": {
+            "name": "3 Epochs",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 3,
+        }
+    },
+
+    "llava-lvis4v+7b": {
+        "model_id": "llava-lvis4v+7b",
+        "names": ["Base + LVIS-4V"],
+        "description": {
+            "name": "Base + LVIS-4V",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V"],
+            "train_epochs": 1,
+        }
+    },
+    "llava-lrv+7b": {
+        "model_id": "llava-lrv+7b",
+        "names": ["Base + LRV"],
+        "description": {
+            "name": "Base + LRV",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LRV-Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "llava-lvis4v-lrv+7b": {
+        "model_id": "llava-lvis4v-lrv+7b",
+        "names": ["Base + LVIS-4V + LRV"],
+        "description": {
+            "name": "Base + LVIS-4V + LRV",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Vicuña v1.5 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 1,
+        }
+    },
+
+    # ===
+
+    # === CLIP Prism Models ===
+    "prism-clip-controlled+7b": {
+        "model_id": "prism-clip-controlled+7b",
+        "names": ["Prism-CLIP 7B (Controlled)"],
+        "description": {
+            "name": "CLIP Prism 7B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-clip-controlled+13b": {
+        "model_id": "prism-clip-controlled+13b",
+        "names": ["Prism-CLIP 13B (Controlled)"],
+        "description": {
+            "name": "CLIP Prism 13B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-clip+7b": {
+        "model_id": "prism-clip+7b",
+        "names": ["Prism-CLIP 7B"],
+        "description": {
+            "name": "CLIP Prism 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        },
+    },
+    "prism-clip+13b": {
+        "model_id": "prism-clip+13b",
+        "names": ["Prism-CLIP 13B"],
+        "description": {
+            "name": "CLIP Prism 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        },
+    },
+
+    # === SigLIP Prism Models ==
+    "prism-siglip-controlled+7b": {
+        "model_id": "prism-siglip-controlled+7b",
+        "names": ["Prism-SigLIP 7B (Controlled)"],
+        "description": {
+            "name": "SigLIP Prism 7B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-siglip-controlled+13b": {
+        "model_id": "prism-siglip-controlled+7b",
+        "names": ["Prism-SigLIP 13B (Controlled)"],
+        "description": {
+            "name": "SigLIP Prism 13B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-siglip+7b": {
+        "model_id": "prism-siglip+7b",
+        "names": ["Prism-SigLIP 7B"],
+        "description": {
+            "name": "SigLIP Prism 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        }
+    },
+    "prism-siglip+13b": {
+        "model_id": "prism-siglip+13b",
+        "names": ["Prism-SigLIP 13B"],
+        "description": {
+            "name": "SigLIP Prism 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        }
+    },
+
+    # === DINOSigLIP Prism Models ===
+    "prism-dinosiglip-controlled+7b": {
+        "model_id": "prism-dinosiglip-controlled+7b",
+        "names": ["Prism-DINOSigLIP 7B (Controlled)", "Prism 7B (Controlled)"],
+        "description": {
+            "name": "DINOSigLIP Prism 7B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-dinosiglip-controlled+13b": {
+        "model_id": "prism-dinosiglip-controlled+13b",
+        "names": ["Prism-DINOSigLIP 13B (Controlled)", "Prism 13B (Controlled)"],
+        "description": {
+            "name": "DINOSigLIP Prism 13B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-dinosiglip+7b": {
+        "model_id": "prism-dinosiglip+7b",
+        "names": ["Prism-DINOSigLIP 7B"],
+        "description": {
+            "name": "DINOSigLIP Prism 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        },
+    },
+    "prism-dinosiglip+13b": {
+        "model_id": "prism-dinosiglip+13b",
+        "names": ["Prism-DINOSigLIP 13B"],
+        "description": {
+            "name": "DINOSigLIP Prism 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO/14 @ 384px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 13B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        },
+    },
+
+    # === DINOSigLIP 224px Prism Models ===
+    "prism-dinosiglip-224px-controlled+7b": {
+        "model_id": "prism-dinosiglip-224px-controlled+7b",
+        "names": ["Prism-DINOSigLIP 224px 7B (Controlled)"],
+        "description": {
+            "name": "DINOSigLIP 224px 7B (Controlled)",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO 14 @ 224px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "prism-dinosiglip-224px+7b": {
+        "model_id": "prism-dinosiglip-224px+7b",
+        "names": ["Prism-DINOSigLIP 224px 7B"],
+        "description": {
+            "name": "DINOSigLIP 224px 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "DINOv2 ViT-L/14 + SigLIP ViT-SO 14 @ 224px",
+            "image_processing": "Naive Resize",
+            "language_model": "Llama-2 7B",
+            "datasets": ["LLaVa v1.5 Instruct", "LVIS-Instruct-4V", "LRV-Instruct"],
+            "train_epochs": 2,
+        }
+    },
+
+    # === Additional LLM Backbones ===
+    "llama2-chat+7b": {
+        "model_id": "llama2-chat+7b",
+        "names": ["Llama-2 Chat 7B"],
+        "description": {
+            "name": "Llama-2 Chat 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Llama-2 Chat 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "llama2-chat+13b": {
+        "model_id": "llama2-chat+13b",
+        "names": ["Llama-2 Chat 13B"],
+        "description": {
+            "name": "Llama-2 Chat 13B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Llama-2 Chat 13B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "mistral-v0.1+7b": {
+        "model_id": "mistral-v0.1+7b",
+        "names": ["Mistral v0.1 7B"],
+        "description": {
+            "name": "Mistral v0.1 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Mistral v0.1 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "mistral-instruct-v0.1+7b": {
+        "model_id": "mistral-instruct-v0.1+7b",
+        "names": ["Mistral Instruct v0.1 7B"],
+        "description": {
+            "name": "Mistral Instruct v0.1 7B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Mistral Instruct v0.1 7B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+    "phi-2+3b": {
+        "model_id": "phi-2+3b",
+        "names": ["Phi-2 3B"],
+        "description": {
+            "name": "Phi-2 3B",
+            "optimization_procedure": "single-stage",
+            "visual_representation": "CLIP ViT-L/14 @ 336px",
+            "image_processing": "Letterbox",
+            "language_model": "Phi-2 3B",
+            "datasets": ["LLaVa v1.5 Instruct"],
+            "train_epochs": 1,
+        }
+    },
+}
+
+# Build Global Registry (Model ID, Name) -> Metadata
+GLOBAL_REGISTRY = {name: v for k, v in MODEL_REGISTRY.items() for name in [k] + v["names"]}
+
+# fmt: on
diff --git a/prismatic/models/vlas/__init__.py b/prismatic/models/vlas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e0ac7bb2da66c21b13073a1b1a742765c9a0b19
--- /dev/null
+++ b/prismatic/models/vlas/__init__.py
@@ -0,0 +1,2 @@
+from .openvla import OpenVLA
+from .slotvla import SlotVLA
diff --git a/prismatic/models/vlas/openvla.py b/prismatic/models/vlas/openvla.py
new file mode 100644
index 0000000000000000000000000000000000000000..a451a8e6e4a689d8711ee12acc0a2bf25de5c5fb
--- /dev/null
+++ b/prismatic/models/vlas/openvla.py
@@ -0,0 +1,130 @@
+"""
+openvla.py
+
+PyTorch Module defining OpenVLA as a lightweight wrapper around a PrismaticVLM; defines custom logic around
+discretizing actions with the ActionTokenizer.
+"""
+
+from typing import Dict, List, Optional
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import LlamaTokenizerFast
+
+from prismatic.models.vlms.prismatic import PrismaticVLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.action_tokenizer import ActionTokenizer
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+class OpenVLA(PrismaticVLM):
+    def __init__(
+        self,
+        *args,
+        norm_stats: Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]],
+        action_tokenizer: ActionTokenizer,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.norm_stats = norm_stats
+        self.action_tokenizer = action_tokenizer
+
+    @torch.inference_mode()
+    def predict_action(
+        self, image: Image, instruction: str, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """
+        Core function for VLA inference; maps input image and task instruction to continuous action (de-tokenizes).
+
+        @param image: PIL Image as [height, width, 3]
+        @param instruction: Task instruction string
+        @param unnorm_key: Optional dataset name for retrieving un-normalizing statistics; if None, checks that model
+                           was trained only on a single dataset, and retrieves those statistics.
+
+        @return Unnormalized (continuous) action vector --> end-effector deltas.
+        """
+        image_transform, tokenizer = self.vision_backbone.image_transform, self.llm_backbone.tokenizer
+
+        # Build VLA Prompt
+        prompt_builder = self.get_prompt_builder()
+        prompt_builder.add_turn(role="human", message=f"What action should the robot take to {instruction.lower()}?")
+        prompt_text = prompt_builder.get_prompt()
+
+        # Prepare Inputs
+        input_ids = tokenizer(prompt_text, truncation=True, return_tensors="pt").input_ids.to(self.device)
+        if isinstance(tokenizer, LlamaTokenizerFast):
+            # Note: We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
+            #       in order for the predictions to match the training configuration and be accurate.
+            input_ids = torch.cat(
+                (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(self.device)), dim=1
+            )
+        else:
+            raise ValueError(f"Unsupported `tokenizer` type = {type(tokenizer)}")
+
+        # Preprocess Image
+        pixel_values = image_transform(image)
+        if isinstance(pixel_values, torch.Tensor):
+            pixel_values = pixel_values[None, ...].to(self.device)
+        elif isinstance(pixel_values, dict):
+            pixel_values = {k: v[None, ...].to(self.device) for k, v in pixel_values.items()}
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Invoke super().generate --> taps into `GenerationMixin` which (redirects) to `forward()`
+        autocast_dtype = self.llm_backbone.half_precision_dtype
+        with torch.autocast("cuda", dtype=autocast_dtype, enabled=self.enable_mixed_precision_training):
+            # fmt: off
+            generated_ids = super(PrismaticVLM, self).generate(
+                input_ids=input_ids,                            # Shape: [1, seq]
+                pixel_values=pixel_values,                      # Shape: [1, 3, res, res] or Dict[str, ...]
+                max_new_tokens=self.get_action_dim(unnorm_key),
+                **kwargs
+            )
+            # fmt: on
+
+        # Extract predicted action tokens and translate into (normalized) continuous actions
+        predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :]
+        normalized_actions = self.action_tokenizer.decode_token_ids_to_actions(predicted_action_token_ids.cpu().numpy())
+
+        # Un-normalize Actions
+        action_norm_stats = self.get_action_stats(unnorm_key)
+        mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+        action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict, unnorm_key: str) -> str:
+        if unnorm_key is None:
+            assert len(norm_stats) == 1, (
+                f"Your model was trained on more than one dataset, please pass a `unnorm_key` from the following "
+                f"options to choose the statistics used for un-normalizing actions: {norm_stats.keys()}"
+            )
+            unnorm_key = next(iter(norm_stats.keys()))
+
+        # Error Handling
+        assert (
+            unnorm_key in norm_stats
+        ), f"The `unnorm_key` you chose is not in the set of available statistics; choose from: {norm_stats.keys()}"
+
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+
+        return len(self.norm_stats[unnorm_key]["action"]["q01"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict:
+        """Dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+
+        return self.norm_stats[unnorm_key]["action"]
diff --git a/prismatic/models/vlas/slotvla.py b/prismatic/models/vlas/slotvla.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dd2c855bc79e34240fc04fdd3e7a3287dd93573
--- /dev/null
+++ b/prismatic/models/vlas/slotvla.py
@@ -0,0 +1,359 @@
+"""
+openvla.py
+
+PyTorch Module defining OpenVLA as a lightweight wrapper around a PrismaticVLM; defines custom logic around
+discretizing actions with the ActionTokenizer.
+"""
+
+from typing import Dict, List, Optional
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import LlamaTokenizerFast
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from prismatic.models.vlms.prismatic import PrismaticVLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.action_tokenizer import ActionTokenizer
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Type, Union
+
+from prismatic.models.backbones.llm import LLMBackbone
+from prismatic.models.backbones.vision import VisionBackbone
+from prismatic.models.addons.slot_attention import SlotAttention
+
+# HuggingFace Default / LLaMa-2 IGNORE_INDEX (for labels)
+IGNORE_INDEX = -100
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+class SlotVLA(PrismaticVLM):
+    def __init__(
+        self,
+        *args,
+        norm_stats: Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]],
+        action_tokenizer: ActionTokenizer,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self.norm_stats = norm_stats
+        self.action_tokenizer = action_tokenizer
+        self.slot_attention = SlotAttention()
+
+    @torch.inference_mode()
+    def predict_action(
+        self, image: Image, instruction: str, unnorm_key: Optional[str] = None, **kwargs: str
+    ) -> np.ndarray:
+        """
+        Core function for VLA inference; maps input image and task instruction to continuous action (de-tokenizes).
+
+        @param image: PIL Image as [height, width, 3]
+        @param instruction: Task instruction string
+        @param unnorm_key: Optional dataset name for retrieving un-normalizing statistics; if None, checks that model
+                           was trained only on a single dataset, and retrieves those statistics.
+
+        @return Unnormalized (continuous) action vector --> end-effector deltas.
+        """
+        image_transform, tokenizer = self.vision_backbone.image_transform, self.llm_backbone.tokenizer
+
+        # Build VLA Prompt
+        prompt_builder = self.get_prompt_builder()
+        prompt_builder.add_turn(role="human", message=f"What action should the robot take to {instruction.lower()}?")
+        prompt_text = prompt_builder.get_prompt()
+
+        # Prepare Inputs
+        input_ids = tokenizer(prompt_text, truncation=True, return_tensors="pt").input_ids.to(self.device)
+        if isinstance(tokenizer, LlamaTokenizerFast):
+            # Note: We need to add this special empty token ('') after the colon (':') token in "ASSISTANT:"
+            #       in order for the predictions to match the training configuration and be accurate.
+            input_ids = torch.cat(
+                (input_ids, torch.unsqueeze(torch.Tensor([29871]).long(), dim=0).to(self.device)), dim=1
+            )
+        else:
+            raise ValueError(f"Unsupported `tokenizer` type = {type(tokenizer)}")
+
+        # Preprocess Image
+        pixel_values = image_transform(image)
+        if isinstance(pixel_values, torch.Tensor):
+            pixel_values = pixel_values[None, ...].to(self.device)
+        elif isinstance(pixel_values, dict):
+            pixel_values = {k: v[None, ...].to(self.device) for k, v in pixel_values.items()}
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Invoke super().generate --> taps into `GenerationMixin` which (redirects) to `forward()`
+        autocast_dtype = self.llm_backbone.half_precision_dtype
+        with torch.autocast("cuda", dtype=autocast_dtype, enabled=self.enable_mixed_precision_training):
+            # fmt: off
+            generated_ids = super(PrismaticVLM, self).generate(
+                input_ids=input_ids,                            # Shape: [1, seq]
+                pixel_values=pixel_values,                      # Shape: [1, 3, res, res] or Dict[str, ...]
+                max_new_tokens=self.get_action_dim(unnorm_key),
+                **kwargs
+            )
+            # fmt: on
+
+        # Extract predicted action tokens and translate into (normalized) continuous actions
+        predicted_action_token_ids = generated_ids[0, -self.get_action_dim(unnorm_key) :]
+        normalized_actions = self.action_tokenizer.decode_token_ids_to_actions(predicted_action_token_ids.cpu().numpy())
+
+        # Un-normalize Actions
+        action_norm_stats = self.get_action_stats(unnorm_key)
+        mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+        action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_checkpoint: Path,
+        model_id: str,
+        vision_backbone: VisionBackbone,
+        llm_backbone: LLMBackbone,
+        enable_mixed_precision_training: bool = True,
+        arch_specifier: str = "gelu-mlp",
+        freeze_weights: bool = True,
+        **kwargs,
+    ) -> PrismaticVLM:
+        """Initialize a PrismaticVLM from a pretrained checkpoint, freezing all weights, tailored for inference."""
+        vlm = cls(
+            model_id,
+            vision_backbone,
+            llm_backbone,
+            enable_mixed_precision_training=enable_mixed_precision_training,
+            arch_specifier=arch_specifier,
+            **kwargs,
+        )
+
+        # Load from Checkpoint (Custom --> should load both *projector* and *llm* weights)
+        model_state_dict = torch.load(pretrained_checkpoint, map_location="cpu")["model"]
+        assert (
+            "projector" in model_state_dict and "llm_backbone" in model_state_dict
+        ), "PrismaticVLM `from_pretrained` expects checkpoint with keys for `projector` AND `llm_backbone`!"
+
+        vlm.projector.load_state_dict(model_state_dict["projector"])
+        vlm.llm_backbone.load_state_dict(model_state_dict["llm_backbone"])
+
+        if "vision_backbone" in model_state_dict.keys():
+            vlm.vision_backbone.load_state_dict(model_state_dict["vision_backbone"])
+        
+        if "slot_attention" in model_state_dict.keys():
+            vlm.slot_attention.load_state_dict(model_state_dict["slot_attention"])
+
+        # Freeze Weights
+        if freeze_weights:
+            vlm.requires_grad_(False)
+            vlm.eval()
+
+        return vlm
+
+
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict, unnorm_key: str) -> str:
+        if unnorm_key is None:
+            assert len(norm_stats) == 1, (
+                f"Your model was trained on more than one dataset, please pass a `unnorm_key` from the following "
+                f"options to choose the statistics used for un-normalizing actions: {norm_stats.keys()}"
+            )
+            unnorm_key = next(iter(norm_stats.keys()))
+
+        # Error Handling
+        assert (
+            unnorm_key in norm_stats
+        ), f"The `unnorm_key` you chose is not in the set of available statistics; choose from: {norm_stats.keys()}"
+
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+
+        return len(self.norm_stats[unnorm_key]["action"]["q01"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict:
+        """Dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+
+        return self.norm_stats[unnorm_key]["action"]
+
+
+    # ruff: noqa: C901
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        multimodal_indices: Optional[torch.LongTensor] = None,
+    ) -> CausalLMOutputWithPast:
+        """Run a forward pass through the VLM, returning a CausalLMOutputWithPast instance (contains loss)."""
+
+        # Handle Inference (leverage cache, short-circuit on just LLM forward)
+        if input_ids.shape[1] == 1 and past_key_values is not None:
+            # We're leveraging the cache, so just redirect to `self.llm_backbone` with `input_ids` and `past_key_values`
+            output = self.llm_backbone(
+                input_ids=input_ids,
+                attention_mask=None,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            return output
+
+        elif input_ids.shape[1] == 1 or pixel_values is None:
+            raise RuntimeError("Invalid `forward()` call!")
+
+        # Handle Multimodal Indices is None --> pretend like the batch is fully multimodal (always image + text)!
+        if multimodal_indices is None:
+            multimodal_indices = torch.arange(len(input_ids), dtype=torch.long, device=input_ids.device)
+
+        # Handle Multimodal Indices is Empty (len == 0) --> simple unimodal forward
+        elif len(multimodal_indices) == 0:
+            return self.llm_backbone(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # Run Visual Feature Extraction
+        with torch.set_grad_enabled(self.vision_backbone_requires_grad):
+            if isinstance(pixel_values, dict):
+                patch_features = self.vision_backbone({k: pixel_values[k][multimodal_indices] for k in pixel_values})
+            else:
+                patch_features = self.vision_backbone(pixel_values[multimodal_indices])
+
+        # Projection Logic :: [bsz, num_patches, llm_embed_dim] =>> num_patches = (2 *) (256 + 1) for ViT-L + CLS
+        projected_patch_embeddings = self.projector(patch_features)
+        projected_patch_attention_mask = None
+        if attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                True,
+                dtype=attention_mask.dtype,
+                device=attention_mask.device,
+            )
+
+        # Get Input Embeddings from LLM Backbone :: [bsz, input_seq_len, llm_embed_dim]
+        input_embeddings = self.llm_backbone.embed_input_ids(input_ids)
+
+        # Build Multimodal Embeddings (and build resulting attention mask)
+        multimodal_embeddings = torch.cat(
+            [
+                input_embeddings[multimodal_indices, :1, :],
+                projected_patch_embeddings,
+                input_embeddings[multimodal_indices, 1:, :],
+            ],
+            dim=1,
+        )
+        multimodal_attention_mask = None
+        if attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [
+                    attention_mask[multimodal_indices, :1],
+                    projected_patch_attention_mask,
+                    attention_mask[multimodal_indices, 1:],
+                ],
+                dim=1,
+            )
+
+        # [Contract] We assume the first token of `labels` (associated with <BOS>) is already marked as "IGNORE"
+        #   => We'll ignore the per-token outputs for each of the patch embeddings as well!
+        multimodal_labels = None
+        if labels is not None:
+            projected_patch_labels = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                IGNORE_INDEX,
+                dtype=labels.dtype,
+                device=labels.device,
+            )
+            multimodal_labels = torch.cat(
+                [labels[multimodal_indices, :1], projected_patch_labels, labels[multimodal_indices, 1:]], dim=1
+            )
+
+        # === Add Unimodal Handling ===
+
+        # Create Fused Embeddings, Attention Mask, and Labels by Merging with "unimodal" Inputs (if applicable)
+        unimodal_indices = torch.tensor(
+            [idx for idx in range(len(input_ids)) if idx not in multimodal_indices],
+            dtype=torch.long,
+            device=multimodal_indices.device,
+        )
+
+        # No "unimodal" data --> Fused == Multimodal
+        if len(unimodal_indices) == 0:
+            fused_embeddings = multimodal_embeddings
+            fused_attention_mask = multimodal_attention_mask
+            fused_labels = multimodal_labels
+
+        else:
+            # Otherwise --> Merge w/ unimodal data
+
+            # This doesn't matter --> but in the "normal" case this is the embedding of the <PAD> token
+            #   => NOTE :: Verified that `zeros/randn/empty/<PAD> embedding` all return the same result!
+            unimodal_embeddings_pad = torch.zeros(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1], input_embeddings.shape[2]),
+                dtype=input_embeddings.dtype,
+                device=input_embeddings.device,
+            )
+            unimodal_attention_pad = torch.full(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1]),
+                False,
+                dtype=attention_mask.dtype,
+                device=attention_mask.device,
+            )
+            unimodal_labels_pad = torch.full(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1]),
+                IGNORE_INDEX,
+                dtype=labels.dtype,
+                device=labels.device,
+            )
+
+            unimodal_embeddings = torch.cat([input_embeddings[unimodal_indices], unimodal_embeddings_pad], dim=1)
+            unimodal_attention_mask = torch.cat([attention_mask[unimodal_indices], unimodal_attention_pad], dim=1)
+            unimodal_labels = torch.cat([labels[unimodal_indices], unimodal_labels_pad], dim=1)
+
+            # Create "Fused" Tensors by Stacking Multimodal & Unimodal
+            fused_embeddings = torch.vstack([multimodal_embeddings, unimodal_embeddings])
+            fused_attention_mask = torch.vstack([multimodal_attention_mask, unimodal_attention_mask])
+            fused_labels = torch.vstack([multimodal_labels, unimodal_labels])
+
+        # Run LLM Forward --> returns CausalLMOutputWithPast!
+        return self.llm_backbone(
+            input_ids=None,
+            attention_mask=fused_attention_mask,
+            position_ids=None,
+            past_key_values=past_key_values,
+            inputs_embeds=fused_embeddings,
+            labels=fused_labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
diff --git a/prismatic/models/vlms/__init__.py b/prismatic/models/vlms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a31406e6f47bd5f3f75df046d8f64def2356a99
--- /dev/null
+++ b/prismatic/models/vlms/__init__.py
@@ -0,0 +1 @@
+from .prismatic import PrismaticVLM
diff --git a/prismatic/models/vlms/base_vlm.py b/prismatic/models/vlms/base_vlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c1049501b1156c02acf116891df3296e1177c3f
--- /dev/null
+++ b/prismatic/models/vlms/base_vlm.py
@@ -0,0 +1,108 @@
+"""
+base_vlm.py
+
+Abstract class definition of a Vision-Language Model (VLM), with full annotations of class methods, utility functions,
+and initialization logic. This is mostly to future-proof the codebase; while all our experiments instantiate
+from PrismaticVLM, theoretically, this base class should be general enough to cover almost all models (e.g., IDEFICS,
+PALI, Fuyu) in the future.
+
+We use Abstract base classes *sparingly* -- mostly as a way to encapsulate any redundant logic or nested inheritance
+(e.g., dependence on nn.Module, HF PretrainedModel, etc.). For other abstract objects (e.g., Tokenizers/Transforms),
+prefer Protocol definitions instead.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Callable, List, Optional
+
+import torch
+import torch.nn as nn
+from transformers import GenerationMixin, PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from prismatic.models.backbones.llm import LLMBackbone
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import VisionBackbone
+
+
+# === Abstract Base Class for arbitrary Vision-Language Models ===
+class VLM(nn.Module, GenerationMixin, ABC):
+    def __init__(
+        self,
+        model_family: str,
+        model_id: str,
+        vision_backbone: VisionBackbone,
+        llm_backbone: LLMBackbone,
+        enable_mixed_precision_training: bool = True,
+    ) -> None:
+        super().__init__()
+        self.model_family, self.model_id = model_family, model_id
+        self.vision_backbone, self.llm_backbone = vision_backbone, llm_backbone
+        self.enable_mixed_precision_training = enable_mixed_precision_training
+
+        # Instance Attributes for a generic VLM
+        self.all_module_keys, self.trainable_module_keys = None, None
+
+        # === GenerationMixin Expected Attributes =>> *DO NOT MODIFY* ===
+        self.generation_config = self.llm_backbone.llm.generation_config
+        self.main_input_name = "input_ids"
+
+    @property
+    def device(self) -> torch.device:
+        """Borrowed from `transformers.modeling_utils.py` -- checks parameter device; assumes model on *ONE* device!"""
+        return next(self.parameters()).device
+
+    @classmethod
+    @abstractmethod
+    def from_pretrained(
+        cls,
+        pretrained_checkpoint: Path,
+        model_family: str,
+        model_id: str,
+        vision_backbone: VisionBackbone,
+        llm_backbone: LLMBackbone,
+        **kwargs: str,
+    ) -> VLM: ...
+
+    @abstractmethod
+    def get_prompt_builder(self, system_prompt: Optional[str] = None) -> PromptBuilder: ...
+
+    @abstractmethod
+    def freeze_backbones(self, stage: str) -> None: ...
+
+    @abstractmethod
+    def load_from_checkpoint(self, stage: str, run_dir: Path, pretrained_checkpoint: Optional[Path] = None) -> None: ...
+
+    @abstractmethod
+    def get_fsdp_wrapping_policy(self) -> Callable: ...
+
+    @abstractmethod
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        multimodal_indices: Optional[torch.LongTensor] = None,
+    ) -> CausalLMOutputWithPast: ...
+
+    # === GenerationMixin Expected Properties & Methods (DO NOT MODIFY) ===
+    @staticmethod
+    def can_generate() -> bool:
+        return True
+
+    @property
+    def config(self) -> PretrainedConfig:
+        return self.llm_backbone.llm.config
+
+    # => Beam Search Utility
+    def _reorder_cache(self, past_key_values, beam_idx):
+        return self.llm_backbone.llm._reorder_cache(past_key_values, beam_idx)
diff --git a/prismatic/models/vlms/prismatic.py b/prismatic/models/vlms/prismatic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6d1a6aac97b724d9148dec818581a91790c285f
--- /dev/null
+++ b/prismatic/models/vlms/prismatic.py
@@ -0,0 +1,621 @@
+"""
+prismatic.py
+
+PyTorch Module defining a PrismaticVLM, our general interface for defining the various different VLMs in our work.
+
+Notes:
+    - For now, we don't subclass `transformers.PretrainedModel` (or CausalLM). Instead, we assume a very limited subset
+      of the {Model}ForCausalLM API that enables dispatch to the underlying LLM's `generate` utilities (feeding inputs
+      through our custom projection shim).
+"""
+
+from __future__ import annotations
+
+from functools import partial
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Type, Union
+
+import torch
+from PIL import Image
+from torch.distributed.fsdp.wrap import _module_wrap_policy, _or_policy
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from prismatic.models.backbones.llm import LLMBackbone
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import VisionBackbone
+from prismatic.models.vlms.base_vlm import VLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.util.nn_utils import FusedMLPProjector, LinearProjector, MLPProjector
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# HuggingFace Default / LLaMa-2 IGNORE_INDEX (for labels)
+IGNORE_INDEX = -100
+
+
+class PrismaticVLM(VLM):
+    def __init__(
+        self,
+        model_id: str,
+        vision_backbone: VisionBackbone,
+        llm_backbone: LLMBackbone,
+        enable_mixed_precision_training: bool = True,
+        arch_specifier: str = "gelu-mlp",
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            "prismatic",
+            model_id,
+            vision_backbone,
+            llm_backbone,
+            enable_mixed_precision_training=enable_mixed_precision_training,
+        )
+
+        # Set Weight Initialization Seed for Projector Consistency
+        torch.manual_seed(vision_backbone.embed_dim)
+
+        # Initialize Projection (Adapter) based on `arch_specifier`
+        self.arch_specifier = arch_specifier
+        if arch_specifier == "linear":
+            self.projector = LinearProjector(vision_backbone.embed_dim, llm_backbone.embed_dim)
+        elif arch_specifier.endswith("fused-gelu-mlp"):
+            self.projector = FusedMLPProjector(vision_backbone.embed_dim, llm_backbone.embed_dim)
+        elif arch_specifier.endswith("gelu-mlp"):
+            self.projector = MLPProjector(vision_backbone.embed_dim, llm_backbone.embed_dim)
+        else:
+            raise ValueError(f"PrismaticVLM with `{arch_specifier = }` is not supported!")
+
+        # Trackers
+        self.vision_backbone_requires_grad = False
+
+        # Set Module Keys =>> used in Checkpoint Saving / Model Loading
+        self.all_module_keys = ["vision_backbone", "llm_backbone", "projector"]
+        self.trainable_module_keys = []
+
+        # === Generation Utilities ===
+        #   => For computing likelihoods --> get tokens corresponding to "True", "False" and "Yes", "No"
+        self.string2idx = {}
+        for trigger_string in ["True", "False", "Yes", "No"] + [chr(ord("A") + i) for i in range(26)]:
+            token_idx_list = self.llm_backbone.tokenizer.encode(trigger_string, add_special_tokens=False)
+            assert len(token_idx_list) == 1, f'String "{trigger_string}" is tokenized as more than one token!'
+            self.string2idx[trigger_string] = token_idx_list[0]
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_checkpoint: Path,
+        model_id: str,
+        vision_backbone: VisionBackbone,
+        llm_backbone: LLMBackbone,
+        enable_mixed_precision_training: bool = True,
+        arch_specifier: str = "gelu-mlp",
+        freeze_weights: bool = True,
+        **kwargs,
+    ) -> PrismaticVLM:
+        """Initialize a PrismaticVLM from a pretrained checkpoint, freezing all weights, tailored for inference."""
+        vlm = cls(
+            model_id,
+            vision_backbone,
+            llm_backbone,
+            enable_mixed_precision_training=enable_mixed_precision_training,
+            arch_specifier=arch_specifier,
+            **kwargs,
+        )
+
+        # Load from Checkpoint (Custom --> should load both *projector* and *llm* weights)
+        model_state_dict = torch.load(pretrained_checkpoint, map_location="cpu")["model"]
+        assert (
+            "projector" in model_state_dict and "llm_backbone" in model_state_dict
+        ), "PrismaticVLM `from_pretrained` expects checkpoint with keys for `projector` AND `llm_backbone`!"
+
+        vlm.projector.load_state_dict(model_state_dict["projector"])
+        vlm.llm_backbone.load_state_dict(model_state_dict["llm_backbone"])
+        if "vision_backbone" in model_state_dict.keys():
+            vlm.vision_backbone.load_state_dict(model_state_dict["vision_backbone"])
+
+        # Freeze Weights
+        if freeze_weights:
+            vlm.requires_grad_(False)
+            vlm.eval()
+
+        return vlm
+
+    def get_prompt_builder(self, system_prompt: Optional[str] = None) -> PromptBuilder:
+        prompt_initializer: Type[PromptBuilder] = self.llm_backbone.prompt_builder_fn
+        return prompt_initializer(self.model_family, system_prompt=system_prompt)
+
+    def freeze_backbones(self, stage: str) -> None:
+        """
+        This function sets `requires_grad_` on each of the component modules explicitly, depending on stage.
+
+        We support two separate stages --> "align" and "finetune".
+            => "align" --> vision_backbone*, llm_backbone* are frozen; only the `projector` is trained.
+            => "finetune" --> vision_backbone* is frozen; both `projector` and `llm_backbone` are trained.
+
+        :param stage: Pretraining stage in < "align" | "finetune" | "full-finetune" | "vla-train" | "vla-full-train" >
+        """
+        if stage == "align":
+            self.vision_backbone.requires_grad_(False)
+            self.llm_backbone.requires_grad_(False)
+            self.projector.requires_grad_(True)
+
+            # Add to `self.trainable_module_keys`
+            self.trainable_module_keys = ["projector"]
+
+            # Update Trackers
+            self.vision_backbone_requires_grad = False
+
+            # Explicitly Log Frozen / Trainable Components
+            overwatch.info(f"[Frozen]    🥶 =>> Vision Backbone `{self.vision_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[Frozen]    🥶 =>> LLM Backbone `{self.llm_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[TRAINABLE] 🔥 =>> Projector `{self.arch_specifier}`", ctx_level=1)
+
+        elif stage in {"finetune", "vla-train"}:
+            self.vision_backbone.requires_grad_(False)
+            self.llm_backbone.requires_grad_(True)
+            self.projector.requires_grad_(True)
+
+            # Add to `self.trainable_module_keys`
+            self.trainable_module_keys = ["projector", "llm_backbone"]
+
+            # Update Trackers
+            self.vision_backbone_requires_grad = False
+
+            # Explicitly Log Frozen / Unfrozen Components
+            overwatch.info(f"[Frozen]    🥶 =>> Vision Backbone `{self.vision_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[TRAINABLE] 🔥 =>> LLM Backbone `{self.llm_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[TRAINABLE] 🔥 =>> Projector `{self.arch_specifier}`", ctx_level=1)
+
+        elif stage in {"full-finetune", "vla-full-train"}:
+            self.vision_backbone.dtype = torch.float32
+            self.vision_backbone.requires_grad_(True)
+            self.llm_backbone.requires_grad_(True)
+            self.projector.requires_grad_(True)
+
+            # Add to `self.trainable_module_keys`
+            self.trainable_module_keys = ["vision_backbone", "projector", "llm_backbone"]
+
+            # Update Trackers
+            self.vision_backbone_requires_grad = True
+
+            # Explicitly Log Frozen / Unfrozen Components
+            overwatch.info(f"[TRAINABLE] 🔥 =>> Vision Backbone `{self.vision_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[TRAINABLE] 🔥 =>> LLM Backbone `{self.llm_backbone.identifier}`", ctx_level=1)
+            overwatch.info(f"[TRAINABLE] 🔥 =>> Projector `{self.arch_specifier}`", ctx_level=1)
+
+        elif stage in {"last-layer-finetune", "vla-last-layer-train"}:
+            self.vision_backbone.requires_grad_(False)
+            self.projector.requires_grad_(False)
+            self.llm_backbone.requires_grad_(False)
+
+            # Unfreeze final LLM layer
+            for module in self.llm_backbone.last_layer_finetune_modules:
+                module.requires_grad_(True)
+
+            # Add to `self.trainable_module_keys`
+            self.trainable_module_keys = ["llm_backbone"]
+
+            # Update Trackers
+            self.vision_backbone_requires_grad = False
+
+            # Explicitly Log Frozen / Unfrozen Components
+            # fmt: off
+            overwatch.info(f"[Frozen]                    🥶   =>> Vision Backbone `{self.vision_backbone.identifier}`", ctx_level=1)  # noqa: E501
+            overwatch.info(f"[Frozen, except last layer] 🥶🔥 =>> LLM Backbone `{self.llm_backbone.identifier}`", ctx_level=1)  # noqa: E501
+            overwatch.info(f"[Frozen]                    🥶   =>> Projector `{self.arch_specifier}`", ctx_level=1)
+            # fmt: on
+
+        elif stage in {"vla-sandwich-train"}:
+            self.vision_backbone.dtype = torch.float32
+            self.vision_backbone.requires_grad_(True)
+            self.projector.requires_grad_(True)
+            self.llm_backbone.requires_grad_(False)
+
+            # Unfreeze final LLM layer
+            for module in self.llm_backbone.last_layer_finetune_modules:
+                module.requires_grad_(True)
+
+            # Add to `self.trainable_module_keys`
+            self.trainable_module_keys = ["vision_backbone", "projector", "llm_backbone"]
+
+            # Update Trackers
+            self.vision_backbone_requires_grad = True
+
+            # Explicitly Log Frozen / Unfrozen Components
+            # fmt: off
+            overwatch.info(f"[TRAINABLE]                 🔥   =>> Vision Backbone `{self.vision_backbone.identifier}`", ctx_level=1)  # noqa: E501
+            overwatch.info(f"[Frozen, except last layer] 🥶🔥 =>> LLM Backbone `{self.llm_backbone.identifier}`", ctx_level=1)  # noqa: E501
+            overwatch.info(f"[TRAINABLE]                 🔥   =>> Projector `{self.arch_specifier}`", ctx_level=1)
+            # fmt: on
+
+        else:
+            raise ValueError(f"Stage `{stage}` is not supported for LLaVa! Try < align | finetune >")
+
+        overwatch.debug("##################################################")
+        overwatch.debug("#####      Trainable Network Parameters:     #####")
+        overwatch.debug("##################################################")
+        for name, param in self.named_parameters():
+            if param.requires_grad:
+                overwatch.debug(name)
+
+    def load_from_checkpoint(self, stage: str, run_dir: Path, pretrained_checkpoint: Optional[Path] = None) -> None:
+        """Load weights from checkpoint (if required by the given stage)."""
+        assert stage in {"align", "finetune", "full-finetune"}, f"Stage {stage} is not supported!"
+
+        # If we're running a `no-align` architecture, we're good!
+        if self.arch_specifier.startswith("no-align"):
+            overwatch.info(
+                f"PrismaticVLM with `{self.arch_specifier = }` does not require pretrained weights!", ctx_level=1
+            )
+            return
+
+        # Otherwise, handle stage-specific logic!
+        if stage == "align":
+            overwatch.info("Stage `align` does not require pretrained weights =>> Starting Training", ctx_level=1)
+            return
+
+        # Otherwise, load from `pretrained_checkpoint` or match on `run_dir` (s/+stage-finetune/+stage-align/g)
+        overwatch.info("Stage `finetune` requires `align` pretrained weights", ctx_level=1)
+
+        # Config specifies path to a checkpoint to load
+        if pretrained_checkpoint is not None:
+            overwatch.info(f"Loading from Provided Checkpoint `{pretrained_checkpoint}`", ctx_level=1)
+            model_state_dict = torch.load(pretrained_checkpoint)["model"]
+            self.projector.load_state_dict(model_state_dict["projector"])
+
+            return
+
+        # [Contract] If no `pretrained_checkpoint`, assume `align` lives in the run directory; string substitution!
+        model, scale, _, seed = run_dir.name.split("+")
+        align_dirs = [
+            d
+            for d in run_dir.parent.iterdir()
+            if (d.name.startswith(f"{model}+{scale}") and d.name.endswith(f"+stage-align+{seed}"))
+        ]
+        assert len(align_dirs) == 1, "Multiple or No Valid Pretrained Directories Exist -- Double Check `runs`!"
+        if (pretrained_checkpoint := (align_dirs[0] / "checkpoints" / "latest-checkpoint.pt")).exists():
+            overwatch.info(f"Loading from Discovered Checkpoint `{pretrained_checkpoint}`", ctx_level=1)
+            model_state_dict = torch.load(pretrained_checkpoint)["model"]
+            self.projector.load_state_dict(model_state_dict["projector"])
+        else:
+            raise ValueError(f"Could not find valid `align` checkpoint at {pretrained_checkpoint}!")
+
+    def get_fsdp_wrapping_policy(self) -> Callable:
+        """Return an FSDP _or_policy over the policies returned by each individual backbone (and our VLM policy)."""
+        vision_fsdp_wrapping_policy = self.vision_backbone.get_fsdp_wrapping_policy()
+        llm_fsdp_wrapping_policy = self.llm_backbone.get_fsdp_wrapping_policy()
+
+        # Get Prismatic Wrapping Policy =>> just a module wrapping policy around `self.projector`
+        prismatic_fsdp_wrapping_policy = partial(
+            _module_wrap_policy,
+            module_classes={LinearProjector, MLPProjector, FusedMLPProjector},
+        )
+
+        # Return union (_or_) over constituent policies
+        #   => Note: there is *not* a fall-through policy; any module that isn't covered by the above constituents will
+        #            automatically be folded into the root VLM FSDP instance.
+        return partial(
+            _or_policy,
+            policies=[
+                vision_fsdp_wrapping_policy,
+                llm_fsdp_wrapping_policy,
+                prismatic_fsdp_wrapping_policy,
+            ],
+        )
+
+    # Note =>> We're not explicitly subclassing `PreTrainedModel` because we don't need the bloat; however, `forward()`
+    #          *must* match the signature of a `{Model}ForCausalLM` so that we can inherit from `GenerationMixin`
+
+    # ruff: noqa: C901
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        multimodal_indices: Optional[torch.LongTensor] = None,
+    ) -> CausalLMOutputWithPast:
+        """Run a forward pass through the VLM, returning a CausalLMOutputWithPast instance (contains loss)."""
+
+        # Handle Inference (leverage cache, short-circuit on just LLM forward)
+        if input_ids.shape[1] == 1 and past_key_values is not None:
+            # We're leveraging the cache, so just redirect to `self.llm_backbone` with `input_ids` and `past_key_values`
+            output = self.llm_backbone(
+                input_ids=input_ids,
+                attention_mask=None,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            return output
+
+        elif input_ids.shape[1] == 1 or pixel_values is None:
+            raise RuntimeError("Invalid `forward()` call!")
+
+        # Handle Multimodal Indices is None --> pretend like the batch is fully multimodal (always image + text)!
+        if multimodal_indices is None:
+            multimodal_indices = torch.arange(len(input_ids), dtype=torch.long, device=input_ids.device)
+
+        # Handle Multimodal Indices is Empty (len == 0) --> simple unimodal forward
+        elif len(multimodal_indices) == 0:
+            return self.llm_backbone(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=None,
+                past_key_values=past_key_values,
+                inputs_embeds=None,
+                labels=labels,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        # Run Visual Feature Extraction
+        with torch.set_grad_enabled(self.vision_backbone_requires_grad):
+            if isinstance(pixel_values, dict):
+                patch_features = self.vision_backbone({k: pixel_values[k][multimodal_indices] for k in pixel_values})
+            else:
+                patch_features = self.vision_backbone(pixel_values[multimodal_indices])
+
+        # Projection Logic :: [bsz, num_patches, llm_embed_dim] =>> num_patches = (2 *) (256 + 1) for ViT-L + CLS
+        projected_patch_embeddings = self.projector(patch_features)
+        projected_patch_attention_mask = None
+        if attention_mask is not None:
+            projected_patch_attention_mask = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                True,
+                dtype=attention_mask.dtype,
+                device=attention_mask.device,
+            )
+
+        # Get Input Embeddings from LLM Backbone :: [bsz, input_seq_len, llm_embed_dim]
+        input_embeddings = self.llm_backbone.embed_input_ids(input_ids)
+
+        # Build Multimodal Embeddings (and build resulting attention mask)
+        multimodal_embeddings = torch.cat(
+            [
+                input_embeddings[multimodal_indices, :1, :],
+                projected_patch_embeddings,
+                input_embeddings[multimodal_indices, 1:, :],
+            ],
+            dim=1,
+        )
+        multimodal_attention_mask = None
+        if attention_mask is not None:
+            multimodal_attention_mask = torch.cat(
+                [
+                    attention_mask[multimodal_indices, :1],
+                    projected_patch_attention_mask,
+                    attention_mask[multimodal_indices, 1:],
+                ],
+                dim=1,
+            )
+
+        # [Contract] We assume the first token of `labels` (associated with <BOS>) is already marked as "IGNORE"
+        #   => We'll ignore the per-token outputs for each of the patch embeddings as well!
+        multimodal_labels = None
+        if labels is not None:
+            projected_patch_labels = torch.full(
+                (projected_patch_embeddings.shape[0], projected_patch_embeddings.shape[1]),
+                IGNORE_INDEX,
+                dtype=labels.dtype,
+                device=labels.device,
+            )
+            multimodal_labels = torch.cat(
+                [labels[multimodal_indices, :1], projected_patch_labels, labels[multimodal_indices, 1:]], dim=1
+            )
+
+        # === Add Unimodal Handling ===
+
+        # Create Fused Embeddings, Attention Mask, and Labels by Merging with "unimodal" Inputs (if applicable)
+        unimodal_indices = torch.tensor(
+            [idx for idx in range(len(input_ids)) if idx not in multimodal_indices],
+            dtype=torch.long,
+            device=multimodal_indices.device,
+        )
+
+        # No "unimodal" data --> Fused == Multimodal
+        if len(unimodal_indices) == 0:
+            fused_embeddings = multimodal_embeddings
+            fused_attention_mask = multimodal_attention_mask
+            fused_labels = multimodal_labels
+
+        else:
+            # Otherwise --> Merge w/ unimodal data
+
+            # This doesn't matter --> but in the "normal" case this is the embedding of the <PAD> token
+            #   => NOTE :: Verified that `zeros/randn/empty/<PAD> embedding` all return the same result!
+            unimodal_embeddings_pad = torch.zeros(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1], input_embeddings.shape[2]),
+                dtype=input_embeddings.dtype,
+                device=input_embeddings.device,
+            )
+            unimodal_attention_pad = torch.full(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1]),
+                False,
+                dtype=attention_mask.dtype,
+                device=attention_mask.device,
+            )
+            unimodal_labels_pad = torch.full(
+                (len(unimodal_indices), projected_patch_embeddings.shape[1]),
+                IGNORE_INDEX,
+                dtype=labels.dtype,
+                device=labels.device,
+            )
+
+            unimodal_embeddings = torch.cat([input_embeddings[unimodal_indices], unimodal_embeddings_pad], dim=1)
+            unimodal_attention_mask = torch.cat([attention_mask[unimodal_indices], unimodal_attention_pad], dim=1)
+            unimodal_labels = torch.cat([labels[unimodal_indices], unimodal_labels_pad], dim=1)
+
+            # Create "Fused" Tensors by Stacking Multimodal & Unimodal
+            fused_embeddings = torch.vstack([multimodal_embeddings, unimodal_embeddings])
+            fused_attention_mask = torch.vstack([multimodal_attention_mask, unimodal_attention_mask])
+            fused_labels = torch.vstack([multimodal_labels, unimodal_labels])
+
+        # Run LLM Forward --> returns CausalLMOutputWithPast!
+        return self.llm_backbone(
+            input_ids=None,
+            attention_mask=fused_attention_mask,
+            position_ids=None,
+            past_key_values=past_key_values,
+            inputs_embeds=fused_embeddings,
+            labels=fused_labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+    # === GenerationMixin Methods ===
+    #   => Note: The following methods override the functionality of `transformers.GenerationMixin`; these expect the
+    #            contract in each of the function signatures, and also expect our `forward` function to roughly take
+    #            the same arguments as the underlying LLM (see `LlamaModelForCausalLM` as an example)
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        **kwargs: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        """Borrowed from `LlamaForCausalLM` --> in general, just handles caching logic during generation."""
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        # Make sure `pixel_values` are preserved in `model_inputs`
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "pixel_values": pixel_values,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+            }
+        )
+
+        return model_inputs
+
+    @torch.inference_mode()
+    def generate_batch(
+        self,
+        pixel_values: Union[torch.Tensor, Dict[str, torch.Tensor]],
+        texts: List[str],
+        return_string_probabilities: Optional[List[str]] = None,
+        **kwargs: str,
+    ) -> Union[List[str], List[List[float]]]:
+        # For now, only support generation with a batch size of 1 for simplicity
+        tokenizer = self.llm_backbone.tokenizer
+
+        # Prepare Inputs
+        batch_input_ids = [
+            tokenizer(text, truncation=True, return_tensors="pt").input_ids.to(self.device) for text in texts
+        ]
+        if isinstance(pixel_values, torch.Tensor):
+            pixel_values = pixel_values[None, ...].to(self.device)
+        elif isinstance(pixel_values, dict):
+            pixel_values = {k: v[None, ...].to(self.device) for k, v in pixel_values.items()}
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Create Output Lists
+        gen_texts, gen_probabilities = [], []
+
+        # Invoke super().generate --> taps into `GenerationMixin` which (redirects) to `forward()`
+        autocast_dtype = self.llm_backbone.half_precision_dtype
+        with torch.autocast("cuda", dtype=autocast_dtype, enabled=self.enable_mixed_precision_training):
+            for idx, input_ids in enumerate(batch_input_ids):
+                if isinstance(pixel_values, torch.Tensor):
+                    pixel_values = pixel_values[idx]
+                elif isinstance(pixel_values, dict):
+                    pixel_values = {k: pixel_values[k][idx] for k in pixel_values}
+                else:
+                    raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+                # Handle `return_string_probabilities`
+                if return_string_probabilities is None:
+                    full_out_ids = super().generate(input_ids=input_ids, pixel_values=pixel_values, **kwargs)
+                    gen_ids = full_out_ids[0, input_ids.shape[1] :]
+
+                    # Decode `gen_ids` and strip any <EOS> tokens
+                    gen_texts.append(tokenizer.decode(gen_ids, skip_special_tokens=True).strip())
+
+                else:
+                    full_out_dict = super().generate(
+                        input_ids=input_ids,
+                        pixel_values=pixel_values,
+                        output_scores=True,
+                        return_dict_in_generate=True,
+                        **kwargs,
+                    )
+
+                    # Generation pattern should usually be [TOKEN] <EOS> for True/False and Yes/No Generations
+                    gen_ids = full_out_dict.sequences[0, input_ids.shape[1] :]
+
+                    # [Debug] Verify that the first token generated is in `self.string2idx.values()`
+                    # assert gen_ids[0] in self.string2idx.values(), "Generated ID not in mapping!"
+
+                    # Decode `gen_ids` and strip any <EOS> tokens
+                    gen_texts.append(tokenizer.decode(gen_ids, skip_special_tokens=True).strip())
+
+                    # Get all token probabilities --> softmax over logits
+                    token_probs = torch.softmax(full_out_dict.scores[0][0], dim=0)
+
+                    # Get *normalized* probabilities for all values in `return_token_probabilities`
+                    slice_idxs = torch.tensor([self.string2idx[s] for s in return_string_probabilities])
+                    string_probs_unnormalized = token_probs[slice_idxs]
+                    string_probs = string_probs_unnormalized / string_probs_unnormalized.sum()
+                    gen_probabilities.append(string_probs.cpu().numpy().tolist())
+
+        return gen_texts if return_string_probabilities is None else gen_probabilities
+
+    @torch.inference_mode()
+    def generate(self, image: Image, prompt_text: str, **kwargs: str) -> str:
+        # For now, only support generation with a batch size of 1 for simplicity
+        image_transform, tokenizer = self.vision_backbone.image_transform, self.llm_backbone.tokenizer
+
+        # Prepare Inputs
+        input_ids = tokenizer(prompt_text, truncation=True, return_tensors="pt").input_ids.to(self.device)
+        pixel_values = image_transform(image)
+        if isinstance(pixel_values, torch.Tensor):
+            pixel_values = pixel_values[None, ...].to(self.device)
+        elif isinstance(pixel_values, dict):
+            pixel_values = {k: v[None, ...].to(self.device) for k, v in pixel_values.items()}
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Invoke super().generate --> taps into `GenerationMixin` which (redirects) to `forward()`
+        autocast_dtype = self.llm_backbone.half_precision_dtype
+        with torch.autocast("cuda", dtype=autocast_dtype, enabled=self.enable_mixed_precision_training):
+            # fmt: off
+            generated_ids = super().generate(
+                input_ids=input_ids,            # Shape: [1, seq]
+                pixel_values=pixel_values,      # Shape: [1, 3, res, res] or Dict[str, Shape[1, 3, res, res]]
+                **kwargs
+            )
+            # fmt: on
+
+        generated_text = tokenizer.decode(generated_ids[0, input_ids.shape[1] :], skip_special_tokens=True).strip()
+
+        return generated_text
diff --git a/prismatic/overwatch/__init__.py b/prismatic/overwatch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6897a047fc2741f7e434bcdaa78f6a14c473fec9
--- /dev/null
+++ b/prismatic/overwatch/__init__.py
@@ -0,0 +1 @@
+from .overwatch import initialize_overwatch
diff --git a/prismatic/overwatch/overwatch.py b/prismatic/overwatch/overwatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7c40e65a695cc9287e1bcb6fef062904df5aace
--- /dev/null
+++ b/prismatic/overwatch/overwatch.py
@@ -0,0 +1,147 @@
+"""
+overwatch.py
+
+Utility class for creating a centralized/standardized logger (built on Rich) and accelerate handler.
+"""
+
+import logging
+import logging.config
+import os
+from contextlib import nullcontext
+from logging import LoggerAdapter
+from typing import Any, Callable, ClassVar, Dict, MutableMapping, Tuple, Union
+
+# Overwatch Default Format String
+RICH_FORMATTER, DATEFMT = "| >> %(message)s", "%m/%d [%H:%M:%S]"
+
+# Set Logging Configuration
+LOG_CONFIG = {
+    "version": 1,
+    "disable_existing_loggers": True,
+    "formatters": {"simple-console": {"format": RICH_FORMATTER, "datefmt": DATEFMT}},
+    "handlers": {
+        "console": {
+            "class": "rich.logging.RichHandler",
+            "formatter": "simple-console",
+            "markup": True,
+            "rich_tracebacks": True,
+            "show_level": True,
+            "show_path": True,
+            "show_time": True,
+        }
+    },
+    "root": {"level": "INFO", "handlers": ["console"]},
+}
+logging.config.dictConfig(LOG_CONFIG)
+
+
+# === Custom Contextual Logging Logic ===
+class ContextAdapter(LoggerAdapter):
+    CTX_PREFIXES: ClassVar[Dict[int, str]] = {**{0: "[*] "}, **{idx: "|=> ".rjust(4 + (idx * 4)) for idx in [1, 2, 3]}}
+
+    def process(self, msg: str, kwargs: MutableMapping[str, Any]) -> Tuple[str, MutableMapping[str, Any]]:
+        ctx_level = kwargs.pop("ctx_level", 0)
+        return f"{self.CTX_PREFIXES[ctx_level]}{msg}", kwargs
+
+
+class DistributedOverwatch:
+    def __init__(self, name: str) -> None:
+        """Initializer for an Overwatch object that wraps logging & `accelerate.PartialState`."""
+        from accelerate import PartialState
+
+        # Note that PartialState is always safe to initialize regardless of `accelerate launch` or `torchrun`
+        #   =>> However, might be worth actually figuring out if we need the `accelerate` dependency at all!
+        self.logger, self.distributed_state = ContextAdapter(logging.getLogger(name), extra={}), PartialState()
+
+        # Logger Delegation (for convenience; would be nice to just compose & dynamic dispatch eventually)
+        self.debug = self.logger.debug
+        self.info = self.logger.info
+        self.warning = self.logger.warning
+        self.error = self.logger.error
+        self.critical = self.logger.critical
+
+        # Logging Defaults =>> only Log `INFO` on Main Process, `ERROR` on others!
+        self.logger.setLevel(logging.INFO if self.distributed_state.is_main_process else logging.ERROR)
+
+    @property
+    def rank_zero_only(self) -> Callable[..., Any]:
+        return self.distributed_state.on_main_process
+
+    @property
+    def local_zero_only(self) -> Callable[..., Any]:
+        return self.distributed_state.on_local_main_process
+
+    @property
+    def rank_zero_first(self) -> Callable[..., Any]:
+        return self.distributed_state.main_process_first
+
+    @property
+    def local_zero_first(self) -> Callable[..., Any]:
+        return self.distributed_state.local_main_process_first
+
+    def is_rank_zero(self) -> bool:
+        return self.distributed_state.is_main_process
+
+    def rank(self) -> int:
+        return self.distributed_state.process_index
+
+    def local_rank(self) -> int:
+        return self.distributed_state.local_process_index
+
+    def world_size(self) -> int:
+        return self.distributed_state.num_processes
+
+
+class PureOverwatch:
+    def __init__(self, name: str) -> None:
+        """Initializer for an Overwatch object that just wraps logging."""
+        self.logger = ContextAdapter(logging.getLogger(name), extra={})
+
+        # Logger Delegation (for convenience; would be nice to just compose & dynamic dispatch eventually)
+        self.debug = self.logger.debug
+        self.info = self.logger.info
+        self.warning = self.logger.warning
+        self.error = self.logger.error
+        self.critical = self.logger.critical
+
+        # Logging Defaults =>> INFO
+        self.logger.setLevel(logging.INFO)
+
+    @staticmethod
+    def get_identity_ctx() -> Callable[..., Any]:
+        def identity(fn: Callable[..., Any]) -> Callable[..., Any]:
+            return fn
+
+        return identity
+
+    @property
+    def rank_zero_only(self) -> Callable[..., Any]:
+        return self.get_identity_ctx()
+
+    @property
+    def local_zero_only(self) -> Callable[..., Any]:
+        return self.get_identity_ctx()
+
+    @property
+    def rank_zero_first(self) -> Callable[..., Any]:
+        return nullcontext
+
+    @property
+    def local_zero_first(self) -> Callable[..., Any]:
+        return nullcontext
+
+    @staticmethod
+    def is_rank_zero() -> bool:
+        return True
+
+    @staticmethod
+    def rank() -> int:
+        return 0
+
+    @staticmethod
+    def world_size() -> int:
+        return 1
+
+
+def initialize_overwatch(name: str) -> Union[DistributedOverwatch, PureOverwatch]:
+    return DistributedOverwatch(name) if int(os.environ.get("WORLD_SIZE", -1)) != -1 else PureOverwatch(name)
diff --git a/prismatic/preprocessing/__init__.py b/prismatic/preprocessing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b62598ef246df852419c118a3dc40a6ebddf4bd6
--- /dev/null
+++ b/prismatic/preprocessing/__init__.py
@@ -0,0 +1,2 @@
+from .download import convert_to_jpg, download_extract
+from .materialize import get_dataset_and_collator
diff --git a/prismatic/preprocessing/datasets/__init__.py b/prismatic/preprocessing/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a642948d2d042def8edd1848053ec7846fd0009
--- /dev/null
+++ b/prismatic/preprocessing/datasets/__init__.py
@@ -0,0 +1 @@
+from .datasets import AlignDataset, FinetuneDataset
diff --git a/prismatic/preprocessing/datasets/datasets.py b/prismatic/preprocessing/datasets/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..35f866eda36c17e95df861063b2a41f171b68e1a
--- /dev/null
+++ b/prismatic/preprocessing/datasets/datasets.py
@@ -0,0 +1,200 @@
+"""
+datasets.py
+
+PyTorch Dataset Definitions for Prismatic models; supports processing for both the `align` and `finetune` stages, with
+utilities for formatting conversations during the `finetune` stage subject to the given LLM backbone's expected
+formatting (e.g., SYS_PROMPT + USER: ... ASSISTANT: ... for Vicuña v1.5 Chat models).
+
+We currently only support Map-style Datasets; assumes that all files (annotations, images) are on local disk, and that
+random access image reading is relatively cheap/fast.
+"""
+
+import copy
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple, Type
+
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from transformers import CodeGenTokenizerFast, LlamaTokenizerFast, PreTrainedTokenizerBase
+
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import ImageTransform
+
+# HuggingFace Default / LLaMa-2 IGNORE_INDEX (for labels)
+IGNORE_INDEX = -100
+
+
+class AlignDataset(Dataset[Dict[str, torch.Tensor]]):
+    def __init__(
+        self,
+        chat_json: Path,
+        image_dir: Path,
+        image_transform: ImageTransform,
+        tokenizer: PreTrainedTokenizerBase,
+    ) -> None:
+        super().__init__()
+        self.chat_json, self.image_dir = chat_json, image_dir
+        self.image_transform, self.tokenizer = image_transform, tokenizer
+        self.dataset_type = "align"
+
+        # Create Prompt Template
+        self.prompt_template = "{caption}" + self.tokenizer.eos_token
+
+        # Load Chat JSON
+        with open(self.chat_json, "r") as f:
+            self.examples = json.load(f)
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        """
+        Following the *actual* code executed from the LLaVa codebase, during the "align" phase, we actually discard
+        the "prompt" from the human, and instead directly predict the caption from the image.
+
+        As a concrete example given the "raw data" for the first example:
+            example = self.examples[0]["conversations"]` = {
+                [
+                    {"from": "human", "value": "Render a clear and concise summary of the photo.\n<image>"},
+                    {"from": "gpt", "value": "select luxury furniture 3 - inch gel memory foam mattress topper"}
+                ]
+            }
+
+        Return =>> self.tokenizer("<image> select luxury furniture 3 - inch gel memory foam mattress topper\n")
+
+        :param idx: Index to retrieve from the dataset.
+
+        :return: Dictionary of {"pixel_values": torch.Tensor, "input_ids": torch.Tensor, "labels": torch.Tensor}
+        """
+        image_path, conversation = Path(self.examples[idx]["image"]), self.examples[idx]["conversations"]
+        assert (len(conversation) == 2) and ("<image>" not in conversation[-1]["value"]), "Unexpected text!"
+
+        # Format Caption --> {caption}{eos_token}
+        caption = self.prompt_template.format(caption=conversation[-1]["value"].strip())
+
+        # We treat image patches as "tokens = [p1 p2 p3, ...]"; we need to specify ordering of text/patch tokens.
+        #   => Critically, we find that inserting *after* the BOS token leads to the strongest performance!
+        #       - input_ids = "<s> p1 p2 p3 ... <caption_text> \n"
+        #       - labels = "IGNORE IGNORE ..." (copy `input_ids` replacing <s> and p{1...K} with IGNORE)
+        #
+        # IMPORTANT => IF WE'RE USING HF LLM.forward(... labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids = self.tokenizer(caption, truncation=True, return_tensors="pt").input_ids[0]
+        labels = copy.deepcopy(input_ids)
+
+        # Set the <BOS> token's label to IGNORE_INDEX (since we're inserting the image patches right after)
+        labels[0] = IGNORE_INDEX
+
+        # Process Image --> get "pixel_values" (will either be a torch.Tensor OR a Dict[str,torch.Tensor])
+        pixel_values = self.image_transform(Image.open(self.image_dir / image_path).convert("RGB"))
+
+        return dict(pixel_values=pixel_values, input_ids=input_ids, labels=labels)
+
+    def get_modality_lengths(self, n_image_patches: int) -> List[Tuple[bool, int]]:
+        """Get a list of modalities (unimodal / text-only vs. multimodal) and length of conversations per example."""
+        modality_lengths = []
+        for example in self.examples:
+            is_multimodal = "image" in example
+            n_words = sum([len(turn["value"].replace("<image>", "").split()) for turn in example["conversations"]])
+            modality_lengths.append((is_multimodal, (n_image_patches + n_words) if is_multimodal else n_words))
+        return modality_lengths
+
+    def __len__(self) -> int:
+        return len(self.examples)
+
+
+class FinetuneDataset(Dataset[Dict[str, torch.Tensor]]):
+    def __init__(
+        self,
+        instruct_json: Path,
+        image_dir: Path,
+        image_transform: ImageTransform,
+        tokenizer: PreTrainedTokenizerBase,
+        prompt_builder_fn: Type[PromptBuilder],
+    ) -> None:
+        super().__init__()
+        self.instruct_json, self.image_dir = instruct_json, image_dir
+        self.image_transform, self.tokenizer = image_transform, tokenizer
+        self.prompt_builder_fn = prompt_builder_fn
+        self.dataset_type = "finetune"
+
+        # Load Instruct JSON
+        with open(self.instruct_json, "r") as f:
+            self.examples = json.load(f)
+
+    # === Unimodal + Multimodal Handling ===
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        """
+        Unlike the *align* stage handling, for the *finetune* stage, we actually need to handle multiple "turns" of
+        dialog grounded in a single image.
+
+        To do this, we leverage the `prompt_builder_fn` which instantiates a PromptBuilder object. By calling the
+        methods for adding turns and getting a prompt, we ensure proper formatting and consistency for each example.
+
+        :param idx: Index to retrieve from the dataset.
+
+        :return: Dictionary of {"pixel_values": torch.Tensor, "input_ids": torch.Tensor, "labels": torch.Tensor}
+        """
+        conversation = self.examples[idx]["conversations"]
+
+        # Create Prompt Builder --> add each message sequentially
+        prompt_builder, input_ids, labels = self.prompt_builder_fn(model_family="prismatic"), [], []
+        for turn_idx, turn in enumerate(conversation):
+            # Get "effective" string added to prompt --> handle whitespace for tokenizer type!
+            msg = prompt_builder.add_turn(turn["from"], turn["value"])
+
+            # Llama Tokenizer (Fast) adds extra character if a string ends in whitespace --> strip if non-empty!
+            if isinstance(self.tokenizer, LlamaTokenizerFast):
+                msg = msg.rstrip()
+
+            # Phi-2 Tokenizer == CodeGenTokenizer (Fast) -- no special handling!
+            elif isinstance(self.tokenizer, CodeGenTokenizerFast):
+                pass
+
+            else:
+                raise ValueError(f"Tokenizer of type `{type(self.tokenizer)}` is not explicitly handled!")
+
+            # Tokenize Input IDs
+            turn_input_ids = self.tokenizer(msg, add_special_tokens=turn_idx == 0).input_ids
+
+            # [CRITICAL] We do not want to take the loss for the "USER: <msg>" prompts =>> just the responses!
+            turn_labels = (
+                [IGNORE_INDEX for _ in range(len(turn_input_ids))] if (turn_idx % 2) == 0 else list(turn_input_ids)
+            )
+
+            # Add to Trackers
+            input_ids.extend(turn_input_ids)
+            labels.extend(turn_labels)
+
+        # Tensorize =>> Set the <BOS> token's label to IGNORE_INDEX (since we're inserting the image patches after)
+        #   - IMPORTANT => IF WE'RE USING HF LLM.forward(... labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+
+        # Handle Truncation (if necessary)
+        input_ids, labels = input_ids[: self.tokenizer.model_max_length], labels[: self.tokenizer.model_max_length]
+
+        # === Handle "unimodal" (language-only) vs. "multimodal" ===
+        if "image" in self.examples[idx]:
+            image_path = Path(self.examples[idx]["image"])
+
+            # Set the <BOS> token's label to IGNORE_INDEX (since we're inserting the image patches right after)
+            labels[0] = IGNORE_INDEX
+
+            # Process Image --> get "pixel_values" (will either be a torch.Tensor OR a Dict[str,torch.Tensor])
+            pixel_values = self.image_transform(Image.open(self.image_dir / image_path).convert("RGB"))
+
+            return dict(pixel_values=pixel_values, input_ids=input_ids, labels=labels)
+
+        else:
+            # No image --> return `pixel_values` = None; Collator will do the smart batch handling for us!
+            return dict(pixel_values=None, input_ids=input_ids, labels=labels)
+
+    def get_modality_lengths(self) -> List[Tuple[bool, int]]:
+        """Get a list of modalities (unimodal / text-only vs. multimodal) and length of conversations per example."""
+        modality_lengths = []
+        for example in self.examples:
+            is_multimodal = "image" in example
+            n_words = sum([len(turn["value"].split()) for turn in example["conversations"]])
+            modality_lengths.append((is_multimodal, n_words))
+        return modality_lengths
+
+    def __len__(self) -> int:
+        return len(self.examples)
diff --git a/prismatic/preprocessing/download.py b/prismatic/preprocessing/download.py
new file mode 100644
index 0000000000000000000000000000000000000000..cff294489e8465471be3da3a07bb4000bf4b7a63
--- /dev/null
+++ b/prismatic/preprocessing/download.py
@@ -0,0 +1,207 @@
+"""
+download.py
+
+Utility functions for downloading and extracting various datasets to (local) disk.
+"""
+
+import os
+import shutil
+from pathlib import Path
+from typing import Dict, List, TypedDict
+from zipfile import ZipFile
+
+import requests
+from PIL import Image
+from rich.progress import BarColumn, DownloadColumn, MofNCompleteColumn, Progress, TextColumn, TransferSpeedColumn
+from tqdm import tqdm
+
+from prismatic.overwatch import initialize_overwatch
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# === Dataset Registry w/ Links ===
+# fmt: off
+DatasetComponent = TypedDict(
+    "DatasetComponent",
+    {"name": str, "extract": bool, "extract_type": str, "url": str, "do_rename": bool},
+    total=False
+)
+
+DATASET_REGISTRY: Dict[str, List[DatasetComponent]] = {
+    # === LLaVa v1.5 Dataset(s) ===
+
+    # Note =>> This is the full suite of datasets included in the LLaVa 1.5 "finetuning" stage; all the LLaVa v1.5
+    #          models are finetuned on this split. We use this dataset for all experiments in our paper.
+    "llava-laion-cc-sbu-558k": [
+        {
+            "name": "chat.json",        # Contains the "chat" traces :: {"human" => <prompt>, "gpt" => <caption>}
+            "extract": False,
+            "url": "https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/resolve/main/blip_laion_cc_sbu_558k.json",
+            "do_rename": True,
+        },
+        {
+            "name": "images",           # Contains the LLaVa Processed Images (jpgs, 224x224 resolution)
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/resolve/main/images.zip",
+            "do_rename": False,
+        }
+    ],
+
+    "llava-v1.5-instruct": [
+        {
+            "name": "llava_v1_5_mix665k.json",
+            "extract": False,
+            "url": (
+                "https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/resolve/main/llava_v1_5_mix665k.json"
+            ),
+            "do_rename": True,
+        },
+        {
+            "name": "coco/train2017",       # Visual Instruct Tuning images are all sourced from COCO Train 2017
+            "extract": True,
+            "extract_type": "directory",
+            "url": "http://images.cocodataset.org/zips/train2017.zip",
+            "do_rename": True,
+        },
+        {
+            "name": "gqa/images",
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip",
+            "do_rename": True,
+        },
+        {
+            "name": "ocr_vqa/images",
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://huggingface.co/datasets/qnguyen3/ocr_vqa/resolve/main/ocr_vqa.zip",
+            "do_rename": True,
+        },
+        {
+            "name": "textvqa/train_images",
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip",
+            "do_rename": True,
+        },
+        {
+            "name": "vg/VG_100K",
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip",
+            "do_rename": True,
+        },
+        {
+            "name": "vg/VG_100K_2",
+            "extract": True,
+            "extract_type": "directory",
+            "url": "https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip",
+            "do_rename": True,
+        },
+    ]
+}
+# fmt: on
+
+
+def convert_to_jpg(image_dir: Path) -> None:
+    """Handling for OCR-VQA Images specifically; iterates through directory, converts all GIFs/PNGs."""
+    overwatch.info(f"Converting all Images in `{image_dir}` to JPG")
+
+    for image_fn in tqdm(list(image_dir.iterdir())):
+        if image_fn.suffix in {".jpg", ".jpeg"} or (jpg_fn := image_dir / f"{image_fn.stem}.jpg").exists():
+            continue
+
+        if image_fn.suffix == ".gif":
+            gif = Image.open(image_fn)
+            gif.seek(0)
+            gif.convert("RGB").save(jpg_fn)
+        elif image_fn.suffix == ".png":
+            Image.open(image_fn).convert("RGB").save(jpg_fn)
+        else:
+            raise ValueError(f"Unexpected image format `{image_fn.suffix}`")
+
+
+def download_with_progress(url: str, download_dir: Path, chunk_size_bytes: int = 1024) -> Path:
+    """Utility function for downloading files from the internet, with a handy Rich-based progress bar."""
+    overwatch.info(f"Downloading {(dest_path := download_dir / Path(url).name)} from `{url}`", ctx_level=1)
+    if dest_path.exists():
+        return dest_path
+
+    # Otherwise --> fire an HTTP Request, with `stream = True`
+    response = requests.get(url, stream=True)
+
+    # Download w/ Transfer-Aware Progress
+    #   => Reference: https://github.com/Textualize/rich/blob/master/examples/downloader.py
+    with Progress(
+        TextColumn("[bold]{task.description} - {task.fields[fname]}"),
+        BarColumn(bar_width=None),
+        "[progress.percentage]{task.percentage:>3.1f}%",
+        "•",
+        DownloadColumn(),
+        "•",
+        TransferSpeedColumn(),
+        transient=True,
+    ) as dl_progress:
+        dl_tid = dl_progress.add_task(
+            "Downloading", fname=dest_path.name, total=int(response.headers.get("content-length", "None"))
+        )
+        with open(dest_path, "wb") as f:
+            for data in response.iter_content(chunk_size=chunk_size_bytes):
+                dl_progress.advance(dl_tid, f.write(data))
+
+    return dest_path
+
+
+def extract_with_progress(archive_path: Path, download_dir: Path, extract_type: str, cleanup: bool = False) -> Path:
+    """Utility function for extracting compressed archives, with a handy Rich-based progress bar."""
+    assert archive_path.suffix == ".zip", "Only `.zip` compressed archives are supported for now!"
+    overwatch.info(f"Extracting {archive_path.name} to `{download_dir}`", ctx_level=1)
+
+    # Extract w/ Progress
+    with Progress(
+        TextColumn("[bold]{task.description} - {task.fields[aname]}"),
+        BarColumn(bar_width=None),
+        "[progress.percentage]{task.percentage:>3.1f}%",
+        "•",
+        MofNCompleteColumn(),
+        transient=True,
+    ) as ext_progress:
+        with ZipFile(archive_path) as zf:
+            ext_tid = ext_progress.add_task("Extracting", aname=archive_path.name, total=len(members := zf.infolist()))
+            extract_path = Path(zf.extract(members[0], download_dir))
+            if extract_type == "file":
+                assert len(members) == 1, f"Archive `{archive_path}` with extract type `{extract_type} has > 1 member!"
+            elif extract_type == "directory":
+                for member in members[1:]:
+                    zf.extract(member, download_dir)
+                    ext_progress.advance(ext_tid)
+            else:
+                raise ValueError(f"Extract type `{extract_type}` for archive `{archive_path}` is not defined!")
+
+    # Cleanup (if specified)
+    if cleanup:
+        archive_path.unlink()
+
+    return extract_path
+
+
+def download_extract(dataset_id: str, root_dir: Path) -> None:
+    """Download all files for a given dataset (querying registry above), extracting archives if necessary."""
+    os.makedirs(download_dir := root_dir / "download" / dataset_id, exist_ok=True)
+
+    # Download Files => Single-Threaded, with Progress Bar
+    dl_tasks = [d for d in DATASET_REGISTRY[dataset_id] if not (download_dir / d["name"]).exists()]
+    for dl_task in dl_tasks:
+        dl_path = download_with_progress(dl_task["url"], download_dir)
+
+        # Extract Files (if specified) --> Note (assumes ".zip" ONLY!)
+        if dl_task["extract"]:
+            dl_path = extract_with_progress(dl_path, download_dir, dl_task["extract_type"])
+            dl_path = dl_path.parent if dl_path.is_file() else dl_path
+
+        # Rename Path --> dl_task["name"]
+        if dl_task["do_rename"]:
+            shutil.move(dl_path, download_dir / dl_task["name"])
diff --git a/prismatic/preprocessing/materialize.py b/prismatic/preprocessing/materialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..b84b0d5c1cbf0650efbac20e3700a8ab3d372091
--- /dev/null
+++ b/prismatic/preprocessing/materialize.py
@@ -0,0 +1,69 @@
+"""
+materialize.py
+
+Factory class for initializing pretraining datasets on a per-VLM basis; provides and exports individual functions for
+clear control flow.
+"""
+
+from typing import Tuple, Type
+
+from torch.utils.data import Dataset
+from transformers import PreTrainedTokenizerBase
+
+from prismatic.conf import DatasetConfig
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import ImageTransform
+from prismatic.preprocessing.datasets import AlignDataset, FinetuneDataset
+from prismatic.util.data_utils import PaddedCollatorForLanguageModeling
+
+# Dataset Initializers =>> Maps Stage --> cls()
+DATASET_INITIALIZER = {"align": AlignDataset, "finetune": FinetuneDataset, "full-finetune": FinetuneDataset}
+
+
+def get_dataset_and_collator(
+    stage: str,
+    dataset_cfg: DatasetConfig,
+    image_transform: ImageTransform,
+    tokenizer: PreTrainedTokenizerBase,
+    prompt_builder_fn: Type[PromptBuilder],
+    default_image_resolution: Tuple[int, int, int],
+    padding_side: str = "right",
+) -> Tuple[Dataset, PaddedCollatorForLanguageModeling]:
+    dataset_cls = DATASET_INITIALIZER[stage]
+    dataset_root_dir = dataset_cfg.dataset_root_dir
+    collator = PaddedCollatorForLanguageModeling(
+        tokenizer.model_max_length, tokenizer.pad_token_id, default_image_resolution, padding_side=padding_side
+    )
+
+    # Switch on `stage`
+    if stage == "align":
+        annotation_json, image_dir = dataset_cfg.align_stage_components
+        dataset = dataset_cls(
+            dataset_root_dir / annotation_json, dataset_root_dir / image_dir, image_transform, tokenizer
+        )
+        return dataset, collator
+
+    elif stage == "finetune":
+        annotation_json, image_dir = dataset_cfg.finetune_stage_components
+        dataset = dataset_cls(
+            dataset_root_dir / annotation_json,
+            dataset_root_dir / image_dir,
+            image_transform,
+            tokenizer,
+            prompt_builder_fn=prompt_builder_fn,
+        )
+        return dataset, collator
+
+    elif stage == "full-finetune":
+        annotation_json, image_dir = dataset_cfg.finetune_stage_components
+        dataset = dataset_cls(
+            dataset_root_dir / annotation_json,
+            dataset_root_dir / image_dir,
+            image_transform,
+            tokenizer,
+            prompt_builder_fn=prompt_builder_fn,
+        )
+        return dataset, collator
+
+    else:
+        raise ValueError(f"Stage `{stage}` is not supported!")
diff --git a/prismatic/py.typed b/prismatic/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/training/__init__.py b/prismatic/training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c7f8c8bf8ef7e9c8507eae82d30055e04fae25
--- /dev/null
+++ b/prismatic/training/__init__.py
@@ -0,0 +1,2 @@
+from .materialize import get_train_strategy
+from .metrics import Metrics, VLAMetrics
diff --git a/prismatic/training/materialize.py b/prismatic/training/materialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e9f364dbd7d4b908fe21ba3381ae2305b053f83
--- /dev/null
+++ b/prismatic/training/materialize.py
@@ -0,0 +1,66 @@
+"""
+materialize.py
+
+Factory class defining functions for instantiating various Training Strategies, supporting different VLMs, backbones,
+and strategy configurations.
+"""
+
+from typing import Callable, Optional
+
+import torch
+
+from prismatic.models.vlms import PrismaticVLM
+from prismatic.training.strategies import FSDPStrategy, TrainingStrategy
+
+# Registry =>> Maps ID --> {cls(), kwargs} :: supports FSDP for now, but DDP handler is also implemented!
+TRAIN_STRATEGIES = {
+    "fsdp-shard-grad-op": {"cls": FSDPStrategy, "kwargs": {"sharding_strategy": "shard-grad-op"}},
+    "fsdp-full-shard": {"cls": FSDPStrategy, "kwargs": {"sharding_strategy": "full-shard"}},
+}
+
+
+def get_train_strategy(
+    train_strategy: str,
+    vlm: PrismaticVLM,
+    device_id: int,
+    stage: str,
+    epochs: int,
+    max_steps: Optional[int],
+    global_batch_size: int,
+    per_device_batch_size: int,
+    learning_rate: float,
+    weight_decay: float,
+    max_grad_norm: float,
+    lr_scheduler_type: str,
+    warmup_ratio: float,
+    enable_gradient_checkpointing: bool = True,
+    enable_mixed_precision_training: bool = True,
+    reduce_in_full_precision: bool = False,
+    mixed_precision_dtype: torch.dtype = torch.bfloat16,
+    worker_init_fn: Optional[Callable[[int], None]] = None,
+) -> TrainingStrategy:
+    if train_strategy in TRAIN_STRATEGIES:
+        strategy_cfg = TRAIN_STRATEGIES[train_strategy]
+        strategy = strategy_cfg["cls"](
+            vlm=vlm,
+            device_id=device_id,
+            stage=stage,
+            epochs=epochs,
+            max_steps=max_steps,
+            global_batch_size=global_batch_size,
+            per_device_batch_size=per_device_batch_size,
+            learning_rate=learning_rate,
+            weight_decay=weight_decay,
+            max_grad_norm=max_grad_norm,
+            lr_scheduler_type=lr_scheduler_type,
+            warmup_ratio=warmup_ratio,
+            enable_gradient_checkpointing=enable_gradient_checkpointing,
+            enable_mixed_precision_training=enable_mixed_precision_training,
+            reduce_in_full_precision=reduce_in_full_precision,
+            mixed_precision_dtype=mixed_precision_dtype,
+            worker_init_fn=worker_init_fn,
+            **strategy_cfg["kwargs"],
+        )
+        return strategy
+    else:
+        raise ValueError(f"Train Strategy `{train_strategy}` is not supported!")
diff --git a/prismatic/training/metrics.py b/prismatic/training/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd56da6886eba3c7a32ecbd1b1e1889d14116499
--- /dev/null
+++ b/prismatic/training/metrics.py
@@ -0,0 +1,348 @@
+"""
+metrics.py
+
+Utility classes defining a Metrics container and multiple Trackers to enable model/stage-specific logging to various
+endpoints (e.g., JSONL local logs, Weights & Biases).
+"""
+
+import time
+from collections import defaultdict, deque
+from pathlib import Path
+from typing import Any, Dict, Optional, Protocol, Tuple, Union
+
+import jsonlines
+import numpy as np
+import torch
+import wandb
+
+from prismatic.overwatch import initialize_overwatch
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# === Define Tracker Interface ===
+class Tracker(Protocol):
+    def write_hyperparameters(self) -> None: ...
+
+    def write(self, global_step: int, metrics: Dict[str, Union[int, float]]) -> None: ...
+
+    def finalize(self) -> None: ...
+
+
+# === Individual Tracker Definitions ===
+class JSONLinesTracker:
+    def __init__(self, run_id: str, run_dir: Path, hparams: Dict[str, Any]) -> None:
+        self.run_id, self.run_dir, self.hparams = run_id, run_dir, hparams
+
+    @overwatch.rank_zero_only
+    def write_hyperparameters(self) -> None:
+        with jsonlines.open(self.run_dir / "run-metrics.jsonl", mode="w", sort_keys=True) as js_tracker:
+            js_tracker.write({"run_id": self.run_id, "hparams": self.hparams})
+
+    @overwatch.rank_zero_only
+    def write(self, _: int, metrics: Dict[str, Union[int, float]]) -> None:
+        with jsonlines.open(self.run_dir / f"{self.run_id}.jsonl", mode="a", sort_keys=True) as js_tracker:
+            js_tracker.write(metrics)
+
+    def finalize(self) -> None:
+        return
+
+
+class WeightsBiasesTracker:
+    def __init__(
+        self,
+        run_id: str,
+        run_dir: Path,
+        hparams: Dict[str, Any],
+        project: str = "prismatic",
+        entity: Optional[str] = None,
+        group: str = "align",
+    ) -> None:
+        self.run_id, self.run_dir, self.hparams = run_id, run_dir, hparams
+
+        # Get W&B-Specific Initialization Parameters
+        self.project, self.entity, self.group, self.wandb_dir = project, entity, group, self.run_dir
+
+        # Call W&B.init()
+        self.initialize()
+
+    @overwatch.rank_zero_only
+    def initialize(self) -> None:
+        wandb.init(
+            name=self.run_id,
+            dir=self.wandb_dir,
+            config=self.hparams,
+            project=self.project,
+            entity=self.entity,
+            group=self.group,
+        )
+
+    @overwatch.rank_zero_only
+    def write_hyperparameters(self) -> None:
+        wandb.config = self.hparams
+
+    @overwatch.rank_zero_only
+    def write(self, global_step: int, metrics: Dict[str, Union[int, float]]) -> None:
+        wandb.log(metrics, step=global_step)
+
+    @staticmethod
+    def finalize() -> None:
+        if overwatch.is_rank_zero():
+            wandb.finish()
+
+        # A job gets 210 seconds to get its affairs in order
+        time.sleep(210)
+
+
+# === Core Metrics Container :: Initializes Trackers => Compiles/Pushes Metrics ===
+
+
+class Metrics:
+    def __init__(
+        self,
+        active_trackers: Tuple[str, ...],
+        run_id: str,
+        run_dir: Path,
+        hparams: Dict[str, Any],
+        stage: str,
+        wandb_project: str = "prismatic",
+        wandb_entity: Optional[str] = None,
+        grad_accumulation_steps: int = 1,
+        window_size: int = 128,
+    ) -> None:
+        self.run_id, self.run_dir, self.hparams, self.stage = run_id, run_dir, hparams, stage
+
+        # Initialize Trackers
+        self.trackers = []
+        for tracker_type in active_trackers:
+            if tracker_type == "jsonl":
+                tracker = JSONLinesTracker(run_id, run_dir, hparams)
+            elif tracker_type == "wandb":
+                tracker = WeightsBiasesTracker(
+                    run_id, run_dir, hparams, project=wandb_project, entity=wandb_entity, group=self.stage
+                )
+            else:
+                raise ValueError(f"Tracker with type `{tracker_type} is not supported!")
+
+            # Add Hyperparameters --> add to `self.trackers`
+            tracker.write_hyperparameters()
+            self.trackers.append(tracker)
+
+        # Create Universal Metrics Buffers
+        self.global_step, self.start_time, self.step_start_time = 0, time.time(), time.time()
+        self.state = {
+            "loss_raw": deque(maxlen=grad_accumulation_steps),
+            "loss": deque(maxlen=window_size),
+            "step_time": deque(maxlen=window_size),
+            "lr": [],
+        }
+
+    def log(self, global_step: int, metrics: Dict[str, Union[int, float]]) -> None:
+        for tracker in self.trackers:
+            tracker.write(global_step, metrics)
+
+    def get_status(self, loss: Optional[torch.Tensor] = None) -> str:
+        lr = self.state["lr"][-1] if len(self.state["lr"]) > 0 else 0
+        if loss is None:
+            return f"=>> [Global Step] {self.global_step:06d} =>> LR :: {lr:.6f}"
+
+        # Otherwise, embed `loss` in status report!
+        return f"=>> [Global Step] {self.global_step:06d} =>> LR :: {lr:.6f} -- Loss :: {loss:.4f}"
+
+    def commit(
+        self, *, global_step: Optional[int] = None, lr: Optional[float] = None, update_step_time: bool = False, **kwargs
+    ) -> None:
+        """Update all metrics in `self.state` by iterating through special positional arguments & kwargs."""
+        if global_step is not None:
+            self.global_step = global_step
+
+        # For all other variables --> only track on rank zero!
+        if not overwatch.is_rank_zero():
+            return
+
+        # Special Positional Arguments
+        if lr is not None:
+            self.state["lr"].append(lr)
+
+        if update_step_time:
+            self.state["step_time"].append(time.time() - self.step_start_time)
+            self.step_start_time = time.time()
+
+        # Generic Keyword Arguments
+        for key, value in kwargs.items():
+            if key == "loss":
+                loss_val = value.detach()
+                self.state["loss_raw"].append(loss_val)
+                self.state["loss"].append(loss_val)
+            else:
+                self.state[key].append(value.detach())
+
+    @overwatch.rank_zero_only
+    def push(self) -> str:
+        # Note :: Raw Loss is an Average over Gradient Accumulation Steps --> No Smoothing!
+        loss_raw = torch.stack(list(self.state["loss_raw"])).mean().item()
+        loss = torch.stack(list(self.state["loss"])).mean().item()
+        step_time, lr = np.mean(list(self.state["step_time"])), self.state["lr"][-1]
+        status = self.get_status(loss)
+
+        # Fire to Trackers
+        prefix = self.stage.capitalize()
+        self.log(
+            self.global_step,
+            metrics={
+                f"{prefix}/Step": self.global_step,
+                f"{prefix}/Loss": loss,
+                f"{prefix}/Loss (Raw)": loss_raw,
+                f"{prefix}/Learning Rate": lr,
+                f"{prefix}/Step Time": step_time,
+            },
+        )
+        return status
+
+    def finalize(self) -> str:
+        for tracker in self.trackers:
+            tracker.finalize()
+
+
+class VLAMetrics:
+    def __init__(
+        self,
+        active_trackers: Tuple[str, ...],
+        run_id: str,
+        run_dir: Path,
+        hparams: Dict[str, Any],
+        wandb_project: str = "openvla",
+        wandb_entity: Optional[str] = "stanford-voltron",
+        grad_accumulation_steps: int = 1,
+        window_size: int = 1,
+        resume_step: Optional[int] = None,
+        resume_epoch: Optional[int] = None,
+    ) -> None:
+        self.run_id, self.run_dir, self.hparams = run_id, run_dir, hparams
+
+        # Initialize Trackers
+        self.trackers = []
+        for tracker_type in active_trackers:
+            if tracker_type == "jsonl":
+                tracker = JSONLinesTracker(run_id, run_dir, hparams)
+            elif tracker_type == "wandb":
+                tracker = WeightsBiasesTracker(
+                    run_id, run_dir, hparams, project=wandb_project, entity=wandb_entity, group="vla-train"
+                )
+            else:
+                raise ValueError(f"Tracker with type `{tracker_type} is not supported!")
+
+            # Add Hyperparameters --> add to `self.trackers`
+            tracker.write_hyperparameters()
+            self.trackers.append(tracker)
+
+        # Create Universal Metrics Buffers
+        self.global_step = 0 if resume_step is None else resume_step
+        self.epoch = 0 if resume_epoch is None else resume_epoch
+        self.start_time, self.step_start_time = time.time(), time.time()
+        self.state = {
+            "loss_raw": deque(maxlen=grad_accumulation_steps),
+            "loss": deque(maxlen=window_size),
+            "l1_loss": deque(maxlen=window_size),
+            "action_accuracy": deque(maxlen=window_size),
+            "step_time": deque(maxlen=window_size),
+            "lr": [],
+        }
+
+        # Created metrics buffers for individual tracked datasets
+        self.dataset_trackers = defaultdict(lambda: VLAMetrics([], "", "", {}))
+        self.window_size = window_size
+
+    def log(self, global_step: int, metrics: Dict[str, Union[int, float]]) -> None:
+        for tracker in self.trackers:
+            tracker.write(global_step, metrics)
+
+    def get_status(self, loss: Optional[torch.Tensor] = None) -> str:
+        lr = self.state["lr"][-1] if len(self.state["lr"]) > 0 else 0
+        if loss is None:
+            return f"=>> [Epoch {self.epoch:03d}] Global Step {self.global_step:06d} =>> LR :: {lr:.6f}"
+
+        # Otherwise, embed `loss` in status report!
+        return f"=>> [Epoch {self.epoch:03d}] Global Step {self.global_step:06d} =>> LR :: {lr:.6f} - Loss :: {loss:.4f}"
+
+    def commit(
+        self,
+        *,
+        global_step: Optional[int] = None,
+        epoch: Optional[int] = None,
+        lr: Optional[float] = None,
+        update_step_time: bool = False,
+        **kwargs,
+    ) -> None:
+        """Update all metrics in `self.state` by iterating through special positional arguments & kwargs."""
+        if global_step is not None:
+            self.global_step = global_step
+
+        if epoch is not None:
+            self.epoch = epoch
+
+        # For all other variables --> only track on rank zero!
+        if not overwatch.is_rank_zero():
+            return
+
+        # Special Positional Arguments
+        if lr is not None:
+            self.state["lr"].append(lr)
+
+        if update_step_time:
+            self.state["step_time"].append(time.time() - self.step_start_time)
+            self.step_start_time = time.time()
+
+        # Generic Keyword Arguments
+        for key, value in kwargs.items():
+            if key == "loss":
+                loss_val = value.detach()
+                self.state["loss_raw"].append(loss_val)
+                self.state["loss"].append(loss_val)
+            else:
+                self.state[key].append(value.detach())
+
+    def commit_for_dataset(self, dataset_name: str, **kwargs) -> None:
+        self.dataset_trackers[dataset_name].commit(**kwargs)
+
+    @overwatch.rank_zero_only
+    def push(self, prefix="VLA Train") -> str:
+        # Note :: Raw Loss is an Average over Gradient Accumulation Steps --> No Smoothing!
+        loss_raw = torch.stack(list(self.state["loss_raw"])).mean().item()
+        loss = torch.stack(list(self.state["loss"])).mean().item()
+        l1_loss = torch.stack(list(self.state["l1_loss"])).mean().item()
+        action_accuracy = torch.stack(list(self.state["action_accuracy"])).mean().item()
+        step_time, lr = np.mean(list(self.state["step_time"])), self.state["lr"][-1]
+        status = self.get_status(loss)
+
+        # Get metrics per dataset
+        dataset_metrics = {}
+        for ds, tracker in self.dataset_trackers.items():
+            dataset_metrics.update(
+                {
+                    f"{ds}/L1 Loss": torch.stack(list(tracker.state["l1_loss"])).mean().item(),
+                    f"{ds}/Action Token Accuracy": torch.stack(list(tracker.state["action_accuracy"])).mean().item(),
+                }
+            )
+
+        # Fire to Trackers
+        self.log(
+            self.global_step,
+            metrics={
+                f"{prefix}/Step": self.global_step,
+                f"{prefix}/Epoch": self.epoch,
+                f"{prefix}/Loss": loss,
+                f"{prefix}/L1 Loss": l1_loss,
+                f"{prefix}/Action Token Accuracy": action_accuracy,
+                f"{prefix}/Loss (Raw)": loss_raw,
+                f"{prefix}/Learning Rate": lr,
+                f"{prefix}/Step Time": step_time,
+                **dataset_metrics,
+            },
+        )
+        return status
+
+    def finalize(self) -> str:
+        for tracker in self.trackers:
+            tracker.finalize()
diff --git a/prismatic/training/strategies/__init__.py b/prismatic/training/strategies/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d73eb1069c982ed3969ba3af56479c0359051a1b
--- /dev/null
+++ b/prismatic/training/strategies/__init__.py
@@ -0,0 +1,3 @@
+from .base_strategy import TrainingStrategy
+from .ddp import DDPStrategy
+from .fsdp import FSDPStrategy
diff --git a/prismatic/training/strategies/base_strategy.py b/prismatic/training/strategies/base_strategy.py
new file mode 100644
index 0000000000000000000000000000000000000000..d75aacb3d1ed9eb4c2ecc925f54c0fc7a77599da
--- /dev/null
+++ b/prismatic/training/strategies/base_strategy.py
@@ -0,0 +1,726 @@
+"""
+base_strategy.py
+
+Abstract class definition of a (distributed) training strategy, with full annotations of class methods, utility
+functions, and initialization logic.
+
+Training Strategies (DDP, FSDP-Grad, FSDP-Full) tend to have a lot of repeated components; this class does a lot of
+heavy lifting.
+"""
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Callable, Optional
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import DataLoader, Dataset, DistributedSampler, IterableDataset
+from tqdm import tqdm
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from prismatic.models.vlms import PrismaticVLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.training.metrics import Metrics, VLAMetrics
+from prismatic.util import check_bloat16_supported
+from prismatic.util.batching_utils import SplitModalitySampler
+from prismatic.util.data_utils import PaddedCollatorForActionPrediction, PaddedCollatorForLanguageModeling
+from prismatic.vla.action_tokenizer import ActionTokenizer
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# === Abstract Base Class for an arbitrary Training Strategy ===
+class TrainingStrategy(ABC):
+    def __init__(
+        self,
+        vlm: PrismaticVLM,
+        device_id: int,
+        stage: str,
+        epochs: int,
+        max_steps: Optional[int],
+        global_batch_size: int,
+        per_device_batch_size: int,
+        learning_rate: float,
+        weight_decay: float,
+        max_grad_norm: float,
+        lr_scheduler_type: str,
+        warmup_ratio: float,
+        enable_gradient_checkpointing: bool = True,
+        enable_mixed_precision_training: bool = True,
+        reduce_in_full_precision: bool = False,
+        mixed_precision_dtype: torch.dtype = torch.bfloat16,
+        worker_init_fn: Optional[Callable[[int], None]] = None,
+        **_: str,
+    ) -> None:
+        self.vlm, self.device_id, self.stage = vlm, device_id, stage
+
+        # Get relevant VLM instance parameters before they get (potentially) wrapped
+        self.all_module_keys, self.trainable_module_keys = self.vlm.all_module_keys, self.vlm.trainable_module_keys
+        self.llm_transformer_layer_cls = self.vlm.llm_backbone.transformer_layer_cls
+
+        # Optimization Parameters
+        self.epochs, self.max_steps = epochs, max_steps
+        self.global_batch_size, self.per_device_batch_size = global_batch_size, per_device_batch_size
+
+        self.learning_rate, self.weight_decay, self.max_grad_norm = learning_rate, weight_decay, max_grad_norm
+        self.lr_scheduler_type, self.warmup_ratio = lr_scheduler_type, warmup_ratio
+
+        # Generic Strategy Parameters
+        self.enable_gradient_checkpointing = enable_gradient_checkpointing
+        self.enable_mixed_precision_training = enable_mixed_precision_training
+        self.reduce_in_full_precision = reduce_in_full_precision
+        self.mixed_precision_dtype = mixed_precision_dtype
+
+        # DataLoader Parameters
+        self.worker_init_fn = worker_init_fn
+
+        # Optimizers & Scheduler (initialized in `run_setup`)
+        self.optimizer, self.lr_scheduler = None, None
+
+        # Lightweight Validation
+        assert (
+            self.global_batch_size % self.per_device_batch_size == 0
+        ), "Per-device batch size must evenly divide global batch size!"
+        self.grad_accumulation_steps = self.global_batch_size // self.per_device_batch_size // overwatch.world_size()
+        if self.enable_mixed_precision_training:
+            assert self.mixed_precision_dtype == torch.bfloat16, "Only BF16 mixed precision training is supported!"
+            assert check_bloat16_supported(), "BFloat16 is not supported on this hardware; unset `mixed_precision`"
+
+    @abstractmethod
+    def save_checkpoint(
+        self,
+        run_dir: Path,
+        global_step: int,
+        epoch: int,
+        train_loss: Optional[float] = None,
+        only_trainable: bool = True,
+    ) -> None: ...
+
+    @abstractmethod
+    def run_setup(self, run_dir: Path, n_train_examples: int) -> None: ...
+
+    @abstractmethod
+    def clip_grad_norm(self) -> None: ...
+
+    def run_training(
+        self,
+        dataset: Dataset,
+        collator: PaddedCollatorForLanguageModeling,
+        metrics: Metrics,
+        stage: str = "finetune",
+        batch_construction_strategy: str = "split-modality",
+        seed: int = 7,
+    ) -> None:
+        """Run the training loop for the given `dataset` and `collator`; log losses, results to `metrics`"""
+        if "finetune" in stage and batch_construction_strategy == "split-modality":
+            # Instantiate the split-modality sampler; if you want to extend with other batch construction schemes,
+            #   (e.g., grouping by length) =>> can easily add them here!
+            modality_lengths = dataset.get_modality_lengths()
+            sampler = SplitModalitySampler(
+                dataset,
+                modality_lengths,
+                global_batch_size=self.global_batch_size,
+                num_replicas=overwatch.world_size(),
+                rank=overwatch.rank(),
+                seed=seed,
+                drop_last=False,
+            )
+
+        else:
+            sampler = DistributedSampler(
+                dataset,
+                num_replicas=overwatch.world_size(),
+                rank=overwatch.rank(),
+                shuffle=True,
+                seed=seed,
+                drop_last=False,
+            )
+
+        # Create a DataLoader with the initialized sampler, per-device-bsz, and collator
+        dataloader = DataLoader(
+            dataset,
+            batch_size=self.per_device_batch_size,
+            sampler=sampler,
+            collate_fn=collator,
+            num_workers=2,
+            worker_init_fn=self.worker_init_fn,
+        )
+
+        # Max Steps vs. Epochs Computation
+        steps_per_epoch = len(dataloader) // self.grad_accumulation_steps
+        if self.max_steps is not None and steps_per_epoch < self.max_steps:
+            # Just set `epochs` to some large number --> we'll short-circuit based on steps anyway
+            self.epochs = 100
+
+        # === Train ===
+        status = metrics.get_status()
+        with tqdm(
+            total=(
+                (self.epochs * (len(dataloader) // self.grad_accumulation_steps))
+                if self.max_steps is None
+                else self.max_steps
+            ),
+            desc=status,
+            leave=False,
+            disable=not overwatch.is_rank_zero(),
+        ) as progress:
+            for epoch in range(self.epochs):
+                self.vlm.train()
+                sampler.set_epoch(epoch)
+
+                # Zero-Gradients (just in case)
+                self.optimizer.zero_grad()
+
+                # Note that we'll unpack batch (and let AMP/FSDP do its thing) in the VLM.forward() call
+                #   => Basically, if we're using mixed precision (or not), autocast()/FSDP will move to device!
+                for train_idx, batch in enumerate(dataloader):
+                    # [Contract] self.vlm.forward() must automatically compute `loss` and return!
+                    with torch.autocast(
+                        "cuda",
+                        dtype=self.mixed_precision_dtype,
+                        enabled=self.enable_mixed_precision_training,
+                    ):
+                        output: CausalLMOutputWithPast = self.vlm(
+                            input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            pixel_values=batch["pixel_values"],
+                            labels=batch["labels"],
+                            multimodal_indices=batch["multimodal_indices"],
+                        )
+                        loss = output.loss
+
+                    # Commit Loss (Prior to Gradient Accumulation Normalization)
+                    metrics.commit(loss=loss)
+
+                    # Normalize Loss to account for Gradient Accumulation --> Backward!
+                    # [IMPORTANT] Technically speaking, doing gradient accumulation in this way is "incorrect"; this is
+                    #             because in general, each batch has a *different number of masked out tokens* (because
+                    #             we're instruct-tuning). Taking the mean over two unbalanced means != the right thing!
+                    #
+                    #             HOWEVER -- at least at the 7B scale, the "naive" approach is just as performant as
+                    #             the "correct" implementation, without adding extra complexity.
+                    #
+                    # That being said =>> at the 13B scale, *no matter what we tried, ANY gradient accumulation is just
+                    #   really bad for downstream performance. Initial investigation shows that BF16 accumulation
+                    #   just really tanks in precision... and don't have a good/clean way to fix this. Would love for
+                    #   someone to PR and fix this (and I'd greatly appreciate it!!!)
+                    normalized_loss = loss / self.grad_accumulation_steps
+                    normalized_loss.backward()
+
+                    # Step =>> Only if Done w/ Gradient Accumulation
+                    if (train_idx + 1) % self.grad_accumulation_steps == 0:
+                        metrics.commit(update_step_time=True)
+
+                        # Clip Gradients --> this is custom, per-strategy because of DDP vs. FSDP locality-assumptions
+                        self.clip_grad_norm()
+
+                        # Optimizer & LR Scheduler Step
+                        self.optimizer.step()
+                        self.lr_scheduler.step()
+                        self.optimizer.zero_grad()
+
+                        # Push Metrics
+                        metrics.commit(global_step=metrics.global_step + 1, lr=self.lr_scheduler.get_last_lr()[0])
+                        status = metrics.push()
+
+                        # Check for Termination & Save Final Checkpoint (in case `max_steps` is not None)
+                        if self.max_steps is not None and metrics.global_step >= self.max_steps:
+                            self.save_checkpoint(metrics.run_dir, metrics.global_step, epoch, loss.item())
+                            dist.barrier()
+
+                            return
+
+                        # Update Progress Bar
+                        progress.update()
+                        progress.set_description(status)
+
+            # Save checkpoint at end each epoch (if `self.max_steps` is None)
+            if self.max_steps is None:
+                self.save_checkpoint(metrics.run_dir, metrics.global_step, epoch, loss.item())
+                dist.barrier()
+
+    # === VLA Training ===
+
+    def run_vla_training(
+        self,
+        vla_dataset: IterableDataset,
+        collator: PaddedCollatorForActionPrediction,
+        action_tokenizer: ActionTokenizer,
+        metrics: VLAMetrics,
+        save_interval: int = 2500,
+        save_full_model: bool = True,
+    ) -> None:
+        """Run the VLA training loop for the given `dataset` and `collator`; log losses, action metrics to `metrics`."""
+        assert isinstance(vla_dataset, IterableDataset), "VLA training expects an IterableDataset!"
+        assert self.grad_accumulation_steps == 1, "VLA training does not support gradient accumulation!"
+
+        # Create a DataLoader =>> Set `num_workers` to 0; RLDS loader handles parallelism!
+        dataloader = DataLoader(
+            vla_dataset,
+            batch_size=self.per_device_batch_size,
+            sampler=None,
+            collate_fn=collator,
+            num_workers=0,
+            worker_init_fn=self.worker_init_fn,
+        )
+
+        # === Train ===
+        status = metrics.get_status()
+        with tqdm(
+            total=(self.epochs * len(dataloader)) if self.max_steps is None else self.max_steps,
+            desc=status,
+            leave=False,
+            disable=not overwatch.is_rank_zero(),
+        ) as progress:
+            self.vlm.train()
+
+            # Zero Gradients (just in case)
+            self.optimizer.zero_grad()
+
+            # [Contract] DataLoader wraps RLDS Loader (`.as_numpy_iterator() =>> implicit `.repeat()`)
+            #   => This means looping over the DataLoader is basically "infinite" (so no outer loop over epochs).
+            #      Slightly breaks default PyTorch semantics, which is why we adaptively compute `epoch` below.
+            for batch in dataloader:
+                # Note that we'll unpack batch (and let AMP/FSDP do its thing) in the VLM.forward() call
+                #   => Basically, if we're using mixed precision (or not), autocast()/FSDP will move to device!
+                with torch.autocast(
+                    "cuda", dtype=self.mixed_precision_dtype, enabled=self.enable_mixed_precision_training
+                ):
+                    # [Contract] self.vlm.forward() must automatically compute `loss` and return!
+                    output: CausalLMOutputWithPast = self.vlm(
+                        input_ids=batch["input_ids"],
+                        attention_mask=batch["attention_mask"],
+                        pixel_values=batch["pixel_values"],
+                        labels=batch["labels"],
+                    )
+                    loss = output.loss
+
+                # Commit Loss =>> Backward!
+                metrics.commit(loss=loss)
+                loss.backward()
+
+                # === Compute Action Token Accuracy & L1 Loss ===
+
+                # To compute action token accuracy, we need to identify the locations of the action tokens
+                # in both `output.logits` and `batch["labels"]`. We know that when "right" padding, we
+                # insert `self.vlm.vision_backbone.num_patches` at index 1.
+                #
+                # Computing `action_prediction_accuracy` is then pretty straightforward:
+                #   1) Extract "aligned" predictions & labels
+                #   2) Compute boolean "mask" where "labels > 2" (where 2 is ID for `EOS_TOKEN`)
+                #           => If masking out EOS, then it's just "labels != -100 (IGNORE_INDEX)
+                #   3) Compute masked accuracy as `(preds == logits) & mask` --> sum/divide by # unmasked!
+                action_preds = output.logits[:, self.vlm.vision_backbone.num_patches : -1].argmax(dim=2)
+                action_gt = batch["labels"][:, 1:].to(action_preds.device)
+                mask = action_gt > action_tokenizer.action_token_begin_idx
+
+                # Compute Accuracy
+                correct_preds = (action_preds == action_gt) & mask
+                action_accuracy = correct_preds.sum().float() / mask.sum().float()
+
+                # Compute L1 Loss on Predicted (Continuous) Actions
+                continuous_actions_pred = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_preds[mask].cpu().numpy())
+                )
+                continuous_actions_gt = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_gt[mask].cpu().numpy())
+                )
+                action_l1_loss = torch.nn.functional.l1_loss(continuous_actions_pred, continuous_actions_gt)
+
+                # Commit Metrics
+                metrics.commit(action_accuracy=action_accuracy, l1_loss=action_l1_loss, update_step_time=True)
+
+                # Compute metrics per dataset --> only on rank_zero since we don't log them on other workers anyways
+                if overwatch.is_rank_zero():
+                    datasets = set(batch["dataset_names"])
+                    if len(datasets) > 1:
+                        for ds in datasets:
+                            ds_mask = torch.tensor([elem == ds for elem in batch["dataset_names"]])
+                            action_accuracy_ds = correct_preds[ds_mask].sum().float() / mask[ds_mask].sum().float()
+                            continuous_actions_pred_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_preds[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            continuous_actions_gt_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_gt[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            action_l1_loss_ds = torch.nn.functional.l1_loss(
+                                continuous_actions_pred_ds, continuous_actions_gt_ds
+                            )
+                            metrics.commit_for_dataset(
+                                dataset_name=ds.decode(), action_accuracy=action_accuracy_ds, l1_loss=action_l1_loss_ds
+                            )
+
+                # === Gradient Step ===
+
+                # Clip Gradients --> this is custom, per-strategy because of DDP vs. FSDP locality assumptions
+                self.clip_grad_norm()
+
+                # Optimizer & LR Scheduler Step
+                self.optimizer.step()
+                self.lr_scheduler.step()
+                self.optimizer.zero_grad()
+
+                # Compute epoch value using number of completed gradient steps
+                epoch = (metrics.global_step + 1) // (len(vla_dataset) // self.global_batch_size)
+
+                # Push Metrics
+                metrics.commit(global_step=metrics.global_step + 1, epoch=epoch, lr=self.lr_scheduler.get_last_lr()[0])
+                status = metrics.push()
+
+                # Check for Save Interval or Max Steps & Save Checkpoint
+                if (terminate := (self.max_steps is not None and metrics.global_step >= self.max_steps)) or (
+                    (metrics.global_step % save_interval) == 0
+                ):
+                    self.save_checkpoint(
+                        metrics.run_dir, metrics.global_step, epoch, loss.item(), only_trainable=not save_full_model
+                    )
+                    dist.barrier()
+
+                    if terminate:
+                        return
+
+                # Update Progress Bar
+                progress.update()
+                progress.set_description(status)
+
+
+    def run_vla_training_val(
+        self,
+        vla_dataset_train: IterableDataset,
+        vla_dataset_val: IterableDataset,
+        collator: PaddedCollatorForActionPrediction,
+        action_tokenizer: ActionTokenizer,
+        metrics_train: VLAMetrics,
+        metrics_val: VLAMetrics,
+        eval_interval: int = 5000,
+        save_interval: int = 2500,
+        save_full_model: bool = True,
+    ) -> None:
+        """Run the VLA training loop for the given `dataset` and `collator`; log losses, action metrics to `metrics`."""
+        assert isinstance(vla_dataset_train, IterableDataset), "VLA training expects an IterableDataset!"
+        assert isinstance(vla_dataset_val, IterableDataset), "VLA training expects an IterableDataset!"
+        assert self.grad_accumulation_steps == 1, "VLA training does not support gradient accumulation!"
+
+        # Create a DataLoader =>> Set `num_workers` to 0; RLDS loader handles parallelism!
+        dataloader_train = DataLoader(
+            vla_dataset_train,
+            batch_size=1, # self.per_device_batch_size,
+            sampler=None,
+            collate_fn=collator,
+            num_workers=0,
+            worker_init_fn=self.worker_init_fn,
+        )
+        dataloader_val = DataLoader(
+            vla_dataset_val,
+            batch_size=10, # self.per_device_batch_size,
+            sampler=None,
+            collate_fn=collator,
+            num_workers=0,
+            worker_init_fn=self.worker_init_fn,
+        )
+
+        # # === Warming Up the Data Loaders ===
+        # for step, batch in enumerate(dataloader_train):
+        #     break
+        # for step, batch in enumerate(dataloader_val):
+        #     break
+        
+        # === First Validation ===
+        max_val_steps = metrics_val.window_size
+        val_status = metrics_val.get_status()
+        with tqdm(
+            total=max_val_steps // dataloader_val.batch_size,
+            desc=val_status,
+            leave=True,
+            disable=not overwatch.is_rank_zero(),
+        ) as subprogress:
+            self.vlm.eval()
+
+            for val_step, batch in enumerate(dataloader_val):
+                with torch.autocast(
+                    "cuda", dtype=self.mixed_precision_dtype, enabled=self.enable_mixed_precision_training
+                ):
+                    with torch.no_grad():
+                        # [Contract] self.vlm.forward() must automatically compute `loss` and return!
+                        output: CausalLMOutputWithPast = self.vlm(
+                            input_ids=batch["input_ids"],
+                            attention_mask=batch["attention_mask"],
+                            pixel_values=batch["pixel_values"],
+                            labels=batch["labels"],
+                        )
+                        loss = output.loss
+
+                # Commit Loss =>> Backward!
+                metrics_val.commit(loss=loss)
+
+                # === Compute Action Token Accuracy & L1 Loss ===
+                action_preds = output.logits[:, self.vlm.vision_backbone.num_patches : -1].argmax(dim=2)
+                action_gt = batch["labels"][:, 1:].to(action_preds.device)
+                mask = action_gt > action_tokenizer.action_token_begin_idx
+
+                # Compute Accuracy
+                correct_preds = (action_preds == action_gt) & mask
+                action_accuracy = correct_preds.sum().float() / mask.sum().float()
+
+                # Compute L1 Loss on Predicted (Continuous) Actions
+                continuous_actions_pred = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_preds[mask].cpu().numpy())
+                )
+                continuous_actions_gt = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_gt[mask].cpu().numpy())
+                )
+                action_l1_loss = torch.nn.functional.l1_loss(continuous_actions_pred, continuous_actions_gt)
+
+                # Commit Metrics
+                metrics_val.commit(action_accuracy=action_accuracy, l1_loss=action_l1_loss, update_step_time=True)
+
+                # Compute metrics per dataset --> only on rank_zero since we don't log them on other workers anyways
+                if overwatch.is_rank_zero():
+                    datasets = set(batch["dataset_names"])
+                    if len(datasets) > 1:
+                        for ds in datasets:
+                            ds_mask = torch.tensor([elem == ds for elem in batch["dataset_names"]])
+                            action_accuracy_ds = correct_preds[ds_mask].sum().float() / mask[ds_mask].sum().float()
+                            continuous_actions_pred_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_preds[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            continuous_actions_gt_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_gt[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            action_l1_loss_ds = torch.nn.functional.l1_loss(
+                                continuous_actions_pred_ds, continuous_actions_gt_ds
+                            )
+                            metrics_val.commit_for_dataset(
+                                dataset_name=ds.decode(), action_accuracy=action_accuracy_ds, l1_loss=action_l1_loss_ds
+                            )
+
+                # === Gradient Step ===
+                metrics_val.commit(global_step=0, epoch=0, lr=self.lr_scheduler.get_last_lr()[0])
+
+                # Update Progress Bar
+                subprogress.update()
+
+                if val_step == (max_val_steps // dataloader_val.batch_size - 1):
+                    break
+
+            val_status = metrics_val.push(prefix="VLA Val")
+            subprogress.set_description(val_status)
+
+        # === Train ===
+        eval_mode = False
+        status = metrics_train.get_status()
+        with tqdm(
+            total=self.max_steps,
+            desc=status,
+            leave=True,
+            disable=not overwatch.is_rank_zero(),
+        ) as progress:
+            self.vlm.train()
+            epoch = 0
+            # Zero Gradients (just in case)
+            self.optimizer.zero_grad()
+
+            # [Contract] DataLoader wraps RLDS Loader (`.as_numpy_iterator() =>> implicit `.repeat()`)
+            #   => This means looping over the DataLoader is basically "infinite" (so no outer loop over epochs).
+            #      Slightly breaks default PyTorch semantics, which is why we adaptively compute `epoch` below.
+            for step, batch in enumerate(dataloader_train):
+                
+                # Note that we'll unpack batch (and let AMP/FSDP do its thing) in the VLM.forward() call
+                #   => Basically, if we're using mixed precision (or not), autocast()/FSDP will move to device!
+                with torch.autocast(
+                    "cuda", dtype=self.mixed_precision_dtype, enabled=self.enable_mixed_precision_training
+                ):
+                    # [Contract] self.vlm.forward() must automatically compute `loss` and return!
+                    output: CausalLMOutputWithPast = self.vlm(
+                        input_ids=batch["input_ids"],
+                        attention_mask=batch["attention_mask"],
+                        pixel_values=batch["pixel_values"],
+                        labels=batch["labels"],
+                    )
+                    loss = output.loss
+
+                # Commit Loss =>> Backward!
+                metrics_train.commit(loss=loss)
+                loss.backward()
+
+                # === Compute Action Token Accuracy & L1 Loss ===
+
+                # To compute action token accuracy, we need to identify the locations of the action tokens
+                # in both `output.logits` and `batch["labels"]`. We know that when "right" padding, we
+                # insert `self.vlm.vision_backbone.num_patches` at index 1.
+                #
+                # Computing `action_prediction_accuracy` is then pretty straightforward:
+                #   1) Extract "aligned" predictions & labels
+                #   2) Compute boolean "mask" where "labels > 2" (where 2 is ID for `EOS_TOKEN`)
+                #           => If masking out EOS, then it's just "labels != -100 (IGNORE_INDEX)
+                #   3) Compute masked accuracy as `(preds == logits) & mask` --> sum/divide by # unmasked!
+                action_preds = output.logits[:, self.vlm.vision_backbone.num_patches : -1].argmax(dim=2)
+                action_gt = batch["labels"][:, 1:].to(action_preds.device)
+                mask = action_gt > action_tokenizer.action_token_begin_idx
+
+                # Compute Accuracy
+                correct_preds = (action_preds == action_gt) & mask
+                action_accuracy = correct_preds.sum().float() / mask.sum().float()
+
+                # Compute L1 Loss on Predicted (Continuous) Actions
+                continuous_actions_pred = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_preds[mask].cpu().numpy())
+                )
+                continuous_actions_gt = torch.tensor(
+                    action_tokenizer.decode_token_ids_to_actions(action_gt[mask].cpu().numpy())
+                )
+                action_l1_loss = torch.nn.functional.l1_loss(continuous_actions_pred, continuous_actions_gt)
+
+                # Commit Metrics
+                metrics_train.commit(action_accuracy=action_accuracy, l1_loss=action_l1_loss, update_step_time=True)
+
+                # Compute metrics per dataset --> only on rank_zero since we don't log them on other workers anyways
+                if overwatch.is_rank_zero():
+                    datasets = set(batch["dataset_names"])
+                    if len(datasets) > 1:
+                        for ds in datasets:
+                            ds_mask = torch.tensor([elem == ds for elem in batch["dataset_names"]])
+                            action_accuracy_ds = correct_preds[ds_mask].sum().float() / mask[ds_mask].sum().float()
+                            continuous_actions_pred_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_preds[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            continuous_actions_gt_ds = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(
+                                    action_gt[ds_mask][mask[ds_mask]].cpu().numpy()
+                                )
+                            )
+                            action_l1_loss_ds = torch.nn.functional.l1_loss(
+                                continuous_actions_pred_ds, continuous_actions_gt_ds
+                            )
+                            metrics_train.commit_for_dataset(
+                                dataset_name=ds.decode(), action_accuracy=action_accuracy_ds, l1_loss=action_l1_loss_ds
+                            )
+
+                # === Gradient Step ===
+
+                # Clip Gradients --> this is custom, per-strategy because of DDP vs. FSDP locality assumptions
+                self.clip_grad_norm()
+
+                # Optimizer & LR Scheduler Step
+                self.optimizer.step()
+                self.lr_scheduler.step()
+                self.optimizer.zero_grad()
+
+                # Push Metrics
+                metrics_train.commit(global_step=metrics_train.global_step + 1, epoch=epoch, lr=self.lr_scheduler.get_last_lr()[0])
+                status = metrics_train.push()
+
+                # Update Progress Bar
+                progress.update()
+                progress.set_description(status)
+
+                # Set the evaluation flag
+                if metrics_train.global_step % eval_interval == 0:
+                    eval_mode = True
+                else:
+                    eval_mode = False
+                
+                # Go to evaluation once necessary
+                if eval_mode:
+                    # === Val ===
+                    max_val_steps = metrics_val.window_size
+                    val_status = metrics_val.get_status()
+                    with tqdm(
+                        total=max_val_steps // dataloader_val.batch_size,
+                        desc=val_status,
+                        leave=True,
+                        disable=not overwatch.is_rank_zero(),
+                    ) as subprogress:
+                        self.vlm.eval()
+
+                        for val_step, batch in enumerate(dataloader_val):
+                            with torch.autocast(
+                                "cuda", dtype=self.mixed_precision_dtype, enabled=self.enable_mixed_precision_training
+                            ):
+                                with torch.no_grad():
+                                    # [Contract] self.vlm.forward() must automatically compute `loss` and return!
+                                    output: CausalLMOutputWithPast = self.vlm(
+                                        input_ids=batch["input_ids"],
+                                        attention_mask=batch["attention_mask"],
+                                        pixel_values=batch["pixel_values"],
+                                        labels=batch["labels"],
+                                    )
+                                    loss = output.loss
+
+                            # Commit Loss =>> Backward!
+                            metrics_val.commit(loss=loss)
+
+                            # === Compute Action Token Accuracy & L1 Loss ===
+                            action_preds = output.logits[:, self.vlm.vision_backbone.num_patches : -1].argmax(dim=2)
+                            action_gt = batch["labels"][:, 1:].to(action_preds.device)
+                            mask = action_gt > action_tokenizer.action_token_begin_idx
+
+                            # Compute Accuracy
+                            correct_preds = (action_preds == action_gt) & mask
+                            action_accuracy = correct_preds.sum().float() / mask.sum().float()
+
+                            # Compute L1 Loss on Predicted (Continuous) Actions
+                            continuous_actions_pred = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(action_preds[mask].cpu().numpy())
+                            )
+                            continuous_actions_gt = torch.tensor(
+                                action_tokenizer.decode_token_ids_to_actions(action_gt[mask].cpu().numpy())
+                            )
+                            action_l1_loss = torch.nn.functional.l1_loss(continuous_actions_pred, continuous_actions_gt)
+
+                            # Commit Metrics
+                            metrics_val.commit(action_accuracy=action_accuracy, l1_loss=action_l1_loss, update_step_time=True)
+
+                            # Compute metrics per dataset --> only on rank_zero since we don't log them on other workers anyways
+                            if overwatch.is_rank_zero():
+                                datasets = set(batch["dataset_names"])
+                                if len(datasets) > 1:
+                                    for ds in datasets:
+                                        ds_mask = torch.tensor([elem == ds for elem in batch["dataset_names"]])
+                                        action_accuracy_ds = correct_preds[ds_mask].sum().float() / mask[ds_mask].sum().float()
+                                        continuous_actions_pred_ds = torch.tensor(
+                                            action_tokenizer.decode_token_ids_to_actions(
+                                                action_preds[ds_mask][mask[ds_mask]].cpu().numpy()
+                                            )
+                                        )
+                                        continuous_actions_gt_ds = torch.tensor(
+                                            action_tokenizer.decode_token_ids_to_actions(
+                                                action_gt[ds_mask][mask[ds_mask]].cpu().numpy()
+                                            )
+                                        )
+                                        action_l1_loss_ds = torch.nn.functional.l1_loss(
+                                            continuous_actions_pred_ds, continuous_actions_gt_ds
+                                        )
+                                        metrics_val.commit_for_dataset(
+                                            dataset_name=ds.decode(), action_accuracy=action_accuracy_ds, l1_loss=action_l1_loss_ds
+                                        )
+
+                            # === Gradient Step ===
+                            metrics_val.commit(global_step=metrics_train.global_step, epoch=epoch, lr=self.lr_scheduler.get_last_lr()[0])
+
+                            # Update Progress Bar
+                            subprogress.update()
+
+                            if val_step == (max_val_steps // dataloader_val.batch_size - 1):
+                                break
+
+                        val_status = metrics_val.push(prefix="VLA Val")
+                        subprogress.set_description(val_status)
+
+                    # Check for Save Interval or Max Steps & Save Checkpoint
+                    if (metrics_train.global_step % save_interval == 0):
+                        self.save_checkpoint(
+                            metrics_train.run_dir, metrics_train.global_step, epoch, loss.item(), only_trainable=not save_full_model
+                        )
+                        dist.barrier()
diff --git a/prismatic/training/strategies/ddp.py b/prismatic/training/strategies/ddp.py
new file mode 100644
index 0000000000000000000000000000000000000000..be6c1dd20ef1d315eba1aaf77a94b196ea38af45
--- /dev/null
+++ b/prismatic/training/strategies/ddp.py
@@ -0,0 +1,128 @@
+"""
+ddp.py
+
+Core class definition for a strategy implementing Torch native Distributed Data Parallel Training; note that on most
+GPU hardware and LLM backbones >= 5-7B parameters, DDP training will OOM, which is why we opt for FSDP.
+"""
+
+import shutil
+from pathlib import Path
+from typing import Optional
+
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import AdamW
+from transformers.optimization import get_constant_schedule, get_cosine_schedule_with_warmup
+
+from prismatic.overwatch import initialize_overwatch
+from prismatic.training.strategies.base_strategy import TrainingStrategy
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+class DDPStrategy(TrainingStrategy):
+    @overwatch.rank_zero_only
+    def save_checkpoint(
+        self,
+        run_dir: Path,
+        global_step: int,
+        epoch: int,
+        train_loss: Optional[float] = None,
+        only_trainable: bool = True,
+    ) -> None:
+        """Save a checkpoint to the `run_dir` only containing the state_dicts for trainable parameters by default."""
+        assert isinstance(self.vlm, DDP), "save_checkpoint assumes VLM is already wrapped in DDP!"
+
+        # Splinter State Dictionary by Top-Level Submodules (or subset, if `only_trainable`)
+        model_state_dicts = {
+            mkey: getattr(self.vlm.module, mkey).state_dict()
+            for mkey in (self.trainable_module_keys if only_trainable else self.all_module_keys)
+        }
+        optimizer_state_dict = self.optimizer.state_dict()
+
+        # Set Checkpoint Path =>> Embed *minimal* training statistics!
+        checkpoint_dir = run_dir / "checkpoints"
+        if train_loss is None:
+            checkpoint_path = checkpoint_dir / f"step-{global_step:06d}-epoch-{epoch:02d}-loss=inf.pt"
+        else:
+            checkpoint_path = checkpoint_dir / f"step-{global_step:06d}-epoch-{epoch:02d}-loss={train_loss:.4f}.pt"
+
+        # Save Checkpoint & Copy Latest to `latest-checkpoint.pt`
+        torch.save({"model": model_state_dicts, "optimizer": optimizer_state_dict}, checkpoint_path)
+        shutil.copy(checkpoint_path, checkpoint_dir / "latest-checkpoint.pt")
+
+    def run_setup(self, run_dir: Path, n_train_examples: int) -> None:
+        # Gradient Checkpointing Setup
+        if self.enable_gradient_checkpointing:
+            # For Gradient Checkpointing --> we make the assumption that the "bulk" of activation memory is taken up
+            #     by the LLM; because we also make the explicit assumption that each LLM is derived from a HF
+            #     pretrained model, the only thing we *need* to do (technically) is call `gradient_checkpoint_enable`
+            #     on `self.llm_backbone`.
+            #
+            # What does it actually do? --> runs the *generic* custom_forward + torch.utils.checkpoint.checkpoint logic
+            #   => github.com/huggingface/transformers/.../models/llama/modeling_llama.py#L692-L706
+            #
+            # Additional Reference (to better understand gradient checkpointing in PyTorch writ large)
+            #   => github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb
+            overwatch.info("Enabling Gradient Checkpointing on LLM Backbone", ctx_level=1)
+            self.vlm.llm_backbone.gradient_checkpointing_enable()
+
+        # Move to Device =>> Note parameters are in full precision (*mixed precision* will only autocast as appropriate)
+        overwatch.info("Placing Entire VLM (Vision Backbone, LLM Backbone, Projector Weights) on GPU", ctx_level=1)
+        self.vlm.to(self.device_id)
+
+        # Wrap with Distributed Data Parallel
+        #   => Note: By default, wrapping naively with DDP(self.vlm) will initialize a *separate* buffer on GPU that
+        #            is the same size/dtype as the model parameters; this will *double* GPU memory!
+        # - stackoverflow.com/questions/68949954/model-takes-twice-the-memory-footprint-with-distributed-data-parallel
+        overwatch.info("Wrapping VLM with Distributed Data Parallel", ctx_level=1)
+        self.vlm = DDP(self.vlm, device_ids=[self.device_id], gradient_as_bucket_view=True)
+
+        # Create Optimizer and LR Scheduler =>> note that most of the LR Schedulers we use require `max_steps/epochs`
+        #   => Optimizer should only operate on parameters that are *unfrozen* / trainable!
+        trainable_params = [param for param in self.vlm.parameters() if param.requires_grad]
+        if self.max_steps is None:
+            num_training_steps = (n_train_examples * self.epochs) // self.global_batch_size
+        else:
+            num_training_steps = self.max_steps
+
+        if self.lr_scheduler_type == "linear-warmup+cosine-decay":
+            # Set warmup steps (floor) based on `warmup_ratio` (should be 0.03 - 0.05)
+            num_warmup_steps = int(num_training_steps * self.warmup_ratio)
+
+            assert self.weight_decay == 0, "DDP training does not currently support `weight_decay` > 0!"
+            self.optimizer = AdamW(trainable_params, lr=self.learning_rate, weight_decay=self.weight_decay)
+            self.lr_scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps, num_training_steps)
+            for param_group in self.optimizer.param_groups:
+                param_group["lr"] = 0.0
+
+        elif self.lr_scheduler_type == "constant":
+            num_warmup_steps = 0
+
+            assert self.weight_decay == 0, "DDP training does not currently support `weight_decay` > 0!"
+            self.optimizer = AdamW(trainable_params, lr=self.learning_rate, weight_decay=self.weight_decay)
+            self.lr_scheduler = get_constant_schedule(self.optimizer)
+
+        else:
+            raise ValueError(f"Learning Rate Schedule with type `{self.lr_scheduler_type}` is not supported!")
+
+        # Finalize Setup =>> Log
+        overwatch.info(
+            "DDP Strategy =>> Finalized Training Setup:\n"
+            f"         |-> Global (Effective) Batch Size = {self.global_batch_size}\n"
+            f"         |-> Per-Device Batch Size = {self.per_device_batch_size}\n"
+            f"         |-> Distributed World Size = {overwatch.world_size()}\n"
+            f"         |-> Gradient Accumulation Steps = {self.grad_accumulation_steps}\n\n"
+            f"         |-> LLM Backbone Gradient Checkpointing = {self.enable_gradient_checkpointing}\n"
+            f"         |-> Use Native AMP = {self.enable_mixed_precision_training} ({self.mixed_precision_dtype})\n\n"
+            f"         |-> Default AdamW LR = {self.learning_rate}\n"
+            f"         |-> AdamW Weight Decay = {self.weight_decay}\n"
+            f"         |-> LR Scheduler Type = {self.lr_scheduler_type}\n"
+            f"         |-> LR Scheduler Warmup Steps (Ratio) = {num_warmup_steps} ({self.warmup_ratio})\n"
+            f"         |-> Dataset Size = {n_train_examples} Examples\n"
+            f"         |-> Max Steps = {num_training_steps}\n"
+        )
+
+    def clip_grad_norm(self) -> None:
+        torch.nn.utils.clip_grad_norm_(self.vlm.parameters(), max_norm=self.max_grad_norm)
diff --git a/prismatic/training/strategies/fsdp.py b/prismatic/training/strategies/fsdp.py
new file mode 100644
index 0000000000000000000000000000000000000000..9af28f474908af1bbb048a28968c986629ecc5a5
--- /dev/null
+++ b/prismatic/training/strategies/fsdp.py
@@ -0,0 +1,270 @@
+"""
+fsdp.py
+
+Core class definition for a strategy implementing Torch native Fully Sharded Data Parallel Training (with support for
+fine-grained control over wrapping policies and mixed precision per component).
+"""
+
+import math
+from collections import OrderedDict
+from functools import partial
+from pathlib import Path
+from typing import Callable, Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    CheckpointImpl,
+    apply_activation_checkpointing,
+    checkpoint_wrapper,
+)
+from torch.distributed.fsdp import (
+    FullStateDictConfig,
+    MixedPrecision,
+    ShardingStrategy,
+    StateDictType,
+)
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.optim import AdamW
+from transformers.optimization import get_constant_schedule, get_cosine_schedule_with_warmup
+
+from prismatic.models.vlms import PrismaticVLM
+from prismatic.overwatch import initialize_overwatch
+from prismatic.training.strategies.base_strategy import TrainingStrategy
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+class FSDPStrategy(TrainingStrategy):
+    def __init__(
+        self,
+        vlm: PrismaticVLM,
+        device_id: int,
+        stage: str,
+        epochs: int,
+        max_steps: Optional[int],
+        global_batch_size: int,
+        per_device_batch_size: int,
+        learning_rate: float,
+        weight_decay: float,
+        max_grad_norm: float,
+        lr_scheduler_type: str,
+        warmup_ratio: float,
+        enable_gradient_checkpointing: bool = True,
+        enable_mixed_precision_training: bool = True,
+        reduce_in_full_precision: bool = False,
+        mixed_precision_dtype: torch.dtype = torch.bfloat16,
+        worker_init_fn: Optional[Callable[[int], None]] = None,
+        sharding_strategy: str = "shard-grad-op",
+        state_dict_type: StateDictType = StateDictType.FULL_STATE_DICT,
+    ) -> None:
+        super().__init__(
+            vlm=vlm,
+            device_id=device_id,
+            stage=stage,
+            epochs=epochs,
+            max_steps=max_steps,
+            global_batch_size=global_batch_size,
+            per_device_batch_size=per_device_batch_size,
+            learning_rate=learning_rate,
+            weight_decay=weight_decay,
+            max_grad_norm=max_grad_norm,
+            lr_scheduler_type=lr_scheduler_type,
+            warmup_ratio=warmup_ratio,
+            enable_gradient_checkpointing=enable_gradient_checkpointing,
+            enable_mixed_precision_training=enable_mixed_precision_training,
+            reduce_in_full_precision=reduce_in_full_precision,
+            mixed_precision_dtype=mixed_precision_dtype,
+            worker_init_fn=worker_init_fn,
+        )
+
+        # FSDP-Specific Parameters
+        if sharding_strategy == "shard-grad-op":
+            self.fsdp_sharding_strategy = ShardingStrategy._HYBRID_SHARD_ZERO2
+        elif sharding_strategy == "full-shard":
+            self.fsdp_sharding_strategy = ShardingStrategy.HYBRID_SHARD
+        else:
+            raise ValueError(f"FSDP Sharding Strategy {sharding_strategy} is not supported!")
+
+        assert state_dict_type == StateDictType.FULL_STATE_DICT, "Sharded state saving is not yet implemented!"
+        self.fsdp_state_dict_type = state_dict_type
+        self.fsdp_save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+
+    def save_checkpoint(
+        self,
+        run_dir: Path,
+        global_step: int,
+        epoch: int,
+        train_loss: Optional[float] = None,
+        only_trainable: bool = True,
+    ) -> None:
+        """Save a checkpoint to the `run_dir` only containing the state_dicts for trainable parameters by default."""
+        assert isinstance(self.vlm, FSDP), "FSDPStrategy.save_checkpoint assumes VLM is already wrapped in FSDP!"
+
+        # Summon Full State Dictionary =>> Reconstitute from Shards
+        with FSDP.state_dict_type(self.vlm, self.fsdp_state_dict_type, self.fsdp_save_policy):
+            full_vlm_state_dict = self.vlm.state_dict()
+            model_state_dicts = {
+                mkey: OrderedDict() for mkey in (self.trainable_module_keys if only_trainable else self.all_module_keys)
+            }
+
+            # Iterate through `full_vlm_state_dict` and split `mkey.{full_dotted_path}` -> `mkey: {full_dotted_path}`
+            for key, param in full_vlm_state_dict.items():
+                for mkey in model_state_dicts:
+                    if key.startswith(mprefix := f"{mkey}."):
+                        model_state_dicts[mkey][key.removeprefix(mprefix)] = param
+
+            # Save on rank zero *only*
+            if overwatch.is_rank_zero():
+                checkpoint_dir = run_dir / "checkpoints"
+                if train_loss is None:
+                    checkpoint_path = checkpoint_dir / f"step-{global_step:06d}-epoch-{epoch:02d}-loss=inf.pt"
+                else:
+                    checkpoint_path = (
+                        checkpoint_dir / f"step-{global_step:06d}-epoch-{epoch:02d}-loss={train_loss:.4f}.pt"
+                    )
+
+                # Save Checkpoint & Copy Latest to `latest-checkpoint.pt`
+                torch.save({"model": model_state_dicts}, checkpoint_path)
+
+                # TODO (siddk) :: This breaks w/ Sagemaker default permissions (root vs. <user>)... skip?
+                # shutil.copy(checkpoint_path, checkpoint_dir / "latest-checkpoint.pt")
+
+    def run_setup(self, run_dir: Path, n_train_examples: int) -> None:
+        # Iteratively Assemble FSDP Wrapping Policy by fetching the wrapping policies for each backbone/constituent
+        vlm_fsdp_wrapping_policy = self.vlm.get_fsdp_wrapping_policy()
+
+        # Assemble the Default FSDP Mixed Precision Policy
+        if self.enable_mixed_precision_training and self.mixed_precision_dtype == torch.bfloat16:
+            # MixedPrecision `param_dtype` specifies *compute* dtype (for forward/backward only)
+            #   => Reference: https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.MixedPrecision
+            reduce_buffer_dtype = torch.bfloat16 if not self.reduce_in_full_precision else torch.float32
+            fsdp_precision_policy = MixedPrecision(
+                param_dtype=torch.bfloat16, reduce_dtype=reduce_buffer_dtype, buffer_dtype=reduce_buffer_dtype
+            )
+
+            # When running FSDP with a frozen vision backbone --> move to half precision!
+            if self.stage not in {"full-finetune", "vla-full-train", "vla-sandwich-train"}:
+                overwatch.info("Casting Vision Backbone to *Half Precision* via `.to(dtype=...)`")
+                self.vlm.vision_backbone.to(dtype=self.vlm.vision_backbone.half_precision_dtype)
+
+        else:
+            # If we're not using mixed precision, everything is in default full precision!
+            fsdp_precision_policy = MixedPrecision(
+                param_dtype=torch.float32, reduce_dtype=torch.float32, buffer_dtype=torch.float32
+            )
+
+        # <FSDP> => note that FSDP will automatically take care of device placement (similar to `autocast`)
+        self.vlm = FSDP(
+            self.vlm,
+            auto_wrap_policy=vlm_fsdp_wrapping_policy,
+            mixed_precision=fsdp_precision_policy,
+            sharding_strategy=self.fsdp_sharding_strategy,
+            device_id=torch.cuda.current_device(),
+            limit_all_gathers=True,
+            use_orig_params=True,
+        )
+
+        # Gradient Checkpoint Setup
+        if self.enable_gradient_checkpointing:
+            # For Gradient Checkpointing under FSDP --> we make the same assumption as in the DDP/other strategies; the
+            #   bulk of activation memory is taken up by the LLM activations. However, unlike other strategies, we
+            #   cannot rely on the HF Transformers default `gradient_checkpointing_enable()` --> FSDP breaks semantics!
+            #
+            # Instead, we need to write our own *NO-REENTRANT* wrapper, and apply it to the LLM's Transformer Layer.
+            non_reentrant_wrapper = partial(checkpoint_wrapper, checkpoint_impl=CheckpointImpl.NO_REENTRANT)
+
+            def check_fn(submodule: nn.Module) -> bool:
+                return isinstance(submodule, self.llm_transformer_layer_cls)
+
+            # Note that the terms "activation checkpointing" and "gradient checkpointing" are synonymous!
+            apply_activation_checkpointing(self.vlm, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=check_fn)
+
+        # Barrier =>> Sharding takes a minute?
+        dist.barrier()
+
+        # Create Optimizer and LR Scheduler =>> note that most of the LR Schedulers we use require `max_steps/epochs`
+        #   => Optimizer should only operate on parameters that are *unfrozen* / trainable!
+        n_train_examples = math.ceil(n_train_examples / self.global_batch_size) * self.global_batch_size
+        if self.max_steps is None:
+            num_training_steps = (n_train_examples * self.epochs) // self.global_batch_size
+        else:
+            num_training_steps = self.max_steps
+
+        if self.lr_scheduler_type == "linear-warmup+cosine-decay":
+            # Set warmup steps (floor) based on `warmup_ratio` (should be 0.03 - 0.05)
+            num_warmup_steps = int(num_training_steps * self.warmup_ratio)
+
+            # Default AdamW w/ specified LR & Linear Warmup / Cosine Decay & Weight Decay
+            #   => Create Parameter Groups --> bias terms, normalization layer parameters shouldn't be decayed!
+            decay, no_decay = [], []
+            for name, param in self.vlm.named_parameters():
+                if not param.requires_grad:
+                    continue
+
+                # Check on any parameters with fewer than 2 dimensions or with "bias" in the name
+                if param.ndim <= 1 or name.endswith(".bias"):
+                    no_decay.append(param)
+                else:
+                    decay.append(param)
+
+            # Build Parameter Groups
+            groups = [{"params": decay, "weight_decay": self.weight_decay}, {"params": no_decay, "weight_decay": 0.0}]
+
+            # Create Optimizer & LR Scheduler
+            self.optimizer = AdamW(groups, lr=self.learning_rate)
+            self.lr_scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps, num_training_steps)
+            for param_group in self.optimizer.param_groups:
+                param_group["lr"] = 0.0
+
+        elif self.lr_scheduler_type == "constant":
+            num_warmup_steps = 0
+
+            # Default AdamW w/ specified LR & Linear Warmup / Cosine Decay & Weight Decay
+            #   => Create Parameter Groups --> bias terms, normalization layer parameters shouldn't be decayed!
+            decay, no_decay = [], []
+            for name, param in self.vlm.named_parameters():
+                if not param.requires_grad:
+                    continue
+
+                # Check on any parameters with fewer than 2 dimensions or with "bias" in the name
+                if param.ndim <= 1 or name.endswith(".bias"):
+                    no_decay.append(param)
+                else:
+                    decay.append(param)
+
+            # Build Parameter Groups
+            groups = [{"params": decay, "weight_decay": self.weight_decay}, {"params": no_decay, "weight_decay": 0.0}]
+
+            # Create Optimizer & LR Scheduler
+            self.optimizer = AdamW(groups, lr=self.learning_rate)
+            self.lr_scheduler = get_constant_schedule(self.optimizer)
+
+        else:
+            raise ValueError(f"Learning Rate Schedule with type `{self.lr_scheduler_type}` is not supported!")
+
+        # Finalize Setup =>> Log!
+        overwatch.info(
+            "FSDP Full-Shard Strategy =>> Finalized Training Setup:\n"
+            f"         |-> Global (Effective) Batch Size = {self.global_batch_size}\n"
+            f"         |-> Per-Device Batch Size = {self.per_device_batch_size}\n"
+            f"         |-> Distributed World Size = {overwatch.world_size()}\n"
+            f"         |-> Gradient Accumulation Steps = {self.grad_accumulation_steps}\n\n"
+            f"         |-> LLM Backbone FSDP Gradient Checkpointing = {self.enable_gradient_checkpointing}\n"
+            f"         |-> Use FSDP Mixed Precision = {self.enable_mixed_precision_training}\n"
+            f"                 |-> Parameter Precision = {fsdp_precision_policy.param_dtype}\n"
+            f"                 |-> Reduction Precision = {fsdp_precision_policy.reduce_dtype}\n"
+            f"                 |-> Buffer Precision = {fsdp_precision_policy.buffer_dtype}\n\n"
+            f"         |-> Default AdamW LR = {self.learning_rate}\n"
+            f"         |-> AdamW Weight Decay = {self.weight_decay}\n"
+            f"         |-> LR Scheduler Type = {self.lr_scheduler_type}\n"
+            f"         |-> LR Scheduler Warmup Steps (Ratio) = {num_warmup_steps} ({self.warmup_ratio})\n"
+            f"         |-> Dataset Size = {n_train_examples} Examples\n"
+            f"         |-> Max Steps = {num_training_steps}\n"
+        )
+
+    def clip_grad_norm(self) -> None:
+        # Note =>> FSDP uses a custom `clip_grad_norm_` function; requires *uniform grad dtype*
+        self.vlm.clip_grad_norm_(max_norm=self.max_grad_norm)
diff --git a/prismatic/util/__init__.py b/prismatic/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3473f952d5fd1ddabcd6e0e372a74f4db1f407c3
--- /dev/null
+++ b/prismatic/util/__init__.py
@@ -0,0 +1 @@
+from .torch_utils import check_bloat16_supported, set_global_seed
diff --git a/prismatic/util/batching_utils.py b/prismatic/util/batching_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5610348e2f5ad5406f71023e014105c98ce5eeff
--- /dev/null
+++ b/prismatic/util/batching_utils.py
@@ -0,0 +1,212 @@
+"""
+batching_utils.py
+
+Core definitions of (Distributed) Samplers for VLM finetuning; provides functionality for construction and allocating
+"split-modality" batches as described in the LLaVa paper; this makes sure that a given device/batch is either entirely
+(vision, language) or (language-only) data, which leads to sizeable efficiency gains.
+"""
+
+import math
+from typing import Iterator, List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from torch.utils.data import Dataset, Sampler
+
+
+# High-Fidelity Bitwise Reproduction of the LLaVa Codebase Sampler Strategy + Per-Rank Allocation Scheme (following
+#   the default batching behavior of HF's Trainer Class --> derived from `accelerate`).
+#
+#   =>> Reference: https://github.com/haotian-liu/LLaVA/blob/main/llava/train/llava_trainer.py#L60
+#   =>> Reference: https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_pt_utils.py#L603
+class SplitModalitySampler(Sampler):
+    def __init__(
+        self,
+        dataset: Dataset,
+        modality_lengths: List[Tuple[bool, int]],
+        global_batch_size: int,
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        seed: int = 0,
+        drop_last: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_replicas = num_replicas if num_replicas is not None else dist.get_world_size()
+        self.rank = rank if rank is not None else dist.get_rank()
+        self.seed, self.epoch = seed, 0
+
+        # Custom Parameters
+        self.dataset, self.modality_lengths, self.drop_last = dataset, modality_lengths, drop_last
+        self.global_batch_size = global_batch_size
+
+        # For our purposes, `drop_last` is always False!
+        assert not self.drop_last, "SplitModalitySampler must set `drop_last = False`!"
+        self.total_size = math.ceil(len(self.dataset) / self.global_batch_size) * self.global_batch_size
+        self.num_samples = self.total_size // self.num_replicas
+
+    @staticmethod
+    def reindex_batch(batch_idxs: List[int], idx2lengths: List[int], n_buckets: int) -> List[List[int]]:
+        """Re-indexes a batch in a way that is conducive to DistributedSampler + grouping by seqlen per rank."""
+        assert len(batch_idxs) % n_buckets == 0, "Batch length is not divisible by `num_replicas`!"
+
+        # Establish initial buckets, capacities, and max number of elements per bucket
+        n_examples_per_bucket = len(batch_idxs) // n_buckets
+        bucket_indices = [[] for _ in range(n_buckets)]
+        bucket_lengths = [0 for _ in range(n_buckets)]
+
+        # Note that `batch_idxs` is already sorted by corresponding length (in descending order)
+        for idx in batch_idxs:
+            shortest_bucket_idx = bucket_lengths.index(min(bucket_lengths))
+            bucket_indices[shortest_bucket_idx].append(idx)
+
+            # Update `bucket_lengths` --> set length to infinity if at capacity!
+            bucket_lengths[shortest_bucket_idx] += idx2lengths[idx]
+            if len(bucket_indices[shortest_bucket_idx]) == n_examples_per_bucket:
+                bucket_lengths[shortest_bucket_idx] = float("inf")
+
+        return bucket_indices
+
+    def get_modality_and_length_grouped_indices(self, generator: torch.Generator) -> List[int]:
+        """
+        Returns a list of indices so that each slice of `global_batch_size` consecutive indices corresponds to elements
+        of the same modality with each sub-sequence of `per_replica_batch_size` (the batch size each unique device sees
+        during distributed training) is roughly grouped by sequence length (for training efficiency).
+        """
+        multimodal_indices, multimodal_lengths = zip(
+            *[(idx, length) for idx, (is_multimodal, length) in enumerate(self.modality_lengths) if is_multimodal]
+        )
+
+        # Handle Special Case --> no "unimodal" inputs
+        unimodal_split = [
+            (idx, length) for idx, (is_multimodal, length) in enumerate(self.modality_lengths) if not is_multimodal
+        ]
+        if len(unimodal_split) == 0:
+            unimodal_indices, unimodal_lengths = [], []
+        else:
+            unimodal_indices, unimodal_lengths = zip(*unimodal_split)
+
+        # Create a permutation of indices for each of the multimodal and unimodal data
+        mm_shuffled_idxs = torch.randperm(len(multimodal_indices), generator=generator)
+        uni_shuffled_idxs = torch.randperm(len(unimodal_indices), generator=generator)
+
+        # We're going to be running sorting/grouping relative to `self.global_batch_size` and `self.num_replicas`
+        g_bsz = self.global_batch_size
+
+        # Break each of the permutations into batches of length `global_batch_size`
+        mm_batch_idxs = [mm_shuffled_idxs[i : i + g_bsz].tolist() for i in range(0, len(mm_shuffled_idxs), g_bsz)]
+        uni_batch_idxs = [uni_shuffled_idxs[i : i + g_bsz].tolist() for i in range(0, len(uni_shuffled_idxs), g_bsz)]
+
+        # If "last" batch is not of length `g_bsz` --> PAD by stealing indices from the first batch!
+        if len(mm_batch_idxs[-1]) < g_bsz:
+            n_missing = g_bsz - len(mm_batch_idxs[-1])
+            mm_batch_idxs[-1].extend(mm_batch_idxs[0][:n_missing])
+
+        if len(uni_batch_idxs) > 0 and len(uni_batch_idxs[-1]) < g_bsz:
+            n_missing = g_bsz - len(uni_batch_idxs[-1])
+            uni_batch_idxs[-1].extend(uni_batch_idxs[0][:n_missing])
+
+        # Now we're going to sort each batch by length --> this will aid in grouping by length by rank (efficiency!)
+        mm_sorted_batch_idxs = [sorted(b, key=lambda i: multimodal_lengths[i], reverse=True) for b in mm_batch_idxs]
+        uni_sorted_batch_idxs = [sorted(b, key=lambda i: unimodal_lengths[i], reverse=True) for b in uni_batch_idxs]
+
+        # IMPORTANT :: At this point, for each modality, we have a list of "batches" (made up of indices) where indices
+        # are sorted by example sequence length *within* each batch. To make this more concrete, consider the following:
+        #   => World Size (`num_replicas`) = 2
+        #   => Global Batch Size (`g_bsz`) = 4
+        #   => `multimodal_indices` = [0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11]
+        #      `multimodal_lengths` = [20, 90, 21, 22, 91, 18, 89, 19, 93, 88, 92, 17]
+        #
+        # At this point in the code, `mm_sorted_batch_idxs` might then look like the following (length in parenthesis):
+        #   => `mm_sorted_batch_idxs`: [
+        #       [4  (91), 3  (21), 0  (20), 5  (18)]    => Batch 1
+        #       [6  (89), 9  (88), 7  (19), 11 (17)]    => Batch 2
+        #       [8  (93), 10 (92), 1  (90), 2  (21)]    => Batch 3
+        #   ]
+        #
+        # In practice: `g_bsz` is large (= 128), and for contiguous mini-batch "slices", length variance is low.
+
+        # PROBLEM :: We want to split these "global batches" into equal-sized pieces, so that each "replica" (GPU)
+        # sees a "mini-batch" of roughly the same sequence lengths; this is super useful for efficient training.
+
+        # HOWEVER :: The default "access pattern" for splitting a large batch into mini-batches by a DistributedSampler
+        # is akin to a "take every k" where `k` is equal to the number of replicas (GPUs) you're training on. Or, in
+        # Python notation --> `rank_k_indices = flatten(mm_sorted_batch_idxs)[k::num_replicas].
+        #
+        # Naively translating this our example means each GPU (in our world of 2 total) sees the following indices
+        # (grouped by "mini-batch" = `g_bsz / num_replicas` = 2 for convenience):
+        #   => `rank_0_indices`: [ [4 (91), 0 (20)] =>> [6 (89), 7  (19)] =>> [8  (93), 1 (90)] ]
+        #   => `rank_1_indices`: [ [3 (21), 5 (18)] =>> [9 (88), 11 (17)] =>> [10 (92), 2 (21)] ]
+        #
+        # We get lucky sometimes, but for the most part, each "mini-batch" has VASTLY DIFFERENT lengths! Bad!
+
+        # FIX :: If we "undo" the access pattern with the following code and re-arrange the way we allocate batches
+        # inside the __iter__ method below, we can allocate indices appropriately. Running the following code gives us
+        # the following indices (grouped by "mini-batch" again for convenience):
+        #   => `rank_0_indices`: [ [4 (91), 3 (21)] =>> [6  (89), 9 (88)] =>> [8 (93), 10 (92)] ]
+        #   => `rank_1_indices`: [ [5 (18), 0 (20)] =>> [11 (17), 7 (19)] =>> [2 (21),  1 (90)] ]
+        #
+        # Much better! As `g_bsz` and `dataset` grow, we're more often than not getting *decent* groupings!
+        mm_length_bucketed_idxs = [
+            self.reindex_batch(batch, multimodal_lengths, self.num_replicas) for batch in mm_sorted_batch_idxs
+        ]
+        uni_length_bucketed_idxs = [
+            self.reindex_batch(batch, unimodal_lengths, self.num_replicas) for batch in uni_sorted_batch_idxs
+        ]
+
+        # Note :: Because of the initial `randperm` --> we're indexing both sets from 0 (we're clobbering the range)
+        #   => Flatten indices --> index into original `{modality}_indices` then re-batch!
+        mm_output_idxs = [idx for batch in mm_length_bucketed_idxs for bucket in batch for idx in bucket]
+        mm_reindexed = [multimodal_indices[idx] for idx in mm_output_idxs]
+        mm_batches = [mm_reindexed[i : i + g_bsz] for i in range(0, len(mm_reindexed), g_bsz)]
+
+        uni_output_idxs = [idx for batch in uni_length_bucketed_idxs for bucket in batch for idx in bucket]
+        uni_reindexed = [unimodal_indices[idx] for idx in uni_output_idxs]
+        uni_batches = [uni_reindexed[i : i + g_bsz] for i in range(0, len(uni_reindexed), g_bsz)]
+
+        # Finally, randomly permute the multimodal & unimodal batches, merging into a single stream of indices
+        merged_batches = mm_batches + uni_batches
+        merge_idxs = torch.randperm(len(merged_batches), generator=generator)
+        all_batches = [merged_batches[idx] for idx in merge_idxs]
+
+        # [Quality of Life] Shift "max length" batch to index 0 --> if we OOM, it happens immediately!
+        all_lengths = [length + ((_n_patches := 24 * 24) if is_mm else 0) for is_mm, length in self.modality_lengths]
+        all_batches_max_lengths = []
+        for batch in all_batches:
+            all_batches_max_lengths.append(max([all_lengths[idx] for idx in batch]))
+
+        # Identify Batch with "max length" --> Swap into Index 0
+        longest_batch_idx = np.argmax(all_batches_max_lengths)
+        all_batches[0], all_batches[longest_batch_idx] = all_batches[longest_batch_idx], all_batches[0]
+
+        # Flatten & Return all Indices
+        indices = [idx for batch in all_batches for idx in batch]
+        return indices
+
+    def __iter__(self) -> Iterator:
+        """Deterministically shuffle, then split indices by modality and length."""
+        g = torch.Generator()
+        g.manual_seed(self.seed + self.epoch)
+        indices = self.get_modality_and_length_grouped_indices(g)
+        assert len(set(indices)) == len(self.modality_lengths) == len(self.dataset), "Oops!"
+        assert (len(indices) % self.global_batch_size == 0) and (len(indices) % self.num_replicas) == 0, "Oops"
+
+        # Note :: We compute per-replica batch size as a function of `global_batch` and `num_replicas` to ensure that
+        # gradient accumulation doesn't affect what indices are assigned a given rank.
+        per_replica_batch_size = self.global_batch_size // self.num_replicas
+
+        # Tensorize & Unravel --> rather than yielding via a `take_every` --> we want to partition a global batch
+        # across replicas by assigning each a contiguous sub-sequence.
+        indices_t = torch.as_tensor(indices)
+        per_replica_batch_indices_t = indices_t.reshape(-1, per_replica_batch_size)
+        replica_indices_t = per_replica_batch_indices_t[self.rank :: self.num_replicas]
+
+        replica_indices = replica_indices_t.flatten().tolist()
+        return iter(replica_indices)
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def set_epoch(self, epoch: int) -> None:
+        """To be called *between* epochs, prior to DataLoader instantiation; ensures random order across epochs."""
+        self.epoch = epoch
diff --git a/prismatic/util/data_utils.py b/prismatic/util/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..dedec6122bd8d1c9cad088d89d9a170aa3e1e83b
--- /dev/null
+++ b/prismatic/util/data_utils.py
@@ -0,0 +1,755 @@
+"""
+data_utils.py
+
+General utilities and classes for facilitating data loading and collation.
+"""
+
+from dataclasses import dataclass
+from typing import Callable, Dict, Sequence, Tuple
+import numpy as np
+
+import torch
+from torch.nn.utils.rnn import pad_sequence
+
+# HuggingFace Default / LLaMa-2 IGNORE_INDEX (for labels)
+IGNORE_INDEX = -100
+
+
+def tree_map(fn: Callable, tree: dict) -> dict:
+    """Maps a function over a nested dictionary."""
+    return {k: tree_map(fn, v) if isinstance(v, dict) else fn(v) for k, v in tree.items()}
+
+
+def tree_map_with_key(fn: Callable, tree: dict, keys: Sequence = ()) -> dict:
+    """Maps a function over a nested dictionary."""
+    return {
+        k: tree_map_with_key(fn, v, (*keys, k)) if isinstance(v, dict) else fn((*keys, k), v) for k, v in tree.items()
+    }
+
+
+@dataclass
+class PaddedCollatorForLanguageModeling:
+    model_max_length: int
+    pad_token_id: int
+    default_image_resolution: Tuple[int, int, int]
+    padding_side: str = "right"
+    pixel_values_dtype: torch.dtype = torch.float32
+
+    def __post_init__(self) -> None:
+        self.dummy_pixel_values = torch.zeros(self.default_image_resolution, dtype=self.pixel_values_dtype)
+
+    def __call__(self, instances: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        pixel_values = [instance["pixel_values"] for instance in instances]
+
+        # For now, we only support Tokenizers with `padding_side = "right"` during Training (but plan to extend!)
+        #   => Handle padding via RNN Utils => `pad_sequence`
+        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id)
+        labels = pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        # Truncate (if necessary)
+        input_ids, labels = input_ids[:, : self.model_max_length], labels[:, : self.model_max_length]
+
+        # Get `attention_mask` by checking for `pad_token_id`
+        attention_mask = input_ids.ne(self.pad_token_id)
+
+        # === Handle "unimodal" (language-only) vs. "multimodal" ===
+
+        # Some examples are "language-only" --> build a Tensor of `multimodal_indices` that we can slice into easily
+        multimodal_indices = torch.tensor(
+            [idx for idx in range(len(pixel_values)) if pixel_values[idx] is not None], dtype=torch.long
+        )
+
+        # Stack all `pixel_values` --> depending on type (torch.Tensor, or Dict[str, torch.Tensor]) & presence of None
+        if len(multimodal_indices) == 0:
+            pixel_values = torch.stack([self.dummy_pixel_values for _ in range(len(input_ids))])
+        elif isinstance(pv_example := pixel_values[multimodal_indices[0]], torch.Tensor):
+            pixel_values = torch.stack(
+                [
+                    pixel_values[idx] if idx in multimodal_indices else self.dummy_pixel_values
+                    for idx in range(len(input_ids))
+                ]
+            )
+        elif isinstance(pv_example, dict):
+            pixel_values = {
+                k: torch.stack(
+                    [
+                        pixel_values[idx][k] if idx in multimodal_indices else self.dummy_pixel_values
+                        for idx in range(len(input_ids))
+                    ]
+                )
+                for k in pv_example
+            }
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        return dict(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            multimodal_indices=multimodal_indices,
+        )
+
+
+@dataclass
+class PaddedCollatorForActionPrediction:
+    model_max_length: int
+    pad_token_id: int
+    padding_side: str = "right"
+    pixel_values_dtype: torch.dtype = torch.float32
+
+    def __call__(self, instances: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        pixel_values = [instance["pixel_values"] for instance in instances]
+        if "dataset_name" in instances[0]:
+            dataset_names = [instance["dataset_name"] for instance in instances]
+        else:
+            dataset_names = None
+
+        # For now, we only support Tokenizers with `padding_side = "right"` during training
+        #   => Handle padding via RNN Utils => `pad_sequence`
+        assert self.padding_side == "right", f"Invalid Tokenizer `{self.padding_side = }`"
+        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id)
+        labels = pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        # Truncate (if necessary)
+        input_ids, labels = input_ids[:, : self.model_max_length], labels[:, : self.model_max_length]
+
+        # Get `attention_mask` by checking for `pad_token_id`
+        attention_mask = input_ids.ne(self.pad_token_id)
+
+        # [Contract] For VLA Training =>> No "Unimodal" Data!
+        assert all([pv is not None for pv in pixel_values]), "Invalid VLA Example with `pixel_values = None`!"
+
+        # Stack all `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        if isinstance(pixel_values[0], torch.Tensor):
+            pixel_values = torch.stack(pixel_values)
+        elif isinstance(pixel_values[0], dict):
+            pixel_values = {
+                k: torch.stack([pixel_values[idx][k] for idx in range(len(input_ids))]) for k in pixel_values[0]
+            }
+        else:
+            raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        output = dict(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+        )
+        if dataset_names is not None:
+            output["dataset_names"] = dataset_names
+        return output
+
+
+@dataclass
+class PaddedCollatorForActionPredictionV2:
+    model_max_length: int
+    pad_token_id: int
+    padding_side: str = "right"
+    pixel_values_dtype: torch.dtype = torch.float32
+
+    def __call__(self, instances: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        if "dataset_name" in instances[0]:
+            dataset_names = [instance["dataset_name"] for instance in instances]
+        else:
+            dataset_names = None
+
+        # For now, we only support Tokenizers with `padding_side = "right"` during training
+        #   => Handle padding via RNN Utils => `pad_sequence`
+        assert self.padding_side == "right", f"Invalid Tokenizer `{self.padding_side = }`"
+        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id)
+        labels = pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        # Truncate (if necessary)
+        input_ids, labels = input_ids[:, : self.model_max_length], labels[:, : self.model_max_length]
+
+        # Get `attention_mask` by checking for `pad_token_id`
+        attention_mask = input_ids.ne(self.pad_token_id)
+
+        # # Stack all `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        # pixel_values = [instance["pixel_values"] for instance in instances]
+        # # [Contract] For VLA Training =>> No "Unimodal" Data!
+        # assert all([pv is not None for pv in pixel_values]), "Invalid VLA Example with `pixel_values = None`!"
+        # if isinstance(pixel_values[0], torch.Tensor):
+        #     pixel_values = torch.stack(pixel_values)
+        # elif isinstance(pixel_values[0], dict):
+        #     pixel_values = {
+        #         k: torch.stack([pixel_values[idx][k] for idx in range(len(input_ids))]) for k in pixel_values[0]
+        #     }
+        # else:
+        #     raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Stack all additional `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        all_pixel_values = [instance["all_pixel_values"] for instance in instances]
+        all_pixel_values = torch.stack(all_pixel_values)
+        if instances[0]["all_wrist_values"] is not None:
+            all_wrist_values = [instance["all_wrist_values"] for instance in instances]
+            all_wrist_values = torch.stack(all_wrist_values)
+        else:
+            all_wrist_values = None
+
+        output = dict(
+            # pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            all_pixel_values=all_pixel_values,
+            all_wrist_values=all_wrist_values,
+        )
+        if dataset_names is not None:
+            output["dataset_names"] = dataset_names
+        return output
+
+
+@dataclass
+class PaddedCollatorForActionPredictionV3:
+    model_max_length: int
+    pad_token_id: int
+    padding_side: str = "right"
+    pixel_values_dtype: torch.dtype = torch.float32
+
+    def __call__(self, instances: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        input_ids, windowed_labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        windowed_labels = torch.stack(windowed_labels)
+        if "dataset_name" in instances[0]:
+            dataset_names = [instance["dataset_name"] for instance in instances]
+        else:
+            dataset_names = None
+
+        # For now, we only support Tokenizers with `padding_side = "right"` during training
+        #   => Handle padding via RNN Utils => `pad_sequence`
+        assert self.padding_side == "right", f"Invalid Tokenizer `{self.padding_side = }`"
+        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id)
+
+        # Truncate (if necessary)
+        input_ids = input_ids[:, : self.model_max_length]
+
+        # Windowed labels
+        _windowed_labels = []
+        for idx in range(windowed_labels.shape[1]):
+            labels = windowed_labels[:,idx]
+            labels = pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+            labels = labels[:, : self.model_max_length]
+            _windowed_labels.append(labels)
+        windowed_labels = torch.stack(_windowed_labels, dim=1)
+
+        # Get `attention_mask` by checking for `pad_token_id`
+        attention_mask = input_ids.ne(self.pad_token_id)
+
+        # # Stack all `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        # pixel_values = [instance["pixel_values"] for instance in instances]
+        # # [Contract] For VLA Training =>> No "Unimodal" Data!
+        # assert all([pv is not None for pv in pixel_values]), "Invalid VLA Example with `pixel_values = None`!"
+        # if isinstance(pixel_values[0], torch.Tensor):
+        #     pixel_values = torch.stack(pixel_values)
+        # elif isinstance(pixel_values[0], dict):
+        #     pixel_values = {
+        #         k: torch.stack([pixel_values[idx][k] for idx in range(len(input_ids))]) for k in pixel_values[0]
+        #     }
+        # else:
+        #     raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+        
+        # Stack all additional `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        all_pixel_values = [instance["all_pixel_values"] for instance in instances]
+        all_pixel_values = torch.stack(all_pixel_values)
+        if instances[0]["all_wrist_values"] is not None:
+            all_wrist_values = [instance["all_wrist_values"] for instance in instances]
+            all_wrist_values = torch.stack(all_wrist_values)
+        else:
+            all_wrist_values = None
+        if instances[0]["all_pixel_depth_values"] is not None:
+            all_pixel_depth_values = [instance["all_pixel_depth_values"] for instance in instances]
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+        else:
+            all_pixel_depth_values = None
+        if instances[0]["all_wrist_depth_values"] is not None:
+            all_wrist_depth_values = [instance["all_wrist_depth_values"] for instance in instances]
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        else:
+            all_wrist_depth_values = None
+        if instances[0]["all_pixel_seg_values"] is not None:
+            all_pixel_seg_values = [instance["all_pixel_seg_values"] for instance in instances]
+            all_pixel_seg_values = torch.stack(all_pixel_seg_values)
+        else:
+            all_pixel_seg_values = None
+        if instances[0]["all_wrist_seg_values"] is not None:
+            all_wrist_seg_values = [instance["all_wrist_seg_values"] for instance in instances]
+            all_wrist_seg_values = torch.stack(all_wrist_seg_values)
+        else:
+            all_wrist_seg_values = None
+
+        if "reasoning_on_image" in instances[0]:
+            reasoning_on_image = [instance["reasoning_on_image"] for instance in instances]
+        else:
+            reasoning_on_image = None
+        if "reasoning_on_wrist_image" in instances[0]:
+            reasoning_on_wrist_image = [instance["reasoning_on_wrist_image"] for instance in instances]
+        else:
+            reasoning_on_wrist_image = None
+        if "lang_nouns_ids" in instances[0]:
+            lang_nouns_ids = [instance["lang_nouns_ids"] for instance in instances]
+        else:
+            lang_nouns_ids = None
+
+        if "task" in instances[0]:
+            tasks = [instance["task"] for instance in instances]
+        else:
+            tasks = None
+
+        output = dict(
+            # pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=windowed_labels,
+            all_pixel_values=all_pixel_values,
+            all_wrist_values=all_wrist_values,
+            all_pixel_depth_values=all_pixel_depth_values,
+            all_wrist_depth_values=all_wrist_depth_values,
+            all_pixel_seg_values=all_pixel_seg_values,
+            all_wrist_seg_values=all_wrist_seg_values,
+            reasoning_on_image=reasoning_on_image,
+            reasoning_on_wrist_image=reasoning_on_wrist_image,
+            lang_nouns_ids=lang_nouns_ids,
+            tasks=tasks
+        )
+        if dataset_names is not None:
+            output["dataset_names"] = dataset_names
+        return output
+
+
+@dataclass
+class PaddedCollatorForActionPredictionV3T:
+    model_max_length: int
+    pad_token_id: int
+    padding_side: str = "right"
+    pixel_values_dtype: torch.dtype = torch.float32
+
+    def __call__(self, instances: Sequence[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        full_input_ids, full_labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+        if "dataset_name" in instances[0]:
+            dataset_names = [instance["dataset_name"] for instance in instances]
+        else:
+            dataset_names = None
+
+        # For now, we only support Tokenizers with `padding_side = "right"` during training
+        #   => Handle padding via RNN Utils => `pad_sequence`
+        assert self.padding_side == "right", f"Invalid Tokenizer `{self.padding_side = }`"
+        input_ids = []
+        for datum in full_input_ids:
+            input_ids += datum
+        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.pad_token_id)
+
+        labels = []
+        for datum in full_labels:
+            labels += datum
+        labels = pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        # Truncate (if necessary)
+        input_ids, labels = input_ids[:, : self.model_max_length], labels[:, : self.model_max_length]
+
+        # Get `attention_mask` by checking for `pad_token_id`
+        attention_mask = input_ids.ne(self.pad_token_id)
+
+        # # Stack all `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        # pixel_values = [instance["pixel_values"] for instance in instances]
+        # # [Contract] For VLA Training =>> No "Unimodal" Data!
+        # assert all([pv is not None for pv in pixel_values]), "Invalid VLA Example with `pixel_values = None`!"
+        # if isinstance(pixel_values[0], torch.Tensor):
+        #     pixel_values = torch.stack(pixel_values)
+        # elif isinstance(pixel_values[0], dict):
+        #     pixel_values = {
+        #         k: torch.stack([pixel_values[idx][k] for idx in range(len(input_ids))]) for k in pixel_values[0]
+        #     }
+        # else:
+        #     raise ValueError(f"Unsupported `pixel_values` type = {type(pixel_values)}")
+
+        # Stack all additional `pixel_values` --> depending on type is torch.Tensor or Dict[str, torch.Tensor]
+        all_pixel_values = [instance["all_pixel_values"] for instance in instances]
+        all_pixel_values = torch.stack(all_pixel_values)
+        if instances[0]["all_wrist_values"] is not None:
+            all_wrist_values = [instance["all_wrist_values"] for instance in instances]
+            all_wrist_values = torch.stack(all_wrist_values)
+        else:
+            all_wrist_values = None
+        if instances[0]["all_pixel_depth_values"] is not None:
+            all_pixel_depth_values = [instance["all_pixel_depth_values"] for instance in instances]
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+        else:
+            all_pixel_depth_values = None
+        if instances[0]["all_wrist_depth_values"] is not None:
+            all_wrist_depth_values = [instance["all_wrist_depth_values"] for instance in instances]
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        else:
+            all_wrist_depth_values = None
+
+        if "reasoning_on_image" in instances[0]:
+            reasoning_on_image = [instance["reasoning_on_image"] for instance in instances]
+        else:
+            reasoning_on_image = None
+        if "reasoning_on_wrist_image" in instances[0]:
+            reasoning_on_wrist_image = [instance["reasoning_on_wrist_image"] for instance in instances]
+        else:
+            reasoning_on_wrist_image = None
+        if "lang_nouns_ids" in instances[0]:
+            lang_nouns_ids = [instance["lang_nouns_ids"] for instance in instances]
+        else:
+            lang_nouns_ids = None
+
+        if "task" in instances[0]:
+            tasks = [instance["task"] for instance in instances]
+        else:
+            tasks = None
+
+        output = dict(
+            # pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            all_pixel_values=all_pixel_values,
+            all_wrist_values=all_wrist_values,
+            all_pixel_depth_values=all_pixel_depth_values,
+            all_wrist_depth_values=all_wrist_depth_values,
+            reasoning_on_image=reasoning_on_image,
+            reasoning_on_wrist_image=reasoning_on_wrist_image,
+            lang_nouns_ids=lang_nouns_ids,
+            tasks=tasks
+        )
+        if dataset_names is not None:
+            output["dataset_names"] = dataset_names
+        return output
+
+
+
+def preprocess_reasoning_bboxes(reasoning_bboxes, device=None):
+    # reasoning_bboxes : List[List[tuple(name, box)]]
+    batched_rois = reasoning_bboxes
+    revised_reasoning_bboxes = []
+    for i in range(len(batched_rois)):
+        rois = batched_rois[i]
+        rois = [torch.tensor(roi[1]) for roi in rois]
+        revised_reasoning_bboxes.append(torch.stack(rois, dim=0).to(device))
+
+    return revised_reasoning_bboxes
+
+def preprocess_reasoning_bboxes_v2(reasoning_bboxes, device=None):
+    # reasoning_bboxes : List[List[tuple(name, box)]]
+    bz = len(reasoning_bboxes)
+    horizon = len(reasoning_bboxes[0])
+    
+    revised_reasoning_bboxes = []
+    for i in range(bz):
+        labeled_reasoning_bboxes = {}
+        for j in range(horizon):
+            for roi in reasoning_bboxes[i][j]:
+                if roi[0] not in labeled_reasoning_bboxes:
+                    labeled_reasoning_bboxes[roi[0]] = []
+                    
+        batch_reasoning_keys = labeled_reasoning_bboxes.keys()
+
+        for j in range(horizon):
+            reasoning_dict_ij = dict(reasoning_bboxes[i][j])
+            for key in batch_reasoning_keys:
+                if key in reasoning_dict_ij:
+                    labeled_reasoning_bboxes[key].append(
+                        torch.tensor(np.concatenate([reasoning_dict_ij[key], [1.0]])).float()
+                    )
+                else:
+                    labeled_reasoning_bboxes[key].append(
+                        torch.tensor(np.zeros(5)).float()
+                    )
+        
+        for key in batch_reasoning_keys:
+            labeled_reasoning_bboxes[key] = torch.stack(labeled_reasoning_bboxes[key], dim=0).to(device)
+        revised_reasoning_bboxes.append(labeled_reasoning_bboxes)
+
+    return revised_reasoning_bboxes
+
+
+def preprocess_reasoning_bboxes_v3(reasoning_bboxes, lang_nouns, device=None):
+    # reasoning_bboxes : List[List[tuple(name, box)]]
+    bz = len(reasoning_bboxes)
+    horizon = len(reasoning_bboxes[0])
+    
+    revised_reasoning_bboxes = []
+    for i in range(bz):
+        labeled_reasoning_bboxes = {}
+        for j in range(horizon):
+            for roi in reasoning_bboxes[i][j]:
+                # if roi[0] not in labeled_reasoning_bboxes:
+                #     labeled_reasoning_bboxes[roi[0]] = []
+                if roi not in labeled_reasoning_bboxes:
+                    labeled_reasoning_bboxes[roi] = []
+                    
+        batch_reasoning_keys = labeled_reasoning_bboxes.keys()
+        active_objs = list(lang_nouns[i])
+
+        for j in range(horizon):
+            reasoning_dict_ij = dict(reasoning_bboxes[i][j])
+            for key in batch_reasoning_keys:
+                if key in reasoning_dict_ij:
+                    if key in active_objs:
+                        labeled_reasoning_bboxes[key].append(
+                            torch.tensor(np.concatenate([reasoning_dict_ij[key], [1.0, 1.0]])).float()
+                        )
+                    else:
+                        labeled_reasoning_bboxes[key].append(
+                            torch.tensor(np.concatenate([reasoning_dict_ij[key], [1.0, 0.0]])).float()
+                        )
+                else:
+                    labeled_reasoning_bboxes[key].append(
+                        torch.tensor(np.zeros(6)).float()
+                    )
+        
+        for key in batch_reasoning_keys:
+            labeled_reasoning_bboxes[key] = torch.stack(labeled_reasoning_bboxes[key], dim=0).to(device)
+        revised_reasoning_bboxes.append(labeled_reasoning_bboxes)
+
+    return revised_reasoning_bboxes
+
+# def preprocess_reasoning_bboxes(reasoning_bboxes, lang_nouns=None, device=None):
+#     # reasoning_bboxes : List[{object_name: xywh in [0,1],...}]
+#     # lang_nouns : List[{object_name: tokenized_name,...}]
+#     # english_lang_nouns, revised_reasoning_bboxes, revised_lang_nouns
+    
+#     english_lang_nouns = {}
+#     revised_reasoning_bboxes = {}
+#     revised_lang_nouns = {}
+
+#     for data_name in ['robot', 'objects', 'all']:
+#         _english_lang_nouns = []
+#         _revised_reasoning_bboxes = []
+#         _revised_lang_nouns = []
+
+#         for i, bboxes in enumerate(reasoning_bboxes):
+#             keys = lang_nouns[i].keys()
+#             bbox_list = []
+#             tokens_list = []
+#             selected_keys = []
+#             for key in keys:
+#                 if key not in bboxes:
+#                     continue
+#                 if data_name == 'robot' and key != 'robot':
+#                     continue
+#                 if data_name == 'objects' and key == 'robot':
+#                     continue
+                
+#                 bbox_list.append(torch.tensor(bboxes[key]))
+#                 lang_nouns[i][key][0] = 32000
+#                 tokens_list.append(lang_nouns[i][key])
+#                 selected_keys.append(key)
+#             if len(bbox_list) != 0:
+#                 bbox_list = torch.stack(bbox_list, dim=0).to(device)
+#             else:
+#                 bbox_list = None
+
+#             if len(tokens_list) != 0:
+#                 tokens_list = torch.stack(tokens_list, dim=0).to(device)
+#             else:
+#                 tokens_list = None
+            
+#             _revised_reasoning_bboxes.append(bbox_list)
+#             _revised_lang_nouns.append(tokens_list)
+#             _english_lang_nouns.append(selected_keys)
+        
+#         revised_reasoning_bboxes[data_name] = _revised_reasoning_bboxes
+#         revised_lang_nouns[data_name] = _revised_lang_nouns
+#         english_lang_nouns[data_name] = _english_lang_nouns
+    
+#     return english_lang_nouns, revised_reasoning_bboxes, revised_lang_nouns
+
+
+def split_gripper_object_data(data):
+    return data[:,0:1], data[:,1:]
+
+import os
+import cv2
+from torch.nn import functional as F
+def crop_bboxes_with_score(
+    image_tensor, # torch.Tensor,
+    bboxes, # List[Dict[str: torch.Tensor]] ,
+    patch_resize: tuple = (40, 40),
+    save_debug: bool = False,
+    debug_dir: str = None,
+    is_depth: bool = False
+) -> torch.Tensor:
+    """
+    Crops and resizes bounding boxes from a batch of images.
+
+    Args:
+        image_tensor (torch.Tensor):
+            A batch of horizon of images of shape (B, T, C, H, W).
+        bboxes (List[Dict[str: torch.Tensor]]):
+            A list of dictionaries of len B, each dict has `key`: label, `value`: (T, Ni, 4), 
+            where each bbox in (cx, cy, w, h, score) format [0, 1]. 
+            Coordinates are normalized in [0,1]. There are ~max(Ni)*B*T such bboxes.
+        patch_resize (tuple):
+            The (height, width) to resize each crop.
+        save_debug (bool):
+            If True, saves each cropped patch for debugging via cv2.
+        debug_dir (str):
+            Directory (or prefix) to save debug images.
+
+    Returns:
+        List[Dict[str: torch.Tensor]]: Cropped patches that correspond with `bboxes`.
+    """
+    # Unpack shapes
+    B, T, C, H, W = image_tensor.shape
+
+    # Prepare an output tensor to hold all patches
+    out_patches = []
+
+    # Ensure debug directory exists if we're saving patches
+    if save_debug and debug_dir is not None:
+        os.makedirs(debug_dir, exist_ok=True)
+
+    for b_idx in range(B):
+        batched_patches = {}
+        for key, value in bboxes[b_idx].items():
+            # key   is str
+            # value is tensor of boxes
+            cropped_patches = []
+
+            for t_idx in range(T):
+                cx, cy, w, h, obj = value[t_idx][:5]
+
+                # (Optional) If you want to skip or filter by score, do it here:
+                # if score < 0.5:  # or your threshold
+                #     continue
+
+                # Convert from normalized [0,1] to pixel coordinates
+                cx_pix = cx * W
+                cy_pix = cy * H
+                w_pix  = w  * W
+                h_pix  = h  * H
+
+                # Compute integer pixel bounds for the crop
+                x1 = int(cx_pix - 0.5 * w_pix)
+                y1 = int(cy_pix - 0.5 * h_pix)
+                x2 = int(cx_pix + 0.5 * w_pix)
+                y2 = int(cy_pix + 0.5 * h_pix)
+
+                # Clamp to image boundaries
+                x1 = max(0, min(x1, W))
+                x2 = max(0, min(x2, W))
+                y1 = max(0, min(y1, H))
+                y2 = max(0, min(y2, H))
+                if obj == 0 or (y2-y1 == 0 and x2-x1 == 0):
+                    cropped_patches.append(-torch.ones((1, patch_resize[0], patch_resize[1])).to(image_tensor.device).float())
+                    continue
+                    
+                # Crop the patch [shape: (C, crop_h, crop_w)]
+                patch = image_tensor[b_idx, t_idx, :, y1:y2, x1:x2].unsqueeze(0)
+
+                # Resize to (patch_resize[0], patch_resize[1]) using bilinear interpolation
+                patch_resized = F.interpolate(
+                    patch,
+                    size=patch_resize,
+                    mode='bilinear',
+                    align_corners=False
+                ).squeeze(0)  # => shape (C, patch_resize[0], patch_resize[1])
+                if is_depth:
+                    patch_resized = patch_resized[:1,:,:].float()
+
+                cropped_patches.append(patch_resized)
+
+                if save_debug and debug_dir is not None:
+                    # Convert to NumPy for cv2
+                    patch_np = patch_resized.permute(1, 2, 0).cpu().numpy()
+
+                    # If your tensor is in [0,1], you may want to scale up to [0,255].
+                    patch_np = (patch_np * 255).astype("uint8")
+                    
+                    # Note: By default, this is in RGB. If you need BGR for OpenCV, do:
+                    # patch_np = patch_np[..., ::-1]
+
+                    # Write out the patch
+                    out_path = os.path.join(
+                        debug_dir, f"patch_b{b_idx}_{key}_t{t_idx}.png"
+                    )
+                    cv2.imwrite(out_path, patch_np)
+            batched_patches[key] = torch.stack(cropped_patches, dim=0)
+        out_patches.append(batched_patches)
+
+    # for b_idx in range(B):
+    #     print(bboxes[b_idx].keys())
+    #     print(out_patches[b_idx].keys())
+    # 1/0
+    return out_patches
+
+def filter_by_active_labels(reasoning_data, lang_nouns):
+    revised_reasoning_data = []
+    # print(reasoning_data)
+    # print(lang_nouns)
+    for bi, data in enumerate(reasoning_data):
+        revised_reasoning_data.append({})
+        active_objs = list(lang_nouns[bi])
+        for key, value in data.items():
+            if key in active_objs:
+                revised_reasoning_data[bi][key] = value
+    # print(revised_reasoning_data)
+    return revised_reasoning_data
+
+def _merge_bboxes_util(bbox1, bbox2):
+    # Extract bounding box coordinates: cx, cy, w, h
+    cx1, cy1, w1, h1, score = bbox1[:,0], bbox1[:,1], bbox1[:,2], bbox1[:,3], bbox1[:,4]
+    cx2, cy2, w2, h2, score = bbox2[:,0], bbox2[:,1], bbox2[:,2], bbox2[:,3], bbox2[:,4]
+
+    # Convert bbox1 to corner coordinates (x_min, y_min, x_max, y_max)
+    x_min1 = cx1 - w1 / 2
+    y_min1 = cy1 - h1 / 2
+    x_max1 = cx1 + w1 / 2
+    y_max1 = cy1 + h1 / 2
+
+    # Convert bbox2 to corner coordinates (x_min, y_min, x_max, y_max)
+    x_min2 = cx2 - w2 / 2
+    y_min2 = cy2 - h2 / 2
+    x_max2 = cx2 + w2 / 2
+    y_max2 = cy2 + h2 / 2
+
+    # Calculate the merged bounding box (min and max)
+    x_min_merged = torch.min(x_min1, x_min2)
+    y_min_merged = torch.min(y_min1, y_min2)
+    x_max_merged = torch.max(x_max1, x_max2)
+    y_max_merged = torch.max(y_max1, y_max2)
+
+    # Convert the merged bounding box back to cx, cy, w, h format
+    cx_merged = (x_min_merged + x_max_merged) / 2
+    cy_merged = (y_min_merged + y_max_merged) / 2
+    w_merged = x_max_merged - x_min_merged
+    h_merged = y_max_merged - y_min_merged
+
+    if bbox1.shape[1] > 5:
+        interactable = bbox1[:,5]
+        res = torch.stack([cx_merged, cy_merged, w_merged, h_merged, score, interactable], dim=-1)
+    else:
+        res = torch.stack([cx_merged, cy_merged, w_merged, h_merged, score], dim=-1)
+    return res
+
+def merge_bboxes_to_interaction(reasoning_data):
+    revised_reasoning_data = []
+    # print(reasoning_data)
+    # print(lang_nouns)
+    for bi, data in enumerate(reasoning_data):
+        revised_reasoning_data.append({})
+        for key, value in data.items():
+            revised_reasoning_data[bi][key] = _merge_bboxes_util(value, reasoning_data[bi]['robot'])
+    # print(revised_reasoning_data)
+    return revised_reasoning_data
+
+
+def merge_bboxes(reasoning_data):
+    revised_reasoning_data = []
+    # print(reasoning_data)
+    # print(lang_nouns)
+    for bi, data in enumerate(reasoning_data):
+        revised_reasoning_data.append({})
+        for key, value in data.items():
+            if 'interaction' not in revised_reasoning_data[bi]:
+                revised_reasoning_data[bi]['interaction'] = value
+            else:
+                revised_reasoning_data[bi]['interaction'] = _merge_bboxes_util(revised_reasoning_data[bi]['interaction'], value)
+    # print(revised_reasoning_data)
+    return revised_reasoning_data
diff --git a/prismatic/util/nn_utils.py b/prismatic/util/nn_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b3f6150f2914fde0b1cb80bfb3ad981ad9181ed
--- /dev/null
+++ b/prismatic/util/nn_utils.py
@@ -0,0 +1,53 @@
+"""
+nn_utils.py
+
+Utility functions and PyTorch submodule definitions.
+"""
+
+import torch
+import torch.nn as nn
+
+
+# === Definitions for Various Projection Modules, with Signature :: [..., in_dim] --> [..., out_dim] ===
+class LinearProjector(nn.Module):
+    def __init__(self, vision_dim: int, llm_dim: int) -> None:
+        super().__init__()
+        self.projector = nn.Linear(vision_dim, llm_dim, bias=True)
+
+    def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
+        return self.projector(img_patches)
+
+
+class MLPProjector(nn.Module):
+    def __init__(self, vision_dim: int, llm_dim: int, mlp_type: str = "gelu-mlp") -> None:
+        super().__init__()
+        if mlp_type == "gelu-mlp":
+            self.projector = nn.Sequential(
+                nn.Linear(vision_dim, llm_dim, bias=True),
+                nn.GELU(),
+                nn.Linear(llm_dim, llm_dim, bias=True),
+            )
+        else:
+            raise ValueError(f"Projector with `{mlp_type = }` is not supported!")
+
+    def forward(self, img_patches: torch.Tensor) -> torch.Tensor:
+        return self.projector(img_patches)
+
+
+class FusedMLPProjector(nn.Module):
+    def __init__(self, fused_vision_dim: int, llm_dim: int, mlp_type: str = "fused-gelu-mlp") -> None:
+        super().__init__()
+        self.initial_projection_dim = fused_vision_dim * 4
+        if mlp_type == "fused-gelu-mlp":
+            self.projector = nn.Sequential(
+                nn.Linear(fused_vision_dim, self.initial_projection_dim, bias=True),
+                nn.GELU(),
+                nn.Linear(self.initial_projection_dim, llm_dim, bias=True),
+                nn.GELU(),
+                nn.Linear(llm_dim, llm_dim, bias=True),
+            )
+        else:
+            raise ValueError(f"Fused Projector with `{mlp_type = }` is not supported!")
+
+    def forward(self, fused_img_patches: torch.Tensor) -> torch.Tensor:
+        return self.projector(fused_img_patches)
diff --git a/prismatic/util/torch_utils.py b/prismatic/util/torch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fc08f6ba74afee76ace76897c8de148ede0611f
--- /dev/null
+++ b/prismatic/util/torch_utils.py
@@ -0,0 +1,95 @@
+"""
+torch_utils.py
+
+General utilities for randomness, mixed precision training, and miscellaneous checks in PyTorch.
+
+Random `set_global_seed` functionality is taken directly from PyTorch-Lighting:
+    > Ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/utilities/seed.py
+
+This is pretty important to get right if we're every randomly generating our masks (or prefix dropout) inside our
+Dataset __getitem__() with multiple workers... if not handled properly, we will get repeated augmentations anytime
+we inject randomness from non-PyTorch sources (e.g., numpy, random)!
+    > Ref: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/
+
+Terminology
+    -> World Size :: Total number of processes distributed over (# nodes x # devices) -- assumed homogenous!
+    -> Rank :: Integer index of current process in the total world size
+    -> Local Rank :: Local index on given node in [0, Devices per Node]
+"""
+
+import os
+import random
+from typing import Callable, Optional
+
+import numpy as np
+import torch
+
+# === Randomness ===
+
+
+def set_global_seed(seed: int, get_worker_init_fn: bool = False) -> Optional[Callable[[int], None]]:
+    """Sets seed for all randomness libraries (mostly random, numpy, torch) and produces a `worker_init_fn`"""
+    assert np.iinfo(np.uint32).min < seed < np.iinfo(np.uint32).max, "Seed outside the np.uint32 bounds!"
+
+    # Set Seed as an Environment Variable
+    os.environ["EXPERIMENT_GLOBAL_SEED"] = str(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    return worker_init_function if get_worker_init_fn else None
+
+
+def worker_init_function(worker_id: int) -> None:
+    """
+    Borrowed directly from PyTorch-Lightning; inspired by this issue comment in the PyTorch repo:
+        > Ref: https://github.com/pytorch/pytorch/issues/5059#issuecomment-817392562
+
+    Intuition: You can think of the seed sequence spawn function as a "janky" torch.Generator() or jax.PRNGKey that
+    you can run iterative splitting on to get new (predictable) randomness.
+
+    :param worker_id: Identifier for the given worker [0, num_workers) for the Dataloader in question.
+    """
+    # Get current `rank` (if running distributed) and `process_seed`
+    global_rank, process_seed = int(os.environ["LOCAL_RANK"]), torch.initial_seed()
+
+    # Back out the "base" (original) seed - the per-worker seed is set in PyTorch:
+    #   > https://pytorch.org/docs/stable/data.html#data-loading-randomness
+    base_seed = process_seed - worker_id
+
+    # "Magic" code --> basically creates a seed sequence that mixes different "sources" and seeds every library...
+    seed_seq = np.random.SeedSequence([base_seed, worker_id, global_rank])
+
+    # Use 128 bits (4 x 32-bit words) to represent seed --> generate_state(k) produces a `k` element array!
+    np.random.seed(seed_seq.generate_state(4))
+
+    # Spawn distinct child sequences for PyTorch (reseed) and stdlib random
+    torch_seed_seq, random_seed_seq = seed_seq.spawn(2)
+
+    # Torch Manual seed takes 64 bits (so just specify a dtype of uint64
+    torch.manual_seed(torch_seed_seq.generate_state(1, dtype=np.uint64)[0])
+
+    # Use 128 Bits for `random`, but express as integer instead of as an array
+    random_seed = (random_seed_seq.generate_state(2, dtype=np.uint64).astype(list) * [1 << 64, 1]).sum()
+    random.seed(random_seed)
+
+
+# === BFloat16 Support ===
+
+
+def check_bloat16_supported() -> bool:
+    try:
+        import packaging.version
+        import torch.cuda.nccl as nccl
+        import torch.distributed as dist
+
+        return (
+            (torch.version.cuda is not None)
+            and torch.cuda.is_bf16_supported()
+            and (packaging.version.parse(torch.version.cuda).release >= (11, 0))
+            and dist.is_nccl_available()
+            and (nccl.version() >= (2, 10))
+        )
+
+    except Exception:
+        return False
diff --git a/prismatic/vla/__init__.py b/prismatic/vla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d808837784b917050223d70ee5a3d851bc8e3a1
--- /dev/null
+++ b/prismatic/vla/__init__.py
@@ -0,0 +1,2 @@
+from .materialize import get_vla_dataset_and_collator
+from .materialize import get_vla_trainval_dataset_and_collator
diff --git a/prismatic/vla/action_tokenizer.py b/prismatic/vla/action_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc1841a714f40ba677a1493782da23db4f9d4f4b
--- /dev/null
+++ b/prismatic/vla/action_tokenizer.py
@@ -0,0 +1,72 @@
+"""
+action_tokenizer.py
+
+Extension class; wraps base LLM/VLM tokenizer with logic to discretize and tokenize continuous robot actions.
+"""
+
+from typing import List, Union
+
+import numpy as np
+from transformers import PreTrainedTokenizerBase
+
+
+class ActionTokenizer:
+    def __init__(
+        self, tokenizer: PreTrainedTokenizerBase, bins: int = 256, min_action: int = -1, max_action: int = 1
+    ) -> None:
+        """
+        Discretizes continuous robot actions into N bins per dimension and maps to the least used tokens.
+
+        NOTE =>> by default, assumes a BPE-style tokenizer akin to the LlamaTokenizer, where *the least used tokens*
+                 appear at the end of the vocabulary!
+
+        :param tokenizer: Base LLM/VLM tokenizer to extend.
+        :param bins: Number of bins for each continuous value; we'll adopt a uniform binning strategy.
+        :param min_action: Minimum action value (for clipping, setting lower bound on bin interval).
+        :param max_action: Maximum action value (for clipping, setting upper bound on bin interval).
+        """
+        self.tokenizer, self.n_bins, self.min_action, self.max_action = tokenizer, bins, min_action, max_action
+
+        # Create Uniform Bins + Compute Bin Centers
+        self.bins = np.linspace(min_action, max_action, self.n_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        # [Contract] Set "action_token_begin_idx" based on `self.tokenizer.vocab_size - (self.n_bins + 1)`
+        #   =>> Assumes we're always overwriting the final `n_bins` tokens of the vocabulary!
+        self.action_token_begin_idx: int = int(self.tokenizer.vocab_size - (self.n_bins + 1))
+
+    def __call__(self, action: np.ndarray) -> Union[str, List[str]]:
+        """Clip & bin actions to *the last `n_bins` tokens* of the vocabulary (e.g., tokenizer.vocab[-256:])."""
+        action = np.clip(action, a_min=float(self.min_action), a_max=float(self.max_action))
+        discretized_action = np.digitize(action, self.bins)
+
+        # Handle single element vs. batch
+        if len(discretized_action.shape) == 1:
+            return self.tokenizer.decode(list(self.tokenizer.vocab_size - discretized_action))
+        else:
+            return self.tokenizer.batch_decode((self.tokenizer.vocab_size - discretized_action).tolist())
+
+    def decode_token_ids_to_actions(self, action_token_ids: np.ndarray) -> np.ndarray:
+        """
+        Returns continuous actions for discrete action token IDs.
+
+        NOTE =>> Because of the way the actions are discretized w.r.t. the bins (and not the bin centers), the
+                 digitization returns bin indices between [1, # bins], inclusive, when there are actually only
+                 (# bins - 1) bin intervals.
+
+                 Therefore, if the digitization returns the last possible index, we map this to the last bin interval.
+
+        EXAMPLE =>> Let's say self._bins has 256 values. Then self._bin_centers has 255 values. Digitization returns
+                    indices between [1, 256]. We subtract 1 from all indices so that they are between [0, 255]. There
+                    is still one index (i==255) that would cause an out-of-bounds error if used to index into
+                    self._bin_centers. Therefore, if i==255, we subtract 1 from it so that it just becomes the index of
+                    the last bin center. We implement this simply via clipping between [0, 255 - 1].
+        """
+        discretized_actions = self.tokenizer.vocab_size - action_token_ids
+        discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+
+        return self.bin_centers[discretized_actions]
+
+    @property
+    def vocab_size(self) -> int:
+        return self.n_bins
diff --git a/prismatic/vla/datasets/__init__.py b/prismatic/vla/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..384ff1405e8227123244c3b7b633eb2fd8e124f1
--- /dev/null
+++ b/prismatic/vla/datasets/__init__.py
@@ -0,0 +1,2 @@
+from .datasets import DummyDataset, EpisodicRLDSDataset, RLDSBatchTransform, RLDSDataset, RLDSBatchTransformV2, RLDSBatchTransformV3, RLDSBatchTransformV2T, RLDSDatasetV3
+from .datasets import RLDSDatasetV3_1, RLDSBatchTransformV3_1
\ No newline at end of file
diff --git a/prismatic/vla/datasets/datasets.py b/prismatic/vla/datasets/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..03198dc2db9ede9f356c363aec404f6117a4417b
--- /dev/null
+++ b/prismatic/vla/datasets/datasets.py
@@ -0,0 +1,1599 @@
+"""
+datasets.py
+
+Lightweight PyTorch Dataset Definition for wrapping RLDS TFDS Pipeline; just defines transform from RLDS default
+format to OpenVLA, IterableDataset shim.
+"""
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Tuple, Type
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, IterableDataset
+from transformers import PreTrainedTokenizerBase
+
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import ImageTransform
+from prismatic.util.data_utils import tree_map
+from prismatic.vla.action_tokenizer import ActionTokenizer
+from prismatic.vla.datasets.rlds import make_interleaved_dataset, make_single_dataset
+from prismatic.vla.datasets.rlds import make_interleaved_dataset_v2, make_single_dataset_v2
+from prismatic.vla.datasets.rlds import make_interleaved_dataset_v3, make_single_dataset_v3
+from prismatic.vla.datasets.rlds.oxe import OXE_NAMED_MIXTURES, get_oxe_dataset_kwargs_and_weights
+from prismatic.vla.datasets.rlds.utils.data_utils import NormalizationType
+
+# HuggingFace Default / LLaMa-2 IGNORE_INDEX (for labels)
+IGNORE_INDEX = -100
+
+def process_reasoning_cues(reasoning, new_size=224, ori_size=256):
+
+    object_dict = {}
+    objects = str(reasoning)[2:-1].split('#')
+    for object in objects:
+        noun, details = object.split(':')
+        # print(noun, len(noun))
+        if noun == 'gripper':
+            noun = 'robot'
+        segid, bbox = details.split(',')
+        if '\\n' in bbox:
+            bbox = bbox.replace('[', '').replace(']', '').replace('\\n', '').strip()
+            bbox = bbox.split(' ')
+        else:
+            bbox = bbox[1:-1].split(' ')
+        bbox = [int(p) for p in bbox if p != '']
+        bbox = np.array(bbox)/ori_size
+        bbox[:2] += bbox[2:]/2
+        object_dict[str(noun)] = bbox
+    # print('')
+    # 1/0
+    return object_dict
+
+def process_reasoning_cues_v2(reasoning, new_size=224, ori_size=256):
+
+    object_dict = {}
+    objects = str(reasoning)[2:-1].split('#')
+    for object in objects:
+        if len(object.split(',')) == 3:
+            noun_segid, bbox, itrn_cnt = object.split(',')
+            noun, segid = noun_segid.split(':')
+        else:
+            noun_segid, bbox = object.split(',')
+            noun, segid = noun_segid.split(':')
+            itrn_cnt = str(0)
+        # print(noun, len(noun))
+        if noun == 'gripper':
+            noun = 'robot'
+        if '\\n' in bbox:
+            bbox = bbox.replace('[', '').replace(']', '').replace('\\n', '').strip()
+            bbox = bbox.split(' ')
+        else:
+            bbox = bbox[1:-1].split(' ')
+        bbox = [int(p) for p in bbox if p != '']
+        bbox = np.array(bbox)/ori_size
+        bbox[:2] += bbox[2:]/2
+        object_dict[str(noun)] = [itrn_cnt, int(segid), bbox]
+    # print('')
+    # 1/0
+    return object_dict
+
+def align_cues_by_name(_reasoning_on_image, _reasoning_on_wrist_image):
+    image_keys = set(_reasoning_on_image.keys())
+    wrist_keys = set(_reasoning_on_wrist_image.keys())
+    elements_in_ab = list(image_keys & wrist_keys)
+    elements_in_a_not_b = list(image_keys - wrist_keys)
+    elements_in_b_not_a = list(wrist_keys - image_keys)
+
+    reasoning_on_image, reasoning_on_wrist_image = [], []
+    for key in elements_in_ab:
+        reasoning_on_image.append((key, _reasoning_on_image[key]))
+        reasoning_on_wrist_image.append((key, _reasoning_on_wrist_image[key]))
+    for key in elements_in_a_not_b:
+        reasoning_on_image.append((key, _reasoning_on_image[key]))
+    for key in elements_in_b_not_a:
+        reasoning_on_wrist_image.append((key, _reasoning_on_wrist_image[key]))
+
+    # print(image_keys); print(wrist_keys)
+    # print(elements_in_ab)
+    # print(elements_in_a_not_b)
+    # print(elements_in_b_not_a)
+    # print(reasoning_on_image)
+    # print(reasoning_on_wrist_image); 1/0
+
+    return reasoning_on_image, reasoning_on_wrist_image
+
+@dataclass
+class RLDSBatchTransform:
+    action_tokenizer: ActionTokenizer
+    base_tokenizer: PreTrainedTokenizerBase
+    image_transform: ImageTransform
+    prompt_builder_fn: Type[PromptBuilder]
+    predict_stop_token: bool = True
+
+    def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+        """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+        dataset_name, action = rlds_batch["dataset_name"], rlds_batch["action"][0]
+        lang = rlds_batch["task"]["language_instruction"].decode().lower()
+        if "language_instruction_nouns" in rlds_batch["task"]:
+            lang_nouns = rlds_batch["task"]["language_instruction_nouns"].decode().lower().replace('gripper', 'robot')
+            lang_nouns = lang_nouns.split('. ')
+
+        # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+        prompt_builder = self.prompt_builder_fn("openvla")
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take to {lang}?"},
+            {"from": "gpt", "value": self.action_tokenizer(action)},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+
+        # Tokenize (w/ `base_tokenizer`)
+        input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        # print(prompt_builder.get_prompt())
+        # print(input_ids)
+        labels = list(input_ids)
+        lang_nouns_ids = {}
+        if "reasoning_on_image" in rlds_batch["observation"]:
+            reasoning_on_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_image"][0])
+            reasoning_on_wrist_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_wrist_image"][0])
+            reasoning_on_image, reasoning_on_wrist_image = align_cues_by_name(reasoning_on_image, reasoning_on_wrist_image)
+            for noun in lang_nouns:
+                noun_ids = self.base_tokenizer(noun).input_ids
+                noun_ids = self.base_tokenizer.pad(
+                    {"input_ids": noun_ids}, 
+                    padding="max_length",  # or "max_length"
+                    max_length=30,      # specify if using "max_length"
+                    return_tensors="pt"
+                ).input_ids
+                lang_nouns_ids[noun] = noun_ids
+        else:
+            reasoning_on_image = {}
+            reasoning_on_wrist_image = {}
+
+        # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+        #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+
+        print("prompt", prompt_builder.get_prompt())
+        print("input ids", input_ids)
+
+        all_pixel_values = []
+        for img_data in rlds_batch["observation"]["image_primary"]:
+            img_data = img_data[:,:,:]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+
+            img = Image.fromarray(img_data)
+            # img.save("img.png")
+            pixel_values = self.image_transform(img)
+            all_pixel_values.append(pixel_values)
+        all_pixel_values = torch.stack(all_pixel_values)
+
+        all_wrist_values = None
+        if "image_wrist" in rlds_batch["observation"]:
+            all_wrist_values = []
+            for img_data in rlds_batch["observation"]["image_wrist"]:
+                img_data = img_data[:,:,:]
+                # import cv2
+                # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+                wrist_img = Image.fromarray(img_data)
+                # wrist_img.save("img_wrist_img.png")
+
+                wrist_values = self.image_transform(wrist_img)
+                all_wrist_values.append(wrist_values)
+            all_wrist_values = torch.stack(all_wrist_values)
+
+        all_pixel_depth_values = None
+        if "depth_primary" in rlds_batch["observation"]:
+            all_pixel_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_primary"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_data_depth.png")
+
+                pixel_values = self.image_transform(img_data)
+                all_pixel_depth_values.append(pixel_values)
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+        all_wrist_depth_values = None
+        if "depth_wrist" in rlds_batch["observation"]:
+            all_wrist_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_wrist"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_wrist_data_depth.png"); 1/0
+
+                wrist_values = self.image_transform(img_data)
+                all_wrist_depth_values.append(wrist_values)
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+
+        # [CRITICAL] We do not want to take the loss for anything but the predicted action tokens!
+        labels[: -(len(action) + 1)] = IGNORE_INDEX
+        if not self.predict_stop_token:
+            labels[-1] = IGNORE_INDEX
+
+        # print(lang_nouns)
+        # print(rlds_batch["observation"]["reasoning_on_image"])
+        # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+        # 1/0
+
+        return dict(pixel_values=all_pixel_values[0], input_ids=input_ids, labels=labels, dataset_name=dataset_name,
+                    all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+                    all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+                    reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+                    lang_nouns_ids=lang_nouns_ids)
+
+
+
+class RLDSDataset(IterableDataset):
+    def __init__(
+        self,
+        data_root_dir: Path,
+        data_mix: str,
+        batch_transform: RLDSBatchTransform,
+        resize_resolution: Tuple[int, int],
+        shuffle_buffer_size: int = 256_000,
+        train: bool = True,
+        image_aug: bool = False,
+        window_size: int = 1,
+        load_camera_views: Tuple[str] = ("primary",),
+        load_depth: bool = False,
+        cropping = True
+    ) -> None:
+        """Lightweight wrapper around RLDS TFDS Pipeline for use with PyTorch/OpenVLA Data Loaders."""
+        self.data_root_dir, self.data_mix, self.batch_transform = data_root_dir, data_mix, batch_transform
+
+        # Configure RLDS Dataset(s)
+        if self.data_mix in OXE_NAMED_MIXTURES:
+            mixture_spec = OXE_NAMED_MIXTURES[self.data_mix]
+        else:
+            # Assume that passed "mixture" name is actually a single dataset -- create single-dataset "mix"
+            mixture_spec = [(self.data_mix, 1.0)]
+
+        # fmt: off
+        per_dataset_kwargs, weights = get_oxe_dataset_kwargs_and_weights(
+            self.data_root_dir,
+            mixture_spec,
+            load_camera_views=load_camera_views,
+            load_depth=load_depth,
+            load_proprio=False,
+            load_language=True,
+            action_proprio_normalization_type=NormalizationType.BOUNDS_Q99,
+        )
+        rlds_config = dict(
+            traj_transform_kwargs=dict(
+                window_size=window_size,                                      # If we wanted to feed / predict more than one step
+                future_action_window_size=0,                        # For action chunking
+                skip_unlabeled=True,                                # Skip trajectories without language labels
+                goal_relabeling_strategy="uniform",                 # Goals are currently unused
+            ),
+            frame_transform_kwargs=dict(
+                resize_size=resize_resolution,
+                depth_resize_size=resize_resolution,
+                num_parallel_calls=16,                          # For CPU-intensive ops (decoding, resizing, etc.)
+            ),
+            dataset_kwargs_list=per_dataset_kwargs,
+            shuffle_buffer_size=shuffle_buffer_size,
+            sample_weights=weights,
+            balance_weights=True,
+            traj_transform_threads=len(mixture_spec),
+            traj_read_threads=len(mixture_spec),
+            train=train,
+        )
+
+        # If applicable, enable image augmentations
+        if image_aug:
+            if cropping:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_resized_crop=dict(scale=[0.9, 0.9], ratio=[1.0, 1.0]),
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_resized_crop",
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+            else:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+
+        # fmt: on
+
+        # Initialize RLDS Dataset
+        if load_depth:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config, multimodal=True)
+        else:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config)
+
+    def make_dataset(self, rlds_config, multimodal=False):
+        if multimodal:
+            return make_interleaved_dataset_v2(**rlds_config)
+        return make_interleaved_dataset(**rlds_config)
+
+    def __iter__(self) -> Dict[str, Any]:
+        for rlds_batch in self.dataset.as_numpy_iterator():
+            yield self.batch_transform(rlds_batch)
+
+    def __len__(self) -> int:
+        return self.dataset_length
+
+    # === Explicitly Unused ===
+    def __getitem__(self, idx: int) -> None:
+        raise NotImplementedError("IterableDataset does not implement map-style __getitem__; see __iter__ instead!")
+
+
+import dlimp as dl
+
+class MyStreamingLoader:
+    def __init__(self, dataset: dl.DLataset, batch_size: int, batch_transform=None, collate_fn=None):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.batch_transform = batch_transform or (lambda x: x)
+        self.collate_fn = collate_fn
+        self.iterator = dataset.iterator()
+        self.slots = [None] * batch_size
+        self.indices = [0] * batch_size
+
+        # Preload initial trajectories
+        for i in range(batch_size):
+            self._load_new_traj(i)
+
+    def _load_new_traj(self, i: int):
+        try:
+            traj = next(self.iterator)
+            self.slots[i] = traj
+            self.indices[i] = 0
+        except StopIteration:
+            self.slots[i] = None
+            self.indices[i] = -1
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        batch_obs, batch_act, batch_task = [], [], []
+
+        for i in range(self.batch_size):
+            traj = self.slots[i]
+            idx = self.indices[i]
+
+            if traj is None or idx >= traj["action"].shape[0]:
+                self._load_new_traj(i)
+                traj = self.slots[i]
+                idx = self.indices[i]
+
+            if traj is None:
+                raise StopIteration
+
+            obs = tf.nest.map_structure(lambda x: x[idx], traj["observation"])
+            act = traj["action"][idx]
+            task = traj["task"]
+
+            batch_obs.append(obs)
+            batch_act.append(act)
+            batch_task.append(task)
+
+            self.indices[i] += 1
+
+        batch = {
+            "observation": tf.nest.map_structure(lambda *x: np.stack(x), *batch_obs),
+            "action": np.stack(batch_act),
+            "task": tf.nest.map_structure(lambda *x: np.stack(x), *batch_task),
+        }
+
+        return self.batch_transform(batch)
+
+
+class RLDSDatasetV3(IterableDataset):
+    def __init__(
+        self,
+        data_root_dir: Path,
+        data_mix: str,
+        batch_transform: RLDSBatchTransform,
+        resize_resolution: Tuple[int, int],
+        shuffle_buffer_size: int = 256_000,
+        train: bool = True,
+        image_aug: bool = False,
+        window_size: int = 1,
+        load_camera_views: Tuple[str] = ("primary",),
+        load_depth: bool = False,
+        cropping = True
+    ) -> None:
+        """Lightweight wrapper around RLDS TFDS Pipeline for use with PyTorch/OpenVLA Data Loaders."""
+        self.data_root_dir, self.data_mix, self.batch_transform = data_root_dir, data_mix, batch_transform
+
+        # Configure RLDS Dataset(s)
+        if self.data_mix in OXE_NAMED_MIXTURES:
+            mixture_spec = OXE_NAMED_MIXTURES[self.data_mix]
+        else:
+            # Assume that passed "mixture" name is actually a single dataset -- create single-dataset "mix"
+            mixture_spec = [(self.data_mix, 1.0)]
+
+        # fmt: off
+        per_dataset_kwargs, weights = get_oxe_dataset_kwargs_and_weights(
+            self.data_root_dir,
+            mixture_spec,
+            load_camera_views=load_camera_views,
+            load_depth=load_depth,
+            load_proprio=False,
+            load_language=True,
+            action_proprio_normalization_type=NormalizationType.BOUNDS_Q99,
+        )
+        rlds_config = dict(
+            traj_transform_kwargs=dict(
+                window_size=window_size,                                      # If we wanted to feed / predict more than one step
+                future_action_window_size=0,                        # For action chunking
+                skip_unlabeled=True,                                # Skip trajectories without language labels
+                goal_relabeling_strategy="uniform",                 # Goals are currently unused
+            ),
+            frame_transform_kwargs=dict(
+                resize_size=resize_resolution,
+                depth_resize_size=resize_resolution,
+                num_parallel_calls=16,                          # For CPU-intensive ops (decoding, resizing, etc.)
+            ),
+            dataset_kwargs_list=per_dataset_kwargs,
+            shuffle_buffer_size=shuffle_buffer_size,
+            sample_weights=weights,
+            balance_weights=True,
+            traj_transform_threads=len(mixture_spec),
+            traj_read_threads=len(mixture_spec),
+            train=train,
+        )
+
+        # If applicable, enable image augmentations
+        if image_aug:
+            if cropping:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_resized_crop=dict(scale=[0.9, 0.9], ratio=[1.0, 1.0]),
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_resized_crop",
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+            else:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+
+        # fmt: on
+
+        # Initialize RLDS Dataset
+        if load_depth:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config, multimodal=True)
+        else:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config)
+
+    def make_dataset(self, rlds_config, multimodal=False):
+        # if multimodal:
+        #     return make_interleaved_dataset_v2(**rlds_config)
+        return make_interleaved_dataset_v3(**rlds_config)
+
+    def __iter__(self) -> Dict[str, Any]:
+        for rlds_batch in self.dataset.as_numpy_iterator():
+            yield self.batch_transform(rlds_batch)
+
+    def __len__(self) -> int:
+        return self.dataset_length
+
+    # === Explicitly Unused ===
+    def __getitem__(self, idx: int) -> None:
+        raise NotImplementedError("IterableDataset does not implement map-style __getitem__; see __iter__ instead!")
+
+
+class RLDSDatasetV3_1(IterableDataset):
+    def __init__(
+        self,
+        data_root_dir: Path,
+        data_mix: str,
+        batch_transform: RLDSBatchTransform,
+        resize_resolution: Tuple[int, int],
+        shuffle_buffer_size: int = 256_000,
+        train: bool = True,
+        image_aug: bool = False,
+        window_size: int = 1,
+        future_action_window_size: int = 5,
+        load_camera_views: Tuple[str] = ("primary",),
+        load_depth: bool = False,
+        cropping = True
+    ) -> None:
+        """Lightweight wrapper around RLDS TFDS Pipeline for use with PyTorch/OpenVLA Data Loaders."""
+        self.data_root_dir, self.data_mix, self.batch_transform = data_root_dir, data_mix, batch_transform
+
+        # Configure RLDS Dataset(s)
+        if self.data_mix in OXE_NAMED_MIXTURES:
+            mixture_spec = OXE_NAMED_MIXTURES[self.data_mix]
+        else:
+            # Assume that passed "mixture" name is actually a single dataset -- create single-dataset "mix"
+            mixture_spec = [(self.data_mix, 1.0)]
+
+        # fmt: off
+        per_dataset_kwargs, weights = get_oxe_dataset_kwargs_and_weights(
+            self.data_root_dir,
+            mixture_spec,
+            load_camera_views=load_camera_views,
+            load_depth=load_depth,
+            load_proprio=False,
+            load_language=True,
+            action_proprio_normalization_type=NormalizationType.BOUNDS_Q99,
+        )
+        rlds_config = dict(
+            traj_transform_kwargs=dict(
+                window_size=window_size,                                      # If we wanted to feed / predict more than one step
+                future_action_window_size=future_action_window_size,                        # For action chunking
+                skip_unlabeled=True,                                # Skip trajectories without language labels
+                goal_relabeling_strategy="uniform",                 # Goals are currently unused
+            ),
+            frame_transform_kwargs=dict(
+                resize_size=resize_resolution,
+                depth_resize_size=resize_resolution,
+                num_parallel_calls=16,                          # For CPU-intensive ops (decoding, resizing, etc.)
+            ),
+            dataset_kwargs_list=per_dataset_kwargs,
+            shuffle_buffer_size=shuffle_buffer_size,
+            sample_weights=weights,
+            balance_weights=True,
+            traj_transform_threads=len(mixture_spec),
+            traj_read_threads=len(mixture_spec),
+            train=train,
+        )
+
+        # If applicable, enable image augmentations
+        if image_aug:
+            if cropping:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_resized_crop=dict(scale=[0.9, 0.9], ratio=[1.0, 1.0]),
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_resized_crop",
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+            else:
+                rlds_config["frame_transform_kwargs"].update({"image_augment_kwargs" : dict(
+                    random_brightness=[0.2],
+                    random_contrast=[0.8, 1.2],
+                    random_saturation=[0.8, 1.2],
+                    random_hue=[0.05],
+                    augment_order=[
+                        "random_brightness",
+                        "random_contrast",
+                        "random_saturation",
+                        "random_hue",
+                    ],
+                )}),
+
+        # fmt: on
+
+        # Initialize RLDS Dataset
+        if load_depth:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config, multimodal=True)
+        else:
+            self.dataset, self.dataset_length, self.dataset_statistics = self.make_dataset(rlds_config)
+
+    def make_dataset(self, rlds_config, multimodal=False):
+        # if multimodal:
+        #     return make_interleaved_dataset_v2(**rlds_config)
+        return make_interleaved_dataset_v3(**rlds_config)
+
+    def __iter__(self) -> Dict[str, Any]:
+        for rlds_batch in self.dataset.as_numpy_iterator():
+            yield self.batch_transform(rlds_batch)
+
+    def __len__(self) -> int:
+        return self.dataset_length
+
+    # === Explicitly Unused ===
+    def __getitem__(self, idx: int) -> None:
+        raise NotImplementedError("IterableDataset does not implement map-style __getitem__; see __iter__ instead!")
+
+
+class EpisodicRLDSDataset(RLDSDataset):
+    """Returns full episodes as list of steps instead of individual transitions (useful for visualizations)."""
+
+    def make_dataset(self, rlds_config):
+        per_dataset_kwargs = rlds_config["dataset_kwargs_list"]
+        assert len(per_dataset_kwargs) == 1, "Only support single-dataset `mixes` for episodic datasets."
+
+        return make_single_dataset(
+            per_dataset_kwargs[0],
+            train=rlds_config["train"],
+            traj_transform_kwargs=rlds_config["traj_transform_kwargs"],
+            frame_transform_kwargs=rlds_config["frame_transform_kwargs"],
+        )
+
+    def __iter__(self) -> Dict[str, Any]:
+        for rlds_batch in self.dataset.as_numpy_iterator():
+            out = [
+                self.batch_transform(tree_map(lambda x: x[i], rlds_batch))  # noqa: B023
+                for i in range(rlds_batch["action"].shape[0])
+            ]
+            yield out
+
+
+class DummyDataset(Dataset):
+    def __init__(
+        self,
+        action_tokenizer: ActionTokenizer,
+        base_tokenizer: PreTrainedTokenizerBase,
+        image_transform: ImageTransform,
+        prompt_builder_fn: Type[PromptBuilder],
+    ) -> None:
+        self.action_tokenizer = action_tokenizer
+        self.base_tokenizer = base_tokenizer
+        self.image_transform = image_transform
+        self.prompt_builder_fn = prompt_builder_fn
+
+        # Note =>> We expect the dataset to store statistics for action de-normalization. Specifically, we store the
+        # per-dimension 1st and 99th action quantile. The values below correspond to "no normalization" for simplicity.
+        self.dataset_statistics = {
+            "dummy_dataset": {
+                "action": {"q01": np.zeros((7,), dtype=np.float32), "q99": np.ones((7,), dtype=np.float32)}
+            }
+        }
+
+    def __len__(self):
+        # TODO =>> Replace with number of elements in your dataset!
+        return 10000
+
+    def __getitem__(self, idx):
+        # TODO =>> Load image, action and instruction from disk -- we use dummy values
+        image = Image.fromarray(np.asarray(np.random.rand(224, 224, 3) * 255.0, dtype=np.uint8))
+        action = np.asarray(np.random.rand(7), dtype=np.float32)
+        instruction = "do something spectacular"
+
+        # Add instruction to VLA prompt
+        prompt_builder = self.prompt_builder_fn("openvla")
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take to {instruction}?"},
+            {"from": "gpt", "value": self.action_tokenizer(action)},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+
+        # Tokenize (w/ `base_tokenizer`)
+        input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        labels = list(input_ids)
+
+        # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+        #   =>> IMPORTANT :: IF WE'RE USING HF .forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+        pixel_values = self.image_transform(image)
+        print("prompt", prompt_builder.get_prompt())
+        print("input ids", input_ids)
+
+        # [CRITICAL] We do not want to take the loss for anything but the predicted action tokens!
+        labels[: -(len(action) + 1)] = IGNORE_INDEX
+
+        return dict(pixel_values=pixel_values, input_ids=input_ids, labels=labels)
+
+
+# @dataclass
+# class RLDSBatchTransformV3:
+#     action_tokenizer: ActionTokenizer
+#     base_tokenizer: PreTrainedTokenizerBase
+#     image_transform: ImageTransform
+#     prompt_builder_fn: Type[PromptBuilder]
+#     predict_stop_token: bool = True
+
+#     def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+#         """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+#         dataset_name, action = rlds_batch["dataset_name"], rlds_batch["action"][0]
+#         lang = rlds_batch["task"]["language_instruction"].decode().lower()
+#         language_instruction_nouns = ['none']
+#         if "language_instruction_nouns" in rlds_batch["task"]:
+#             language_instruction_nouns = rlds_batch["task"]["language_instruction_nouns"].decode().lower().replace('gripper', 'robot')
+#             language_instruction_nouns = list(language_instruction_nouns.split('. '))
+
+#         # Label tokens.        
+#         prompt_builder = self.prompt_builder_fn("openvla")
+#         conversation = [
+#             {"from": "human", "value": f"What action should the robot take to {lang}?"},
+#             {"from": "gpt", "value": self.action_tokenizer(action)},
+#         ]
+#         for turn in conversation:
+#             prompt_builder.add_turn(turn["from"], turn["value"])
+#         # Tokenize (w/ `base_tokenizer`)
+#         labels = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+#         labels = list(labels)
+#         # print(prompt_builder.get_prompt())
+#         # print(labels)
+
+#         # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+#         prompt_builder = self.prompt_builder_fn("openvla")
+#         conversation = [
+#             {"from": "human", "value": f"What action should the robot take to {lang}?"},
+#             {"from": "gpt", "value": "_ _ _ _ _ _ _ _"},
+#         ]
+#         for turn in conversation:
+#             prompt_builder.add_turn(turn["from"], turn["value"])
+#         # Tokenize (w/ `base_tokenizer`)
+#         input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+#         input_ids[-9] = 29871
+#         # print(prompt_builder.get_prompt())
+#         # print(input_ids)
+
+#         lang_nouns_ids = {}
+#         if "reasoning_on_image" in rlds_batch["observation"]:
+#             reasoning_on_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_image"][0])
+#             reasoning_on_wrist_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_wrist_image"][0])
+#             lang_nouns = list(set(list(reasoning_on_image.keys()) + list(reasoning_on_wrist_image.keys()) + language_instruction_nouns))
+#             for noun in lang_nouns:
+#                 noun_ids = self.base_tokenizer(noun).input_ids
+#                 noun_ids = self.base_tokenizer.pad(
+#                     {"input_ids": noun_ids}, 
+#                     padding="max_length",  # or "max_length"
+#                     max_length=30,      # specify if using "max_length"
+#                     return_tensors="pt"
+#                 ).input_ids
+#                 lang_nouns_ids[noun] = noun_ids
+#             reasoning_on_image, reasoning_on_wrist_image = align_cues_by_name(reasoning_on_image, reasoning_on_wrist_image)
+#         else:
+#             reasoning_on_image = []
+#             reasoning_on_wrist_image = []
+
+#         # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+#         #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+#         input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+#         if input_ids.shape != labels.shape:
+#             print(input_ids)
+#             print(labels)
+
+#         all_pixel_values = []
+#         for img_data in rlds_batch["observation"]["image_primary"]:
+#             img_data = img_data[:,:,:]
+#             # import cv2
+#             # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+
+#             img = Image.fromarray(img_data)
+#             # img.save("img.png")
+#             pixel_values = self.image_transform(img)
+#             all_pixel_values.append(pixel_values)
+#         all_pixel_values = torch.stack(all_pixel_values)
+
+#         all_wrist_values = None
+#         if "image_wrist" in rlds_batch["observation"]:
+#             all_wrist_values = []
+#             for img_data in rlds_batch["observation"]["image_wrist"]:
+#                 img_data = img_data[:,:,:]
+#                 # import cv2
+#                 # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+#                 wrist_img = Image.fromarray(img_data)
+#                 # wrist_img.save("img_wrist_img.png")
+
+#                 wrist_values = self.image_transform(wrist_img)
+#                 all_wrist_values.append(wrist_values)
+#             all_wrist_values = torch.stack(all_wrist_values)
+
+#         all_pixel_depth_values = None
+#         if "depth_primary" in rlds_batch["observation"]:
+#             all_pixel_depth_values = []
+#             for img_data in rlds_batch["observation"]["depth_primary"]:
+#                 img_data = img_data[:,:,:]
+#                 img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+#                 # import cv2
+#                 # cv2.imwrite('./tmp_depth_img_training.png', img_data); 1/0
+
+#                 img_data = Image.fromarray(img_data)
+#                 # img_data.save("img_data_depth.png")
+
+#                 pixel_values = self.image_transform(img_data)
+#                 all_pixel_depth_values.append(pixel_values)
+#             all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+#         all_wrist_depth_values = None
+#         if "depth_wrist" in rlds_batch["observation"]:
+#             all_wrist_depth_values = []
+#             for img_data in rlds_batch["observation"]["depth_wrist"]:
+#                 img_data = img_data[:,:,:]
+#                 img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+#                 img_data = Image.fromarray(img_data)
+#                 # img_data.save("img_wrist_data_depth.png"); 1/0
+
+#                 wrist_values = self.image_transform(img_data)
+#                 all_wrist_depth_values.append(wrist_values)
+#             all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        
+#         # [CRITICAL] We do not want to take the loss for anything but the predicted action tokens!
+#         labels[: -(len(action) + 1)] = IGNORE_INDEX
+#         if not self.predict_stop_token:
+#             labels[-1] = IGNORE_INDEX
+
+#         # print(lang_nouns)
+#         # print(rlds_batch["observation"]["reasoning_on_image"])
+#         # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+#         # 1/0
+
+#         task = {
+#             'instruction': prompt_builder.get_prompt(),
+#             'instruction_nouns': language_instruction_nouns
+#         }
+
+#         return dict(pixel_values=all_pixel_values[0], input_ids=input_ids, labels=labels, dataset_name=dataset_name,
+#                     all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+#                     all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+#                     reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+#                     lang_nouns_ids=lang_nouns_ids, task=task)
+
+
+@dataclass
+class RLDSBatchTransformV2:
+    action_tokenizer: ActionTokenizer
+    base_tokenizer: PreTrainedTokenizerBase
+    image_transform: ImageTransform
+    prompt_builder_fn: Type[PromptBuilder]
+    predict_stop_token: bool = True
+
+    def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+        """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+        dataset_name, action = rlds_batch["dataset_name"], rlds_batch["action"][0]
+        lang = rlds_batch["task"]["language_instruction"].decode().lower()
+        language_instruction_nouns = ['none']
+        if "language_instruction_nouns" in rlds_batch["task"]:
+            language_instruction_nouns = rlds_batch["task"]["language_instruction_nouns"].decode().lower().replace('gripper', 'robot')
+            language_instruction_nouns = list(language_instruction_nouns.split('. '))
+
+        # Label tokens.        
+        prompt_builder = self.prompt_builder_fn("openvla")
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take to {lang}?"},
+            {"from": "gpt", "value": self.action_tokenizer(action)},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+        # Tokenize (w/ `base_tokenizer`)
+        labels = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        labels = list(labels)
+        # print(prompt_builder.get_prompt())
+        # print(labels)
+
+        # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+        prompt_builder = self.prompt_builder_fn("openvla")
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take to {lang}?"},
+            {"from": "gpt", "value": "_ _ _ _ _ _ _ _"},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+        # Tokenize (w/ `base_tokenizer`)
+        input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        input_ids[-9] = 29871
+        # print(prompt_builder.get_prompt())
+        # print(input_ids)
+
+        lang_nouns_ids = {}
+        if "reasoning_on_image" in rlds_batch["observation"]:
+            reasoning_on_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_image"][0])
+            reasoning_on_wrist_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_wrist_image"][0])
+            lang_nouns = list(set(list(reasoning_on_image.keys()) + list(reasoning_on_wrist_image.keys()) + language_instruction_nouns))
+            for noun in lang_nouns:
+                noun_ids = self.base_tokenizer(noun).input_ids
+                noun_ids = self.base_tokenizer.pad(
+                    {"input_ids": noun_ids}, 
+                    padding="max_length",  # or "max_length"
+                    max_length=30,      # specify if using "max_length"
+                    return_tensors="pt"
+                ).input_ids
+                lang_nouns_ids[noun] = noun_ids
+            reasoning_on_image, reasoning_on_wrist_image = align_cues_by_name(reasoning_on_image, reasoning_on_wrist_image)
+        else:
+            reasoning_on_image = []
+            reasoning_on_wrist_image = []
+
+        # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+        #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+        if input_ids.shape != labels.shape:
+            print(input_ids)
+            print(labels)
+
+        all_pixel_values = []
+        for img_data in rlds_batch["observation"]["image_primary"]:
+            img_data = img_data[:,:,:]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+
+            img = Image.fromarray(img_data)
+            # img.save("img.png")
+            pixel_values = self.image_transform(img)
+            all_pixel_values.append(pixel_values)
+        all_pixel_values = torch.stack(all_pixel_values)
+
+        all_wrist_values = None
+        if "image_wrist" in rlds_batch["observation"]:
+            all_wrist_values = []
+            for img_data in rlds_batch["observation"]["image_wrist"]:
+                img_data = img_data[:,:,:]
+                # import cv2
+                # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+                wrist_img = Image.fromarray(img_data)
+                # wrist_img.save("img_wrist_img.png")
+
+                wrist_values = self.image_transform(wrist_img)
+                all_wrist_values.append(wrist_values)
+            all_wrist_values = torch.stack(all_wrist_values)
+
+        all_pixel_depth_values = None
+        if "depth_primary" in rlds_batch["observation"]:
+            all_pixel_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_primary"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                # import cv2
+                # cv2.imwrite('./tmp_depth_img_training.png', img_data); 1/0
+
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_data_depth.png")
+
+                pixel_values = self.image_transform(img_data)
+                all_pixel_depth_values.append(pixel_values)
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+        all_wrist_depth_values = None
+        if "depth_wrist" in rlds_batch["observation"]:
+            all_wrist_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_wrist"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_wrist_data_depth.png"); 1/0
+
+                wrist_values = self.image_transform(img_data)
+                all_wrist_depth_values.append(wrist_values)
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        
+        # [CRITICAL] We do not want to take the loss for anything but the predicted action tokens!
+        labels[: -(len(action) + 1)] = IGNORE_INDEX
+        if not self.predict_stop_token:
+            labels[-1] = IGNORE_INDEX
+
+        # print(lang_nouns)
+        # print(rlds_batch["observation"]["reasoning_on_image"])
+        # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+        # 1/0
+
+        task = {
+            'instruction': prompt_builder.get_prompt(),
+            'instruction_nouns': language_instruction_nouns
+        }
+
+        return dict(pixel_values=all_pixel_values[0], input_ids=input_ids, labels=labels, dataset_name=dataset_name,
+                    all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+                    all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+                    reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+                    lang_nouns_ids=lang_nouns_ids, task=task)
+
+
+@dataclass
+class RLDSBatchTransformV2T:
+    action_tokenizer: ActionTokenizer
+    base_tokenizer: PreTrainedTokenizerBase
+    image_transform: ImageTransform
+    prompt_builder_fn: Type[PromptBuilder]
+    predict_stop_token: bool = True
+
+    def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+        """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+        dataset_name = rlds_batch["dataset_name"]
+        lang = rlds_batch["task"]["language_instruction"].decode().lower()
+        if "language_instruction_nouns" in rlds_batch["task"]:
+            language_instruction_nouns = rlds_batch["task"]["language_instruction_nouns"].decode().lower().replace('gripper', 'robot')
+            language_instruction_nouns = list(language_instruction_nouns.split('. '))
+
+        full_labels = []
+        full_input_ids = []
+        # print(len(rlds_batch["action"]))
+        # print(rlds_batch["action"])
+        for t in range(len(rlds_batch["action"])):
+            action = rlds_batch["action"][t]
+            # Label tokens.        
+            prompt_builder = self.prompt_builder_fn("openvla")
+            conversation = [
+                {"from": "human", "value": f"What action should the robot take to {lang}?"},
+                {"from": "gpt", "value": self.action_tokenizer(action)},
+            ]
+            for turn in conversation:
+                prompt_builder.add_turn(turn["from"], turn["value"])
+            # Tokenize (w/ `base_tokenizer`)
+            labels = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+            labels = list(labels)
+            # print(prompt_builder.get_prompt())
+            # print(labels)
+
+            # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+            prompt_builder = self.prompt_builder_fn("openvla")
+            conversation = [
+                {"from": "human", "value": f"What action should the robot take to {lang}?"},
+                {"from": "gpt", "value": "_ _ _ _ _ _ _ _"},
+            ]
+            for turn in conversation:
+                prompt_builder.add_turn(turn["from"], turn["value"])
+            # Tokenize (w/ `base_tokenizer`)
+            input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+            input_ids[-9] = 29871
+            # print(prompt_builder.get_prompt())
+            # print(input_ids)
+
+            # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+            #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+            input_ids, labels = torch.tensor(input_ids), torch.tensor(labels)
+            if input_ids.shape != labels.shape:
+                print(input_ids)
+                print(labels)
+                1/0
+            
+            # [CRITICAL] We do not want to take the loss for anything but the predicted action tokens!
+            labels[: -(len(action) + 1)] = IGNORE_INDEX
+            if not self.predict_stop_token:
+                labels[-1] = IGNORE_INDEX
+
+            full_labels.append(labels)
+            full_input_ids.append(input_ids)
+
+        full_labels = full_labels
+        full_input_ids = full_input_ids
+
+        lang_nouns_ids = {}
+        if "reasoning_on_image" in rlds_batch["observation"]:
+            reasoning_on_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_image"][0])
+            reasoning_on_wrist_image = process_reasoning_cues(rlds_batch["observation"]["reasoning_on_wrist_image"][0])
+            lang_nouns = list(set(list(reasoning_on_image.keys()) + list(reasoning_on_wrist_image.keys()) + language_instruction_nouns))
+            for noun in lang_nouns:
+                noun_ids = self.base_tokenizer(noun).input_ids
+                noun_ids = self.base_tokenizer.pad(
+                    {"input_ids": noun_ids}, 
+                    padding="max_length",  # or "max_length"
+                    max_length=30,      # specify if using "max_length"
+                    return_tensors="pt"
+                ).input_ids
+                lang_nouns_ids[noun] = noun_ids
+            reasoning_on_image, reasoning_on_wrist_image = align_cues_by_name(reasoning_on_image, reasoning_on_wrist_image)
+        else:
+            reasoning_on_image = []
+            reasoning_on_wrist_image = []
+
+        all_pixel_values = []
+        for img_data in rlds_batch["observation"]["image_primary"]:
+            img_data = img_data[:,:,:]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+
+            img = Image.fromarray(img_data)
+            # img.save("img.png")
+            pixel_values = self.image_transform(img)
+            all_pixel_values.append(pixel_values)
+        all_pixel_values = torch.stack(all_pixel_values)
+
+        all_wrist_values = None
+        if "image_wrist" in rlds_batch["observation"]:
+            all_wrist_values = []
+            for img_data in rlds_batch["observation"]["image_wrist"]:
+                img_data = img_data[:,:,:]
+                # import cv2
+                # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+                wrist_img = Image.fromarray(img_data)
+                # wrist_img.save("img_wrist_img.png")
+
+                wrist_values = self.image_transform(wrist_img)
+                all_wrist_values.append(wrist_values)
+            all_wrist_values = torch.stack(all_wrist_values)
+
+        all_pixel_depth_values = None
+        if "depth_primary" in rlds_batch["observation"]:
+            all_pixel_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_primary"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                # import cv2
+                # cv2.imwrite('./tmp_depth_img_training.png', img_data); 1/0
+
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_data_depth.png")
+
+                pixel_values = self.image_transform(img_data)
+                all_pixel_depth_values.append(pixel_values)
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+        all_wrist_depth_values = None
+        if "depth_wrist" in rlds_batch["observation"]:
+            all_wrist_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_wrist"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_wrist_data_depth.png"); 1/0
+
+                wrist_values = self.image_transform(img_data)
+                all_wrist_depth_values.append(wrist_values)
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        
+        # print(lang_nouns)
+        # print(rlds_batch["observation"]["reasoning_on_image"])
+        # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+        # 1/0
+
+        task = {
+            'instruction': prompt_builder.get_prompt(),
+            'instruction_nouns': language_instruction_nouns
+        }
+
+        return dict(pixel_values=all_pixel_values[0], input_ids=full_input_ids, labels=full_labels, dataset_name=dataset_name,
+                    all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+                    all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+                    reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+                    lang_nouns_ids=lang_nouns_ids, task=task)
+
+
+LANGUAGE_INSTRUCTION_NOUNS = {
+    "put the carrot on the white plate": ['robot', 'carrot', 'plate'],
+    "open the middle drawer of the cabinet": ['robot', 'middle drawer', 'cabinet'],
+    "put the bowl on the stove": ['robot', 'bowl', 'stove'],
+    "put the wine bottle on top of the cabinet": ['robot', 'bottle', 'cabinet'],
+    "open the top drawer and put the bowl inside": ['robot', 'top drawer', 'bowl'],
+    "put the bowl on top of the cabinet": ['robot', 'bowl', 'cabinet'],
+    "push the plate to the front of the stove": ['robot', 'plate', 'stove'],
+    "put the cream cheese in the bowl": ['robot', 'cream cheese', 'bowl'],
+    "turn on the stove": ['robot', 'stove'],
+    "put the bowl on the plate": ['robot', 'bowl', 'plate'],
+    "put the wine bottle on the rack": ['robot', 'bottle', 'rack'],
+    "swap the 2 bowls using the intermediary plate": ['robot', 'bowl', 'plate'],
+    "swap the 3 bowls from left to right using the intermediary plate": ['robot', 'bowl', 'plate'],
+    "pick the bowl from the plate and place it back 1 time": ['robot', 'bowl', 'plate'],
+    "pick the bowl from the plate and place it back 3 times": ['robot', 'bowl', 'plate'],
+    "pick the bowl from the plate and place it back 5 times": ['robot', 'bowl', 'plate'],
+    "pick the bowl from the plate and place it back 7 times": ['robot', 'bowl', 'plate'],
+
+    "pick up the black bowl between the plate and the ramekin and place it on the plate": ['robot', 'bowl', 'plate', 'ramekin'],
+    "pick up the black bowl from table center and place it on the plate": ['robot', 'bowl', 'plate'],
+    "pick up the black bowl in the top drawer of the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+    "pick up the black bowl next to the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "pick up the black bowl next to the plate and place it on the plate": ['robot', 'bowl', 'plate'],
+    "pick up the black bowl next to the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "pick up the black bowl on the cookie box and place it on the plate": ['robot', 'bowl', 'cookies', 'plate'],
+    "pick up the black bowl on the ramekin and place it on the plate": ['robot', 'bowl', 'ramekin', 'plate'],
+    "pick up the black bowl on the stove and place it on the plate": ['robot', 'bowl', 'stove', 'plate'],
+    "pick up the black bowl on the wooden cabinet and place it on the plate": ['robot', 'bowl', 'cabinet', 'plate'],
+
+    "turn on the stove and put the moka pot on it": ['robot', 'stove', 'moka pot'],
+    "put the black bowl in the bottom drawer of the cabinet and close it": ['robot', 'bowl', 'cabinet'],
+    "put the yellow and white mug in the microwave and close it": ['robot', 'yellow and white mug', 'microwave'],
+    "put both moka pots on the stove": ['robot', 'moka pot', 'stove'],
+    "put both the alphabet soup and the cream cheese box in the basket": ['robot', 'alphabet soup', 'cream cheese'],
+    "put both the alphabet soup and the tomato sauce in the basket": ['robot', 'alphabet soup', 'tomato'],
+    "put both the cream cheese box and the butter in the basket": ['robot', 'cream cheese', 'butter', 'basket'],
+    "put the white mug on the left plate and put the yellow and white mug on the right plate": ['robot', 'white mug', 'plate', 'yellow and white mug'],
+    "put the white mug on the plate and put the chocolate pudding to the right of the plate": ['robot', 'white mug', 'plate', 'chocolate pudding'],
+    "pick up the book and place it in the back compartment of the caddy": ['robot', 'book', 'caddy'],
+
+    "pick up the bowl and place it back on the plate": ['robot', 'pick up', 'bowl', 'place back on', 'plate'],
+    "lift the bottle and put it down on the plate": ['robot', 'lift', 'bottle', 'put down on', 'plate'],
+    "lift the bowl and place it back on the plate 3 times": ['robot', 'lift', 'bowl', 'place back on', 'plate', 'three times'],
+    "pick up the bottle and put it down the plate 3 times": ['robot', 'pick up', 'bottle', 'put down', 'plate', 'three times'],
+    "lift the bowl and place it back on the plate 5 times": ['robot', 'lift', 'bowl', 'place back on', 'plate', 'five times'],
+    "pick up the bowl and place it on the plate 7 times": ['robot', 'pick up', 'bowl', 'place on', 'plate', 'seven times'],
+    "swap the 2 bowls on their plates using the empty plate": ['robot', 'swap', '2 bowls', 'on their plates', 'using', 'empty plate'],
+    "rotate the 3 bowls on their plates from left to right using the empty plate": ['robot', 'rotate', '3 bowls', 'on their plates', 'from left to right', 'using', 'empty plate'],
+    "put the cream cheese in the nearest basket and place that basket in the center": ['robot', 'put', 'cream cheese', 'in', 'nearest basket', 'place', 'that basket', 'in the center'],
+    "put the cream cheese in the nearest basket and place the empty basket in the center": ['robot', 'put', 'cream cheese', 'in', 'nearest basket', 'place', 'empty basket', 'in the center'],
+
+    "pick up the butter and place it in the basket": ['robot', 'pick up', 'butter', 'place', 'in', 'basket'],
+    "pick up the bbq sauce and place it in the basket": ['robot', 'pick up', 'bbq sauce', 'place', 'in', 'basket'],
+    "pick up the cream cheese and place it in the basket": ['robot', 'pick up', 'cream cheese', 'place', 'in', 'basket'],
+    "pick up the salad dressing and place it in the basket": ['robot', 'pick up', 'salad dressing', 'place', 'in', 'basket'],
+    "pick up the orange juice and place it in the basket": ['robot', 'pick up', 'orange juice', 'place', 'in', 'basket'],
+    "pick up the alphabet soup and place it in the basket": ['robot', 'pick up', 'alphabet soup', 'place', 'in', 'basket'],
+    "pick up the tomato sauce and place it in the basket": ['robot', 'pick up', 'tomato sauce', 'place', 'in', 'basket'],
+    "pick up the ketchup and place it in the basket": ['robot', 'pick up', 'ketchup', 'place', 'in', 'basket'],
+    "pick up the chocolate pudding and place it in the basket": ['robot', 'pick up', 'chocolate pudding', 'place', 'in', 'basket'],
+    "pick up the milk and place it in the basket": ['robot', 'pick up', 'milk', 'place', 'in', 'basket'],
+}
+
+import cv2
+
+@dataclass
+class RLDSBatchTransformV3:
+    action_tokenizer: ActionTokenizer
+    base_tokenizer: PreTrainedTokenizerBase
+    image_transform: ImageTransform
+    prompt_builder_fn: Type[PromptBuilder]
+    predict_stop_token: bool = True
+
+    def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+        """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+        horizon = len(rlds_batch["action"])
+        dataset_name = rlds_batch["dataset_name"]
+        lang = rlds_batch["task"]["language_instruction"].decode().lower()
+        if "language_instruction_nouns" in rlds_batch["task"] and lang in LANGUAGE_INSTRUCTION_NOUNS:
+            language_instruction_nouns = LANGUAGE_INSTRUCTION_NOUNS[lang]
+        else:
+            language_instruction_nouns = ['robot']
+
+        windowed_labels = []
+        for hidx in range(horizon):
+            action = rlds_batch["action"][hidx]
+            # Label tokens.        
+            prompt_builder = self.prompt_builder_fn("openvla")
+            conversation = [
+                {"from": "human", "value": f"What action should the robot take?"}, #  to {lang}  , we are relying on CLIP's embeddings for true actions
+                {"from": "gpt", "value": self.action_tokenizer(action)},
+            ]
+            for turn in conversation:
+                prompt_builder.add_turn(turn["from"], turn["value"])
+            # Tokenize (w/ `base_tokenizer`)
+            labels = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+            labels = list(labels)
+            windowed_labels.append(labels)
+            # print(prompt_builder.get_prompt())
+            # print(labels)
+            
+        # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+        prompt_builder = self.prompt_builder_fn("openvla")
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take?"}, #  to {lang}  , we are relying on CLIP's embeddings for true actions
+            {"from": "gpt", "value": "_ _ _ _ _ _ _ _"},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+        # Tokenize (w/ `base_tokenizer`)
+        input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        input_ids[-9] = 29871
+        # print(prompt_builder.get_prompt())
+        # print(input_ids)
+
+        # here we get nouns, bboxes of interest across `horizon`
+        lang_nouns_ids = {}
+        reasoning_on_image = []
+        reasoning_on_wrist_image = []
+        if "reasoning_on_image" in rlds_batch["observation"]:
+            _lang_nouns = list(language_instruction_nouns)
+            for hidx in range(len(rlds_batch["observation"]["reasoning_on_image"])):
+                _reasoning_on_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_image"][hidx])
+                if 'reasoning_on_wrist_image' in rlds_batch["observation"]:
+                    _reasoning_on_wrist_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_wrist_image"][hidx])
+                    # _lang_nouns = list(set(list(_reasoning_on_image.keys()) + list(_reasoning_on_wrist_image.keys()) + _lang_nouns))
+                    # _reasoning_on_image, _reasoning_on_wrist_image = align_cues_by_name(_reasoning_on_image, _reasoning_on_wrist_image)
+                    reasoning_on_wrist_image.append(_reasoning_on_wrist_image)
+                reasoning_on_image.append(_reasoning_on_image)
+
+            for noun in _lang_nouns:
+                noun_ids = self.base_tokenizer(noun).input_ids
+                noun_ids = self.base_tokenizer.pad(
+                    {"input_ids": noun_ids}, 
+                    padding="max_length",  # or "max_length"
+                    max_length=30,      # specify if using "max_length"
+                    return_tensors="pt"
+                ).input_ids
+                lang_nouns_ids[noun] = noun_ids
+
+        # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+        #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, windowed_labels = torch.tensor(input_ids), torch.tensor(windowed_labels)
+        if input_ids.shape != windowed_labels[0].shape:
+            print('input_ids', input_ids.shape)
+            print('windowed_labels', windowed_labels.shape)
+            1/0
+
+        all_pixel_values = []
+        for img_data in rlds_batch["observation"]["image_primary"]:
+            img_data = img_data[:,:,:]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data); 1/0
+
+            img = Image.fromarray(img_data)
+            # img.save("img.png")
+            pixel_values = self.image_transform(img)
+            all_pixel_values.append(pixel_values)
+        all_pixel_values = torch.stack(all_pixel_values)
+        all_pixel_seg_values = []
+        for img_data in rlds_batch["observation"]["image_primary_seg"]:
+            img_data = img_data[:,:,0]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+            # img.save("img.png")
+            pixel_values = torch.from_numpy(img_data)
+            all_pixel_seg_values.append(pixel_values)
+        all_pixel_seg_values = torch.stack(all_pixel_seg_values)
+
+        all_wrist_values = None
+        all_wrist_seg_values = None
+        if "image_wrist" in rlds_batch["observation"]:
+            all_wrist_values = []
+            for img_data in rlds_batch["observation"]["image_wrist"]:
+                img_data = img_data[:,:,:]
+                # import cv2
+                # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+                wrist_img = Image.fromarray(img_data)
+                # wrist_img.save("img_wrist_img.png")
+
+                wrist_values = self.image_transform(wrist_img)
+                all_wrist_values.append(wrist_values)
+            all_wrist_values = torch.stack(all_wrist_values)
+
+            all_wrist_seg_values = []
+            for img_data in rlds_batch["observation"]["image_wrist_seg"]:
+                img_data = img_data[:,:,0]
+                # import cv2
+                # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+                # img.save("img.png")
+                wrist_values = torch.from_numpy(img_data)
+                all_wrist_seg_values.append(wrist_values)
+            all_wrist_seg_values = torch.stack(all_wrist_seg_values)
+
+        all_pixel_depth_values = None
+        if "depth_primary" in rlds_batch["observation"]:
+            all_pixel_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_primary"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                # import cv2
+                # cv2.imwrite('./tmp_depth_img_training.png', img_data); 1/0
+
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_data_depth.png")
+
+                pixel_values = self.image_transform(img_data)
+                all_pixel_depth_values.append(pixel_values)
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+        all_wrist_depth_values = None
+        if "depth_wrist" in rlds_batch["observation"]:
+            all_wrist_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_wrist"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_wrist_data_depth.png"); 1/0
+
+                wrist_values = self.image_transform(img_data)
+                all_wrist_depth_values.append(wrist_values)
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        
+        # print(lang_nouns)
+        # print(rlds_batch["observation"]["reasoning_on_image"])
+        # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+        # 1/0
+
+        task = {
+            'full_instruction': prompt_builder.get_prompt(),
+            'instruction': lang,
+            'instruction_nouns': language_instruction_nouns
+        }
+
+        return dict(pixel_values=all_pixel_values[0], input_ids=input_ids, labels=windowed_labels, dataset_name=dataset_name,
+                    all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+                    all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+                    all_pixel_seg_values=all_pixel_seg_values, all_wrist_seg_values=all_wrist_seg_values,
+                    reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+                    lang_nouns_ids=lang_nouns_ids, task=task)
+
+
+@dataclass
+class RLDSBatchTransformV3_1:
+    action_tokenizer: ActionTokenizer
+    base_tokenizer: PreTrainedTokenizerBase
+    image_transform: ImageTransform
+    prompt_builder_fn: Type[PromptBuilder]
+    predict_stop_token: bool = True
+
+    def __call__(self, rlds_batch: Dict[str, Any]) -> Dict[str, Any]:
+        """Converts a RLDS batch to the format expected by the OpenVLA collator/models."""
+        horizon = len(rlds_batch["observation"]["reasoning_on_image"])
+        future_horizon = len(rlds_batch["action"]) - horizon
+        action_dim = len(rlds_batch["action"][0])
+        dataset_name = rlds_batch["dataset_name"]
+        lang = rlds_batch["task"]["language_instruction"].decode().lower()
+        if "language_instruction_nouns" in rlds_batch["task"] and lang in LANGUAGE_INSTRUCTION_NOUNS:
+            language_instruction_nouns = LANGUAGE_INSTRUCTION_NOUNS[lang]
+        else:
+            language_instruction_nouns = ['robot']
+
+        windowed_labels = []
+        for hidx in range(horizon):
+            action_sequence = ''
+            for fidx in range(future_horizon):
+                action = self.action_tokenizer(rlds_batch["action"][hidx+fidx])
+                action_sequence = action_sequence + action
+
+            # Label tokens.        
+            prompt_builder = self.prompt_builder_fn("openvla")
+            conversation = [
+                {"from": "human", "value": f"What action should the robot take?"}, #  to {lang}  , we are relying on CLIP's embeddings for true actions
+                {"from": "gpt", "value": action_sequence},
+            ]
+            for turn in conversation:
+                prompt_builder.add_turn(turn["from"], turn["value"])
+            # Tokenize (w/ `base_tokenizer`)
+            labels = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+            labels = list(labels)
+            windowed_labels.append(labels)
+            # print(prompt_builder.get_prompt())
+            # print(labels)
+            
+        # Construct Chat-based Prompt =>> Input is default query + language instruction, output are the action tokens
+        prompt_builder = self.prompt_builder_fn("openvla")
+        placeholder_seg = "_ _ _ _ _ _ _ _"
+        for fidx in range(future_horizon-1):
+            placeholder_seg += " _ _ _ _ _ _ _"
+        conversation = [
+            {"from": "human", "value": f"What action should the robot take?"}, #  to {lang}  , we are relying on CLIP's embeddings for true actions
+            {"from": "gpt", "value": placeholder_seg},
+        ]
+        for turn in conversation:
+            prompt_builder.add_turn(turn["from"], turn["value"])
+        # Tokenize (w/ `base_tokenizer`)
+        input_ids = self.base_tokenizer(prompt_builder.get_prompt(), add_special_tokens=True).input_ids
+        input_ids[-2-(action_dim*future_horizon)] = 29871
+        # print(prompt_builder.get_prompt())
+        # print(len(input_ids)); 1/0
+
+        # here we get nouns, bboxes of interest across `horizon`
+        lang_nouns_ids = {}
+        reasoning_on_image = []
+        reasoning_on_wrist_image = []
+        if "reasoning_on_image" in rlds_batch["observation"]:
+            _lang_nouns = list(language_instruction_nouns)
+            for hidx in range(len(rlds_batch["observation"]["reasoning_on_image"])):
+                _reasoning_on_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_image"][hidx])
+                if 'reasoning_on_wrist_image' in rlds_batch["observation"]:
+                    _reasoning_on_wrist_image = process_reasoning_cues_v2(rlds_batch["observation"]["reasoning_on_wrist_image"][hidx])
+                    # _lang_nouns = list(set(list(_reasoning_on_image.keys()) + list(_reasoning_on_wrist_image.keys()) + _lang_nouns))
+                    # _reasoning_on_image, _reasoning_on_wrist_image = align_cues_by_name(_reasoning_on_image, _reasoning_on_wrist_image)
+                    reasoning_on_wrist_image.append(_reasoning_on_wrist_image)
+                reasoning_on_image.append(_reasoning_on_image)
+
+            for noun in _lang_nouns:
+                noun_ids = self.base_tokenizer(noun).input_ids
+                noun_ids = self.base_tokenizer.pad(
+                    {"input_ids": noun_ids}, 
+                    padding="max_length",  # or "max_length"
+                    max_length=30,      # specify if using "max_length"
+                    return_tensors="pt"
+                ).input_ids
+                lang_nouns_ids[noun] = noun_ids
+
+        # Tensorize =>> Run Image Transform to get `pixel_values` =>> Return
+        #   =>> IMPORTANT :: IF WE'RE USING HF LLM.forward(..., labels=labels), SHIFTING HAPPENS _INSIDE_ MODEL!
+        input_ids, windowed_labels = torch.tensor(input_ids), torch.tensor(windowed_labels)
+        if input_ids.shape != windowed_labels[0].shape:
+            print('input_ids\n', input_ids)
+            print('windowed_labels\n', windowed_labels[0])
+            1/0
+
+        all_pixel_values = []
+        for img_data in rlds_batch["observation"]["image_primary"]:
+            img_data = img_data[:,:,:]
+            import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data); 1/0
+
+            img = Image.fromarray(img_data)
+            # img.save("img.png")
+            pixel_values = self.image_transform(img)
+            all_pixel_values.append(pixel_values)
+        all_pixel_values = torch.stack(all_pixel_values)
+        all_pixel_seg_values = []
+        for img_data in rlds_batch["observation"]["image_primary_seg"]:
+            img_data = img_data[:,:,0]
+            # import cv2
+            # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+            # img.save("img.png")
+            pixel_values = torch.from_numpy(img_data)
+            all_pixel_seg_values.append(pixel_values)
+        all_pixel_seg_values = torch.stack(all_pixel_seg_values)
+
+        all_wrist_values = None
+        all_wrist_seg_values = None
+        if "image_wrist" in rlds_batch["observation"]:
+            all_wrist_values = []
+            for img_data in rlds_batch["observation"]["image_wrist"]:
+                img_data = img_data[:,:,:]
+                # import cv2
+                # cv2.imwrite('./tmp_wrist_img_training.png', img_data); 1/0
+
+                wrist_img = Image.fromarray(img_data)
+                # wrist_img.save("img_wrist_img.png")
+
+                wrist_values = self.image_transform(wrist_img)
+                all_wrist_values.append(wrist_values)
+            all_wrist_values = torch.stack(all_wrist_values)
+
+            all_wrist_seg_values = []
+            for img_data in rlds_batch["observation"]["image_wrist_seg"]:
+                img_data = img_data[:,:,0]
+                # import cv2
+                # cv2.imwrite('./tmp_img_training.png', img_data) #; 1/0
+                # img.save("img.png")
+                wrist_values = torch.from_numpy(img_data)
+                all_wrist_seg_values.append(wrist_values)
+            all_wrist_seg_values = torch.stack(all_wrist_seg_values)
+
+        all_pixel_depth_values = None
+        if "depth_primary" in rlds_batch["observation"]:
+            all_pixel_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_primary"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                # import cv2
+                # cv2.imwrite('./tmp_depth_img_training.png', img_data); 1/0
+
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_data_depth.png")
+
+                pixel_values = self.image_transform(img_data)
+                all_pixel_depth_values.append(pixel_values)
+            all_pixel_depth_values = torch.stack(all_pixel_depth_values)
+
+        all_wrist_depth_values = None
+        if "depth_wrist" in rlds_batch["observation"]:
+            all_wrist_depth_values = []
+            for img_data in rlds_batch["observation"]["depth_wrist"]:
+                img_data = img_data[:,:,:]
+                img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
+                img_data = Image.fromarray(img_data)
+                # img_data.save("img_wrist_data_depth.png"); 1/0
+
+                wrist_values = self.image_transform(img_data)
+                all_wrist_depth_values.append(wrist_values)
+            all_wrist_depth_values = torch.stack(all_wrist_depth_values)
+        
+        # print(lang_nouns)
+        # print(rlds_batch["observation"]["reasoning_on_image"])
+        # print(rlds_batch["observation"]["reasoning_on_wrist_image"])
+        # 1/0
+
+        task = {
+            'full_instruction': prompt_builder.get_prompt(),
+            'instruction': lang,
+            'instruction_nouns': language_instruction_nouns
+        }
+
+        return dict(pixel_values=all_pixel_values[0], input_ids=input_ids, labels=windowed_labels, dataset_name=dataset_name,
+                    all_pixel_values=all_pixel_values, all_wrist_values=all_wrist_values,
+                    all_pixel_depth_values=all_pixel_depth_values, all_wrist_depth_values=all_wrist_depth_values,
+                    all_pixel_seg_values=all_pixel_seg_values, all_wrist_seg_values=all_wrist_seg_values,
+                    reasoning_on_image=reasoning_on_image, reasoning_on_wrist_image=reasoning_on_wrist_image,
+                    lang_nouns_ids=lang_nouns_ids, task=task)
diff --git a/prismatic/vla/datasets/rlds/__init__.py b/prismatic/vla/datasets/rlds/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa2293e081142609d6d0d150aca08bbbcce086fe
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/__init__.py
@@ -0,0 +1,3 @@
+from .dataset import make_interleaved_dataset, make_single_dataset
+from .dataset_v2 import make_interleaved_dataset_v2, make_single_dataset_v2
+from .dataset_v3 import make_interleaved_dataset_v3, make_single_dataset_v3
diff --git a/prismatic/vla/datasets/rlds/dataset.py b/prismatic/vla/datasets/rlds/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..c18b6708b68de7e1d3b3139096ac1d421523ea42
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/dataset.py
@@ -0,0 +1,613 @@
+"""
+dataset.py
+
+Core interface script for configuring and initializing RLDS datasets.
+"""
+
+import copy
+import inspect
+import json
+from functools import partial
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import dlimp as dl
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.datasets.rlds import obs_transforms, traj_transforms
+from prismatic.vla.datasets.rlds.utils import goal_relabeling, task_augmentation
+from prismatic.vla.datasets.rlds.utils.data_utils import (
+    NormalizationType,
+    allocate_threads,
+    get_dataset_statistics,
+    normalize_action_and_proprio,
+    pprint_data_mixture,
+    tree_map,
+)
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# Configure Tensorflow with *no GPU devices* (to prevent clobber with PyTorch)
+tf.config.set_visible_devices([], "GPU")
+
+
+# ruff: noqa: B006
+def make_dataset_from_rlds(
+    name: str,
+    data_dir: str,
+    *,
+    train: bool,
+    standardize_fn: Optional[Callable[[dict], dict]] = None,
+    shuffle: bool = True,
+    image_obs_keys: Dict[str, Optional[str]] = {},
+    depth_obs_keys: Dict[str, Optional[str]] = {},
+    state_obs_keys: List[Optional[str]] = (),
+    language_key: Optional[str] = None,
+    action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL,
+    dataset_statistics: Optional[Union[dict, str]] = None,
+    absolute_action_mask: Optional[List[bool]] = None,
+    action_normalization_mask: Optional[List[bool]] = None,
+    num_parallel_reads: int = tf.data.AUTOTUNE,
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> Tuple[dl.DLataset, dict]:
+    """
+    This function is responsible for loading a specific RLDS dataset from storage and getting it into a standardized
+    format. Yields a dataset of trajectories. Does not include CPU-intensive operations.
+
+    If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the trajectory
+    into a standard format, which includes the keys "observation" and "action". Entry "observation" should be a
+    dictionary containing some number of additional keys, which will be extracted into an even more standardized format
+    according to the "*_obs_keys" arguments.
+
+    The `image_obs_keys` and `depth_obs_keys` arguments are mappings from new names to old names, or None in place of an
+    old name to insert padding. For example, if after `standardize_fn`, your "observation" dict has RGB images called
+    "workspace" and "wrist", and `image_obs_keys={"primary": "workspace", "secondary": None, "wrist": "wrist"}`, then
+    the resulting dataset will have an "observation" dict containing the keys "image_primary", "image_secondary", and
+    "image_wrist", where "image_primary" corresponds to "workspace", "image_secondary" is a padding image, and
+    "image_wrist" corresponds to "wrist".
+
+    Entry `state_obs_keys` is a list of 1-dimensional proprioceptive keys to concatenate into a single array, which will
+    be placed in the "proprio" key of the "observation" dict. A single padding element (zero) will be inserted for each
+    None entry.
+
+    The dataset will also include a "task" dict. If `language_key` is provided, then the "task" dict will contain the
+    key "language_instruction", extracted from `traj[language_key]`.
+
+    Args:
+        name (str): The name of the RLDS dataset (usually "name" or "name:version").
+        data_dir (str): The path to the data directory.
+        train (bool): Whether to use the training or validation split.
+        shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle the dataset, since one
+            file usually contains many trajectories)!
+        standardize_fn (Callable[[dict], dict], optional): A function that, if provided, will be the first
+            thing applied to each trajectory.
+        image_obs_keys (Mapping[str, str|None]): Mapping from {new: old} indicating which RGB images to extract from the
+            "observation" dict. `new_obs = {f"image_{new}": old_obs[old] for new, old in image_obs_keys.items()}`.
+            If a value of `old` is None, inserts a padding image instead (empty string).
+        depth_obs_keys (Mapping[str, str|None]): Same as `image_obs_keys`, but for depth images. Keys will be
+            prefixed with "depth_" instead of "image_".
+        state_obs_keys (Sequence[str|None]): List of 1-dimensional proprioception keys to be extracted from the
+            "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of padding for each None entry.
+        language_key (str, optional): If provided, the "task" dict will contain the key "language_instruction",
+            extracted from `traj[language_key]`.
+        action_proprio_normalization_type (str, optional): The type of normalization to perform on the action,
+            proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]).
+        dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics
+            for normalization. If `action_proprio_normalization_type` is "normal", this should contain "mean" and
+            "std" keys. If `action_proprio_normalization_type` is "bounds", this should contain "min" and "max"
+            keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for
+            `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly.
+        absolute_action_mask (Sequence[bool], optional): By default, all action dimensions are assumed to be
+            relative. This is important for when `future_action_window_size > 0`: actions that are taken
+            from beyond the end of the trajectory (or beyond the goal timestep when goal relabeling is used)
+            need to be made "neutral" to indicate that the task has been completed. For relative actions,
+            "neutral" means zero, but for absolute actions, "neutral" means repeating the last valid action.
+            This mask, if provided, indicates which action dimensions are absolute.
+        action_normalization_mask (Sequence[bool], optional): If provided, indicates which action dimensions
+            should be normalized. For example, you might not want to normalize the gripper action dimension if
+            it's always exactly 0 or 1. By default, all action dimensions are normalized.
+        num_parallel_reads (int): number of parallel read workers. Default to AUTOTUNE.
+        num_parallel_calls (int): number of parallel calls for traj_map operations. Default to AUTOTUNE.
+    Returns:
+        Dataset of trajectories where each step has the following fields:
+        - observation:
+            - image_{name1, name2, ...} # RGB image observations
+            - depth_{name1, name2, ...} # depth image observations
+            - proprio                   # 1-dimensional array of proprioceptive observations
+            - timestep                  # timestep of each frame
+        - task:
+            - language_instruction      # language instruction, present if `language_key` is provided
+        - action                        # action vector
+        - dataset_name                  # name of the dataset
+    """
+    REQUIRED_KEYS = {"observation", "action"}
+    if language_key is not None:
+        REQUIRED_KEYS.add(language_key)
+
+    def restructure(traj):
+        # apply a standardization function, if provided
+        if standardize_fn is not None:
+            traj = standardize_fn(traj)
+
+        if not all(k in traj for k in REQUIRED_KEYS):
+            raise ValueError(
+                f"Trajectory is missing keys: {REQUIRED_KEYS - set(traj.keys())}. " "Did you write a `standardize_fn`?"
+            )
+
+        # extracts images, depth images and proprio from the "observation" dict
+        traj_len = tf.shape(traj["action"])[0]
+        old_obs = traj["observation"]
+        new_obs = {}
+        for new, old in image_obs_keys.items():
+            if old is None:
+                new_obs[f"image_{new}"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"image_{new}"] = old_obs[old]
+
+        for new, old in depth_obs_keys.items():
+            if old is None:
+                new_obs[f"depth_{new}"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"depth_{new}"] = old_obs[old]
+
+        if state_obs_keys:
+            new_obs["proprio"] = tf.concat(
+                [
+                    (
+                        tf.zeros((traj_len, 1), dtype=tf.float32)  # padding
+                        if key is None
+                        else tf.cast(old_obs[key], tf.float32)
+                    )
+                    for key in state_obs_keys
+                ],
+                axis=1,
+            )
+
+        # add timestep info
+        new_obs["timestep"] = tf.range(traj_len)
+
+        # extracts `language_key` into the "task" dict
+        task = {}
+        if language_key is not None:
+            if traj[language_key].dtype != tf.string:
+                raise ValueError(
+                    f"Language key {language_key} has dtype {traj[language_key].dtype}, " "but it must be tf.string."
+                )
+            task["language_instruction"] = traj.pop(language_key)
+
+        traj = {
+            "observation": new_obs,
+            "task": task,
+            "action": tf.cast(traj["action"], tf.float32),
+            "dataset_name": tf.repeat(name, traj_len),
+        }
+
+        if absolute_action_mask is not None:
+            if len(absolute_action_mask) != traj["action"].shape[-1]:
+                raise ValueError(
+                    f"Length of absolute_action_mask ({len(absolute_action_mask)}) "
+                    f"does not match action dimension ({traj['action'].shape[-1]})."
+                )
+            traj["absolute_action_mask"] = tf.tile(
+                tf.convert_to_tensor(absolute_action_mask, dtype=tf.bool)[None],
+                [traj_len, 1],
+            )
+
+        return traj
+
+    if 'droid' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/droid/droid_dataset/1.0.0')
+    elif 'real' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/physical_01/data_20241202/custom_ur10e_real/1.0.0')
+    elif 'robomimic_lift' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/lift/lift/lift_ph_image/1.0.1')
+    elif 'robomimic_can' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/can/can/can_ph_image/1.0.1')
+    elif 'robomimic_square' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/square/square/square_ph_image/1.0.1')
+    elif 'robomimic_tool_hang' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/tool_hang/tool_hang/tool_hang_ph_image/1.0.1')
+    elif 'libero_spatial' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_spatial/1.0.0')
+    elif 'libero_object' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_object/1.0.0')
+    elif 'libero_goal' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__small/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_goal/1.0.0')
+    elif 'libero_10' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/liber_o10/1.0.0')
+    else:
+        builder = tfds.builder(name, data_dir=data_dir)
+
+    # load or compute dataset statistics
+    if isinstance(dataset_statistics, str):
+        with tf.io.gfile.GFile(dataset_statistics, "r") as f:
+            dataset_statistics = json.load(f)
+    elif dataset_statistics is None:
+        full_dataset = dl.DLataset.from_rlds(
+            builder, split="all", shuffle=False, num_parallel_reads=num_parallel_reads
+        ).traj_map(restructure, num_parallel_calls)
+        # tries to load from cache, otherwise computes on the fly
+        dataset_statistics = get_dataset_statistics(
+            full_dataset,
+            hash_dependencies=(
+                str(builder.info),
+                str(state_obs_keys),
+                inspect.getsource(standardize_fn) if standardize_fn is not None else "",
+            ),
+            save_dir=builder.data_dir,
+        )
+    dataset_statistics = tree_map(np.array, dataset_statistics)
+
+    # skip normalization for certain action dimensions
+    if action_normalization_mask is not None:
+        if len(action_normalization_mask) != dataset_statistics["action"]["mean"].shape[-1]:
+            raise ValueError(
+                f"Length of skip_normalization_mask ({len(action_normalization_mask)}) "
+                f"does not match action dimension ({dataset_statistics['action']['mean'].shape[-1]})."
+            )
+        dataset_statistics["action"]["mask"] = np.array(action_normalization_mask)
+
+    # construct the dataset
+    if "val" not in builder.info.splits:
+        split = "train[:95%]" if train else "train[95%:]"
+    else:
+        split = "train" if train else "val"
+
+    dataset = dl.DLataset.from_rlds(builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads)
+    # print(split, len(dataset))
+    dataset = dataset.traj_map(restructure, num_parallel_calls)
+    dataset = dataset.traj_map(
+        partial(
+            normalize_action_and_proprio,
+            metadata=dataset_statistics,
+            normalization_type=action_proprio_normalization_type,
+        ),
+        num_parallel_calls,
+    )
+
+    return dataset, dataset_statistics
+
+
+def apply_trajectory_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    goal_relabeling_strategy: Optional[str] = None,
+    goal_relabeling_kwargs: dict = {},
+    window_size: int = 1,
+    future_action_window_size: int = 0,
+    subsample_length: Optional[int] = None,
+    skip_unlabeled: bool = False,
+    max_action: Optional[float] = None,
+    max_proprio: Optional[float] = None,
+    task_augment_strategy: Optional[str] = None,
+    task_augment_kwargs: dict = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a trajectory level. Such transforms are usually some sort of "relabeling"
+    (e.g., filtering, chunking, adding goals, dropping keys).
+
+    Transforms in this function should have the following properties:
+        - They require access to an entire trajectory (i.e., they cannot be applied frame-wise).
+        - They are generally not CPU-intensive, mostly involving moving and copying data.
+        - They do not require decoded images.
+
+    Args:
+        dataset (dl.DLataset): The dataset to transform.
+        train (bool): Whether the dataset is for training (affects subsampling).
+        goal_relabeling_strategy (str, optional): The goal relabeling strategy to use, or None for
+            no goal relabeling. See `goal_relabeling.py`.
+        goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function.
+        window_size (int, optional): The length of the snippets that trajectories are chunked into.
+        future_action_window_size (int, optional): The number of future actions beyond window_size to include
+            in the chunked actions.
+        subsample_length (int, optional): If provided, trajectories longer than this will be subsampled to
+            this length (after goal relabeling and chunking).
+        skip_unlabeled (bool, optional): Whether to skip trajectories with no language labels.
+        max_action: (float, optional): If provided, trajectories in which *any* action dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        max_proprio: (float, optional): If provided, trajectories in which *any* proprio dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        task_augment_strategy (str, optional): The task augmentation strategy to use, or None for no task
+            augmentation. See `task_augmentation.py`.
+        task_augment_kwargs (dict, optional): Additional keyword arguments to pass to the task augmentation
+            function.
+        num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE.
+    """
+    if skip_unlabeled:
+        if "language_instruction" not in dataset.element_spec["task"]:
+            raise ValueError("skip_unlabeled=True but dataset does not have language labels.")
+
+        dataset = dataset.filter(lambda x: tf.math.reduce_any(x["task"]["language_instruction"] != ""))
+
+    if max_action is not None:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["action"]) <= max_action))
+
+    if max_proprio is not None and "proprio" in dataset.element_spec["observation"]:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["observation"]["proprio"]) <= max_proprio))
+
+    # marks which entires of the observation and task dicts are padding
+    dataset = dataset.traj_map(traj_transforms.add_pad_mask_dict, num_parallel_calls)
+
+    # updates the "task" dict
+    if goal_relabeling_strategy is not None:
+        dataset = dataset.traj_map(
+            partial(getattr(goal_relabeling, goal_relabeling_strategy), **goal_relabeling_kwargs),
+            num_parallel_calls,
+        )
+
+    # must run task augmentation before chunking, in case it changes goal timesteps
+    if train and task_augment_strategy is not None:
+        # perform task augmentation (e.g., dropping keys)
+        dataset = dataset.traj_map(
+            partial(
+                getattr(task_augmentation, task_augment_strategy),
+                **task_augment_kwargs,
+            ),
+            num_parallel_calls,
+        )
+
+    # chunks observations and actions, giving them a new axis at index 1 of size `window_size` and
+    # `window_size + future_action_window_size`, respectively
+    dataset = dataset.traj_map(
+        partial(
+            traj_transforms.chunk_act_obs,
+            window_size=window_size,
+            future_action_window_size=future_action_window_size,
+        ),
+        num_parallel_calls,
+    )
+
+    if train and subsample_length is not None:
+        dataset = dataset.traj_map(
+            partial(traj_transforms.subsample, subsample_length=subsample_length),
+            num_parallel_calls,
+        )
+
+    return dataset
+
+
+def apply_per_dataset_frame_transforms(
+    dataset: dl.DLataset,
+    chunk_filter_fn: Optional[Callable] = None,
+):
+    """
+    Optionally applied *per-dataset* transforms that happen at a frame level.
+
+    Args:
+        chunk_filter_fn (callable, optional): Filter function for chunks.
+    """
+    if chunk_filter_fn:
+        dataset = dataset.filter(chunk_filter_fn)
+    return dataset
+
+
+def apply_frame_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    image_augment_kwargs: Union[Dict, Dict[str, Dict]] = {},
+    resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    depth_resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a frame level. These transforms are usually more CPU-intensive, (e.g.,
+    decoding or resizing images).
+
+    Args:
+        train (bool): Whether the dataset is for training (affects image augmentation).
+        dataset (dl.DLataset): The dataset to transform.
+        image_augment_kwargs (dict|Mapping[str, dict]): Keyword arguments to pass to the image augmentation
+            function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a dict of
+            dicts is provided, then key "k" will be used for "image_{k}" (names determined by `image_obs_keys`
+            in `make_dataset_from_rlds`). Augmentation will be skipped for missing keys (so pass an empty dict
+            to skip augmentation for all images).
+        resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): If provided, images will be resized to
+            this size. If a dict of tuples is provided, then key "k" will be used for "image_{k}" (names
+            determined by `image_obs_keys` in `make_dataset_from_rlds`). Resizing will be skipped for missing
+            keys (so pass an empty dict to skip resizing for all images).
+        depth_resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): Same as resize_size, but for depth
+            images.
+        num_parallel_calls (int): number of parallel calls for frame_map operations. Default to AUTOTUNE.
+    """
+
+    # Convenience wrapper that takes a function that operates on a non-chunked "observation" dict and applies
+    # it to the chunked "observation" dict as well as the non-chunked "task" dict
+    def apply_obs_transform(fn: Callable[[Dict], Dict], frame: Dict) -> Dict:
+        frame["task"] = fn(frame["task"])
+        frame["observation"] = dl.vmap(fn)(frame["observation"])
+        return frame
+
+    # Decode + resize images (and depth images)
+    dataset = dataset.frame_map(
+        partial(
+            apply_obs_transform,
+            partial(obs_transforms.decode_and_resize, resize_size=resize_size, depth_resize_size=depth_resize_size),
+        ),
+        num_parallel_calls,
+    )
+
+    if train:
+        # Augment all images with the same seed, skipping padding images
+        def aug(frame: dict):
+            seed = tf.random.uniform([2], maxval=tf.dtypes.int32.max, dtype=tf.int32)
+            aug_fn = partial(obs_transforms.augment, seed=seed, augment_kwargs=image_augment_kwargs)
+            return apply_obs_transform(aug_fn, frame)
+
+        dataset = dataset.frame_map(aug, num_parallel_calls)
+
+    return dataset
+
+
+def make_single_dataset(
+    dataset_kwargs: dict,
+    *,
+    train: bool,
+    traj_transform_kwargs: dict = {},
+    frame_transform_kwargs: dict = {},
+) -> dl.DLataset:
+    """Creates a single dataset from kwargs. Returns a dataset of trajectories.
+
+    Args:
+        dataset_kwargs: kwargs passed to `make_dataset_from_rlds` that are dataset-specific.
+        train: whether this is a training or validation dataset.
+        traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'.
+        frame_transform_kwargs: kwargs passed to 'get_frame_transforms'.
+    """
+    dataset, dataset_statistics = make_dataset_from_rlds(
+        **dataset_kwargs,
+        train=train,
+    )
+    dataset = apply_trajectory_transforms(dataset, **traj_transform_kwargs, train=train)
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+
+    # this seems to reduce memory usage without affecting speed
+    dataset = dataset.with_ram_budget(1)
+
+    # save for later
+    return dataset, dataset_statistics["num_trajectories"], dataset_statistics
+
+
+# === Core Initializer ===
+def make_interleaved_dataset(
+    dataset_kwargs_list: List[Dict],
+    sample_weights: Optional[List[float]] = None,
+    *,
+    train: bool,
+    shuffle_buffer_size: int,
+    traj_transform_kwargs: Optional[Dict] = None,
+    frame_transform_kwargs: Optional[Dict] = None,
+    batch_size: Optional[int] = None,
+    balance_weights: bool = False,
+    traj_transform_threads: Optional[int] = None,
+    traj_read_threads: Optional[int] = None,
+) -> dl.DLataset:
+    """
+    Creates an interleaved dataset from list of dataset configs (kwargs). Returns a dataset of batched frames.
+
+    Args:
+        dataset_kwargs_list: list of kwargs, each element of which is passed to `make_dataset_from_rlds`.
+            "num_parallel_calls" and "num_parallel_reads" are overridden using `traj_transform_threads` and
+            `traj_read_threads`, respectively.
+        sample_weights: sampling weights for each dataset in list. If None, defaults to uniform.
+        train: whether this is a training or validation dataset.
+        shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames).
+        traj_transform_kwargs: kwargs passed to `apply_trajectory_transforms`. "num_parallel_calls" is
+            overridden using `traj_transform_threads`.
+        frame_transform_kwargs: kwargs passed to `apply_frame_transforms`.
+        batch_size: batch size, if not provided output is not batched.
+        balance_weights: if True, the sample weights are multiplied by the number of frames in each dataset.
+            This makes it so that, if all the sample weights are equal, one full iteration through the interleaved
+            dataset will correspond to one full iteration through each individual dataset (only in expectation,
+            since in practice the sampling is random).
+        traj_transform_threads: total number of parallel calls for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+        traj_read_threads: total number of parallel read workers for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+    """
+    # Default to uniform sampling (if `sample_weights` is not specified)
+    if not sample_weights:
+        sample_weights = [1.0] * len(dataset_kwargs_list)
+
+    if len(sample_weights) != len(dataset_kwargs_list):
+        raise ValueError(f"sample_weights must be None or have length {len(dataset_kwargs_list)}.")
+
+    # Check valid `traj_transform_kwargs` and `frame_transform_kwargs`
+    if (traj_transform_kwargs is None) or (frame_transform_kwargs is None):
+        raise ValueError("Missing `traj_transform_kwargs` and `frame_transform_kwargs`!")
+
+    # Get Dataset Sizes
+    dataset_sizes, all_dataset_statistics = [], {}
+    for dataset_kwargs in dataset_kwargs_list:
+        data_kwargs = copy.deepcopy(dataset_kwargs)
+        if "dataset_frame_transform_kwargs" in data_kwargs:
+            data_kwargs.pop("dataset_frame_transform_kwargs")
+        _, dataset_statistics = make_dataset_from_rlds(**data_kwargs, train=train)
+        # print(train)
+        # print(dataset_kwargs)
+        # print(dataset_statistics)
+        dataset_sizes.append(dataset_statistics["num_transitions"])
+        all_dataset_statistics[dataset_kwargs["name"]] = dataset_statistics
+
+    # Get the indices of the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    primary_dataset_indices = np.array([idx for idx in range(len(sample_weights)) if sample_weights[idx] == 1.0])
+
+    # Balance and Normalize Weights
+    if balance_weights:
+        sample_weights = np.array(sample_weights) * np.array(dataset_sizes)
+    sample_weights = np.array(sample_weights) / np.sum(sample_weights)
+    pprint_data_mixture(dataset_kwargs_list, sample_weights)
+
+    # Effective Dataset Length = Number of samples until each dataset has completed at least one epoch
+    #   =>> Note :: Only counting the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    dataset_len = int((np.array(dataset_sizes) / sample_weights)[primary_dataset_indices].max())
+
+    # Allocate Threads based on Weights
+    threads_per_dataset = allocate_threads(traj_transform_threads, sample_weights)
+    reads_per_dataset = allocate_threads(traj_read_threads, sample_weights)
+
+    overwatch.info("Threads per Dataset: %s", threads_per_dataset)
+    overwatch.info("Reads per Dataset: %s", reads_per_dataset)
+
+    # Construct Datasets
+    overwatch.info("Constructing datasets...")
+    datasets = []
+    for dataset_kwargs, threads, reads in zip(
+        dataset_kwargs_list,
+        threads_per_dataset,
+        reads_per_dataset,
+    ):
+        dataset_frame_transform_kwargs = (
+            dataset_kwargs.pop("dataset_frame_transform_kwargs")
+            if "dataset_frame_transform_kwargs" in dataset_kwargs
+            else {}
+        )
+        dataset, _ = make_dataset_from_rlds(
+            **dataset_kwargs,
+            train=train,
+            num_parallel_calls=threads,
+            num_parallel_reads=reads,
+            dataset_statistics=all_dataset_statistics[dataset_kwargs["name"]],
+        )
+        dataset = apply_trajectory_transforms(
+            dataset.repeat(),
+            **traj_transform_kwargs,
+            num_parallel_calls=threads,
+            train=train,
+        ).flatten(num_parallel_calls=threads)
+        dataset = apply_per_dataset_frame_transforms(dataset, **dataset_frame_transform_kwargs)
+        datasets.append(dataset)
+
+    # Interleave at the Frame Level
+    dataset: dl.DLataset = dl.DLataset.sample_from_datasets(datasets, sample_weights)
+
+    # Validation =>> fix a single shuffle buffer of data and cache it in RAM; prevents gradual memory increase!
+    if not train:
+        dataset = dataset.take(shuffle_buffer_size).cache()
+
+    # Shuffle the Dataset
+    #   =>> IMPORTANT :: Shuffle AFTER .cache(), or else memory will still leak!
+    dataset = dataset.shuffle(shuffle_buffer_size)
+
+    # Apply Frame Transforms
+    overwatch.info("Applying frame transforms on dataset...")
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+
+    # [Contract] When training VLA Policies, we let the Collator handle Batching!
+    if batch_size is not None:
+        dataset = dataset.batch(batch_size)
+
+    # Note =>> Seems to reduce memory usage without affecting speed?
+    dataset = dataset.with_ram_budget(1)
+
+    # Save for Later
+    dataset.sample_weights = sample_weights
+
+    return dataset, dataset_len, all_dataset_statistics
diff --git a/prismatic/vla/datasets/rlds/dataset_v2.py b/prismatic/vla/datasets/rlds/dataset_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec3334a9c3d1c92c637f4f044fb400709f01ad5e
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/dataset_v2.py
@@ -0,0 +1,633 @@
+"""
+dataset.py
+
+Core interface script for configuring and initializing RLDS datasets.
+"""
+
+import copy
+import inspect
+import json
+from functools import partial
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import dlimp as dl
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.datasets.rlds import obs_transforms, traj_transforms
+from prismatic.vla.datasets.rlds.utils import goal_relabeling, task_augmentation
+from prismatic.vla.datasets.rlds.utils.data_utils import (
+    NormalizationType,
+    allocate_threads,
+    get_dataset_statistics,
+    normalize_action_and_proprio,
+    pprint_data_mixture,
+    tree_map,
+)
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# Configure Tensorflow with *no GPU devices* (to prevent clobber with PyTorch)
+tf.config.set_visible_devices([], "GPU")
+
+
+# ruff: noqa: B006
+def make_dataset_from_rlds(
+    name: str,
+    data_dir: str,
+    *,
+    train: bool,
+    standardize_fn: Optional[Callable[[dict], dict]] = None,
+    shuffle: bool = True,
+    image_obs_keys: Dict[str, Optional[str]] = {},
+    depth_obs_keys: Dict[str, Optional[str]] = {},
+    state_obs_keys: List[Optional[str]] = (),
+    language_key: Optional[str] = None,
+    action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL,
+    dataset_statistics: Optional[Union[dict, str]] = None,
+    absolute_action_mask: Optional[List[bool]] = None,
+    action_normalization_mask: Optional[List[bool]] = None,
+    num_parallel_reads: int = tf.data.AUTOTUNE,
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> Tuple[dl.DLataset, dict]:
+    """
+    This function is responsible for loading a specific RLDS dataset from storage and getting it into a standardized
+    format. Yields a dataset of trajectories. Does not include CPU-intensive operations.
+
+    If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the trajectory
+    into a standard format, which includes the keys "observation" and "action". Entry "observation" should be a
+    dictionary containing some number of additional keys, which will be extracted into an even more standardized format
+    according to the "*_obs_keys" arguments.
+
+    The `image_obs_keys` and `depth_obs_keys` arguments are mappings from new names to old names, or None in place of an
+    old name to insert padding. For example, if after `standardize_fn`, your "observation" dict has RGB images called
+    "workspace" and "wrist", and `image_obs_keys={"primary": "workspace", "secondary": None, "wrist": "wrist"}`, then
+    the resulting dataset will have an "observation" dict containing the keys "image_primary", "image_secondary", and
+    "image_wrist", where "image_primary" corresponds to "workspace", "image_secondary" is a padding image, and
+    "image_wrist" corresponds to "wrist".
+
+    Entry `state_obs_keys` is a list of 1-dimensional proprioceptive keys to concatenate into a single array, which will
+    be placed in the "proprio" key of the "observation" dict. A single padding element (zero) will be inserted for each
+    None entry.
+
+    The dataset will also include a "task" dict. If `language_key` is provided, then the "task" dict will contain the
+    key "language_instruction", extracted from `traj[language_key]`.
+
+    Args:
+        name (str): The name of the RLDS dataset (usually "name" or "name:version").
+        data_dir (str): The path to the data directory.
+        train (bool): Whether to use the training or validation split.
+        shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle the dataset, since one
+            file usually contains many trajectories)!
+        standardize_fn (Callable[[dict], dict], optional): A function that, if provided, will be the first
+            thing applied to each trajectory.
+        image_obs_keys (Mapping[str, str|None]): Mapping from {new: old} indicating which RGB images to extract from the
+            "observation" dict. `new_obs = {f"image_{new}": old_obs[old] for new, old in image_obs_keys.items()}`.
+            If a value of `old` is None, inserts a padding image instead (empty string).
+        depth_obs_keys (Mapping[str, str|None]): Same as `image_obs_keys`, but for depth images. Keys will be
+            prefixed with "depth_" instead of "image_".
+        state_obs_keys (Sequence[str|None]): List of 1-dimensional proprioception keys to be extracted from the
+            "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of padding for each None entry.
+        language_key (str, optional): If provided, the "task" dict will contain the key "language_instruction",
+            extracted from `traj[language_key]`.
+        action_proprio_normalization_type (str, optional): The type of normalization to perform on the action,
+            proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]).
+        dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics
+            for normalization. If `action_proprio_normalization_type` is "normal", this should contain "mean" and
+            "std" keys. If `action_proprio_normalization_type` is "bounds", this should contain "min" and "max"
+            keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for
+            `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly.
+        absolute_action_mask (Sequence[bool], optional): By default, all action dimensions are assumed to be
+            relative. This is important for when `future_action_window_size > 0`: actions that are taken
+            from beyond the end of the trajectory (or beyond the goal timestep when goal relabeling is used)
+            need to be made "neutral" to indicate that the task has been completed. For relative actions,
+            "neutral" means zero, but for absolute actions, "neutral" means repeating the last valid action.
+            This mask, if provided, indicates which action dimensions are absolute.
+        action_normalization_mask (Sequence[bool], optional): If provided, indicates which action dimensions
+            should be normalized. For example, you might not want to normalize the gripper action dimension if
+            it's always exactly 0 or 1. By default, all action dimensions are normalized.
+        num_parallel_reads (int): number of parallel read workers. Default to AUTOTUNE.
+        num_parallel_calls (int): number of parallel calls for traj_map operations. Default to AUTOTUNE.
+    Returns:
+        Dataset of trajectories where each step has the following fields:
+        - observation:
+            - image_{name1, name2, ...} # RGB image observations
+            - depth_{name1, name2, ...} # depth image observations
+            - proprio                   # 1-dimensional array of proprioceptive observations
+            - timestep                  # timestep of each frame
+        - task:
+            - language_instruction      # language instruction, present if `language_key` is provided
+        - action                        # action vector
+        - dataset_name                  # name of the dataset
+    """
+    REQUIRED_KEYS = {"observation", "action"}
+    if language_key is not None:
+        REQUIRED_KEYS.add(language_key)
+
+    def restructure(traj):
+        # apply a standardization function, if provided
+        if standardize_fn is not None:
+            traj = standardize_fn(traj)
+
+        if not all(k in traj for k in REQUIRED_KEYS):
+            raise ValueError(
+                f"Trajectory is missing keys: {REQUIRED_KEYS - set(traj.keys())}. " "Did you write a `standardize_fn`?"
+            )
+
+        # extracts images, depth images and proprio from the "observation" dict
+        traj_len = tf.shape(traj["action"])[0]
+        old_obs = traj["observation"]
+        new_obs = {}
+        for new, old in image_obs_keys.items():
+            if old is None:
+                new_obs[f"image_{new}"] = tf.repeat("", traj_len)  # padding
+                new_obs[f"image_{new}_seg"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"image_{new}"] = old_obs[old]
+                new_obs[f"image_{new}_seg"] = old_obs[old + "_seg"]
+
+        for new, old in depth_obs_keys.items():
+            if old is None:
+                new_obs[f"depth_{new}"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"depth_{new}"] = old_obs[old]
+
+        if state_obs_keys:
+            new_obs["proprio"] = tf.concat(
+                [
+                    (
+                        tf.zeros((traj_len, 1), dtype=tf.float32)  # padding
+                        if key is None
+                        else tf.cast(old_obs[key], tf.float32)
+                    )
+                    for key in state_obs_keys
+                ],
+                axis=1,
+            )
+
+
+        if "image_reasoning" in old_obs:
+            new_obs["reasoning_on_image"] = tf.cast(old_obs["image_reasoning"], tf.string) 
+        if "wrist_image_reasoning" in old_obs: 
+            new_obs["reasoning_on_wrist_image"] = tf.cast(old_obs["wrist_image_reasoning"], tf.string)
+
+        # add timestep info
+        new_obs["timestep"] = tf.range(traj_len)
+
+        # extracts `language_key` into the "task" dict
+        task = {}
+        if language_key is not None:
+            if traj[language_key].dtype != tf.string:
+                raise ValueError(
+                    f"Language key {language_key} has dtype {traj[language_key].dtype}, " "but it must be tf.string."
+                )
+            task["language_instruction"] = traj.pop(language_key)
+        if "language_instruction_nouns" in traj:
+            task["language_instruction_nouns"] = traj.pop("language_instruction_nouns")
+
+        traj = {
+            "observation": new_obs,
+            "task": task,
+            "action": tf.cast(traj["action"], tf.float32),
+            "dataset_name": tf.repeat(name, traj_len),
+        }
+        
+        if absolute_action_mask is not None:
+            if len(absolute_action_mask) != traj["action"].shape[-1]:
+                raise ValueError(
+                    f"Length of absolute_action_mask ({len(absolute_action_mask)}) "
+                    f"does not match action dimension ({traj['action'].shape[-1]})."
+                )
+            traj["absolute_action_mask"] = tf.tile(
+                tf.convert_to_tensor(absolute_action_mask, dtype=tf.bool)[None],
+                [traj_len, 1],
+            )
+
+        return traj
+
+    if 'droid' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/droid/droid_dataset/1.0.0')
+    elif 'real_action' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/physical_01/data_20250304/custom_ur10e_real_action/1.0.0')
+    elif 'real' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/physical_01/data_20250304/custom_ur10e_real/1.0.0')
+    elif 'robomimic_lift' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/lift/lift/lift_ph_image/1.0.1')
+    elif 'robomimic_can' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/can/can/can_ph_image/1.0.1')
+    elif 'robomimic_square' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/square/square/square_ph_image/1.0.1')
+    elif 'robomimic_tool_hang' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/tool_hang/tool_hang/tool_hang_ph_image/1.0.1')
+    elif 'libero_spatial' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_spatial/1.0.0')
+    elif 'libero_object' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_object/1.0.0')
+    elif 'libero_goal_oc' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_goal_oc/libero_goal/1.0.0')
+    elif 'libero_goal' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__revised_v4/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__revised_v2/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__small/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_goal/1.0.0')
+    elif 'libero_10' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/liber_o10/1.0.0')
+    elif 'libero_relation' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_relation/1.0.0')
+    elif 'libero_90' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_90/liber_o90/1.0.0')
+    elif 'libero_mem' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_mem/1.0.0')
+    else:
+        builder = tfds.builder(name, data_dir=data_dir)
+
+    # load or compute dataset statistics
+    if isinstance(dataset_statistics, str):
+        with tf.io.gfile.GFile(dataset_statistics, "r") as f:
+            dataset_statistics = json.load(f)
+    elif dataset_statistics is None:
+        full_dataset = dl.DLataset.from_rlds(
+            builder, split="all", shuffle=False, num_parallel_reads=num_parallel_reads
+        ).traj_map(restructure, num_parallel_calls)
+        # tries to load from cache, otherwise computes on the fly
+        dataset_statistics = get_dataset_statistics(
+            full_dataset,
+            hash_dependencies=(
+                str(builder.info),
+                str(state_obs_keys),
+                inspect.getsource(standardize_fn) if standardize_fn is not None else "",
+            ),
+            save_dir=builder.data_dir,
+        )
+    dataset_statistics = tree_map(np.array, dataset_statistics)
+
+    # skip normalization for certain action dimensions
+    if action_normalization_mask is not None:
+        if len(action_normalization_mask) != dataset_statistics["action"]["mean"].shape[-1]:
+            raise ValueError(
+                f"Length of skip_normalization_mask ({len(action_normalization_mask)}) "
+                f"does not match action dimension ({dataset_statistics['action']['mean'].shape[-1]})."
+            )
+        dataset_statistics["action"]["mask"] = np.array(action_normalization_mask)
+
+    # construct the dataset
+    if "val" not in builder.info.splits:
+        split = "train[:95%]" if train else "train[95%:]"
+    else:
+        split = "train" if train else "val"
+
+    dataset = dl.DLataset.from_rlds(builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads)
+    # print(split, len(dataset))
+    dataset = dataset.traj_map(restructure, num_parallel_calls)
+    dataset = dataset.traj_map(
+        partial(
+            normalize_action_and_proprio,
+            metadata=dataset_statistics,
+            normalization_type=action_proprio_normalization_type,
+        ),
+        num_parallel_calls,
+    )
+    return dataset, dataset_statistics
+
+
+def apply_trajectory_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    goal_relabeling_strategy: Optional[str] = None,
+    goal_relabeling_kwargs: dict = {},
+    window_size: int = 1,
+    future_action_window_size: int = 0,
+    subsample_length: Optional[int] = None,
+    skip_unlabeled: bool = False,
+    max_action: Optional[float] = None,
+    max_proprio: Optional[float] = None,
+    task_augment_strategy: Optional[str] = None,
+    task_augment_kwargs: dict = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a trajectory level. Such transforms are usually some sort of "relabeling"
+    (e.g., filtering, chunking, adding goals, dropping keys).
+
+    Transforms in this function should have the following properties:
+        - They require access to an entire trajectory (i.e., they cannot be applied frame-wise).
+        - They are generally not CPU-intensive, mostly involving moving and copying data.
+        - They do not require decoded images.
+
+    Args:
+        dataset (dl.DLataset): The dataset to transform.
+        train (bool): Whether the dataset is for training (affects subsampling).
+        goal_relabeling_strategy (str, optional): The goal relabeling strategy to use, or None for
+            no goal relabeling. See `goal_relabeling.py`.
+        goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function.
+        window_size (int, optional): The length of the snippets that trajectories are chunked into.
+        future_action_window_size (int, optional): The number of future actions beyond window_size to include
+            in the chunked actions.
+        subsample_length (int, optional): If provided, trajectories longer than this will be subsampled to
+            this length (after goal relabeling and chunking).
+        skip_unlabeled (bool, optional): Whether to skip trajectories with no language labels.
+        max_action: (float, optional): If provided, trajectories in which *any* action dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        max_proprio: (float, optional): If provided, trajectories in which *any* proprio dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        task_augment_strategy (str, optional): The task augmentation strategy to use, or None for no task
+            augmentation. See `task_augmentation.py`.
+        task_augment_kwargs (dict, optional): Additional keyword arguments to pass to the task augmentation
+            function.
+        num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE.
+    """
+    if skip_unlabeled:
+        if "language_instruction" not in dataset.element_spec["task"]:
+            raise ValueError("skip_unlabeled=True but dataset does not have language labels.")
+
+        dataset = dataset.filter(lambda x: tf.math.reduce_any(x["task"]["language_instruction"] != ""))
+
+    if max_action is not None:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["action"]) <= max_action))
+
+    if max_proprio is not None and "proprio" in dataset.element_spec["observation"]:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["observation"]["proprio"]) <= max_proprio))
+
+    # marks which entires of the observation and task dicts are padding
+    dataset = dataset.traj_map(traj_transforms.add_pad_mask_dict, num_parallel_calls)
+
+    # updates the "task" dict
+    if goal_relabeling_strategy is not None:
+        dataset = dataset.traj_map(
+            partial(getattr(goal_relabeling, goal_relabeling_strategy), **goal_relabeling_kwargs),
+            num_parallel_calls,
+        )
+
+    # must run task augmentation before chunking, in case it changes goal timesteps
+    if train and task_augment_strategy is not None:
+        # perform task augmentation (e.g., dropping keys)
+        dataset = dataset.traj_map(
+            partial(
+                getattr(task_augmentation, task_augment_strategy),
+                **task_augment_kwargs,
+            ),
+            num_parallel_calls,
+        )
+
+    # chunks observations and actions, giving them a new axis at index 1 of size `window_size` and
+    # `window_size + future_action_window_size`, respectively
+    dataset = dataset.traj_map(
+        partial(
+            traj_transforms.chunk_act_obs,
+            window_size=window_size,
+            future_action_window_size=future_action_window_size,
+        ),
+        num_parallel_calls,
+    )
+
+    if train and subsample_length is not None:
+        dataset = dataset.traj_map(
+            partial(traj_transforms.subsample, subsample_length=subsample_length),
+            num_parallel_calls,
+        )
+
+    return dataset
+
+
+def apply_per_dataset_frame_transforms(
+    dataset: dl.DLataset,
+    chunk_filter_fn: Optional[Callable] = None,
+):
+    """
+    Optionally applied *per-dataset* transforms that happen at a frame level.
+
+    Args:
+        chunk_filter_fn (callable, optional): Filter function for chunks.
+    """
+    if chunk_filter_fn:
+        dataset = dataset.filter(chunk_filter_fn)
+    return dataset
+
+
+def apply_frame_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    image_augment_kwargs: Union[Dict, Dict[str, Dict]] = {},
+    resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    depth_resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a frame level. These transforms are usually more CPU-intensive, (e.g.,
+    decoding or resizing images).
+
+    Args:
+        train (bool): Whether the dataset is for training (affects image augmentation).
+        dataset (dl.DLataset): The dataset to transform.
+        image_augment_kwargs (dict|Mapping[str, dict]): Keyword arguments to pass to the image augmentation
+            function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a dict of
+            dicts is provided, then key "k" will be used for "image_{k}" (names determined by `image_obs_keys`
+            in `make_dataset_from_rlds`). Augmentation will be skipped for missing keys (so pass an empty dict
+            to skip augmentation for all images).
+        resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): If provided, images will be resized to
+            this size. If a dict of tuples is provided, then key "k" will be used for "image_{k}" (names
+            determined by `image_obs_keys` in `make_dataset_from_rlds`). Resizing will be skipped for missing
+            keys (so pass an empty dict to skip resizing for all images).
+        depth_resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): Same as resize_size, but for depth
+            images.
+        num_parallel_calls (int): number of parallel calls for frame_map operations. Default to AUTOTUNE.
+    """
+
+    # Convenience wrapper that takes a function that operates on a non-chunked "observation" dict and applies
+    # it to the chunked "observation" dict as well as the non-chunked "task" dict
+    def apply_obs_transform(fn: Callable[[Dict], Dict], frame: Dict) -> Dict:
+        frame["task"] = fn(frame["task"])
+        frame["observation"] = dl.vmap(fn)(frame["observation"])
+        return frame
+
+    # Decode + resize images (and depth images)
+    dataset = dataset.frame_map(
+        partial(
+            apply_obs_transform,
+            partial(obs_transforms.decode_and_resize, resize_size=resize_size, depth_resize_size=depth_resize_size),
+        ),
+        num_parallel_calls,
+    )
+
+    if train:
+        # Augment all images with the same seed, skipping padding images
+        def aug(frame: dict):
+            seed = tf.random.uniform([2], maxval=tf.dtypes.int32.max, dtype=tf.int32)
+            aug_fn = partial(obs_transforms.augment, seed=seed, augment_kwargs=image_augment_kwargs)
+            return apply_obs_transform(aug_fn, frame)
+
+        dataset = dataset.frame_map(aug, num_parallel_calls)
+
+    return dataset
+
+
+def make_single_dataset_v2(
+    dataset_kwargs: dict,
+    *,
+    train: bool,
+    traj_transform_kwargs: dict = {},
+    frame_transform_kwargs: dict = {},
+) -> dl.DLataset:
+    """Creates a single dataset from kwargs. Returns a dataset of trajectories.
+
+    Args:
+        dataset_kwargs: kwargs passed to `make_dataset_from_rlds` that are dataset-specific.
+        train: whether this is a training or validation dataset.
+        traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'.
+        frame_transform_kwargs: kwargs passed to 'get_frame_transforms'.
+    """
+    dataset, dataset_statistics = make_dataset_from_rlds(
+        **dataset_kwargs,
+        train=train,
+    )
+    dataset = apply_trajectory_transforms(dataset, **traj_transform_kwargs, train=train)
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+    # this seems to reduce memory usage without affecting speed
+    dataset = dataset.with_ram_budget(1)
+
+    # save for later
+    return dataset, dataset_statistics["num_trajectories"], dataset_statistics
+
+
+# === Core Initializer ===
+def make_interleaved_dataset_v2(
+    dataset_kwargs_list: List[Dict],
+    sample_weights: Optional[List[float]] = None,
+    *,
+    train: bool,
+    shuffle_buffer_size: int,
+    traj_transform_kwargs: Optional[Dict] = None,
+    frame_transform_kwargs: Optional[Dict] = None,
+    batch_size: Optional[int] = None,
+    balance_weights: bool = False,
+    traj_transform_threads: Optional[int] = None,
+    traj_read_threads: Optional[int] = None,
+) -> dl.DLataset:
+    """
+    Creates an interleaved dataset from list of dataset configs (kwargs). Returns a dataset of batched frames.
+
+    Args:
+        dataset_kwargs_list: list of kwargs, each element of which is passed to `make_dataset_from_rlds`.
+            "num_parallel_calls" and "num_parallel_reads" are overridden using `traj_transform_threads` and
+            `traj_read_threads`, respectively.
+        sample_weights: sampling weights for each dataset in list. If None, defaults to uniform.
+        train: whether this is a training or validation dataset.
+        shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames).
+        traj_transform_kwargs: kwargs passed to `apply_trajectory_transforms`. "num_parallel_calls" is
+            overridden using `traj_transform_threads`.
+        frame_transform_kwargs: kwargs passed to `apply_frame_transforms`.
+        batch_size: batch size, if not provided output is not batched.
+        balance_weights: if True, the sample weights are multiplied by the number of frames in each dataset.
+            This makes it so that, if all the sample weights are equal, one full iteration through the interleaved
+            dataset will correspond to one full iteration through each individual dataset (only in expectation,
+            since in practice the sampling is random).
+        traj_transform_threads: total number of parallel calls for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+        traj_read_threads: total number of parallel read workers for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+    """
+    # Default to uniform sampling (if `sample_weights` is not specified)
+    if not sample_weights:
+        sample_weights = [1.0] * len(dataset_kwargs_list)
+
+    if len(sample_weights) != len(dataset_kwargs_list):
+        raise ValueError(f"sample_weights must be None or have length {len(dataset_kwargs_list)}.")
+
+    # Check valid `traj_transform_kwargs` and `frame_transform_kwargs`
+    if (traj_transform_kwargs is None) or (frame_transform_kwargs is None):
+        raise ValueError("Missing `traj_transform_kwargs` and `frame_transform_kwargs`!")
+
+    # Get Dataset Sizes
+    dataset_sizes, all_dataset_statistics = [], {}
+    for dataset_kwargs in dataset_kwargs_list:
+        data_kwargs = copy.deepcopy(dataset_kwargs)
+        if "dataset_frame_transform_kwargs" in data_kwargs:
+            data_kwargs.pop("dataset_frame_transform_kwargs")
+        _, dataset_statistics = make_dataset_from_rlds(**data_kwargs, train=train)
+        # print(train)
+        # print(dataset_kwargs)
+        # print(dataset_statistics)
+        dataset_sizes.append(dataset_statistics["num_transitions"])
+        all_dataset_statistics[dataset_kwargs["name"]] = dataset_statistics
+
+    # Get the indices of the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    primary_dataset_indices = np.array([idx for idx in range(len(sample_weights)) if sample_weights[idx] == 1.0])
+
+    # Balance and Normalize Weights
+    if balance_weights:
+        sample_weights = np.array(sample_weights) * np.array(dataset_sizes)
+    sample_weights = np.array(sample_weights) / np.sum(sample_weights)
+    pprint_data_mixture(dataset_kwargs_list, sample_weights)
+
+    # Effective Dataset Length = Number of samples until each dataset has completed at least one epoch
+    #   =>> Note :: Only counting the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    dataset_len = int((np.array(dataset_sizes) / sample_weights)[primary_dataset_indices].max())
+
+    # Allocate Threads based on Weights
+    threads_per_dataset = allocate_threads(traj_transform_threads, sample_weights)
+    reads_per_dataset = allocate_threads(traj_read_threads, sample_weights)
+
+    overwatch.info("Threads per Dataset: %s", threads_per_dataset)
+    overwatch.info("Reads per Dataset: %s", reads_per_dataset)
+
+    # Construct Datasets
+    overwatch.info("Constructing datasets...")
+    datasets = []
+    for dataset_kwargs, threads, reads in zip(
+        dataset_kwargs_list,
+        threads_per_dataset,
+        reads_per_dataset,
+    ):
+        dataset_frame_transform_kwargs = (
+            dataset_kwargs.pop("dataset_frame_transform_kwargs")
+            if "dataset_frame_transform_kwargs" in dataset_kwargs
+            else {}
+        )
+        dataset, _ = make_dataset_from_rlds(
+            **dataset_kwargs,
+            train=train,
+            num_parallel_calls=threads,
+            num_parallel_reads=reads,
+            dataset_statistics=all_dataset_statistics[dataset_kwargs["name"]],
+        )
+        dataset = apply_trajectory_transforms(
+            dataset.repeat(),
+            **traj_transform_kwargs,
+            num_parallel_calls=threads,
+            train=train,
+        ).flatten(num_parallel_calls=threads)
+        dataset = apply_per_dataset_frame_transforms(dataset, **dataset_frame_transform_kwargs)
+        datasets.append(dataset)
+
+    # Interleave at the Frame Level
+    dataset: dl.DLataset = dl.DLataset.sample_from_datasets(datasets, sample_weights)
+
+    # Validation =>> fix a single shuffle buffer of data and cache it in RAM; prevents gradual memory increase!
+    if not train:
+        dataset = dataset.take(shuffle_buffer_size).cache()
+
+    # Shuffle the Dataset
+    #   =>> IMPORTANT :: Shuffle AFTER .cache(), or else memory will still leak!
+    dataset = dataset.shuffle(shuffle_buffer_size)
+
+    # Apply Frame Transforms
+    overwatch.info("Applying frame transforms on dataset...")
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+
+    # [Contract] When training VLA Policies, we let the Collator handle Batching!
+    if batch_size is not None:
+        dataset = dataset.batch(batch_size)
+
+    # Note =>> Seems to reduce memory usage without affecting speed?
+    dataset = dataset.with_ram_budget(1)
+
+    # Save for Later
+    dataset.sample_weights = sample_weights
+
+    return dataset, dataset_len, all_dataset_statistics
diff --git a/prismatic/vla/datasets/rlds/dataset_v3.py b/prismatic/vla/datasets/rlds/dataset_v3.py
new file mode 100644
index 0000000000000000000000000000000000000000..69197cfc8cc1515ab4475bb51e858eed8278297b
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/dataset_v3.py
@@ -0,0 +1,636 @@
+"""
+dataset.py
+
+Core interface script for configuring and initializing RLDS datasets.
+"""
+
+import copy
+import inspect
+import json
+from functools import partial
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import dlimp as dl
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.datasets.rlds import obs_transforms, traj_transforms
+from prismatic.vla.datasets.rlds.utils import goal_relabeling, task_augmentation
+from prismatic.vla.datasets.rlds.utils.data_utils import (
+    NormalizationType,
+    allocate_threads,
+    get_dataset_statistics,
+    normalize_action_and_proprio,
+    pprint_data_mixture,
+    tree_map,
+)
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+# Configure Tensorflow with *no GPU devices* (to prevent clobber with PyTorch)
+tf.config.set_visible_devices([], "GPU")
+
+
+# ruff: noqa: B006
+def make_dataset_from_rlds(
+    name: str,
+    data_dir: str,
+    *,
+    train: bool,
+    standardize_fn: Optional[Callable[[dict], dict]] = None,
+    shuffle: bool = True,
+    image_obs_keys: Dict[str, Optional[str]] = {},
+    depth_obs_keys: Dict[str, Optional[str]] = {},
+    state_obs_keys: List[Optional[str]] = (),
+    language_key: Optional[str] = None,
+    action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL,
+    dataset_statistics: Optional[Union[dict, str]] = None,
+    absolute_action_mask: Optional[List[bool]] = None,
+    action_normalization_mask: Optional[List[bool]] = None,
+    num_parallel_reads: int = tf.data.AUTOTUNE,
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> Tuple[dl.DLataset, dict]:
+    """
+    This function is responsible for loading a specific RLDS dataset from storage and getting it into a standardized
+    format. Yields a dataset of trajectories. Does not include CPU-intensive operations.
+
+    If `standardize_fn` is provided, it will be applied to each trajectory. This function should get the trajectory
+    into a standard format, which includes the keys "observation" and "action". Entry "observation" should be a
+    dictionary containing some number of additional keys, which will be extracted into an even more standardized format
+    according to the "*_obs_keys" arguments.
+
+    The `image_obs_keys` and `depth_obs_keys` arguments are mappings from new names to old names, or None in place of an
+    old name to insert padding. For example, if after `standardize_fn`, your "observation" dict has RGB images called
+    "workspace" and "wrist", and `image_obs_keys={"primary": "workspace", "secondary": None, "wrist": "wrist"}`, then
+    the resulting dataset will have an "observation" dict containing the keys "image_primary", "image_secondary", and
+    "image_wrist", where "image_primary" corresponds to "workspace", "image_secondary" is a padding image, and
+    "image_wrist" corresponds to "wrist".
+
+    Entry `state_obs_keys` is a list of 1-dimensional proprioceptive keys to concatenate into a single array, which will
+    be placed in the "proprio" key of the "observation" dict. A single padding element (zero) will be inserted for each
+    None entry.
+
+    The dataset will also include a "task" dict. If `language_key` is provided, then the "task" dict will contain the
+    key "language_instruction", extracted from `traj[language_key]`.
+
+    Args:
+        name (str): The name of the RLDS dataset (usually "name" or "name:version").
+        data_dir (str): The path to the data directory.
+        train (bool): Whether to use the training or validation split.
+        shuffle (bool, optional): Whether to shuffle the file read order (does NOT fully shuffle the dataset, since one
+            file usually contains many trajectories)!
+        standardize_fn (Callable[[dict], dict], optional): A function that, if provided, will be the first
+            thing applied to each trajectory.
+        image_obs_keys (Mapping[str, str|None]): Mapping from {new: old} indicating which RGB images to extract from the
+            "observation" dict. `new_obs = {f"image_{new}": old_obs[old] for new, old in image_obs_keys.items()}`.
+            If a value of `old` is None, inserts a padding image instead (empty string).
+        depth_obs_keys (Mapping[str, str|None]): Same as `image_obs_keys`, but for depth images. Keys will be
+            prefixed with "depth_" instead of "image_".
+        state_obs_keys (Sequence[str|None]): List of 1-dimensional proprioception keys to be extracted from the
+            "observation" dict, concatenated, and mapped to "proprio". Inserts 1 element of padding for each None entry.
+        language_key (str, optional): If provided, the "task" dict will contain the key "language_instruction",
+            extracted from `traj[language_key]`.
+        action_proprio_normalization_type (str, optional): The type of normalization to perform on the action,
+            proprio, or both. Can be "normal" (mean 0, std 1) or "bounds" (normalized to [-1, 1]).
+        dataset_statistics: (dict|str, optional): dict (or path to JSON file) that contains dataset statistics
+            for normalization. If `action_proprio_normalization_type` is "normal", this should contain "mean" and
+            "std" keys. If `action_proprio_normalization_type` is "bounds", this should contain "min" and "max"
+            keys. May also provide "num_transitions" and "num_trajectories" keys for downstream usage (e.g., for
+            `make_interleaved_dataset`). If not provided, the statistics will be computed on the fly.
+        absolute_action_mask (Sequence[bool], optional): By default, all action dimensions are assumed to be
+            relative. This is important for when `future_action_window_size > 0`: actions that are taken
+            from beyond the end of the trajectory (or beyond the goal timestep when goal relabeling is used)
+            need to be made "neutral" to indicate that the task has been completed. For relative actions,
+            "neutral" means zero, but for absolute actions, "neutral" means repeating the last valid action.
+            This mask, if provided, indicates which action dimensions are absolute.
+        action_normalization_mask (Sequence[bool], optional): If provided, indicates which action dimensions
+            should be normalized. For example, you might not want to normalize the gripper action dimension if
+            it's always exactly 0 or 1. By default, all action dimensions are normalized.
+        num_parallel_reads (int): number of parallel read workers. Default to AUTOTUNE.
+        num_parallel_calls (int): number of parallel calls for traj_map operations. Default to AUTOTUNE.
+    Returns:
+        Dataset of trajectories where each step has the following fields:
+        - observation:
+            - image_{name1, name2, ...} # RGB image observations
+            - depth_{name1, name2, ...} # depth image observations
+            - proprio                   # 1-dimensional array of proprioceptive observations
+            - timestep                  # timestep of each frame
+        - task:
+            - language_instruction      # language instruction, present if `language_key` is provided
+        - action                        # action vector
+        - dataset_name                  # name of the dataset
+    """
+    REQUIRED_KEYS = {"observation", "action"}
+    if language_key is not None:
+        REQUIRED_KEYS.add(language_key)
+
+    def restructure(traj):
+        # apply a standardization function, if provided
+        if standardize_fn is not None:
+            traj = standardize_fn(traj)
+
+        if not all(k in traj for k in REQUIRED_KEYS):
+            raise ValueError(
+                f"Trajectory is missing keys: {REQUIRED_KEYS - set(traj.keys())}. " "Did you write a `standardize_fn`?"
+            )
+
+        # extracts images, depth images and proprio from the "observation" dict
+        traj_len = tf.shape(traj["action"])[0]
+        old_obs = traj["observation"]
+        new_obs = {}
+        for new, old in image_obs_keys.items():
+            if old is None:
+                new_obs[f"image_{new}"] = tf.repeat("", traj_len)  # padding
+                new_obs[f"image_{new}_seg"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"image_{new}"] = old_obs[old]
+                new_obs[f"image_{new}_seg"] = old_obs[old + "_seg"]
+
+        for new, old in depth_obs_keys.items():
+            if old is None:
+                new_obs[f"depth_{new}"] = tf.repeat("", traj_len)  # padding
+            else:
+                new_obs[f"depth_{new}"] = old_obs[old]
+
+        if state_obs_keys:
+            new_obs["proprio"] = tf.concat(
+                [
+                    (
+                        tf.zeros((traj_len, 1), dtype=tf.float32)  # padding
+                        if key is None
+                        else tf.cast(old_obs[key], tf.float32)
+                    )
+                    for key in state_obs_keys
+                ],
+                axis=1,
+            )
+
+
+        if "image_reasoning" in old_obs:
+            new_obs["reasoning_on_image"] = tf.cast(old_obs["image_reasoning"], tf.string) 
+        if "wrist_image_reasoning" in old_obs: 
+            new_obs["reasoning_on_wrist_image"] = tf.cast(old_obs["wrist_image_reasoning"], tf.string)
+
+        # add timestep info
+        new_obs["timestep"] = tf.range(traj_len)
+
+        # extracts `language_key` into the "task" dict
+        task = {}
+        if language_key is not None:
+            if traj[language_key].dtype != tf.string:
+                raise ValueError(
+                    f"Language key {language_key} has dtype {traj[language_key].dtype}, " "but it must be tf.string."
+                )
+            task["language_instruction"] = traj.pop(language_key)
+        if "language_instruction_nouns" in traj:
+            task["language_instruction_nouns"] = traj.pop("language_instruction_nouns")
+
+        traj = {
+            "observation": new_obs,
+            "task": task,
+            "action": tf.cast(traj["action"], tf.float32),
+            "dataset_name": tf.repeat(name, traj_len),
+        }
+        
+        if absolute_action_mask is not None:
+            if len(absolute_action_mask) != traj["action"].shape[-1]:
+                raise ValueError(
+                    f"Length of absolute_action_mask ({len(absolute_action_mask)}) "
+                    f"does not match action dimension ({traj['action'].shape[-1]})."
+                )
+            traj["absolute_action_mask"] = tf.tile(
+                tf.convert_to_tensor(absolute_action_mask, dtype=tf.bool)[None],
+                [traj_len, 1],
+            )
+
+        return traj
+
+    if 'droid' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/droid/droid_dataset/1.0.0')
+    elif 'real_data' in name:
+        builder = tfds.builder_from_directory(builder_dir='/cm/shared/nhatcm3/workspace/Dataset/rlds_builder/Real_Data/Real_Data/real_data/1.0.0')
+    elif 'robomimic_lift' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/lift/lift/lift_ph_image/1.0.1')
+    elif 'robomimic_can' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/can/can/can_ph_image/1.0.1')
+    elif 'robomimic_square' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/square/square/square_ph_image/1.0.1')
+    elif 'robomimic_tool_hang' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/robomimic_ph/rlds_datasets/tool_hang/tool_hang/tool_hang_ph_image/1.0.1')
+    elif 'libero_goal' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_goal_oc/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__revised_v2/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/__small/libero_goal/1.0.0')
+        # builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_goal/1.0.0')
+    elif 'libero_object' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_object_oc/libero_object/1.0.0')
+    elif 'libero_spatial' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_spatial_oc/libero_spatial/1.0.0')
+    elif 'libero_10' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/liber_o10_oc/liber_o10/1.0.0')
+    elif 'libero_relation' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_relation/1.0.0')
+    elif 'libero_90' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_90/liber_o90/1.0.0')
+    elif 'libero_mem' in name:
+        builder = tfds.builder_from_directory(builder_dir=data_dir + '/libero/rlds_datasets/libero_mem/1.0.0')
+    else:
+        builder = tfds.builder(name, data_dir=data_dir)
+
+    # load or compute dataset statistics
+    if isinstance(dataset_statistics, str):
+        with tf.io.gfile.GFile(dataset_statistics, "r") as f:
+            dataset_statistics = json.load(f)
+    elif dataset_statistics is None:
+        full_dataset = dl.DLataset.from_rlds(
+            builder, split="all", shuffle=False, num_parallel_reads=num_parallel_reads
+        ).traj_map(restructure, num_parallel_calls)
+        # tries to load from cache, otherwise computes on the fly
+        dataset_statistics = get_dataset_statistics(
+            full_dataset,
+            hash_dependencies=(
+                str(builder.info),
+                str(state_obs_keys),
+                inspect.getsource(standardize_fn) if standardize_fn is not None else "",
+            ),
+            save_dir=builder.data_dir,
+        )
+    dataset_statistics = tree_map(np.array, dataset_statistics)
+
+    # skip normalization for certain action dimensions
+    if action_normalization_mask is not None:
+        if len(action_normalization_mask) != dataset_statistics["action"]["mean"].shape[-1]:
+            raise ValueError(
+                f"Length of skip_normalization_mask ({len(action_normalization_mask)}) "
+                f"does not match action dimension ({dataset_statistics['action']['mean'].shape[-1]})."
+            )
+        dataset_statistics["action"]["mask"] = np.array(action_normalization_mask)
+
+    # construct the dataset
+    if "val" not in builder.info.splits:
+        split = "train[:95%]" if train else "train[95%:]"
+    else:
+        split = "train" if train else "val"
+
+    dataset = dl.DLataset.from_rlds(builder, split=split, shuffle=shuffle, num_parallel_reads=num_parallel_reads)
+    # print(split, len(dataset))
+    dataset = dataset.traj_map(restructure, num_parallel_calls)
+    dataset = dataset.traj_map(
+        partial(
+            normalize_action_and_proprio,
+            metadata=dataset_statistics,
+            normalization_type=action_proprio_normalization_type,
+        ),
+        num_parallel_calls,
+    )
+    return dataset, dataset_statistics
+
+
+def apply_trajectory_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    goal_relabeling_strategy: Optional[str] = None,
+    goal_relabeling_kwargs: dict = {},
+    window_size: int = 1,
+    future_action_window_size: int = 0,
+    subsample_length: Optional[int] = None,
+    skip_unlabeled: bool = False,
+    max_action: Optional[float] = None,
+    max_proprio: Optional[float] = None,
+    task_augment_strategy: Optional[str] = None,
+    task_augment_kwargs: dict = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a trajectory level. Such transforms are usually some sort of "relabeling"
+    (e.g., filtering, chunking, adding goals, dropping keys).
+
+    Transforms in this function should have the following properties:
+        - They require access to an entire trajectory (i.e., they cannot be applied frame-wise).
+        - They are generally not CPU-intensive, mostly involving moving and copying data.
+        - They do not require decoded images.
+
+    Args:
+        dataset (dl.DLataset): The dataset to transform.
+        train (bool): Whether the dataset is for training (affects subsampling).
+        goal_relabeling_strategy (str, optional): The goal relabeling strategy to use, or None for
+            no goal relabeling. See `goal_relabeling.py`.
+        goal_relabeling_kwargs (dict, optional): Additional keyword arguments to pass to the goal relabeling function.
+        window_size (int, optional): The length of the snippets that trajectories are chunked into.
+        future_action_window_size (int, optional): The number of future actions beyond window_size to include
+            in the chunked actions.
+        subsample_length (int, optional): If provided, trajectories longer than this will be subsampled to
+            this length (after goal relabeling and chunking).
+        skip_unlabeled (bool, optional): Whether to skip trajectories with no language labels.
+        max_action: (float, optional): If provided, trajectories in which *any* action dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        max_proprio: (float, optional): If provided, trajectories in which *any* proprio dimension
+            of *any* transition has an absolute value larger than this will be skipped.
+        task_augment_strategy (str, optional): The task augmentation strategy to use, or None for no task
+            augmentation. See `task_augmentation.py`.
+        task_augment_kwargs (dict, optional): Additional keyword arguments to pass to the task augmentation
+            function.
+        num_parallel_calls (int, optional): number of parallel calls for map operations. Default to AUTOTUNE.
+    """
+    if skip_unlabeled:
+        if "language_instruction" not in dataset.element_spec["task"]:
+            raise ValueError("skip_unlabeled=True but dataset does not have language labels.")
+
+        dataset = dataset.filter(lambda x: tf.math.reduce_any(x["task"]["language_instruction"] != ""))
+
+    if max_action is not None:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["action"]) <= max_action))
+
+    if max_proprio is not None and "proprio" in dataset.element_spec["observation"]:
+        dataset = dataset.filter(lambda x: tf.math.reduce_all(tf.math.abs(x["observation"]["proprio"]) <= max_proprio))
+
+    # marks which entires of the observation and task dicts are padding
+    dataset = dataset.traj_map(traj_transforms.add_pad_mask_dict, num_parallel_calls)
+
+    # updates the "task" dict
+    if goal_relabeling_strategy is not None:
+        dataset = dataset.traj_map(
+            partial(getattr(goal_relabeling, goal_relabeling_strategy), **goal_relabeling_kwargs),
+            num_parallel_calls,
+        )
+
+    # must run task augmentation before chunking, in case it changes goal timesteps
+    if train and task_augment_strategy is not None:
+        # perform task augmentation (e.g., dropping keys)
+        dataset = dataset.traj_map(
+            partial(
+                getattr(task_augmentation, task_augment_strategy),
+                **task_augment_kwargs,
+            ),
+            num_parallel_calls,
+        )
+
+    # print('b4', dataset)
+    # chunks observations and actions, giving them a new axis at index 1 of size `window_size` and
+    # `window_size + future_action_window_size`, respectively
+    dataset = dataset.traj_map(
+        partial(
+            traj_transforms.chunk_act_obs,
+            window_size=window_size,
+            future_action_window_size=future_action_window_size,
+        ),
+        num_parallel_calls,
+    )
+    # print('af', dataset); 1/0
+
+    if train and subsample_length is not None:
+        dataset = dataset.traj_map(
+            partial(traj_transforms.subsample, subsample_length=subsample_length),
+            num_parallel_calls,
+        )
+
+    return dataset
+
+
+def apply_per_dataset_frame_transforms(
+    dataset: dl.DLataset,
+    chunk_filter_fn: Optional[Callable] = None,
+):
+    """
+    Optionally applied *per-dataset* transforms that happen at a frame level.
+
+    Args:
+        chunk_filter_fn (callable, optional): Filter function for chunks.
+    """
+    if chunk_filter_fn:
+        dataset = dataset.filter(chunk_filter_fn)
+    return dataset
+
+
+def apply_frame_transforms(
+    dataset: dl.DLataset,
+    *,
+    train: bool,
+    image_augment_kwargs: Union[Dict, Dict[str, Dict]] = {},
+    resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    depth_resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]] = {},
+    num_parallel_calls: int = tf.data.AUTOTUNE,
+) -> dl.DLataset:
+    """
+    Applies common transforms that happen at a frame level. These transforms are usually more CPU-intensive, (e.g.,
+    decoding or resizing images).
+
+    Args:
+        train (bool): Whether the dataset is for training (affects image augmentation).
+        dataset (dl.DLataset): The dataset to transform.
+        image_augment_kwargs (dict|Mapping[str, dict]): Keyword arguments to pass to the image augmentation
+            function. See `dlimp.transforms.augment_image` for documentation of these kwargs. If a dict of
+            dicts is provided, then key "k" will be used for "image_{k}" (names determined by `image_obs_keys`
+            in `make_dataset_from_rlds`). Augmentation will be skipped for missing keys (so pass an empty dict
+            to skip augmentation for all images).
+        resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): If provided, images will be resized to
+            this size. If a dict of tuples is provided, then key "k" will be used for "image_{k}" (names
+            determined by `image_obs_keys` in `make_dataset_from_rlds`). Resizing will be skipped for missing
+            keys (so pass an empty dict to skip resizing for all images).
+        depth_resize_size (Tuple[int, int]|Mapping[str, Tuple[int, int]]): Same as resize_size, but for depth
+            images.
+        num_parallel_calls (int): number of parallel calls for frame_map operations. Default to AUTOTUNE.
+    """
+
+    # Convenience wrapper that takes a function that operates on a non-chunked "observation" dict and applies
+    # it to the chunked "observation" dict as well as the non-chunked "task" dict
+    def apply_obs_transform(fn: Callable[[Dict], Dict], frame: Dict) -> Dict:
+        frame["task"] = fn(frame["task"])
+        frame["observation"] = dl.vmap(fn)(frame["observation"])
+        return frame
+
+    # Decode + resize images (and depth images)
+    dataset = dataset.frame_map(
+        partial(
+            apply_obs_transform,
+            partial(obs_transforms.decode_and_resize, resize_size=resize_size, depth_resize_size=depth_resize_size),
+        ),
+        num_parallel_calls,
+    )
+
+    if train:
+        # Augment all images with the same seed, skipping padding images
+        def aug(frame: dict):
+            seed = tf.random.uniform([2], maxval=tf.dtypes.int32.max, dtype=tf.int32)
+            aug_fn = partial(obs_transforms.augment, seed=seed, augment_kwargs=image_augment_kwargs)
+            return apply_obs_transform(aug_fn, frame)
+
+        dataset = dataset.frame_map(aug, num_parallel_calls)
+
+    return dataset
+
+
+def make_single_dataset_v3(
+    dataset_kwargs: dict,
+    *,
+    train: bool,
+    traj_transform_kwargs: dict = {},
+    frame_transform_kwargs: dict = {},
+) -> dl.DLataset:
+    """Creates a single dataset from kwargs. Returns a dataset of trajectories.
+
+    Args:
+        dataset_kwargs: kwargs passed to `make_dataset_from_rlds` that are dataset-specific.
+        train: whether this is a training or validation dataset.
+        traj_transform_kwargs: kwargs passed to 'apply_trajectory_transforms'.
+        frame_transform_kwargs: kwargs passed to 'get_frame_transforms'.
+    """
+    dataset, dataset_statistics = make_dataset_from_rlds(
+        **dataset_kwargs,
+        train=train,
+    )
+    dataset = apply_trajectory_transforms(dataset, **traj_transform_kwargs, train=train)
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+    # this seems to reduce memory usage without affecting speed
+    dataset = dataset.with_ram_budget(1)
+
+    # save for later
+    return dataset, dataset_statistics["num_trajectories"], dataset_statistics
+
+
+# === Core Initializer ===
+def make_interleaved_dataset_v3(
+    dataset_kwargs_list: List[Dict],
+    sample_weights: Optional[List[float]] = None,
+    *,
+    train: bool,
+    shuffle_buffer_size: int,
+    traj_transform_kwargs: Optional[Dict] = None,
+    frame_transform_kwargs: Optional[Dict] = None,
+    batch_size: Optional[int] = None,
+    balance_weights: bool = False,
+    traj_transform_threads: Optional[int] = None,
+    traj_read_threads: Optional[int] = None,
+) -> dl.DLataset:
+    """
+    Creates an interleaved dataset from list of dataset configs (kwargs). Returns a dataset of batched frames.
+
+    Args:
+        dataset_kwargs_list: list of kwargs, each element of which is passed to `make_dataset_from_rlds`.
+            "num_parallel_calls" and "num_parallel_reads" are overridden using `traj_transform_threads` and
+            `traj_read_threads`, respectively.
+        sample_weights: sampling weights for each dataset in list. If None, defaults to uniform.
+        train: whether this is a training or validation dataset.
+        shuffle_buffer_size: size of the dataset shuffle buffer (in number of frames).
+        traj_transform_kwargs: kwargs passed to `apply_trajectory_transforms`. "num_parallel_calls" is
+            overridden using `traj_transform_threads`.
+        frame_transform_kwargs: kwargs passed to `apply_frame_transforms`.
+        batch_size: batch size, if not provided output is not batched.
+        balance_weights: if True, the sample weights are multiplied by the number of frames in each dataset.
+            This makes it so that, if all the sample weights are equal, one full iteration through the interleaved
+            dataset will correspond to one full iteration through each individual dataset (only in expectation,
+            since in practice the sampling is random).
+        traj_transform_threads: total number of parallel calls for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+        traj_read_threads: total number of parallel read workers for trajectory transforms, distributed across
+            datasets according to their sampling weights. If None, defaults to AUTOTUNE for every dataset.
+    """
+    # Default to uniform sampling (if `sample_weights` is not specified)
+    if not sample_weights:
+        sample_weights = [1.0] * len(dataset_kwargs_list)
+
+    if len(sample_weights) != len(dataset_kwargs_list):
+        raise ValueError(f"sample_weights must be None or have length {len(dataset_kwargs_list)}.")
+
+    # Check valid `traj_transform_kwargs` and `frame_transform_kwargs`
+    if (traj_transform_kwargs is None) or (frame_transform_kwargs is None):
+        raise ValueError("Missing `traj_transform_kwargs` and `frame_transform_kwargs`!")
+
+    # Get Dataset Sizes
+    dataset_sizes, all_dataset_statistics = [], {}
+    for dataset_kwargs in dataset_kwargs_list:
+        data_kwargs = copy.deepcopy(dataset_kwargs)
+        if "dataset_frame_transform_kwargs" in data_kwargs:
+            data_kwargs.pop("dataset_frame_transform_kwargs")
+        _, dataset_statistics = make_dataset_from_rlds(**data_kwargs, train=train)
+        # print(train)
+        # print(dataset_kwargs)
+        # print(dataset_statistics)
+        dataset_sizes.append(dataset_statistics["num_transitions"])
+        all_dataset_statistics[dataset_kwargs["name"]] = dataset_statistics
+
+    # Get the indices of the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    primary_dataset_indices = np.array([idx for idx in range(len(sample_weights)) if sample_weights[idx] == 1.0])
+
+    # Balance and Normalize Weights
+    if balance_weights:
+        sample_weights = np.array(sample_weights) * np.array(dataset_sizes)
+    sample_weights = np.array(sample_weights) / np.sum(sample_weights)
+    pprint_data_mixture(dataset_kwargs_list, sample_weights)
+
+    # Effective Dataset Length = Number of samples until each dataset has completed at least one epoch
+    #   =>> Note :: Only counting the "primary" datasets (i.e., datasets with sample_weight == 1.0)
+    dataset_len = int((np.array(dataset_sizes) / sample_weights)[primary_dataset_indices].max())
+
+    # Allocate Threads based on Weights
+    threads_per_dataset = allocate_threads(traj_transform_threads, sample_weights)
+    reads_per_dataset = allocate_threads(traj_read_threads, sample_weights)
+
+    overwatch.info("Threads per Dataset: %s", threads_per_dataset)
+    overwatch.info("Reads per Dataset: %s", reads_per_dataset)
+
+    # Construct Datasets
+    overwatch.info("Constructing datasets...")
+    datasets = []
+    for dataset_kwargs, threads, reads in zip(
+        dataset_kwargs_list,
+        threads_per_dataset,
+        reads_per_dataset,
+    ):
+        dataset_frame_transform_kwargs = (
+            dataset_kwargs.pop("dataset_frame_transform_kwargs")
+            if "dataset_frame_transform_kwargs" in dataset_kwargs
+            else {}
+        )
+        dataset, _ = make_dataset_from_rlds(
+            **dataset_kwargs,
+            train=train,
+            num_parallel_calls=threads,
+            num_parallel_reads=reads,
+            dataset_statistics=all_dataset_statistics[dataset_kwargs["name"]],
+        )
+
+        dataset = apply_trajectory_transforms(
+            dataset.repeat(),
+            **traj_transform_kwargs,
+            num_parallel_calls=threads,
+            train=train,
+        ).flatten(num_parallel_calls=threads)
+
+        dataset = apply_per_dataset_frame_transforms(dataset, **dataset_frame_transform_kwargs)
+        datasets.append(dataset)
+
+    # Interleave at the Frame Level
+    dataset: dl.DLataset = dl.DLataset.sample_from_datasets(datasets, sample_weights)
+    
+    # Validation =>> fix a single shuffle buffer of data and cache it in RAM; prevents gradual memory increase!
+    if not train:
+        dataset = dataset.take(shuffle_buffer_size).cache()
+
+    # return dataset, dataset_len, all_dataset_statistics[dataset_kwargs["name"]]
+    # 1/0
+
+    # Shuffle the Dataset
+    #   =>> IMPORTANT :: Shuffle AFTER .cache(), or else memory will still leak!
+    dataset = dataset.shuffle(shuffle_buffer_size)
+
+    # # Apply Frame Transforms
+    overwatch.info("Applying frame transforms on dataset...")
+    dataset = apply_frame_transforms(dataset, **frame_transform_kwargs, train=train)
+
+    # [Contract] When training VLA Policies, we let the Collator handle Batching!
+    if batch_size is not None:
+        dataset = dataset.batch(batch_size)
+
+    # Note =>> Seems to reduce memory usage without affecting speed?
+    dataset = dataset.with_ram_budget(1)
+
+    # Save for Later
+    dataset.sample_weights = sample_weights
+
+    return dataset, dataset_len, all_dataset_statistics
diff --git a/prismatic/vla/datasets/rlds/obs_transforms.py b/prismatic/vla/datasets/rlds/obs_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8aba472e62c73995ba140f6827125ac238dd6b9
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/obs_transforms.py
@@ -0,0 +1,102 @@
+"""
+obs_transforms.py
+
+Contains observation-level transforms used in the orca data pipeline.
+
+These transforms operate on the "observation" dictionary, and are applied at a per-frame level.
+"""
+
+from typing import Dict, Tuple, Union
+
+import dlimp as dl
+import tensorflow as tf
+from absl import logging
+
+
+# ruff: noqa: B023
+def augment(obs: Dict, seed: tf.Tensor, augment_kwargs: Union[Dict, Dict[str, Dict]]) -> Dict:
+    """Augments images, skipping padding images."""
+    image_names = {key[6:] for key in obs if key.startswith("image_") and 'seg' not in key}
+
+    # "augment_order" is required in augment_kwargs, so if it's there, we can assume that the user has passed
+    # in a single augmentation dict (otherwise, we assume that the user has passed in a mapping from image
+    # name to augmentation dict)
+    if "augment_order" in augment_kwargs:
+        augment_kwargs = {name: augment_kwargs for name in image_names}
+
+    for i, name in enumerate(image_names):
+        if name not in augment_kwargs:
+            continue
+        kwargs = augment_kwargs[name]
+        logging.debug(f"Augmenting image_{name} with kwargs {kwargs}")
+        obs[f"image_{name}"] = tf.cond(
+            obs["pad_mask_dict"][f"image_{name}"],
+            lambda: dl.transforms.augment_image(
+                obs[f"image_{name}"],
+                **kwargs,
+                seed=seed + i,  # augment each image differently
+            ),
+            lambda: obs[f"image_{name}"],  # skip padding images
+        )
+
+    return obs
+
+
+def decode_and_resize(
+    obs: Dict,
+    resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]],
+    depth_resize_size: Union[Tuple[int, int], Dict[str, Tuple[int, int]]],
+) -> Dict:
+    """Decodes images and depth images, and then optionally resizes them."""
+    image_names = {key[6:] for key in obs if key.startswith("image_")}
+    depth_names = {key[6:] for key in obs if key.startswith("depth_")}
+
+    if isinstance(resize_size, tuple):
+        resize_size = {name: resize_size for name in image_names}
+    if isinstance(depth_resize_size, tuple):
+        depth_resize_size = {name: depth_resize_size for name in depth_names}
+
+    for name in image_names:
+        if name not in resize_size:
+            logging.warning(
+                f"No resize_size was provided for image_{name}. This will result in 1x1 "
+                "padding images, which may cause errors if you mix padding and non-padding images."
+            )
+        image = obs[f"image_{name}"]
+        if image.dtype == tf.string:
+            if tf.strings.length(image) == 0:
+                # this is a padding image
+                image = tf.zeros((*resize_size.get(name, (1, 1)), 3), dtype=tf.uint8)
+            else:
+                image = tf.io.decode_image(image, expand_animations=False, dtype=tf.uint8)
+        elif image.dtype != tf.uint8:
+            raise ValueError(f"Unsupported image dtype: found image_{name} with dtype {image.dtype}")
+        if name in resize_size:
+            if '_seg' in name:
+                image = tf.image.resize(image, resize_size[name], method="nearest")
+            else:
+                image = dl.transforms.resize_image(image, size=resize_size[name])
+        obs[f"image_{name}"] = image
+
+    for name in depth_names:
+        if name not in depth_resize_size:
+            logging.warning(
+                f"No depth_resize_size was provided for depth_{name}. This will result in 1x1 "
+                "padding depth images, which may cause errors if you mix padding and non-padding images."
+            )
+        depth = obs[f"depth_{name}"]
+
+        if depth.dtype == tf.string:
+            if tf.strings.length(depth) == 0:
+                depth = tf.zeros((*depth_resize_size.get(name, (1, 1)), 1), dtype=tf.uint8)
+            else:
+                depth = tf.io.decode_image(depth, expand_animations=False, dtype=tf.uint8)
+        elif depth.dtype != tf.uint8:
+            raise ValueError(f"Unsupported depth dtype: found depth_{name} with dtype {depth.dtype}")
+
+        if name in depth_resize_size:
+            depth = dl.transforms.resize_depth_image(depth, size=depth_resize_size[name])
+
+        obs[f"depth_{name}"] = depth
+
+    return obs
diff --git a/prismatic/vla/datasets/rlds/oxe/__init__.py b/prismatic/vla/datasets/rlds/oxe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1502ecb73d70c57c184e0c90e568b02a0fbd11de
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/__init__.py
@@ -0,0 +1,2 @@
+from .materialize import get_oxe_dataset_kwargs_and_weights
+from .mixtures import OXE_NAMED_MIXTURES
diff --git a/prismatic/vla/datasets/rlds/oxe/configs.py b/prismatic/vla/datasets/rlds/oxe/configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ebbafbdff43f9f80b558a9897c82dcbf2f6b7f0
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/configs.py
@@ -0,0 +1,749 @@
+"""
+configs.py
+
+Defines per-dataset configuration (kwargs) for each dataset in Open-X Embodiment.
+
+Configuration adopts the following structure:
+    image_obs_keys:
+        primary: primary external RGB
+        secondary: secondary external RGB
+        wrist: wrist RGB
+
+    depth_obs_keys:
+        primary: primary external depth
+        secondary: secondary external depth
+        wrist: wrist depth
+
+    # Always 8-dim =>> changes based on `StateEncoding`
+    state_obs_keys:
+        StateEncoding.POS_EULER:    EEF XYZ (3) + Roll-Pitch-Yaw (3) + <PAD> (1) + Gripper Open/Close (1)
+        StateEncoding.POS_QUAT:     EEF XYZ (3) + Quaternion (4) + Gripper Open/Close (1)
+        StateEncoding.JOINT:        Joint Angles (7, <PAD> if fewer) + Gripper Open/Close (1)
+
+    state_encoding: Type of `StateEncoding`
+    action_encoding: Type of action encoding (e.g., EEF Position vs. Joint Position)
+"""
+
+from enum import IntEnum
+
+from prismatic.vla.datasets.rlds.oxe.utils.droid_utils import zero_action_filter
+
+
+# Defines Proprioceptive State Encoding Schemes
+class StateEncoding(IntEnum):
+    # fmt: off
+    NONE = -1               # No Proprioceptive State
+    POS_EULER = 1           # EEF XYZ (3) + Roll-Pitch-Yaw (3) + <PAD> (1) + Gripper Open/Close (1)
+    POS_QUAT = 2            # EEF XYZ (3) + Quaternion (4) + Gripper Open/Close (1)
+    JOINT = 3               # Joint Angles (7, <PAD> if fewer) + Gripper Open/Close (1)
+    JOINT_BIMANUAL = 4      # Joint Angles (2 x [ Joint Angles (6) + Gripper Open/Close (1) ])
+    # fmt: on
+
+
+# Defines Action Encoding Schemes
+class ActionEncoding(IntEnum):
+    # fmt: off
+    EEF_POS = 1             # EEF Delta XYZ (3) + Roll-Pitch-Yaw (3) + Gripper Open/Close (1)
+    JOINT_POS = 2           # Joint Delta Position (7) + Gripper Open/Close (1)
+    JOINT_POS_BIMANUAL = 3  # Joint Delta Position (2 x [ Joint Delta Position (6) + Gripper Open/Close (1) ])
+    EEF_R6 = 4              # EEF Delta XYZ (3) + R6 (6) + Gripper Open/Close (1)
+    # fmt: on
+
+
+# === Individual Dataset Configs ===
+OXE_DATASET_CONFIGS = {
+    "fractal20220817_data": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["base_pose_tool_reached", "gripper_closed"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "kuka": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [
+            "clip_function_input/base_pose_tool_reached",
+            "gripper_closed",
+        ],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "bridge_oxe": {  # Version of Bridge V2 in Open X-Embodiment mixture
+        "image_obs_keys": {"primary": "image", "secondary": "image_1", "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "bridge_orig": {  # Original version of Bridge V2 from project website
+        "image_obs_keys": {"primary": "image_0", "secondary": "image_1", "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "bridge_dataset": {  # Original version of Bridge V2 from project website
+        "image_obs_keys": {"primary": "image_0", "secondary": "image_1", "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "taco_play": {
+        "image_obs_keys": {
+            "primary": "rgb_static",
+            "secondary": None,
+            "wrist": "rgb_gripper",
+        },
+        "depth_obs_keys": {
+            "primary": "depth_static",
+            "secondary": None,
+            "wrist": "depth_gripper",
+        },
+        "state_obs_keys": ["state_eef", None, "state_gripper"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "jaco_play": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "image_wrist",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state_eef", None, "state_gripper"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_cable_routing": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": "top_image",
+            "wrist": "wrist45_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["robot_state", None],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "roboturk": {
+        "image_obs_keys": {"primary": "front_rgb", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [None, None, None, None, None, None, None, None],
+        "state_encoding": StateEncoding.NONE,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "nyu_door_opening_surprising_effectiveness": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [None, None, None, None, None, None, None, None],
+        "state_encoding": StateEncoding.NONE,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "viola": {
+        "image_obs_keys": {
+            "primary": "agentview_rgb",
+            "secondary": None,
+            "wrist": "eye_in_hand_rgb",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["joint_states", "gripper_states"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_autolab_ur5": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "hand_image",
+        },
+        "depth_obs_keys": {"primary": "depth", "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "toto": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "language_table": {
+        "image_obs_keys": {"primary": "rgb", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["effector_translation", None, None, None, None, None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "columbia_cairlab_pusht_real": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["robot_state", None, None, None, None, None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["ee_position", "ee_orientation", None],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "nyu_rot_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "stanford_hydra_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "austin_buds_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "nyu_franka_play_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": "image_additional_view",
+            "wrist": None,
+        },
+        "depth_obs_keys": {
+            "primary": "depth",
+            "secondary": "depth_additional_view",
+            "wrist": None,
+        },
+        "state_obs_keys": ["eef_state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "maniskill_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {
+            "primary": "depth",
+            "secondary": None,
+            "wrist": "wrist_depth",
+        },
+        "state_obs_keys": ["tcp_pose", "gripper_state"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "furniture_bench_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "cmu_franka_exploration_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "highres_image",
+            "secondary": None,
+            "wrist": None,
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [None, None, None, None, None, None, None, None],
+        "state_encoding": StateEncoding.NONE,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "ucsd_kitchen_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["joint_state", None],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "ucsd_pick_and_place_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "austin_sailor_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "austin_sirius_dataset_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "bc_z": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [
+            "present/xyz",
+            "present/axis_angle",
+            None,
+            "present/sensed_close",
+        ],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "utokyo_pr2_opening_fridge_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "utokyo_xarm_pick_and_place_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": "image2",
+            "wrist": "hand_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["end_effector_pose", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "utokyo_xarm_bimanual_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["pose_r", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "robo_net": {
+        "image_obs_keys": {"primary": "image", "secondary": "image1", "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_mvp_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["pose", "gripper"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.JOINT_POS,
+    },
+    "berkeley_rpt_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["joint_pos", "gripper"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.JOINT_POS,
+    },
+    "kaist_nonprehensile_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "stanford_mask_vit_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tokyo_u_lsmo_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "dlr_sara_pour_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "dlr_sara_grid_clamp_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "dlr_edan_shared_control_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "asu_table_top_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "stanford_robocook_converted_externally_to_rlds": {
+        "image_obs_keys": {"primary": "image_1", "secondary": "image_2", "wrist": None},
+        "depth_obs_keys": {"primary": "depth_1", "secondary": "depth_2", "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "imperialcollege_sawyer_wrist_cam": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": [None, None, None, None, None, None, None, "state"],
+        "state_encoding": StateEncoding.NONE,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "iamlab_cmu_pickup_insert_converted_externally_to_rlds": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["joint_state", "gripper_state"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "uiuc_d3field": {
+        "image_obs_keys": {"primary": "image_1", "secondary": "image_2", "wrist": None},
+        "depth_obs_keys": {"primary": "depth_1", "secondary": "depth_2", "wrist": None},
+        "state_obs_keys": [None, None, None, None, None, None, None, None],
+        "state_encoding": StateEncoding.NONE,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "utaustin_mutex": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_fanuc_manipulation": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "wrist_image",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["joint_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "cmu_playing_with_food": {
+        "image_obs_keys": {
+            "primary": "image",
+            "secondary": None,
+            "wrist": "finger_vision_1",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "cmu_play_fusion": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "cmu_stretch": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["eef_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_gnm_recon": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_gnm_cory_hall": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "berkeley_gnm_sac_son": {
+        "image_obs_keys": {"primary": None, "secondary": None, "wrist": "image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state", None, None],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "droid": {
+        "image_obs_keys": {
+            "primary": "exterior_image_1_left",
+            "secondary": "exterior_image_2_left",
+            "wrist": "wrist_image_left",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_QUAT,
+        "action_encoding": ActionEncoding.EEF_POS,
+        "aux_kwargs": {
+            "dataset_frame_transform_kwargs": {
+                "chunk_filter_fn": zero_action_filter,
+            },
+        },
+    },
+    "fmb_dataset": {
+        "image_obs_keys": {
+            "primary": "image_side_1",
+            "secondary": "image_side_2",
+            "wrist": "image_wrist_1",
+        },
+        "depth_obs_keys": {
+            "primary": "image_side_1_depth",
+            "secondary": "image_side_2_depth",
+            "wrist": "image_wrist_1_depth",
+        },
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "dobbe": {
+        "image_obs_keys": {"primary": "wrist_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "roboset": {
+        "image_obs_keys": {
+            "primary": "image_left",
+            "secondary": "image_right",
+            "wrist": "image_wrist",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.JOINT,
+        "action_encoding": ActionEncoding.JOINT_POS,
+    },
+    "rh20t": {
+        "image_obs_keys": {
+            "primary": "image_front",
+            "secondary": "image_side_right",
+            "wrist": "image_wrist",
+        },
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    ### T-DROID datasets
+    "tdroid_carrot_in_bowl": {  # "put carrot in bowl" task, 50 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tdroid_pour_corn_in_pot": {  # "pour corn from red bowl into steel pot" task, 50 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tdroid_flip_pot_upright": {  # "flip pot upright" task, 10 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tdroid_move_object_onto_plate": {  # "move <object> onto plate" task, 150 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tdroid_knock_object_over": {  # "knock <object> over" task, 70 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "tdroid_cover_object_with_towel": {  # "cover <object> with towel" task, 45 demos @ 5 Hz control
+        "image_obs_keys": {"primary": "static_image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": "static_depth_image", "secondary": None, "wrist": None},
+        "state_obs_keys": ["EEF_state", None, "gripper_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    ### DROID Finetuning datasets
+    "droid_wipe": {
+        "image_obs_keys": {"primary": "exterior_image_2_left", "secondary": None, "wrist": "wrist_image_left"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    ### robomimic Finetuning datasets
+    "robomimic_lift": {
+        "image_obs_keys": {"primary": "agentview_image", "secondary": None, "wrist": "robot0_eye_in_hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "robomimic_can": {
+        "image_obs_keys": {"primary": "agentview_image", "secondary": None, "wrist": "robot0_eye_in_hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "robomimic_square": {
+        "image_obs_keys": {"primary": "agentview_image", "secondary": None, "wrist": "robot0_eye_in_hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "robomimic_tool_hang": {
+        "image_obs_keys": {"primary": "sideview_image", "secondary": None, "wrist": "robot0_eye_in_hand_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["proprio"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+
+    # LIBERO Finetuning datasets
+    "libero_spatial": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_object": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_goal": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": "image_depth", "secondary": None, "wrist": "wrist_image_depth"},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_goal_oc": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": "image_depth", "secondary": None, "wrist": "wrist_image_depth"},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_90": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_10": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_relation": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+    "libero_mem": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": "wrist_image"},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["robot_state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+
+    # real data
+    "real_data": {
+        "image_obs_keys": {"primary": "image", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+
+    "real_action": {
+        "image_obs_keys": {"primary": "wrist_image_left", "secondary": None, "wrist": None},
+        "depth_obs_keys": {"primary": None, "secondary": None, "wrist": None},
+        "state_obs_keys": ["state"],
+        "state_encoding": StateEncoding.POS_EULER,
+        "action_encoding": ActionEncoding.EEF_POS,
+    },
+
+}
diff --git a/prismatic/vla/datasets/rlds/oxe/materialize.py b/prismatic/vla/datasets/rlds/oxe/materialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..56d0d38feca840ca2005132a1eccb00420ffbd06
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/materialize.py
@@ -0,0 +1,131 @@
+"""
+materialize.py
+
+Factory class for initializing Open-X Embodiment dataset kwargs and other parameters; provides and exports functions for
+clear control flow.
+"""
+
+from copy import deepcopy
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+from prismatic.overwatch import initialize_overwatch
+from prismatic.vla.datasets.rlds.oxe.configs import OXE_DATASET_CONFIGS, ActionEncoding
+from prismatic.vla.datasets.rlds.oxe.transforms import OXE_STANDARDIZATION_TRANSFORMS
+from prismatic.vla.datasets.rlds.utils.data_utils import NormalizationType
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+def make_oxe_dataset_kwargs(
+    dataset_name: str,
+    data_root_dir: Path,
+    load_camera_views: Tuple[str] = ("primary",),
+    load_depth: bool = False,
+    load_proprio: bool = True,
+    load_language: bool = True,
+    action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL,
+) -> Dict[str, Any]:
+    """Generates config (kwargs) for given dataset from Open-X Embodiment."""
+    dataset_kwargs = deepcopy(OXE_DATASET_CONFIGS[dataset_name])
+    if dataset_kwargs["action_encoding"] not in [ActionEncoding.EEF_POS, ActionEncoding.EEF_R6]:
+        raise ValueError(f"Cannot load `{dataset_name}`; only EEF_POS & EEF_R6 actions supported!")
+
+    # [Contract] For EEF_POS & EEF_R6 actions, only the last action dimension (gripper) is absolute!
+    # Normalize all action dimensions *except* the gripper
+    if dataset_kwargs["action_encoding"] is ActionEncoding.EEF_POS:
+        dataset_kwargs["absolute_action_mask"] = [False] * 6 + [True]
+        dataset_kwargs["action_normalization_mask"] = [True] * 6 + [False]
+    elif dataset_kwargs["action_encoding"] is ActionEncoding.EEF_R6:
+        dataset_kwargs["absolute_action_mask"] = [False] * 9 + [True]
+        dataset_kwargs["action_normalization_mask"] = [True] * 9 + [False]
+    dataset_kwargs["action_proprio_normalization_type"] = action_proprio_normalization_type
+
+    # Adjust Loaded Camera Views
+    if len(missing_keys := (set(load_camera_views) - set(dataset_kwargs["image_obs_keys"]))) > 0:
+        raise ValueError(f"Cannot load `{dataset_name}`; missing camera views `{missing_keys}`")
+
+    # Filter
+    dataset_kwargs["image_obs_keys"] = {
+        k: v for k, v in dataset_kwargs["image_obs_keys"].items() if k in load_camera_views
+    }
+    dataset_kwargs["depth_obs_keys"] = {
+        k: v for k, v in dataset_kwargs["depth_obs_keys"].items() if k in load_camera_views
+    }
+
+    # Eliminate Unnecessary Keys
+    dataset_kwargs.pop("state_encoding")
+    dataset_kwargs.pop("action_encoding")
+    if not load_depth:
+        dataset_kwargs.pop("depth_obs_keys")
+    if not load_proprio:
+        dataset_kwargs.pop("state_obs_keys")
+
+    # Load Language
+    if load_language:
+        dataset_kwargs["language_key"] = "language_instruction"
+
+    # Specify Standardization Transform
+    dataset_kwargs["standardize_fn"] = OXE_STANDARDIZATION_TRANSFORMS[dataset_name]
+
+    # Add any aux arguments
+    if "aux_kwargs" in dataset_kwargs:
+        dataset_kwargs.update(dataset_kwargs.pop("aux_kwargs"))
+
+    return {"name": dataset_name, "data_dir": str(data_root_dir), **dataset_kwargs}
+
+
+def get_oxe_dataset_kwargs_and_weights(
+    data_root_dir: Path,
+    mixture_spec: List[Tuple[str, float]],
+    load_camera_views: Tuple[str] = ("primary",),
+    load_depth: bool = False,
+    load_proprio: bool = True,
+    load_language: bool = True,
+    action_proprio_normalization_type: NormalizationType = NormalizationType.NORMAL,
+) -> Tuple[Dict[str, Any], List[float]]:
+    """
+    Generates dataset kwargs for a given dataset mix from the Open X-Embodiment dataset. The returned kwargs
+    (per-dataset configs) and weights can be passed directly to `make_interleaved_dataset`.
+
+    :param data_root_dir: Base directory containing RLDS/TFDS-formatted datasets (from Open-X)
+    :param mixture_spec: List of (dataset_name, sampling_weight) from `oxe.mixtures.OXE_NAMED_MIXTURES`
+    :param load_camera_views: Camera views to load; see `oxe.dataset_configs.py` for available views.
+    :param load_depth: Load depth information in addition to camera RGB.
+    :param load_proprio: Load proprioceptive state.
+    :param load_language: Load language instructions.
+    :param action_proprio_normalization_type: Normalization scheme to use for proprioceptive actions.
+
+    return: Tuple of (per_dataset_kwargs, sampling_weights)
+    """
+    included_datasets, filtered_mixture_spec = set(), []
+    for d_name, d_weight in mixture_spec:
+        if d_name in included_datasets:
+            overwatch.warning(f"Skipping Duplicate Dataset: `{(d_name, d_weight)}`")
+            continue
+
+        included_datasets.add(d_name)
+        filtered_mixture_spec.append((d_name, d_weight))
+
+    # Assemble Dataset Config (kwargs) and Weights
+    per_dataset_kwargs, sampling_weights = [], []
+    for d_name, d_weight in filtered_mixture_spec:
+        try:
+            per_dataset_kwargs.append(
+                make_oxe_dataset_kwargs(
+                    d_name,
+                    data_root_dir,
+                    load_camera_views,
+                    load_depth,
+                    load_proprio,
+                    load_language,
+                    action_proprio_normalization_type,
+                )
+            )
+            sampling_weights.append(d_weight)
+
+        except ValueError as e:
+            overwatch.warning(f"Skipping `{d_name}` due to Error: {e}")
+
+    return per_dataset_kwargs, sampling_weights
diff --git a/prismatic/vla/datasets/rlds/oxe/mixtures.py b/prismatic/vla/datasets/rlds/oxe/mixtures.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d9f4f36ad9ab4ff6d7ee5252cb81c6f457f3b1f
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/mixtures.py
@@ -0,0 +1,201 @@
+"""
+mixtures.py
+
+Defines a registry of dataset mixtures and weights for the Open-X Embodiment Datasets. Each dataset is associated with
+a float "sampling weight"
+"""
+
+from typing import Dict, List, Tuple
+
+# fmt: off
+OXE_NAMED_MIXTURES: Dict[str, List[Tuple[str, float]]] = {
+    # === DROID Dataset ===
+    "droid": [
+        ("droid", 1.0),
+    ],
+
+    # === Bridge V2 Dataset ===
+    "bridge": [
+        # ("bridge_oxe", 1.0),                                    # Version of Bridge V2 in Open-X GCP Bucket
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+    ],
+
+
+    # === [Moderate-Scale] Bridge++ Mixtures ===
+    "bridge_rt_1": [
+        # ("bridge_oxe", 1.0)                                   # Version of Bridge V2 in Open-X GCP Bucket
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+
+        ("fractal20220817_data", 1.0),                          # Google RT-1 Robot Data (Large-Scale)
+    ],
+
+    # === RT-X Mixtures ===
+    "rtx": [
+        ("fractal20220817_data", 0.54087122203),                # Google RT-1 Robot Data (Large-Scale)
+        ("kuka", 0.8341046294),
+        # ("bridge_oxe", 1.0)                                   # Version of Bridge V2 in Open-X GCP Bucket
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+        ("taco_play", 2.0),
+        ("jaco_play", 2.0),
+        ("berkeley_cable_routing", 3.0),
+        ("roboturk", 1.0),
+        # ("nyu_door_opening_surprising_effectiveness", 5.0),   # Note --> only contains wrist camera images (skip?)
+        ("viola", 2.0),
+        ("berkeley_autolab_ur5", 1.0),
+        ("toto", 1.0),
+    ],
+
+    "rtx_franka": [
+        ("fractal20220817_data", 0.54087122203),                # Google RT-1 Robot Data (Large-Scale)
+        ("kuka", 0.8341046294),
+        # ("bridge_oxe", 1.0)                                   # Version of Bridge V2 in Open-X GCP Bucket
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+        ("taco_play", 2.0),
+        ("jaco_play", 2.0),
+        ("berkeley_cable_routing", 3.0),
+        ("roboturk", 1.0),
+        # ("nyu_door_opening_surprising_effectiveness", 5.0),   # Note --> only contains wrist camera images (skip?)
+        ("viola", 2.0),
+        ("berkeley_autolab_ur5", 1.0),
+        ("toto", 1.0),
+
+        ("taco_play", 1.0),
+        ("berkeley_cable_routing", 1.0),
+        ("viola", 1.0),
+        ("toto", 1.0),
+        ("stanford_hydra_dataset_converted_externally_to_rlds", 1.0),
+        ("austin_buds_dataset_converted_externally_to_rlds", 3.0),
+        ("nyu_franka_play_dataset_converted_externally_to_rlds", 3.0),
+        ("maniskill_dataset_converted_externally_to_rlds", 0.1),
+        ("furniture_bench_dataset_converted_externally_to_rlds", 0.1),
+        ("cmu_franka_exploration_dataset_converted_externally_to_rlds", 5.0),
+        ("austin_sailor_dataset_converted_externally_to_rlds", 1.0),
+        ("austin_sirius_dataset_converted_externally_to_rlds", 1.0),
+        ("berkeley_rpt_converted_externally_to_rlds", 1.0),
+        ("kaist_nonprehensile_converted_externally_to_rlds", 3.0),
+        ("stanford_robocook_converted_externally_to_rlds", 1.0),
+        ("iamlab_cmu_pickup_insert_converted_externally_to_rlds", 1.0),
+        ("utaustin_mutex", 1.0),
+        ("cmu_play_fusion", 1.0),
+    ],
+
+    # === Open-X Magic Soup ===
+    "oxe_magic_soup": [
+        ("fractal20220817_data", 0.54087122203),                # Google RT-1 Robot Data (Large-Scale)
+        ("kuka", 0.8341046294),
+        # ("bridge_oxe", 1.0)                                   # Version of Bridge V2 in Open-X GCP Bucket
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+        ("taco_play", 2.0),
+        ("jaco_play", 1.0),
+        ("berkeley_cable_routing", 1.0),
+        ("roboturk", 2.0),
+        # ("nyu_door_opening_surprising_effectiveness", 1.0),   # Note --> only contains wrist camera images (skip?)
+        ("viola", 2.0),
+        ("berkeley_autolab_ur5", 2.0),
+        ("toto", 1.0),
+        ("language_table", 0.1),
+        ("stanford_hydra_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_buds_dataset_converted_externally_to_rlds", 1.0),
+        ("nyu_franka_play_dataset_converted_externally_to_rlds", 3.0),
+        ("furniture_bench_dataset_converted_externally_to_rlds", 0.1),
+        ("ucsd_kitchen_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_sailor_dataset_converted_externally_to_rlds", 1.0),
+        ("austin_sirius_dataset_converted_externally_to_rlds", 1.0),
+        # ("bc_z", 0.2),                                        # Note --> raw data is broken!
+        ("dlr_edan_shared_control_converted_externally_to_rlds", 1.0),
+        ("iamlab_cmu_pickup_insert_converted_externally_to_rlds", 1.0),
+        # ("uiuc_d3field", 1.0),                                # Note --> raw data is broken!
+        ("utaustin_mutex", 1.0),
+        ("berkeley_fanuc_manipulation", 2.0),
+        ("cmu_stretch", 1.0),
+    ],
+
+    # === Open-X Magic Soup++ ===
+    "oxe_magic_soup_plus": [
+        ("fractal20220817_data", 0.54087122203),                # Google RT-1 Robot Data (Large-Scale)
+        ("kuka", 0.8341046294),
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+        ("taco_play", 2.0),
+        ("jaco_play", 1.0),
+        ("berkeley_cable_routing", 1.0),
+        ("roboturk", 2.0),
+        ("viola", 2.0),
+        ("berkeley_autolab_ur5", 2.0),
+        ("toto", 1.0),
+        ("language_table", 0.1),
+        ("stanford_hydra_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_buds_dataset_converted_externally_to_rlds", 1.0),
+        ("nyu_franka_play_dataset_converted_externally_to_rlds", 3.0),
+        ("furniture_bench_dataset_converted_externally_to_rlds", 0.1),
+        ("ucsd_kitchen_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_sailor_dataset_converted_externally_to_rlds", 1.0),
+        ("austin_sirius_dataset_converted_externally_to_rlds", 1.0),
+        ("dlr_edan_shared_control_converted_externally_to_rlds", 1.0),
+        ("iamlab_cmu_pickup_insert_converted_externally_to_rlds", 1.0),
+        ("utaustin_mutex", 1.0),
+        ("berkeley_fanuc_manipulation", 2.0),
+        ("cmu_stretch", 1.0),
+        ## New Datasets in MagicSoup++
+        ("bc_z", 0.2),                                          # Note: use v0.1.0 --> later versions broken
+        ("fmb_dataset", 1.0),
+        ("dobbe", 0.2),
+        ("droid", 0.06),
+    ],
+
+    "oxe_magic_soup_plus_minus": [
+        ("fractal20220817_data", 1.0),                          # Google RT-1 Robot Data (Large-Scale)
+        ("kuka", 0.8341046294),
+        ("bridge_orig", 1.0),                                   # Original Version of Bridge V2 from Project Website
+        ("taco_play", 2.0),
+        ("jaco_play", 1.0),
+        ("berkeley_cable_routing", 1.0),
+        ("roboturk", 2.0),
+        ("viola", 2.0),
+        ("berkeley_autolab_ur5", 2.0),
+        ("toto", 1.0),
+        # ("language_table", 0.1),
+        ("stanford_hydra_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_buds_dataset_converted_externally_to_rlds", 1.0),
+        ("nyu_franka_play_dataset_converted_externally_to_rlds", 3.0),
+        ("furniture_bench_dataset_converted_externally_to_rlds", 0.1),
+        ("ucsd_kitchen_dataset_converted_externally_to_rlds", 2.0),
+        ("austin_sailor_dataset_converted_externally_to_rlds", 1.0),
+        ("austin_sirius_dataset_converted_externally_to_rlds", 1.0),
+        ("dlr_edan_shared_control_converted_externally_to_rlds", 1.0),
+        ("iamlab_cmu_pickup_insert_converted_externally_to_rlds", 1.0),
+        ("utaustin_mutex", 1.0),
+        ("berkeley_fanuc_manipulation", 2.0),
+        ("cmu_stretch", 1.0),
+        ## New Datasets in MagicSoup++
+        ("bc_z", 0.2),                                          # Note: use v0.1.0 --> later versions broken
+        ("fmb_dataset", 1.0),
+        ("dobbe", 0.2),
+        # ("droid", 0.06),
+    ],
+
+    # === T-DROID Dataset ===
+    "tdroid_carrot_in_bowl": [
+        ("tdroid_carrot_in_bowl", 1.0),
+    ],
+    "tdroid_pour_corn_in_pot": [
+        ("tdroid_pour_corn_in_pot", 1.0),
+    ],
+    "tdroid_flip_pot_upright": [
+        ("tdroid_flip_pot_upright", 1.0),
+    ],
+    "tdroid_move_object_onto_plate": [
+        ("tdroid_move_object_onto_plate", 1.0),
+    ],
+    "tdroid_knock_object_over": [
+        ("tdroid_knock_object_over", 1.0),
+    ],
+    "tdroid_cover_object_with_towel": [
+        ("tdroid_cover_object_with_towel", 1.0),
+    ],
+
+    # === DROID Finetuning Datasets ===
+    "droid_wipe": [
+        ("droid_wipe", 1.0),
+    ],
+}
+# fmt: on
diff --git a/prismatic/vla/datasets/rlds/oxe/transforms.py b/prismatic/vla/datasets/rlds/oxe/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..02770794422f1fc1a66d8c1764b55c277eb536b5
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/transforms.py
@@ -0,0 +1,989 @@
+"""
+transforms.py
+
+Defines a registry of per-dataset standardization transforms for each dataset in Open-X Embodiment.
+
+Transforms adopt the following structure:
+    Input: Dictionary of *batched* features (i.e., has leading time dimension)
+    Output: Dictionary `step` =>> {
+        "observation": {
+            <image_keys, depth_image_keys>
+            State (in chosen state representation)
+        },
+        "action": Action (in chosen action representation),
+        "language_instruction": str
+    }
+"""
+
+from typing import Any, Dict
+
+import tensorflow as tf
+
+from prismatic.vla.datasets.rlds.oxe.utils.droid_utils import droid_baseact_transform, droid_finetuning_transform
+from prismatic.vla.datasets.rlds.utils.data_utils import (
+    binarize_gripper_actions,
+    invert_gripper_actions,
+    rel2abs_gripper_actions,
+    relabel_bridge_actions,
+)
+
+
+def bridge_oxe_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Applies to version of Bridge V2 in Open X-Embodiment mixture.
+
+    Note =>> In original Bridge V2 dataset, the first timestep has an all-zero action, so we remove it!
+    """
+    for key in trajectory.keys():
+        if key == "traj_metadata":
+            continue
+        elif key in ["observation", "action"]:
+            for key2 in trajectory[key]:
+                trajectory[key][key2] = trajectory[key][key2][1:]
+        else:
+            trajectory[key] = trajectory[key][1:]
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.cast(trajectory["action"]["open_gripper"][:, None], tf.float32),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    trajectory = relabel_bridge_actions(trajectory)
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def bridge_orig_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Applies to original version of Bridge V2 from the official project website.
+
+    Note =>> In original Bridge V2 dataset, the first timestep has an all-zero action, so we remove it!
+    """
+    for key in trajectory.keys():
+        if key == "traj_metadata":
+            continue
+        elif key == "observation":
+            for key2 in trajectory[key]:
+                trajectory[key][key2] = trajectory[key][key2][1:]
+        else:
+            trajectory[key] = trajectory[key][1:]
+
+    trajectory["action"] = tf.concat(
+        [
+            trajectory["action"][:, :6],
+            binarize_gripper_actions(trajectory["action"][:, -1])[:, None],
+        ],
+        axis=1,
+    )
+    trajectory = relabel_bridge_actions(trajectory)
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def ppgm_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        [
+            trajectory["action"][:, :6],
+            binarize_gripper_actions(trajectory["action"][:, -1])[:, None],
+        ],
+        axis=1,
+    )
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+    return trajectory
+
+
+def rt1_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def kuka_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    # decode compressed state
+    eef_value = tf.io.decode_compressed(
+        trajectory["observation"]["clip_function_input/base_pose_tool_reached"],
+        compression_type="ZLIB",
+    )
+    eef_value = tf.io.decode_raw(eef_value, tf.float32)
+    trajectory["observation"]["clip_function_input/base_pose_tool_reached"] = tf.reshape(eef_value, (-1, 7))
+    gripper_value = tf.io.decode_compressed(trajectory["observation"]["gripper_closed"], compression_type="ZLIB")
+    gripper_value = tf.io.decode_raw(gripper_value, tf.float32)
+    trajectory["observation"]["gripper_closed"] = tf.reshape(gripper_value, (-1, 1))
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def taco_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state_eef"] = trajectory["observation"]["robot_obs"][:, :6]
+    trajectory["observation"]["state_gripper"] = trajectory["observation"]["robot_obs"][:, 7:8]
+    trajectory["action"] = trajectory["action"]["rel_actions_world"]
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            tf.clip_by_value(trajectory["action"][:, -1:], 0, 1),
+        ),
+        axis=-1,
+    )
+
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def jaco_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state_eef"] = trajectory["observation"]["end_effector_cartesian_pos"][:, :6]
+    trajectory["observation"]["state_gripper"] = trajectory["observation"]["end_effector_cartesian_pos"][:, -1:]
+
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            tf.zeros_like(trajectory["action"]["world_vector"]),
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def berkeley_cable_routing_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.zeros_like(trajectory["action"]["world_vector"][:, :1]),
+        ),
+        axis=-1,
+    )
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def roboturk_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert absolute gripper action, +1 = open, 0 = close
+    gripper_action = invert_gripper_actions(tf.clip_by_value(trajectory["action"]["gripper_closedness_action"], 0, 1))
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def nyu_door_opening_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, 0]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def viola_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # make gripper action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"][:, None]
+    gripper_action = tf.clip_by_value(gripper_action, 0, 1)
+    gripper_action = invert_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def berkeley_autolab_ur5_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["robot_state"][:, 6:14]
+    trajectory["observation"]["depth"] = trajectory["observation"].pop("image_with_depth")
+
+    # make gripper action absolute action, +1 = open, 0 = close
+    gripper_action = trajectory["action"]["gripper_closedness_action"]
+    gripper_action = rel2abs_gripper_actions(gripper_action)
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            gripper_action[:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def toto_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            tf.cast(trajectory["action"]["open_gripper"][:, None], tf.float32),
+        ),
+        axis=-1,
+    )
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["observation"]["natural_language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def language_table_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # default to "open" gripper
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"]),
+            tf.ones_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+
+    # decode language instruction
+    instruction_bytes = trajectory["observation"]["instruction"]
+    instruction_encoded = tf.strings.unicode_encode(instruction_bytes, output_encoding="UTF-8")
+    # Remove trailing padding --> convert RaggedTensor to regular Tensor.
+    trajectory["language_instruction"] = tf.strings.split(instruction_encoded, "\x00")[:, :1].to_tensor()[:, 0]
+    return trajectory
+
+
+def pusht_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["world_vector"],
+            trajectory["action"]["rotation_delta"],
+            trajectory["action"]["gripper_closedness_action"][:, None],
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def stanford_kuka_multimodal_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["depth_image"] = trajectory["observation"]["depth_image"][..., 0]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tf.zeros_like(trajectory["action"][:, :3]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def nyu_rot_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][..., :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][..., -1:]
+    trajectory["action"] = trajectory["action"][..., :7]
+    return trajectory
+
+
+def stanford_hydra_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(trajectory["action"][:, -1:]),
+        ),
+        axis=-1,
+    )
+
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :3],
+            trajectory["observation"]["state"][:, 7:10],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -3:-2]
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def austin_buds_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :8]
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def nyu_franka_play_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["depth"] = tf.cast(trajectory["observation"]["depth"][..., 0], tf.float32)
+    trajectory["observation"]["depth_additional_view"] = tf.cast(
+        trajectory["observation"]["depth_additional_view"][..., 0], tf.float32
+    )
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, -6:]
+
+    # clip gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, -8:-2],
+            tf.clip_by_value(trajectory["action"][:, -2:-1], 0, 1),
+        ),
+        axis=-1,
+    )
+
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def maniskill_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][..., 7:8]
+    return trajectory
+
+
+def furniture_bench_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["observation"]["state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :7],
+            trajectory["observation"]["state"][:, -1:],
+        ),
+        axis=-1,
+    )
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_franka_exploration_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def ucsd_kitchen_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :7]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def ucsd_pick_place_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tf.zeros_like(trajectory["action"][:, :3]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def austin_sailor_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def austin_sirius_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def bc_z_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["future/xyz_residual"][:, :3],
+            trajectory["action"]["future/axis_angle_residual"][:, :3],
+            invert_gripper_actions(tf.cast(trajectory["action"]["future/target_close"][:, :1], tf.float32)),
+        ),
+        axis=-1,
+    )
+    trajectory["language_instruction"] = trajectory["observation"]["natural_language_instruction"]
+    return trajectory
+
+
+def tokyo_pr2_opening_fridge_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def tokyo_pr2_tabletop_manipulation_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def utokyo_xarm_pick_place_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    return trajectory
+
+
+def utokyo_xarm_bimanual_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., -7:]
+    return trajectory
+
+
+def robo_net_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :4],
+            tf.zeros_like(trajectory["observation"]["state"][:, :2]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :4],
+            tf.zeros_like(trajectory["action"][:, :2]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def berkeley_mvp_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    return trajectory
+
+
+def berkeley_rpt_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    return trajectory
+
+
+def kaist_nonprehensible_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, -7:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def stanford_mask_vit_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["end_effector_pose"][:, :4],
+            tf.zeros_like(trajectory["observation"]["end_effector_pose"][:, :2]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["end_effector_pose"][:, -1:]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :4],
+            tf.zeros_like(trajectory["action"][:, :2]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def tokyo_lsmo_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def dlr_sara_pour_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    return trajectory
+
+
+def dlr_sara_grid_clamp_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :6]
+    return trajectory
+
+
+def dlr_edan_shared_control_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # invert gripper action, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(trajectory["action"][:, -1:]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def asu_table_top_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["ground_truth_states"]["EE"]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def robocook_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    return trajectory
+
+
+def imperial_wristcam_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def iamlab_pick_insert_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :7]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, 7:8]
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            trajectory["action"][:, 7:8],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def uiuc_d3field_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def utaustin_mutex_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = trajectory["observation"]["state"][:, :8]
+
+    # invert gripper action + clip, +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :6],
+            invert_gripper_actions(tf.clip_by_value(trajectory["action"][:, -1:], 0, 1)),
+        ),
+        axis=-1,
+    )
+
+    # trajectory["language_instruction"] = tf.fill(
+    #     tf.shape(trajectory["language_instruction"]), ""
+    # )  # delete uninformative language instruction
+    return trajectory
+
+
+def berkeley_fanuc_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["joint_state"] = trajectory["observation"]["state"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, 6:7]
+
+    # dataset does not store gripper actions, so use gripper state info, invert so +1 = open, 0 = close
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            invert_gripper_actions(trajectory["observation"]["gripper_state"]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_playing_with_food_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    import tensorflow_graphics.geometry.transformation as tft
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            tft.euler.from_quaternion(trajectory["action"][:, 3:7]),
+            trajectory["action"][:, -1:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def playfusion_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :3],
+            trajectory["action"][:, -4:],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def cmu_stretch_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["eef_state"] = tf.concat(
+        (
+            trajectory["observation"]["state"][:, :3],
+            tf.zeros_like(trajectory["observation"]["state"][:, :3]),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["state"][:, -1:]
+    trajectory["action"] = trajectory["action"][..., :-1]
+    return trajectory
+
+
+def gnm_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["observation"]["state"] = tf.concat(
+        (
+            trajectory["observation"]["position"],
+            tf.zeros_like(trajectory["observation"]["state"][:, :3]),
+            trajectory["observation"]["yaw"],
+        ),
+        axis=-1,
+    )
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"],
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"]),
+            tf.zeros_like(trajectory["action"][:, :1]),
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def fmb_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # every input feature is batched, ie has leading batch dimension
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["eef_pose"],
+            trajectory["observation"]["state_gripper_pose"][..., None],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def dobbe_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # every input feature is batched, ie has leading batch dimension
+    trajectory["observation"]["proprio"] = trajectory["observation"]["state"]
+    return trajectory
+
+
+def roboset_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # every input feature is batched, ie has leading batch dimension
+    trajectory["observation"]["proprio"] = trajectory["observation"]["state"]
+
+    # gripper action is in -1...1 --> clip to 0...1, flip
+    gripper_action = trajectory["action"][:, -1:]
+    gripper_action = invert_gripper_actions(tf.clip_by_value(gripper_action, 0, 1))
+
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"][:, :7],
+            gripper_action,
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def rh20t_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        (
+            trajectory["action"]["tcp_base"],
+            tf.cast(trajectory["action"]["gripper"][:, None], tf.float32),
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["tcp_base"],
+            trajectory["observation"]["gripper_width"][..., None],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def tdroid_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    trajectory["action"] = tf.concat(
+        [
+            trajectory["action"][:, :6],
+            binarize_gripper_actions(trajectory["action"][:, -1])[:, None],
+        ],
+        axis=1,
+    )
+    trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+    return trajectory
+
+########################################################################
+########################################################################
+########################################################################
+# def quaternion_to_euler_tf(quaternions):
+#     # Split the quaternion tensor into w, x, y, z components
+#     w, x, y, z = tf.split(quaternions, 4, axis=1)
+    
+#     # Compute Roll (x-axis rotation)
+#     roll = tf.atan2(2 * (w * x + y * z), 1 - 2 * (x**2 + y**2))
+    
+#     # Compute Pitch (y-axis rotation)
+#     pitch = tf.asin(tf.clip_by_value(2 * (w * y - z * x), -1.0, 1.0))  # Clip to avoid invalid values due to numerical issues
+    
+#     # Compute Yaw (z-axis rotation)
+#     yaw = tf.atan2(2 * (w * z + x * y), 1 - 2 * (y**2 + z**2))
+    
+#     # Convert radians to degrees if needed
+#     roll = tf.math.degrees(roll)
+#     pitch = tf.math.degrees(pitch)
+#     yaw = tf.math.degrees(yaw)
+    
+#     # Concatenate the results into a single tensor of shape [N, 3]
+#     euler_angles = tf.concat([roll, pitch, yaw], axis=1)
+    
+#     return euler_angles
+
+# def gripper_qpos_to_single_variable(gripper_qpos, qpos_min=0.22, qpos_max=0.30):
+#     # Split the input tensor into qpos1 and qpos2 components
+#     qpos1, _ = tf.split(gripper_qpos, 2, axis=1)
+    
+#     # Combine the two gripper joint positions (average)
+#     gripper_position = qpos1
+    
+#     # Normalize to the range [-1, 1]
+#     normalized_gripper_position = (2 * (gripper_position - qpos_min) / (qpos_max - qpos_min)) - 1
+    
+#     return normalized_gripper_position
+
+def robomimic_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    # trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+
+    trajectory["observation"]["proprio"] = tf.concat(
+        [
+            trajectory["observation"]["robot0_eef_pos"],
+            trajectory["observation"]["robot0_eef_quat"],
+            trajectory["observation"]["robot0_gripper_qpos"]
+        ],
+        axis=1,
+    )
+
+    return trajectory
+
+
+def libero_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    # trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+
+    return trajectory
+
+########################################################################
+########################################################################
+########################################################################
+
+def real_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    # trajectory["observation"]["EEF_state"] = trajectory["observation"]["cartesian_position"][:, :6]
+    # trajectory["observation"]["gripper_state"] = trajectory["observation"]["gripper_position"][:, -1:]
+
+    return trajectory
+
+# === Registry ===
+OXE_STANDARDIZATION_TRANSFORMS = {
+    "bridge_oxe": bridge_oxe_dataset_transform,
+    "bridge_orig": bridge_orig_dataset_transform,
+    "bridge_dataset": bridge_orig_dataset_transform,
+    "ppgm": ppgm_dataset_transform,
+    "ppgm_static": ppgm_dataset_transform,
+    "ppgm_wrist": ppgm_dataset_transform,
+    "fractal20220817_data": rt1_dataset_transform,
+    "kuka": kuka_dataset_transform,
+    "taco_play": taco_play_dataset_transform,
+    "jaco_play": jaco_play_dataset_transform,
+    "berkeley_cable_routing": berkeley_cable_routing_dataset_transform,
+    "roboturk": roboturk_dataset_transform,
+    "nyu_door_opening_surprising_effectiveness": nyu_door_opening_dataset_transform,
+    "viola": viola_dataset_transform,
+    "berkeley_autolab_ur5": berkeley_autolab_ur5_dataset_transform,
+    "toto": toto_dataset_transform,
+    "language_table": language_table_dataset_transform,
+    "columbia_cairlab_pusht_real": pusht_dataset_transform,
+    "stanford_kuka_multimodal_dataset_converted_externally_to_rlds": stanford_kuka_multimodal_dataset_transform,
+    "nyu_rot_dataset_converted_externally_to_rlds": nyu_rot_dataset_transform,
+    "stanford_hydra_dataset_converted_externally_to_rlds": stanford_hydra_dataset_transform,
+    "austin_buds_dataset_converted_externally_to_rlds": austin_buds_dataset_transform,
+    "nyu_franka_play_dataset_converted_externally_to_rlds": nyu_franka_play_dataset_transform,
+    "maniskill_dataset_converted_externally_to_rlds": maniskill_dataset_transform,
+    "furniture_bench_dataset_converted_externally_to_rlds": furniture_bench_dataset_transform,
+    "cmu_franka_exploration_dataset_converted_externally_to_rlds": cmu_franka_exploration_dataset_transform,
+    "ucsd_kitchen_dataset_converted_externally_to_rlds": ucsd_kitchen_dataset_transform,
+    "ucsd_pick_and_place_dataset_converted_externally_to_rlds": ucsd_pick_place_dataset_transform,
+    "austin_sailor_dataset_converted_externally_to_rlds": austin_sailor_dataset_transform,
+    "austin_sirius_dataset_converted_externally_to_rlds": austin_sirius_dataset_transform,
+    "bc_z": bc_z_dataset_transform,
+    "utokyo_pr2_opening_fridge_converted_externally_to_rlds": tokyo_pr2_opening_fridge_dataset_transform,
+    "utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds": tokyo_pr2_tabletop_manipulation_dataset_transform,
+    "utokyo_xarm_pick_and_place_converted_externally_to_rlds": utokyo_xarm_pick_place_dataset_transform,
+    "utokyo_xarm_bimanual_converted_externally_to_rlds": utokyo_xarm_bimanual_dataset_transform,
+    "robo_net": robo_net_dataset_transform,
+    "berkeley_mvp_converted_externally_to_rlds": berkeley_mvp_dataset_transform,
+    "berkeley_rpt_converted_externally_to_rlds": berkeley_rpt_dataset_transform,
+    "kaist_nonprehensile_converted_externally_to_rlds": kaist_nonprehensible_dataset_transform,
+    "stanford_mask_vit_converted_externally_to_rlds": stanford_mask_vit_dataset_transform,
+    "tokyo_u_lsmo_converted_externally_to_rlds": tokyo_lsmo_dataset_transform,
+    "dlr_sara_pour_converted_externally_to_rlds": dlr_sara_pour_dataset_transform,
+    "dlr_sara_grid_clamp_converted_externally_to_rlds": dlr_sara_grid_clamp_dataset_transform,
+    "dlr_edan_shared_control_converted_externally_to_rlds": dlr_edan_shared_control_dataset_transform,
+    "asu_table_top_converted_externally_to_rlds": asu_table_top_dataset_transform,
+    "stanford_robocook_converted_externally_to_rlds": robocook_dataset_transform,
+    "imperialcollege_sawyer_wrist_cam": imperial_wristcam_dataset_transform,
+    "iamlab_cmu_pickup_insert_converted_externally_to_rlds": iamlab_pick_insert_dataset_transform,
+    "uiuc_d3field": uiuc_d3field_dataset_transform,
+    "utaustin_mutex": utaustin_mutex_dataset_transform,
+    "berkeley_fanuc_manipulation": berkeley_fanuc_dataset_transform,
+    "cmu_playing_with_food": cmu_playing_with_food_dataset_transform,
+    "cmu_play_fusion": playfusion_dataset_transform,
+    "cmu_stretch": cmu_stretch_dataset_transform,
+    "berkeley_gnm_recon": gnm_dataset_transform,
+    "berkeley_gnm_cory_hall": gnm_dataset_transform,
+    "berkeley_gnm_sac_son": gnm_dataset_transform,
+    "droid": droid_baseact_transform,
+    "fmb_dataset": fmb_dataset_transform,
+    "dobbe": dobbe_dataset_transform,
+    "roboset": roboset_dataset_transform,
+    "rh20t": rh20t_dataset_transform,
+    ### T-DROID datasets
+    "tdroid_carrot_in_bowl": tdroid_dataset_transform,
+    "tdroid_pour_corn_in_pot": tdroid_dataset_transform,
+    "tdroid_flip_pot_upright": tdroid_dataset_transform,
+    "tdroid_move_object_onto_plate": tdroid_dataset_transform,
+    "tdroid_knock_object_over": tdroid_dataset_transform,
+    "tdroid_cover_object_with_towel": tdroid_dataset_transform,
+    ### DROID Finetuning datasets
+    "droid_wipe": droid_finetuning_transform,
+
+    ### Robomimic Finetuning datasets
+    "robomimic_lift": robomimic_dataset_transform,
+    "robomimic_can": robomimic_dataset_transform,
+    "robomimic_square": robomimic_dataset_transform,
+    "robomimic_tool_hang": robomimic_dataset_transform,
+
+    ### LIBERO Finetuning datasets
+    "libero_spatial": libero_dataset_transform,
+    "libero_object": libero_dataset_transform,
+    "libero_goal": libero_dataset_transform,
+    "libero_goal_oc": libero_dataset_transform,
+    "libero_90": libero_dataset_transform,
+    "libero_10": libero_dataset_transform,
+    "libero_relation": libero_dataset_transform,
+    "libero_mem": libero_dataset_transform,
+
+    ### Real dataset
+    "real_data": real_dataset_transform,
+    "real_action": real_dataset_transform,
+}
diff --git a/prismatic/vla/datasets/rlds/oxe/utils/droid_utils.py b/prismatic/vla/datasets/rlds/oxe/utils/droid_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..44175a21cff6c3ae45f7596024852462ea40c68e
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/oxe/utils/droid_utils.py
@@ -0,0 +1,178 @@
+"""Episode transforms for DROID dataset."""
+
+from typing import Any, Dict
+
+import tensorflow as tf
+import tensorflow_graphics.geometry.transformation as tfg
+
+
+def rmat_to_euler(rot_mat):
+    return tfg.euler.from_rotation_matrix(rot_mat)
+
+
+def euler_to_rmat(euler):
+    return tfg.rotation_matrix_3d.from_euler(euler)
+
+
+def invert_rmat(rot_mat):
+    return tfg.rotation_matrix_3d.inverse(rot_mat)
+
+
+def rotmat_to_rot6d(mat):
+    """
+    Converts rotation matrix to R6 rotation representation (first two rows in rotation matrix).
+    Args:
+        mat: rotation matrix
+
+    Returns: 6d vector (first two rows of rotation matrix)
+
+    """
+    r6 = mat[..., :2, :]
+    r6_0, r6_1 = r6[..., 0, :], r6[..., 1, :]
+    r6_flat = tf.concat([r6_0, r6_1], axis=-1)
+    return r6_flat
+
+
+def velocity_act_to_wrist_frame(velocity, wrist_in_robot_frame):
+    """
+    Translates velocity actions (translation + rotation) from base frame of the robot to wrist frame.
+    Args:
+        velocity: 6d velocity action (3 x translation, 3 x rotation)
+        wrist_in_robot_frame: 6d pose of the end-effector in robot base frame
+
+    Returns: 9d velocity action in robot wrist frame (3 x translation, 6 x rotation as R6)
+
+    """
+    R_frame = euler_to_rmat(wrist_in_robot_frame[:, 3:6])
+    R_frame_inv = invert_rmat(R_frame)
+
+    # world to wrist: dT_pi = R^-1 dT_rbt
+    vel_t = (R_frame_inv @ velocity[:, :3][..., None])[..., 0]
+
+    # world to wrist: dR_pi = R^-1 dR_rbt R
+    dR = euler_to_rmat(velocity[:, 3:6])
+    dR = R_frame_inv @ (dR @ R_frame)
+    dR_r6 = rotmat_to_rot6d(dR)
+    return tf.concat([vel_t, dR_r6], axis=-1)
+
+
+def rand_swap_exterior_images(img1, img2):
+    """
+    Randomly swaps the two exterior images (for training with single exterior input).
+    """
+    return tf.cond(tf.random.uniform(shape=[]) > 0.5, lambda: (img1, img2), lambda: (img2, img1))
+
+
+def droid_baseact_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *base* frame of the robot.
+    """
+    dt = trajectory["action_dict"]["cartesian_velocity"][:, :3]
+    dR = trajectory["action_dict"]["cartesian_velocity"][:, 3:6]
+
+    trajectory["action"] = tf.concat(
+        (
+            dt,
+            dR,
+            1 - trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["exterior_image_1_left"], trajectory["observation"]["exterior_image_2_left"] = (
+        rand_swap_exterior_images(
+            trajectory["observation"]["exterior_image_1_left"],
+            trajectory["observation"]["exterior_image_2_left"],
+        )
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def droid_wristact_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *wrist* frame of the robot.
+    """
+    wrist_act = velocity_act_to_wrist_frame(
+        trajectory["action_dict"]["cartesian_velocity"], trajectory["observation"]["cartesian_position"]
+    )
+    trajectory["action"] = tf.concat(
+        (
+            wrist_act,
+            trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["exterior_image_1_left"], trajectory["observation"]["exterior_image_2_left"] = (
+        rand_swap_exterior_images(
+            trajectory["observation"]["exterior_image_1_left"],
+            trajectory["observation"]["exterior_image_2_left"],
+        )
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def droid_finetuning_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    DROID dataset transformation for actions expressed in *base* frame of the robot.
+    """
+    dt = trajectory["action_dict"]["cartesian_velocity"][:, :3]
+    dR = trajectory["action_dict"]["cartesian_velocity"][:, 3:6]
+    trajectory["action"] = tf.concat(
+        (
+            dt,
+            dR,
+            1 - trajectory["action_dict"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    trajectory["observation"]["proprio"] = tf.concat(
+        (
+            trajectory["observation"]["cartesian_position"],
+            trajectory["observation"]["gripper_position"],
+        ),
+        axis=-1,
+    )
+    return trajectory
+
+
+def zero_action_filter(traj: Dict) -> bool:
+    """
+    Filters transitions whose actions are all-0 (only relative actions, no gripper action).
+    Note: this filter is applied *after* action normalization, so need to compare to "normalized 0".
+    """
+    DROID_Q01 = tf.convert_to_tensor(
+        [
+            -0.7776297926902771,
+            -0.5803514122962952,
+            -0.5795090794563293,
+            -0.6464047729969025,
+            -0.7041108310222626,
+            -0.8895104378461838,
+        ]
+    )
+    DROID_Q99 = tf.convert_to_tensor(
+        [
+            0.7597932070493698,
+            0.5726242214441299,
+            0.7351000607013702,
+            0.6705610305070877,
+            0.6464948207139969,
+            0.8897542208433151,
+        ]
+    )
+    DROID_NORM_0_ACT = 2 * (tf.zeros_like(traj["action"][:, :6]) - DROID_Q01) / (DROID_Q99 - DROID_Q01 + 1e-8) - 1
+
+    return tf.reduce_any(tf.math.abs(traj["action"][:, :6] - DROID_NORM_0_ACT) > 1e-5)
diff --git a/prismatic/vla/datasets/rlds/traj_transforms.py b/prismatic/vla/datasets/rlds/traj_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..82cc43a2e7b0a9a3dbaa81d898a4257db560119a
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/traj_transforms.py
@@ -0,0 +1,104 @@
+"""
+traj_transforms.py
+
+Contains trajectory transforms used in the orca data pipeline. Trajectory transforms operate on a dictionary
+that represents a single trajectory, meaning each tensor has the same leading dimension (the trajectory length).
+"""
+
+import logging
+from typing import Dict
+
+import tensorflow as tf
+
+
+def chunk_act_obs(traj: Dict, window_size: int, future_action_window_size: int = 0) -> Dict:
+    """
+    Chunks actions and observations into the given window_size.
+
+    "observation" keys are given a new axis (at index 1) of size `window_size` containing `window_size - 1`
+    observations from the past and the current observation. "action" is given a new axis (at index 1) of size
+    `window_size + future_action_window_size` containing `window_size - 1` actions from the past, the current
+    action, and `future_action_window_size` actions from the future. "pad_mask" is added to "observation" and
+    indicates whether an observation should be considered padding (i.e. if it had come from a timestep
+    before the start of the trajectory).
+    """
+    traj_len = tf.shape(traj["action"])[0]
+    action_dim = traj["action"].shape[-1]
+    chunk_indices = tf.broadcast_to(tf.range(-window_size + 1, 1), [traj_len, window_size]) + tf.broadcast_to(
+        tf.range(traj_len)[:, None], [traj_len, window_size]
+    )
+
+    action_chunk_indices = tf.broadcast_to(
+        tf.range(-window_size + 1, 1 + future_action_window_size),
+        [traj_len, window_size + future_action_window_size],
+    ) + tf.broadcast_to(
+        tf.range(traj_len)[:, None],
+        [traj_len, window_size + future_action_window_size],
+    )
+
+    floored_chunk_indices = tf.maximum(chunk_indices, 0)
+
+    if "timestep" in traj["task"]:
+        goal_timestep = traj["task"]["timestep"]
+    else:
+        goal_timestep = tf.fill([traj_len], traj_len - 1)
+
+    floored_action_chunk_indices = tf.minimum(tf.maximum(action_chunk_indices, 0), goal_timestep[:, None])
+
+    traj["observation"] = tf.nest.map_structure(lambda x: tf.gather(x, floored_chunk_indices), traj["observation"])
+    traj["action"] = tf.gather(traj["action"], floored_action_chunk_indices)
+
+    # indicates whether an entire observation is padding
+    traj["observation"]["pad_mask"] = chunk_indices >= 0
+
+    # if no absolute_action_mask was provided, assume all actions are relative
+    if "absolute_action_mask" not in traj and future_action_window_size > 0:
+        logging.warning(
+            "future_action_window_size > 0 but no absolute_action_mask was provided. "
+            "Assuming all actions are relative for the purpose of making neutral actions."
+        )
+    absolute_action_mask = traj.get("absolute_action_mask", tf.zeros([traj_len, action_dim], dtype=tf.bool))
+    neutral_actions = tf.where(
+        absolute_action_mask[:, None, :],
+        traj["action"],  # absolute actions are repeated (already done during chunking)
+        tf.zeros_like(traj["action"]),  # relative actions are zeroed
+    )
+
+    # actions past the goal timestep become neutral
+    action_past_goal = action_chunk_indices > goal_timestep[:, None]
+    traj["action"] = tf.where(action_past_goal[:, :, None], neutral_actions, traj["action"])
+
+    return traj
+
+
+def subsample(traj: Dict, subsample_length: int) -> Dict:
+    """Subsamples trajectories to the given length."""
+    traj_len = tf.shape(traj["action"])[0]
+    if traj_len > subsample_length:
+        indices = tf.random.shuffle(tf.range(traj_len))[:subsample_length]
+        traj = tf.nest.map_structure(lambda x: tf.gather(x, indices), traj)
+
+    return traj
+
+
+def add_pad_mask_dict(traj: Dict) -> Dict:
+    """
+    Adds a dictionary indicating which elements of the observation/task should be treated as padding.
+        =>> traj["observation"|"task"]["pad_mask_dict"] = {k: traj["observation"|"task"][k] is not padding}
+    """
+    traj_len = tf.shape(traj["action"])[0]
+
+    for key in ["observation", "task"]:
+        pad_mask_dict = {}
+        for subkey in traj[key]:
+            # Handles "language_instruction", "image_*", and "depth_*"
+            if traj[key][subkey].dtype == tf.string:
+                pad_mask_dict[subkey] = tf.strings.length(traj[key][subkey]) != 0
+
+            # All other keys should not be treated as padding
+            else:
+                pad_mask_dict[subkey] = tf.ones([traj_len], dtype=tf.bool)
+
+        traj[key]["pad_mask_dict"] = pad_mask_dict
+
+    return traj
diff --git a/prismatic/vla/datasets/rlds/utils/__init__.py b/prismatic/vla/datasets/rlds/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/prismatic/vla/datasets/rlds/utils/data_utils.py b/prismatic/vla/datasets/rlds/utils/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b0e5ae9ccc9e34473912d491b04b0043fd956b1
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/utils/data_utils.py
@@ -0,0 +1,330 @@
+"""
+data_utils.py
+
+Additional RLDS-specific data utilities.
+"""
+
+import hashlib
+import json
+import os
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import dlimp as dl
+import numpy as np
+import tensorflow as tf
+from tqdm import tqdm
+
+from prismatic.overwatch import initialize_overwatch
+
+# Initialize Overwatch =>> Wraps `logging.Logger`
+overwatch = initialize_overwatch(__name__)
+
+
+def tree_map(fn: Callable, tree: Dict) -> Dict:
+    return {k: tree_map(fn, v) if isinstance(v, dict) else fn(v) for k, v in tree.items()}
+
+
+def tree_merge(*trees: Dict) -> Dict:
+    merged = {}
+    for tree in trees:
+        for k, v in tree.items():
+            if isinstance(v, dict):
+                merged[k] = tree_merge(merged.get(k, {}), v)
+            else:
+                merged[k] = v
+    return merged
+
+
+def to_padding(tensor: tf.Tensor) -> tf.Tensor:
+    if tf.debugging.is_numeric_tensor(tensor):
+        return tf.zeros_like(tensor)
+    elif tensor.dtype == tf.string:
+        return tf.fill(tf.shape(tensor), "")
+    else:
+        raise ValueError(f"Cannot generate padding for tensor of type {tensor.dtype}.")
+
+
+# Defines supported normalization schemes for action and proprioceptive state.
+class NormalizationType(str, Enum):
+    # fmt: off
+    NORMAL = "normal"               # Normalize to Mean = 0, Stdev = 1
+    BOUNDS = "bounds"               # Normalize to Interval = [-1, 1]
+    BOUNDS_Q99 = "bounds_q99"       # Normalize [quantile_01, ..., quantile_99] --> [-1, ..., 1]
+    # fmt: on
+
+
+# === State / Action Processing Primitives ===
+
+
+# ruff: noqa: B023
+def normalize_action_and_proprio(traj: Dict, metadata: Dict, normalization_type: NormalizationType):
+    """Normalizes the action and proprio fields of a trajectory using the given metadata."""
+    keys_to_normalize = {"action": "action", "proprio": "observation/proprio"}
+
+    if normalization_type == NormalizationType.NORMAL:
+        for key, traj_key in keys_to_normalize.items():
+            mask = metadata[key].get("mask", tf.ones_like(metadata[key]["mean"], dtype=tf.bool))
+            traj = dl.transforms.selective_tree_map(
+                traj,
+                match=lambda k, _: k == traj_key,
+                map_fn=lambda x: tf.where(mask, (x - metadata[key]["mean"]) / (metadata[key]["std"] + 1e-8), x),
+            )
+
+        return traj
+
+    elif normalization_type in [NormalizationType.BOUNDS, NormalizationType.BOUNDS_Q99]:
+        for key, traj_key in keys_to_normalize.items():
+            if normalization_type == NormalizationType.BOUNDS:
+                low = metadata[key]["min"]
+                high = metadata[key]["max"]
+            elif normalization_type == NormalizationType.BOUNDS_Q99:
+                low = metadata[key]["q01"]
+                high = metadata[key]["q99"]
+            mask = metadata[key].get("mask", tf.ones_like(metadata[key]["min"], dtype=tf.bool))
+            traj = dl.transforms.selective_tree_map(
+                traj,
+                match=lambda k, _: k == traj_key,
+                map_fn=lambda x: tf.where(
+                    mask,
+                    tf.clip_by_value(2 * (x - low) / (high - low + 1e-8) - 1, -1, 1),
+                    x,
+                ),
+            )
+
+            # Note (Moo Jin): Map unused action dimensions (i.e., dimensions where min == max) to all 0s.
+            zeros_mask = metadata[key]["min"] == metadata[key]["max"]
+            traj = dl.transforms.selective_tree_map(
+                traj, match=lambda k, _: k == traj_key, map_fn=lambda x: tf.where(zeros_mask, 0.0, x)
+            )
+
+        return traj
+
+    raise ValueError(f"Unknown Normalization Type {normalization_type}")
+
+
+def binarize_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    """
+    Converts gripper actions from continuous to binary values (0 and 1).
+
+    We exploit that fact that most of the time, the gripper is fully open (near 1.0) or fully closed (near 0.0). As it
+    transitions between the two, it sometimes passes through a few intermediate values. We relabel those intermediate
+    values based on the state that is reached _after_ those intermediate values.
+
+    In the edge case that the trajectory ends with an intermediate value, we give up on binarizing and relabel that
+    chunk of intermediate values as the last action in the trajectory.
+
+    The `scan_fn` implements the following logic:
+        new_actions = np.empty_like(actions)
+        carry = actions[-1]
+        for i in reversed(range(actions.shape[0])):
+            if in_between_mask[i]:
+                carry = carry
+            else:
+                carry = float(open_mask[i])
+            new_actions[i] = carry
+    """
+    open_mask, closed_mask = actions > 0.95, actions < 0.05
+    in_between_mask = tf.logical_not(tf.logical_or(open_mask, closed_mask))
+    is_open_float = tf.cast(open_mask, tf.float32)
+
+    def scan_fn(carry, i):
+        return tf.cond(in_between_mask[i], lambda: tf.cast(carry, tf.float32), lambda: is_open_float[i])
+
+    return tf.scan(scan_fn, tf.range(tf.shape(actions)[0]), actions[-1], reverse=True)
+
+
+def invert_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    return 1 - actions
+
+
+def rel2abs_gripper_actions(actions: tf.Tensor) -> tf.Tensor:
+    """
+    Converts relative gripper actions (+1 for closing, -1 for opening) to absolute actions (0 = closed; 1 = open).
+
+    Assumes that the first relative gripper is not redundant (i.e. close when already closed)!
+    """
+    # Note =>> -1 for closing, 1 for opening, 0 for no change
+    opening_mask, closing_mask = actions < -0.1, actions > 0.1
+    thresholded_actions = tf.where(opening_mask, 1, tf.where(closing_mask, -1, 0))
+
+    def scan_fn(carry, i):
+        return tf.cond(thresholded_actions[i] == 0, lambda: carry, lambda: thresholded_actions[i])
+
+    # If no relative grasp, assumes open for whole trajectory
+    start = -1 * thresholded_actions[tf.argmax(thresholded_actions != 0, axis=0)]
+    start = tf.cond(start == 0, lambda: 1, lambda: start)
+
+    # Note =>> -1 for closed, 1 for open
+    new_actions = tf.scan(scan_fn, tf.range(tf.shape(actions)[0]), start)
+    new_actions = tf.cast(new_actions, tf.float32) / 2 + 0.5
+
+    return new_actions
+
+
+# === Bridge-V2 =>> Dataset-Specific Transform ===
+def relabel_bridge_actions(traj: Dict[str, Any]) -> Dict[str, Any]:
+    """Relabels actions to use reached proprioceptive state; discards last timestep (no-action)."""
+    movement_actions = traj["observation"]["state"][1:, :6] - traj["observation"]["state"][:-1, :6]
+    traj_truncated = tf.nest.map_structure(lambda x: x[:-1], traj)
+    traj_truncated["action"] = tf.concat([movement_actions, traj["action"][:-1, -1:]], axis=1)
+
+    return traj_truncated
+
+
+# === RLDS Dataset Initialization Utilities ===
+def pprint_data_mixture(dataset_kwargs_list: List[Dict[str, Any]], dataset_weights: List[int]) -> None:
+    print("\n######################################################################################")
+    print(f"# Loading the following {len(dataset_kwargs_list)} datasets (incl. sampling weight):{'': >24} #")
+    for dataset_kwargs, weight in zip(dataset_kwargs_list, dataset_weights):
+        pad = 80 - len(dataset_kwargs["name"])
+        print(f"# {dataset_kwargs['name']}: {weight:=>{pad}f} #")
+    print("######################################################################################\n")
+
+
+def get_dataset_statistics(
+    dataset: dl.DLataset,
+    hash_dependencies: Tuple[str, ...],
+    save_dir: Optional[str] = None,
+) -> Dict:
+    """
+    Either computes the statistics of a dataset or loads them from a cache file if this function has been called before
+    with the same `hash_dependencies`.
+
+    Currently, the statistics include the min/max/mean/std of the actions and proprio as well as the number of
+    transitions and trajectories in the dataset.
+    """
+    unique_hash = hashlib.sha256("".join(hash_dependencies).encode("utf-8"), usedforsecurity=False).hexdigest()
+
+    # Fallback local path for when data_dir is not writable or not provided
+    local_path = os.path.expanduser(os.path.join("~", ".cache", "orca", f"dataset_statistics_{unique_hash}.json"))
+    if save_dir is not None:
+        path = tf.io.gfile.join(save_dir, f"dataset_statistics_{unique_hash}.json")
+    else:
+        path = local_path
+
+    # check if cache file exists and load
+    if tf.io.gfile.exists(path):
+        overwatch.info(f"Loading existing dataset statistics from {path}.")
+        with tf.io.gfile.GFile(path, "r") as f:
+            metadata = json.load(f)
+        return metadata
+
+    if os.path.exists(local_path):
+        overwatch.info(f"Loading existing dataset statistics from {local_path}.")
+        with open(local_path, "r") as f:
+            metadata = json.load(f)
+        return metadata
+
+    dataset = dataset.traj_map(
+        lambda traj: {
+            "action": traj["action"],
+            "proprio": (
+                traj["observation"]["proprio"] if "proprio" in traj["observation"] else tf.zeros_like(traj["action"])
+            ),
+        }
+    )
+
+    cardinality = dataset.cardinality().numpy()
+    if cardinality == tf.data.INFINITE_CARDINALITY:
+        raise ValueError("Cannot compute dataset statistics for infinite datasets.")
+
+    overwatch.info("Computing dataset statistics. This may take a bit, but should only need to happen once.")
+    actions, proprios, num_transitions, num_trajectories = [], [], 0, 0
+    for traj in tqdm(dataset.iterator(), total=cardinality if cardinality != tf.data.UNKNOWN_CARDINALITY else None):
+        actions.append(traj["action"])
+        proprios.append(traj["proprio"])
+        num_transitions += traj["action"].shape[0]
+        num_trajectories += 1
+
+    actions, proprios = np.concatenate(actions), np.concatenate(proprios)
+    metadata = {
+        "action": {
+            "mean": actions.mean(0).tolist(),
+            "std": actions.std(0).tolist(),
+            "max": actions.max(0).tolist(),
+            "min": actions.min(0).tolist(),
+            "q01": np.quantile(actions, 0.01, axis=0).tolist(),
+            "q99": np.quantile(actions, 0.99, axis=0).tolist(),
+        },
+        "proprio": {
+            "mean": proprios.mean(0).tolist(),
+            "std": proprios.std(0).tolist(),
+            "max": proprios.max(0).tolist(),
+            "min": proprios.min(0).tolist(),
+            "q01": np.quantile(proprios, 0.01, axis=0).tolist(),
+            "q99": np.quantile(proprios, 0.99, axis=0).tolist(),
+        },
+        "num_transitions": num_transitions,
+        "num_trajectories": num_trajectories,
+    }
+
+    try:
+        with tf.io.gfile.GFile(path, "w") as f:
+            json.dump(metadata, f)
+    except tf.errors.PermissionDeniedError:
+        overwatch.warning(f"Could not write dataset statistics to {path}. Writing to {local_path} instead.")
+        os.makedirs(os.path.dirname(local_path), exist_ok=True)
+        with open(local_path, "w") as f:
+            json.dump(metadata, f)
+
+    return metadata
+
+
+def save_dataset_statistics(dataset_statistics, run_dir):
+    """Saves a `dataset_statistics.json` file."""
+    out_path = run_dir / "dataset_statistics.json"
+    with open(out_path, "w") as f_json:
+        for _, stats in dataset_statistics.items():
+            for k in stats["action"].keys():
+                if isinstance(stats["action"][k], np.ndarray):
+                    stats["action"][k] = stats["action"][k].tolist()
+            if "proprio" in stats:
+                for k in stats["proprio"].keys():
+                    if isinstance(stats["proprio"][k], np.ndarray):
+                        stats["proprio"][k] = stats["proprio"][k].tolist()
+            if "num_trajectories" in stats:
+                if isinstance(stats["num_trajectories"], np.ndarray):
+                    stats["num_trajectories"] = stats["num_trajectories"].item()
+            if "num_transitions" in stats:
+                if isinstance(stats["num_transitions"], np.ndarray):
+                    stats["num_transitions"] = stats["num_transitions"].item()
+        json.dump(dataset_statistics, f_json, indent=2)
+    overwatch.info(f"Saved dataset statistics file at path {out_path}")
+
+
+def allocate_threads(n: Optional[int], weights: np.ndarray):
+    """
+    Allocates an integer number of threads across datasets based on weights.
+
+    The final array sums to `n`, but each element is no less than 1. If `n` is None, then every dataset is assigned a
+    value of AUTOTUNE.
+    """
+    if n is None:
+        return np.array([tf.data.AUTOTUNE] * len(weights))
+
+    assert np.all(weights >= 0), "Weights must be non-negative"
+    assert len(weights) <= n, "Number of threads must be at least as large as length of weights"
+    weights = np.array(weights) / np.sum(weights)
+
+    allocation = np.zeros_like(weights, dtype=int)
+    while True:
+        # Give the remaining elements that would get less than 1 a 1
+        mask = (weights * n < 1) & (weights > 0)
+        if not mask.any():
+            break
+        n -= mask.sum()
+        allocation += mask.astype(int)
+
+        # Recompute the distribution over the remaining elements
+        weights[mask] = 0
+        weights = weights / weights.sum()
+
+    # Allocate the remaining elements
+    fractional, integral = np.modf(weights * n)
+    allocation += integral.astype(int)
+    n -= integral.sum()
+    for i in np.argsort(fractional)[::-1][: int(n)]:
+        allocation[i] += 1
+
+    return allocation
diff --git a/prismatic/vla/datasets/rlds/utils/goal_relabeling.py b/prismatic/vla/datasets/rlds/utils/goal_relabeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..4864d2b772e53ca75cb03b50efb5921d2deae50c
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/utils/goal_relabeling.py
@@ -0,0 +1,32 @@
+"""
+goal_relabeling.py
+
+Contains simple goal relabeling logic for BC use-cases where rewards and next_observations are not required.
+Each function should add entries to the "task" dict.
+"""
+
+from typing import Dict
+
+import tensorflow as tf
+
+from prismatic.vla.datasets.rlds.utils.data_utils import tree_merge
+
+
+def uniform(traj: Dict) -> Dict:
+    """Relabels with a true uniform distribution over future states."""
+    traj_len = tf.shape(tf.nest.flatten(traj["observation"])[0])[0]
+
+    # Select a random future index for each transition i in the range [i + 1, traj_len)
+    rand = tf.random.uniform([traj_len])
+    low = tf.cast(tf.range(traj_len) + 1, tf.float32)
+    high = tf.cast(traj_len, tf.float32)
+    goal_idxs = tf.cast(rand * (high - low) + low, tf.int32)
+
+    # Sometimes there are floating-point errors that cause an out-of-bounds
+    goal_idxs = tf.minimum(goal_idxs, traj_len - 1)
+
+    # Adds keys to "task" mirroring "observation" keys (`tree_merge` to combine "pad_mask_dict" properly)
+    goal = tf.nest.map_structure(lambda x: tf.gather(x, goal_idxs), traj["observation"])
+    traj["task"] = tree_merge(traj["task"], goal)
+
+    return traj
diff --git a/prismatic/vla/datasets/rlds/utils/task_augmentation.py b/prismatic/vla/datasets/rlds/utils/task_augmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..425b57303a4d06dd60ccdc05b7ef51f328e68b18
--- /dev/null
+++ b/prismatic/vla/datasets/rlds/utils/task_augmentation.py
@@ -0,0 +1,57 @@
+"""
+task_augmentation.py
+
+Contains basic logic for randomly zeroing out keys in the task specification.
+"""
+
+from typing import Dict
+
+import tensorflow as tf
+
+from prismatic.vla.datasets.rlds.utils.data_utils import to_padding
+
+
+def delete_task_conditioning(traj: Dict, keep_image_prob: float) -> Dict:
+    """
+    Randomly drops out either the goal images or the language instruction. Only does something if both of
+    these are present.
+
+    Args:
+        traj: A dictionary containing trajectory data. Should have a "task" key.
+        keep_image_prob: The probability of keeping the goal images. The probability of keeping the language
+            instruction is 1 - keep_image_prob.
+    """
+    if "language_instruction" not in traj["task"]:
+        return traj
+
+    image_keys = {key for key in traj["task"].keys() if key.startswith("image_") or key.startswith("depth_")}
+    if not image_keys:
+        return traj
+
+    traj_len = tf.shape(traj["action"])[0]
+    should_keep_images = tf.random.uniform([traj_len]) < keep_image_prob
+    should_keep_images |= ~traj["task"]["pad_mask_dict"]["language_instruction"]
+
+    for key in image_keys | {"language_instruction"}:
+        should_keep = should_keep_images if key in image_keys else ~should_keep_images
+        # pad out the key
+        traj["task"][key] = tf.where(
+            should_keep,
+            traj["task"][key],
+            to_padding(traj["task"][key]),
+        )
+        # zero out the pad mask dict for the key
+        traj["task"]["pad_mask_dict"][key] = tf.where(
+            should_keep,
+            traj["task"]["pad_mask_dict"][key],
+            tf.zeros_like(traj["task"]["pad_mask_dict"][key]),
+        )
+
+    # when no goal images are present, the goal timestep becomes the final timestep
+    traj["task"]["timestep"] = tf.where(
+        should_keep_images,
+        traj["task"]["timestep"],
+        traj_len - 1,
+    )
+
+    return traj
diff --git a/prismatic/vla/materialize.py b/prismatic/vla/materialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f2d1200e5707b020116b796ac7e1bcf8389dd90
--- /dev/null
+++ b/prismatic/vla/materialize.py
@@ -0,0 +1,103 @@
+"""
+materialize.py
+
+Factory class for initializing Open-X RLDS-backed datasets, given specified data mixture parameters; provides and
+exports individual functions for clear control flow.
+"""
+
+from pathlib import Path
+from typing import Tuple, Type
+
+from torch.utils.data import Dataset
+from transformers import PreTrainedTokenizerBase
+
+from prismatic.models.backbones.llm.prompting import PromptBuilder
+from prismatic.models.backbones.vision import ImageTransform
+from prismatic.util.data_utils import PaddedCollatorForActionPrediction
+from prismatic.vla.action_tokenizer import ActionTokenizer
+from prismatic.vla.datasets import EpisodicRLDSDataset, RLDSBatchTransform, RLDSDataset
+
+
+def get_vla_dataset_and_collator(
+    data_root_dir: Path,
+    data_mix: str,
+    image_transform: ImageTransform,
+    tokenizer: PreTrainedTokenizerBase,
+    prompt_builder_fn: Type[PromptBuilder],
+    default_image_resolution: Tuple[int, int, int],
+    padding_side: str = "right",
+    predict_stop_token: bool = True,
+    shuffle_buffer_size: int = 100_000,
+    train: bool = True,
+    episodic: bool = False,
+    image_aug: bool = False,
+) -> Tuple[Dataset, ActionTokenizer, PaddedCollatorForActionPrediction]:
+    """Initialize RLDS Dataset (wraps TFDS), ActionTokenizer, and initialize transform/collation functions."""
+    action_tokenizer = ActionTokenizer(tokenizer)
+    batch_transform = RLDSBatchTransform(
+        action_tokenizer, tokenizer, image_transform, prompt_builder_fn, predict_stop_token=predict_stop_token
+    )
+    collator = PaddedCollatorForActionPrediction(
+        tokenizer.model_max_length, tokenizer.pad_token_id, padding_side=padding_side
+    )
+
+    # Build RLDS Iterable Dataset
+    cls = RLDSDataset if not episodic else EpisodicRLDSDataset
+    dataset = cls(
+        data_root_dir,
+        data_mix,
+        batch_transform,
+        resize_resolution=default_image_resolution[1:],
+        shuffle_buffer_size=shuffle_buffer_size,
+        train=train,
+        image_aug=image_aug,
+    )
+
+    return dataset, action_tokenizer, collator
+
+
+def get_vla_trainval_dataset_and_collator(
+    data_root_dir: Path,
+    data_mix: str,
+    image_transform: ImageTransform,
+    tokenizer: PreTrainedTokenizerBase,
+    prompt_builder_fn: Type[PromptBuilder],
+    default_image_resolution: Tuple[int, int, int],
+    padding_side: str = "right",
+    predict_stop_token: bool = True,
+    shuffle_buffer_size: int = 100_000,
+    train: bool = True,
+    episodic: bool = False,
+    image_aug: bool = False,
+) -> Tuple[Dataset, ActionTokenizer, PaddedCollatorForActionPrediction]:
+    """Initialize RLDS Dataset (wraps TFDS), ActionTokenizer, and initialize transform/collation functions."""
+    action_tokenizer = ActionTokenizer(tokenizer)
+    batch_transform = RLDSBatchTransform(
+        action_tokenizer, tokenizer, image_transform, prompt_builder_fn, predict_stop_token=predict_stop_token
+    )
+    collator = PaddedCollatorForActionPrediction(
+        tokenizer.model_max_length, tokenizer.pad_token_id, padding_side=padding_side
+    )
+
+    # Build RLDS Iterable Dataset
+    cls = RLDSDataset if not episodic else EpisodicRLDSDataset
+    dataset_train = cls(
+        data_root_dir,
+        data_mix,
+        batch_transform,
+        resize_resolution=default_image_resolution[1:],
+        shuffle_buffer_size=shuffle_buffer_size,
+        train=True,
+        image_aug=image_aug,
+    )
+    dataset_val = cls(
+        data_root_dir,
+        data_mix,
+        batch_transform,
+        resize_resolution=default_image_resolution[1:],
+        shuffle_buffer_size=50000,
+        train=False,
+        image_aug=False,
+    )
+    # print(len(dataset_train), len(dataset_val)); 1/0
+    return dataset_train, dataset_val, action_tokenizer, collator
diff --git a/robot_utils.py b/robot_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f9013f16f19fb9129bc6a13a49e701dbbf2d6d4
--- /dev/null
+++ b/robot_utils.py
@@ -0,0 +1,115 @@
+"""Utils for evaluating robot policies in various environments."""
+
+import os
+import random
+import time
+
+import numpy as np
+import torch
+
+from openvla_utils import (
+    get_vla_action,
+    get_vla_action_v2,
+)
+
+# Initialize important constants and pretty-printing mode in NumPy.
+ACTION_DIM = 7
+DATE = time.strftime("%Y_%m_%d")
+DATE_TIME = time.strftime("%Y_%m_%d-%H_%M_%S")
+DEVICE = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+np.set_printoptions(formatter={"float": lambda x: "{0:0.3f}".format(x)})
+
+# Initialize system prompt for OpenVLA v0.1.
+OPENVLA_V01_SYSTEM_PROMPT = (
+    "A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions."
+)
+
+
+def set_seed_everywhere(seed: int):
+    """Sets the random seed for Python, NumPy, and PyTorch functions."""
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ["PYTHONHASHSEED"] = str(seed)
+
+
+
+
+def get_image_resize_size(cfg):
+    """
+    Gets image resize size for a model class.
+    If `resize_size` is an int, then the resized image will be a square.
+    Else, the image will be a rectangle.
+    """
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        resize_size = 224
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return resize_size
+
+
+def get_action(cfg, model, obs, task_label, processor=None):
+    """Queries the model to get an action."""
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        action = get_vla_action(
+            model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop
+        )
+        assert action.shape == (ACTION_DIM,)
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return action
+
+def get_action_v2(cfg, model, obs, task_label, processor=None):
+    """Queries the model to get an action."""
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        action = get_vla_action_v2(
+            model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop
+        )
+        assert action.shape == (ACTION_DIM,)
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return action
+
+def get_action_v3(cfg, model, obs, task_label, processor=None):
+    """Queries the model to get an action."""
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        action = get_vla_action_v3(
+            model, processor, cfg.pretrained_checkpoint, obs, task_label, cfg.unnorm_key, center_crop=cfg.center_crop
+        )
+        assert action.shape == (ACTION_DIM,)
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return action
+
+def normalize_gripper_action(action, binarize=True):
+    """
+    Changes gripper action (last dimension of action vector) from [0,1] to [-1,+1].
+    Necessary for some environments (not Bridge) because the dataset wrapper standardizes gripper actions to [0,1].
+    Note that unlike the other action dimensions, the gripper action is not normalized to [-1,+1] by default by
+    the dataset wrapper.
+
+    Normalization formula: y = 2 * (x - orig_low) / (orig_high - orig_low) - 1
+    """
+    # Just normalize the last action to [-1,+1].
+    orig_low, orig_high = 0.0, 1.0
+    action[..., -1] = 2 * (action[..., -1] - orig_low) / (orig_high - orig_low) - 1
+
+    if binarize:
+        # Binarize to -1 or +1.
+        action[..., -1] = np.sign(action[..., -1])
+
+    return action
+
+
+def invert_gripper_action(action):
+    """
+    Flips the sign of the gripper action (last dimension of action vector).
+    This is necessary for some environments where -1 = open, +1 = close, since
+    the RLDS dataloader aligns gripper actions such that 0 = close, 1 = open.
+    """
+    action[..., -1] = action[..., -1] * -1.0
+    return action
\ No newline at end of file
diff --git a/run_oc_model_sample.py b/run_oc_model_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..323dc64466b16a1fb95211c37b6814352018fa52
--- /dev/null
+++ b/run_oc_model_sample.py
@@ -0,0 +1,509 @@
+"""
+run_libero_eval.py
+
+Runs a model in a LIBERO simulation environment.
+
+Usage:
+    # OpenVLA:
+    # IMPORTANT: Set `center_crop=True` if model is fine-tuned with augmentations
+    oc slot
+    CUDA_VISIBLE_DEVICES=0 python run_oc_model_sample.py \
+        --model_family openvla \
+        --saved_dir output_oc
+"""
+
+import random
+import os
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional, Union
+import torch
+import draccus
+import numpy as np
+import tqdm
+from transformers import AutoConfig, AutoImageProcessor, AutoModelForVision2Seq, AutoProcessor
+
+# import wandb
+
+# Append current directory so that interpreter can find experiments.robot
+# from libero_utils import (
+#     get_libero_env,
+#     get_libero_image,
+#     save_rollout_video,
+# )
+from robot_utils import (
+    DATE_TIME,
+)
+
+from prismatic.extern.hf.configuration_prismatic import OpenVLAConfig
+from prismatic.extern.hf.modeling_prismatic import OpenVLAForActionPrediction, EmbodiedObjectSlot, EmbodiedRelationSlot, EmbodiedObject_LangSlot, EmbodiedRelation_LangSlot
+from prismatic.extern.hf.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor
+from peft import PeftModel
+from safetensors.torch import load_file
+
+import json
+
+def get_processor(cfg):
+    """Get VLA model's Hugging Face processor."""
+    processor = AutoProcessor.from_pretrained(os.path.dirname(cfg.custom_param_checkpoint), trust_remote_code=False)
+    return processor
+
+def get_vla(cfg):
+    """Loads and returns a VLA model from checkpoint."""
+    # Load VLA checkpoint.
+    print("[*] Instantiating Pretrained VLA model")
+    # print("[*] Loading in F16 with Flash-Attention Enabled")
+    print("[*] Loading in BF16 with Flash-Attention Enabled")
+
+    AutoConfig.register("openvla", OpenVLAConfig)
+    AutoImageProcessor.register(OpenVLAConfig, PrismaticImageProcessor)
+    AutoProcessor.register(OpenVLAConfig, PrismaticProcessor)
+    AutoModelForVision2Seq.register(OpenVLAConfig, OpenVLAForActionPrediction)
+
+    base_vla = AutoModelForVision2Seq.from_pretrained(
+        'output_hf_model_openx', 
+        attn_implementation="flash_attention_2",
+        torch_dtype=torch.bfloat16,
+        # torch_dtype=torch.float16,
+        load_in_8bit=cfg.load_in_8bit,
+        load_in_4bit=cfg.load_in_4bit,
+        low_cpu_mem_usage=True,
+        trust_remote_code=False,
+    ).to('cuda')
+    print("Loaded base.")
+    
+    base_vla = PeftModel.from_pretrained(base_vla, cfg.pretrained_checkpoint)
+    base_vla = base_vla.merge_and_unload()
+    print("Merged CustomOpenVLA LLM LoRA.")
+
+    if cfg.slot_type == 'oc':
+        vla = EmbodiedObject_LangSlot(base_model=base_vla, number_of_slots = cfg.number_of_slots)
+    elif cfg.slot_type == 'orc':
+        vla = EmbodiedRelation_LangSlot(base_model=base_vla, number_of_slots = cfg.number_of_slots)
+    
+    weights = load_file(cfg.custom_param_checkpoint)
+    vla.load_state_dict(weights, strict=False)
+    vla.object_centric_tokenizer.requires_grad_(False)
+    vla.object_centric_bbox_head.requires_grad_(False)
+    vla.object_centric_mask_head.requires_grad_(False)
+    vla.requires_grad_(False)
+    vla = vla.to('cuda')
+
+    # Move model to device.
+    # Note: `.to()` is not supported for 8-bit or 4-bit bitsandbytes models, but the model will
+    #       already be set to the right devices and casted to the correct dtype upon loading.
+    if not cfg.load_in_8bit and not cfg.load_in_4bit:
+        vla = vla.to('cuda')
+
+    # Load dataset stats used during finetuning (for action un-normalization).
+    dataset_statistics_path = os.path.join(os.path.dirname(cfg.custom_param_checkpoint), "dataset_statistics.json")
+    if os.path.isfile(dataset_statistics_path):
+        with open(dataset_statistics_path, "r") as f:
+            norm_stats = json.load(f)
+        vla.norm_stats = norm_stats
+    else:
+        print(
+            "WARNING: No local dataset_statistics.json file found for current checkpoint.\n"
+            "You can ignore this if you are loading the base VLA (i.e. not fine-tuned) checkpoint."
+            "Otherwise, you may run into errors when trying to call `predict_action()` due to an absent `unnorm_key`."
+        )
+
+    return vla
+
+def get_model(cfg, wrap_diffusion_policy_for_droid=False):
+    """Load model for evaluation."""
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        model = get_vla(cfg)
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    print(f"Loaded model: {type(model)}")
+    return model
+
+def visualize_all(visuals):
+    # Get the corresponding visualizations
+    row1 = [
+        visuals['exo_rgb'],  # 'exo rgb'
+        visuals['exo_rgb_boxed'],  # 'exo rgb'
+        visuals['exo_rgb_boxed_contact'],  # 'exo rgb'
+        visuals['exo_depth'],  # 'exo depth'
+        visuals['exo_seg'],  # 'exo seg'
+    ]
+
+    row2 = [
+        visuals['ego_rgb'],  # 'ego rgb'
+        visuals['ego_rgb_boxed'],  # 'ego rgb'
+        visuals['ego_rgb_boxed_contact'],  # 'ego rgb'
+        visuals['ego_depth'],  # 'ego depth'
+        visuals['ego_seg'],  # 'ego seg'
+    ]
+
+    # Function to preprocess images (convert grayscale to RGB and resize)
+    def preprocess_image(image, target_size=(300, 300)):
+        if len(image.shape) == 2:  # Convert grayscale to RGB
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = cv2.resize(image, target_size)
+        return image
+
+    # Process all images to uniform size and color format and concatenate horizontally
+    row1_concat = np.hstack([preprocess_image(img) for img in row1])
+    row2_concat = np.hstack([preprocess_image(img) for img in row2])
+
+    # Concatenate vertically to form a 2x4 grid
+    final_image = np.vstack([row1_concat, row2_concat])
+    cv2.imshow('data', final_image)
+
+from dataclasses import dataclass, field
+
+def get_libero_dummy_action(model_family: str):
+    """Get dummy/no-op action, used to roll out the simulation while the robot does nothing."""
+    return [0, 0, 0, 0, 0, 0, -1]
+
+@dataclass
+class GenerateConfig:
+    # fmt: off
+
+    #################################################################################################################
+    # Model-specific parameters
+    #################################################################################################################
+    model_family: str = "openvla"                    # Model family
+    slot_type: str = "oc"                            # oc
+    number_of_slots: int = 16                        # number of slots in the model
+    pretrained_checkpoint: Union[str, Path] = "oc_model"     # Pretrained checkpoint path
+    custom_param_checkpoint: Union[str, Path] = "oc_model/relate_object_bboxes_w_mask_actionable_s16h1.safetensors"   # Pretrained checkpoint path
+    load_in_8bit: bool = False                       # (For OpenVLA only) Load with 8-bit quantization
+    load_in_4bit: bool = False                       # (For OpenVLA only) Load with 4-bit quantization
+    
+    center_crop: bool = False                         # Center crop? (if trained w/ random crop image aug)
+
+    #################################################################################################################
+    # LIBERO environment-specific parameters
+    #################################################################################################################
+    task_suite_name: str = "real_data"            # Task suite. Options: libero_spatial, libero_object, libero_goal, libero_10, libero_90
+    num_steps_wait: int = 20                        # Number of steps to wait for objects to stabilize in sim
+    num_trials_per_task: int = 20                   # Number of rollouts per task
+
+    #################################################################################################################
+    # Utils
+    #################################################################################################################
+    run_id_note: Optional[str] = None                # Extra note to add in run ID for logging
+    local_log_dir: str = "./experiments/logs"        # Local directory for eval logs
+    saved_dir: str = "libero_goal_01_slots"             # Libero goal slots
+
+    use_wandb: bool = False                          # Whether to also log results in Weights & Biases
+    wandb_project: str = "YOUR_WANDB_PROJECT"        # Name of W&B project to log to (use default!)
+    wandb_entity: str = "YOUR_WANDB_ENTITY"          # Name of entity to log under
+
+    seed: int = 7                                    # Random Seed (for reproducibility)
+
+    writing_extra_waits: bool = False
+    # fmt: on
+
+import re
+
+def natural_key(s):
+    return [int(text) if text.isdigit() else text for text in re.split(r'(\d+)', s)]
+
+import cv2
+import pickle
+
+IMAGE_RESOLUTION = 256
+from PIL import Image
+
+def process_inputs(processor, pixel_values, device):
+    image = Image.fromarray(pixel_values)
+    image = image.convert("RGB")
+    
+    # Build VLA prompt
+    action_dim = 7; future_horizon = 5
+    prompt = f"In: What action should the robot take?\nOut: "
+    placeholder_seg = "_ _ _ _ _ _ _ _"
+    for fidx in range(future_horizon-1):
+        placeholder_seg += " _ _ _ _ _ _ _"
+    prompt = prompt + placeholder_seg + "</s>"
+
+    # Process inputs.
+    inputs = processor(prompt, image).to(device, dtype=torch.float16)
+    inputs['input_ids'][0][-2-(action_dim*future_horizon)] = 29871
+    # for key, value in inputs.items():
+    #     print(key, value.shape)
+    # 1/0
+    return inputs
+
+def set_seed_everywhere(seed: int):
+    """Sets the random seed for Python, NumPy, and PyTorch functions."""
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ["PYTHONHASHSEED"] = str(seed)
+
+
+def get_image_resize_size(cfg):
+    """
+    Gets image resize size for a model class.
+    If `resize_size` is an int, then the resized image will be a square.
+    Else, the image will be a rectangle.
+    """
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        resize_size = 224
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return resize_size
+
+
+def get_current_slots(vla, batch, task_texts, in_past_tokens, device_id='cuda'):
+    pixel_values = batch["pixel_values"].to(torch.bfloat16).to(device_id)
+    outputs = vla.get_obj_slots(pixel_values, task_texts, in_past_tokens)
+    return outputs
+
+def get_normalized_actions(vla, batch, slot_outputs, device_id):
+    # get object-centric dynamics
+    patch_features = slot_outputs['patch_features']
+    slotted_features = slot_outputs['visual_tokens']
+    if 'interactable_features' in slot_outputs:
+        slotted_features, _ = vla.select_top_k_slots(
+                            slotted_features, slot_outputs['interactable_features'], k=4)
+    clip_embeddings = slot_outputs['texts']
+    clip_attention_mask = torch.logical_not(slot_outputs['texts_attn'])
+    llama_input_ids = batch["input_ids"].to(device_id)
+    llama_attention_mask = batch["attention_mask"].to(device_id)
+
+    continuous_actions_pred = vla.decode_continuous_actions(
+        patch_features=patch_features,
+        slotted_features=slotted_features,
+        clip_embeddings=clip_embeddings,
+        clip_attention_mask=clip_attention_mask,
+        llama_input_ids=llama_input_ids,
+        llama_attention_mask=llama_attention_mask,
+        llama_labels=None
+    ) # will return output of [cross-modality-slots, cross-modality-bboxes]
+    action_chunk, action_dim = continuous_actions_pred.shape[1:]
+
+    return continuous_actions_pred[:,0,:]
+
+def denormalize_actions(model, unnorm_key, normalized_actions):
+    # Unnormalize actions
+    action_norm_stats = model.get_action_stats(unnorm_key)
+    mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+    action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+    actions = np.where(
+        mask,
+        0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+        normalized_actions,
+    )
+    return actions
+
+def normalize_gripper_action(action, binarize=True):
+    """
+    Changes gripper action (last dimension of action vector) from [0,1] to [-1,+1].
+    Necessary for some environments (not Bridge) because the dataset wrapper standardizes gripper actions to [0,1].
+    Note that unlike the other action dimensions, the gripper action is not normalized to [-1,+1] by default by
+    the dataset wrapper.
+
+    Normalization formula: y = 2 * (x - orig_low) / (orig_high - orig_low) - 1
+    """
+    # Just normalize the last action to [-1,+1].
+    orig_low, orig_high = 0.0, 1.0
+    action[..., -1] = 2 * (action[..., -1] - orig_low) / (orig_high - orig_low) - 1
+
+    if binarize:
+        # Binarize to -1 or +1.
+        action[..., -1] = np.sign(action[..., -1])
+
+    return action
+
+# from libero.libero import benchmark
+
+@draccus.wrap()
+def eval_libero(cfg: GenerateConfig) -> None:
+    print(cfg.pretrained_checkpoint)
+    assert cfg.pretrained_checkpoint is not None, "cfg.pretrained_checkpoint must not be None!"
+    # if "image_aug" in cfg.pretrained_checkpoint:
+    #     assert cfg.center_crop, "Expecting `center_crop==True` because model was trained with image augmentations!"
+    assert not (cfg.load_in_8bit and cfg.load_in_4bit), "Cannot use both 8-bit and 4-bit quantization!"
+
+    # Set random seed
+    set_seed_everywhere(cfg.seed)
+
+    # [OpenVLA] Set action un-normalization key
+    cfg.unnorm_key = cfg.task_suite_name
+
+    # # Load model
+    model = get_model(cfg)
+
+    # [OpenVLA] Check that the model contains the action un-normalization key
+    if cfg.model_family == "openvla":
+        # In some cases, the key must be manually modified (e.g. after training on a modified version of the dataset
+        # with the suffix "_no_noops" in the dataset name)
+        if cfg.unnorm_key not in model.norm_stats and f"{cfg.unnorm_key}_no_noops" in model.norm_stats:
+            cfg.unnorm_key = f"{cfg.unnorm_key}_no_noops"
+        assert cfg.unnorm_key in model.norm_stats, f"Action un-norm key {cfg.unnorm_key} not found in VLA `norm_stats`!"
+
+    # [OpenVLA] Get Hugging Face processor
+    processor = None
+    if cfg.model_family == "openvla":
+        processor = get_processor(cfg)
+
+    # Initialize local logging
+    run_id = f"EVAL-{cfg.task_suite_name}-{cfg.model_family}-{DATE_TIME}"
+    if cfg.run_id_note is not None:
+        run_id += f"--{cfg.run_id_note}"
+    os.makedirs(cfg.local_log_dir, exist_ok=True)
+    local_log_filepath = os.path.join(cfg.local_log_dir, run_id + ".txt")
+    log_file = open(local_log_filepath, "w")
+    print(f"Logging to local log file: {local_log_filepath}")
+
+    # Initialize LIBERO task suite
+    benchmark_dict = benchmark.get_benchmark_dict()
+    task_suite = benchmark_dict[cfg.task_suite_name]()
+    num_tasks_in_suite = task_suite.n_tasks
+    print(f"Task suite: {cfg.task_suite_name}")
+    log_file.write(f"Task suite: {cfg.task_suite_name}\n")
+
+    # Get expected image dimensions
+    resize_size = get_image_resize_size(cfg)
+
+    # Start evaluation
+    total_episodes, total_successes = 0, 0
+    extra_waits_dict = {}
+    for task_id in tqdm.tqdm(range(num_tasks_in_suite)):
+        # Get task
+        task = task_suite.get_task(task_id)
+
+        # Get default LIBERO initial states
+        initial_states = task_suite.get_task_init_states(task_id)
+
+        # Initialize LIBERO environment and task description
+        env, task_description = get_libero_env(task, cfg.model_family, resolution=256)
+
+        # Start episodes
+        task_episodes, task_successes = 0, 0
+        extra_waits = []
+        for episode_idx in tqdm.tqdm(range(cfg.num_trials_per_task)):
+            print(f"\nTask: {task_description}")
+            log_file.write(f"\nTask: {task_description}\n")
+
+            # Reset environment
+            env.reset()
+
+            # Set initial states
+            obs = env.set_init_state(initial_states[episode_idx])
+            
+            # Setup
+            t = 0
+            replay_images = []
+            if cfg.task_suite_name == "libero_spatial":
+                max_steps = 220  # longest training demo has 193 steps
+            elif cfg.task_suite_name == "libero_object":
+                max_steps = 280  # longest training demo has 254 steps
+            elif cfg.task_suite_name == "libero_goal":
+                max_steps = 300  # longest training demo has 270 steps
+            elif cfg.task_suite_name == "libero_10":
+                max_steps = 520  # longest training demo has 505 steps
+            elif cfg.task_suite_name == "libero_90":
+                max_steps = 400  # longest training demo has 373 steps
+
+            t = 0
+            while t < cfg.num_steps_wait:
+                # try:
+                # IMPORTANT: Do nothing for the first few timesteps because the simulator drops objects
+                # and we need to wait for them to fall
+                obs, reward, done, info = env.step(get_libero_dummy_action(cfg.model_family))
+                t += 1
+            
+            # Process observations and task
+            task_texts = [task_description]
+            texts = ['robot ' + task_description]
+            img = get_libero_image(obs, resize_size)
+            img = img[:,::-1,:]
+            batch_data = process_inputs(processor=processor, pixel_values=img, device='cuda')
+
+            horizon = 1
+            batch_data['pixel_values'] = batch_data['pixel_values'].unsqueeze(1).repeat(1,horizon,1,1,1)            
+            # Replicate frames across temporal dimension for initialization purposes
+            # Get the object-centric slot embeddings
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                slot_outputs = get_current_slots(model, batch_data, texts, in_past_tokens=None, device_id='cuda')
+                    
+            # Query model to get several prelim actions
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                actions = get_normalized_actions(model, batch_data, slot_outputs, device_id='cuda')
+                actions = actions.detach().float().cpu().numpy()
+                unnormalized_actions = denormalize_actions(model, cfg.unnorm_key, actions)
+
+            ## Start running the rest of the model here
+            while t < max_steps + cfg.num_steps_wait:
+                # Get preprocessed image
+                img = get_libero_image(obs, resize_size)
+                img = img[:,::-1,:]
+                batch_data = process_inputs(processor=processor, pixel_values=img, device='cuda')
+                # pixel_values    = batch_data['pixel_values']    #  torch.Size([1, 6, 224, 224])
+                # input_ids       = batch_data['input_ids']       #  torch.Size([1, 50])
+                # attention_mask  = batch_data['attention_mask']  # torch.Size([1, 50])
+                # Save preprocessed image for replay video
+                replay_images.append(img)
+
+                # Getting frames across temporal dimension for initialization purposes
+                batch_data['pixel_values'] = batch_data['pixel_values'].unsqueeze(1)
+                # Get the object-centric slot embeddings
+                with torch.autocast("cuda", dtype=torch.bfloat16):
+                    slot_outputs = get_current_slots(model, batch_data, texts, in_past_tokens=None, device_id='cuda')
+                                        
+                # Query model to get several prelim actions
+                with torch.autocast("cuda", dtype=torch.bfloat16):
+                    actions = get_normalized_actions(model, batch_data, slot_outputs, device_id='cuda')
+                    actions = actions.detach().float().cpu().numpy()
+                    unnormalized_actions = denormalize_actions(model, cfg.unnorm_key, actions)
+                    action = unnormalized_actions[0]
+
+                # Normalize gripper action [0,1] -> [-1,+1] because the environment expects the latter
+                action = normalize_gripper_action(action, binarize=True)
+
+                # [OpenVLA] The dataloader flips the sign of the gripper action to align with other datasets
+                # (0 = close, 1 = open), so flip it back (-1 = open, +1 = close) before executing the action
+                # if cfg.model_family == "openvla":
+                #     action = invert_gripper_action(action)
+
+                # Execute action in environment
+                obs, reward, done, info = env.step(action.tolist())
+                t += 1
+                if done:
+                    task_successes += 1
+                    total_successes += 1
+                    break
+
+            task_episodes += 1
+            total_episodes += 1
+
+            # Save a replay video of the episode
+            cv2.destroyAllWindows()
+            save_rollout_video(
+                replay_images, total_episodes, success=done, task_description=task_description, log_file=log_file, saved_dir=cfg.saved_dir
+            )
+            # # Log current results
+            print(f"Success: {done}")
+            print(f"# episodes completed so far: {total_episodes}")
+            print(f"# successes: {total_successes} ({total_successes / total_episodes * 100:.1f}%)")
+            log_file.write(f"Success: {done}\n")
+            log_file.write(f"# episodes completed so far: {total_episodes}\n")
+            log_file.write(f"# successes: {total_successes} ({total_successes / total_episodes * 100:.1f}%)\n")
+            log_file.flush()
+            # # 1/0
+        # 1/0
+            
+        # Log final results
+        print(f"Current task success rate: {float(task_successes) / float(task_episodes)}")
+        print(f"Current total success rate: {float(total_successes) / float(total_episodes)}")
+        log_file.write(f"Current task success rate: {float(task_successes) / float(task_episodes)}\n")
+        log_file.write(f"Current total success rate: {float(total_successes) / float(total_episodes)}\n")
+        log_file.flush()
+
+    # Save local log file
+    log_file.close()
+
+if __name__ == "__main__":
+    eval_libero()
diff --git a/run_orc_model_sample.py b/run_orc_model_sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..eafad01038e9788cc8d897b44996eae08af3f05a
--- /dev/null
+++ b/run_orc_model_sample.py
@@ -0,0 +1,508 @@
+"""
+run_libero_eval.py
+
+Runs a model in a LIBERO simulation environment.
+
+Usage:
+    # OpenVLA:
+    # IMPORTANT: Set `center_crop=True` if model is fine-tuned with augmentations
+    oc slot
+    CUDA_VISIBLE_DEVICES=0 python run_orc_model_sample.py \
+        --model_family openvla \
+        --saved_dir output_orc
+"""
+
+import random
+import os
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional, Union
+import torch
+import draccus
+import numpy as np
+import tqdm
+from transformers import AutoConfig, AutoImageProcessor, AutoModelForVision2Seq, AutoProcessor
+
+# import wandb
+
+# Append current directory so that interpreter can find experiments.robot
+# from libero_utils import (
+#     get_libero_env,
+#     get_libero_image,
+#     save_rollout_video,
+# )
+from robot_utils import (
+    DATE_TIME,
+)
+
+from prismatic.extern.hf.configuration_prismatic import OpenVLAConfig
+from prismatic.extern.hf.modeling_prismatic import OpenVLAForActionPrediction, EmbodiedObjectSlot, EmbodiedRelationSlot, EmbodiedObject_LangSlot, EmbodiedRelation_LangSlot
+from prismatic.extern.hf.processing_prismatic import PrismaticImageProcessor, PrismaticProcessor
+from peft import PeftModel
+from safetensors.torch import load_file
+
+import json
+
+def get_processor(cfg):
+    """Get VLA model's Hugging Face processor."""
+    processor = AutoProcessor.from_pretrained(os.path.dirname(cfg.custom_param_checkpoint), trust_remote_code=False)
+    return processor
+
+def get_vla(cfg):
+    """Loads and returns a VLA model from checkpoint."""
+    # Load VLA checkpoint.
+    print("[*] Instantiating Pretrained VLA model")
+    # print("[*] Loading in F16 with Flash-Attention Enabled")
+    print("[*] Loading in BF16 with Flash-Attention Enabled")
+
+    AutoConfig.register("openvla", OpenVLAConfig)
+    AutoImageProcessor.register(OpenVLAConfig, PrismaticImageProcessor)
+    AutoProcessor.register(OpenVLAConfig, PrismaticProcessor)
+    AutoModelForVision2Seq.register(OpenVLAConfig, OpenVLAForActionPrediction)
+
+    base_vla = AutoModelForVision2Seq.from_pretrained(
+        'output_hf_model_openx', 
+        attn_implementation="flash_attention_2",
+        torch_dtype=torch.bfloat16,
+        # torch_dtype=torch.float16,
+        load_in_8bit=cfg.load_in_8bit,
+        load_in_4bit=cfg.load_in_4bit,
+        low_cpu_mem_usage=True,
+        trust_remote_code=False,
+    ).to('cuda')
+    print("Loaded base.")
+    
+    base_vla = PeftModel.from_pretrained(base_vla, cfg.pretrained_checkpoint)
+    base_vla = base_vla.merge_and_unload()
+    print("Merged CustomOpenVLA LLM LoRA.")
+
+    if cfg.slot_type == 'oc':
+        vla = EmbodiedObject_LangSlot(base_model=base_vla, number_of_slots = cfg.number_of_slots)
+    elif cfg.slot_type == 'orc':
+        vla = EmbodiedRelation_LangSlot(base_model=base_vla, number_of_slots = cfg.number_of_slots)
+    
+    weights = load_file(cfg.custom_param_checkpoint)
+    vla.load_state_dict(weights, strict=False)
+    vla.object_centric_tokenizer.requires_grad_(False)
+    vla.object_centric_bbox_head.requires_grad_(False)
+    vla.object_centric_mask_head.requires_grad_(False)
+    vla.requires_grad_(False)
+    vla = vla.to('cuda')
+
+    # Move model to device.
+    # Note: `.to()` is not supported for 8-bit or 4-bit bitsandbytes models, but the model will
+    #       already be set to the right devices and casted to the correct dtype upon loading.
+    if not cfg.load_in_8bit and not cfg.load_in_4bit:
+        vla = vla.to('cuda')
+
+    # Load dataset stats used during finetuning (for action un-normalization).
+    dataset_statistics_path = os.path.join(os.path.dirname(cfg.custom_param_checkpoint), "dataset_statistics.json")
+    if os.path.isfile(dataset_statistics_path):
+        with open(dataset_statistics_path, "r") as f:
+            norm_stats = json.load(f)
+        vla.norm_stats = norm_stats
+    else:
+        print(
+            "WARNING: No local dataset_statistics.json file found for current checkpoint.\n"
+            "You can ignore this if you are loading the base VLA (i.e. not fine-tuned) checkpoint."
+            "Otherwise, you may run into errors when trying to call `predict_action()` due to an absent `unnorm_key`."
+        )
+
+    return vla
+
+def get_model(cfg, wrap_diffusion_policy_for_droid=False):
+    """Load model for evaluation."""
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        model = get_vla(cfg)
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    print(f"Loaded model: {type(model)}")
+    return model
+
+def visualize_all(visuals):
+    # Get the corresponding visualizations
+    row1 = [
+        visuals['exo_rgb'],  # 'exo rgb'
+        visuals['exo_rgb_boxed'],  # 'exo rgb'
+        visuals['exo_rgb_boxed_contact'],  # 'exo rgb'
+        visuals['exo_depth'],  # 'exo depth'
+        visuals['exo_seg'],  # 'exo seg'
+    ]
+
+    row2 = [
+        visuals['ego_rgb'],  # 'ego rgb'
+        visuals['ego_rgb_boxed'],  # 'ego rgb'
+        visuals['ego_rgb_boxed_contact'],  # 'ego rgb'
+        visuals['ego_depth'],  # 'ego depth'
+        visuals['ego_seg'],  # 'ego seg'
+    ]
+
+    # Function to preprocess images (convert grayscale to RGB and resize)
+    def preprocess_image(image, target_size=(300, 300)):
+        if len(image.shape) == 2:  # Convert grayscale to RGB
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = cv2.resize(image, target_size)
+        return image
+
+    # Process all images to uniform size and color format and concatenate horizontally
+    row1_concat = np.hstack([preprocess_image(img) for img in row1])
+    row2_concat = np.hstack([preprocess_image(img) for img in row2])
+
+    # Concatenate vertically to form a 2x4 grid
+    final_image = np.vstack([row1_concat, row2_concat])
+    cv2.imshow('data', final_image)
+
+from dataclasses import dataclass, field
+
+def get_libero_dummy_action(model_family: str):
+    """Get dummy/no-op action, used to roll out the simulation while the robot does nothing."""
+    return [0, 0, 0, 0, 0, 0, -1]
+
+@dataclass
+class GenerateConfig:
+    # fmt: off
+
+    #################################################################################################################
+    # Model-specific parameters
+    #################################################################################################################
+    model_family: str = "openvla"                    # Model family
+    slot_type: str = "orc"                            # orc
+    number_of_slots: int = 16                        # number of slots in the model
+    pretrained_checkpoint: Union[str, Path] = "./orc_model"     # Pretrained checkpoint path
+    custom_param_checkpoint: Union[str, Path] = "./orc_model/relate_object_bboxes_w_mask_actionable_s16h1.safetensors"   # Pretrained checkpoint path
+    load_in_8bit: bool = False                       # (For OpenVLA only) Load with 8-bit quantization
+    load_in_4bit: bool = False                       # (For OpenVLA only) Load with 4-bit quantization
+    
+    center_crop: bool = False                         # Center crop? (if trained w/ random crop image aug)
+
+    #################################################################################################################
+    # LIBERO environment-specific parameters
+    #################################################################################################################
+    task_suite_name: str = "real_data"            # Task suite. Options: libero_spatial, libero_object, libero_goal, libero_10, libero_90
+    num_steps_wait: int = 20                        # Number of steps to wait for objects to stabilize in sim
+    num_trials_per_task: int = 20                   # Number of rollouts per task
+
+    #################################################################################################################
+    # Utils
+    #################################################################################################################
+    run_id_note: Optional[str] = None                # Extra note to add in run ID for logging
+    local_log_dir: str = "./experiments/logs"        # Local directory for eval logs
+    saved_dir: str = "libero_goal_01_slots"             # Libero goal slots
+
+    use_wandb: bool = False                          # Whether to also log results in Weights & Biases
+    wandb_project: str = "YOUR_WANDB_PROJECT"        # Name of W&B project to log to (use default!)
+    wandb_entity: str = "YOUR_WANDB_ENTITY"          # Name of entity to log under
+
+    seed: int = 7                                    # Random Seed (for reproducibility)
+
+    writing_extra_waits: bool = False
+    # fmt: on
+
+import re
+
+def natural_key(s):
+    return [int(text) if text.isdigit() else text for text in re.split(r'(\d+)', s)]
+
+import cv2
+import pickle
+
+IMAGE_RESOLUTION = 256
+from PIL import Image
+
+def process_inputs(processor, pixel_values, device):
+    image = Image.fromarray(pixel_values)
+    image = image.convert("RGB")
+    
+    # Build VLA prompt
+    action_dim = 7; future_horizon = 5
+    prompt = f"In: What action should the robot take?\nOut: "
+    placeholder_seg = "_ _ _ _ _ _ _ _"
+    for fidx in range(future_horizon-1):
+        placeholder_seg += " _ _ _ _ _ _ _"
+    prompt = prompt + placeholder_seg + "</s>"
+
+    # Process inputs.
+    inputs = processor(prompt, image).to(device, dtype=torch.float16)
+    inputs['input_ids'][0][-2-(action_dim*future_horizon)] = 29871
+    # for key, value in inputs.items():
+    #     print(key, value.shape)
+    # 1/0
+    return inputs
+
+def set_seed_everywhere(seed: int):
+    """Sets the random seed for Python, NumPy, and PyTorch functions."""
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ["PYTHONHASHSEED"] = str(seed)
+
+
+def get_image_resize_size(cfg):
+    """
+    Gets image resize size for a model class.
+    If `resize_size` is an int, then the resized image will be a square.
+    Else, the image will be a rectangle.
+    """
+    if cfg.model_family == "openvla" or cfg.model_family == "customvla" or cfg.model_family == 'objectvla':
+        resize_size = 224
+    else:
+        raise ValueError("Unexpected `model_family` found in config.")
+    return resize_size
+
+
+def get_current_slots(vla, batch, task_texts, in_past_tokens, device_id='cuda'):
+    pixel_values = batch["pixel_values"].to(torch.bfloat16).to(device_id)
+    outputs = vla.get_obj_slots(pixel_values, task_texts, in_past_tokens)
+    return outputs
+
+def get_normalized_actions(vla, batch, slot_outputs, device_id):
+    # get object-centric dynamics
+    patch_features = slot_outputs['patch_features']
+    slotted_features = slot_outputs['visual_tokens']
+    if 'interactable_features' in slot_outputs:
+        slotted_features, _ = vla.select_top_k_slots(
+                            slotted_features, slot_outputs['interactable_features'], k=4)
+    clip_embeddings = slot_outputs['texts']
+    clip_attention_mask = torch.logical_not(slot_outputs['texts_attn'])
+    llama_input_ids = batch["input_ids"].to(device_id)
+    llama_attention_mask = batch["attention_mask"].to(device_id)
+
+    continuous_actions_pred = vla.decode_continuous_actions(
+        patch_features=patch_features,
+        slotted_features=slotted_features,
+        clip_embeddings=clip_embeddings,
+        clip_attention_mask=clip_attention_mask,
+        llama_input_ids=llama_input_ids,
+        llama_attention_mask=llama_attention_mask,
+        llama_labels=None
+    ) # will return output of [cross-modality-slots, cross-modality-bboxes]
+    action_chunk, action_dim = continuous_actions_pred.shape[1:]
+
+    return continuous_actions_pred[:,0,:]
+
+def denormalize_actions(model, unnorm_key, normalized_actions):
+    # Unnormalize actions
+    action_norm_stats = model.get_action_stats(unnorm_key)
+    mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+    action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+    actions = np.where(
+        mask,
+        0.5 * (normalized_actions + 1) * (action_high - action_low) + action_low,
+        normalized_actions,
+    )
+    return actions
+
+def normalize_gripper_action(action, binarize=True):
+    """
+    Changes gripper action (last dimension of action vector) from [0,1] to [-1,+1].
+    Necessary for some environments (not Bridge) because the dataset wrapper standardizes gripper actions to [0,1].
+    Note that unlike the other action dimensions, the gripper action is not normalized to [-1,+1] by default by
+    the dataset wrapper.
+
+    Normalization formula: y = 2 * (x - orig_low) / (orig_high - orig_low) - 1
+    """
+    # Just normalize the last action to [-1,+1].
+    orig_low, orig_high = 0.0, 1.0
+    action[..., -1] = 2 * (action[..., -1] - orig_low) / (orig_high - orig_low) - 1
+
+    if binarize:
+        # Binarize to -1 or +1.
+        action[..., -1] = np.sign(action[..., -1])
+
+    return action
+
+# from libero.libero import benchmark
+
+@draccus.wrap()
+def eval_libero(cfg: GenerateConfig) -> None:
+    assert cfg.pretrained_checkpoint is not None, "cfg.pretrained_checkpoint must not be None!"
+    # if "image_aug" in cfg.pretrained_checkpoint:
+    #     assert cfg.center_crop, "Expecting `center_crop==True` because model was trained with image augmentations!"
+    assert not (cfg.load_in_8bit and cfg.load_in_4bit), "Cannot use both 8-bit and 4-bit quantization!"
+
+    # Set random seed
+    set_seed_everywhere(cfg.seed)
+
+    # [OpenVLA] Set action un-normalization key
+    cfg.unnorm_key = cfg.task_suite_name
+
+    # # Load model
+    model = get_model(cfg)
+
+    # [OpenVLA] Check that the model contains the action un-normalization key
+    if cfg.model_family == "openvla":
+        # In some cases, the key must be manually modified (e.g. after training on a modified version of the dataset
+        # with the suffix "_no_noops" in the dataset name)
+        if cfg.unnorm_key not in model.norm_stats and f"{cfg.unnorm_key}_no_noops" in model.norm_stats:
+            cfg.unnorm_key = f"{cfg.unnorm_key}_no_noops"
+        assert cfg.unnorm_key in model.norm_stats, f"Action un-norm key {cfg.unnorm_key} not found in VLA `norm_stats`!"
+
+    # [OpenVLA] Get Hugging Face processor
+    processor = None
+    if cfg.model_family == "openvla":
+        processor = get_processor(cfg)
+
+    # Initialize local logging
+    run_id = f"EVAL-{cfg.task_suite_name}-{cfg.model_family}-{DATE_TIME}"
+    if cfg.run_id_note is not None:
+        run_id += f"--{cfg.run_id_note}"
+    os.makedirs(cfg.local_log_dir, exist_ok=True)
+    local_log_filepath = os.path.join(cfg.local_log_dir, run_id + ".txt")
+    log_file = open(local_log_filepath, "w")
+    print(f"Logging to local log file: {local_log_filepath}")
+
+    # Initialize LIBERO task suite
+    benchmark_dict = benchmark.get_benchmark_dict()
+    task_suite = benchmark_dict[cfg.task_suite_name]()
+    num_tasks_in_suite = task_suite.n_tasks
+    print(f"Task suite: {cfg.task_suite_name}")
+    log_file.write(f"Task suite: {cfg.task_suite_name}\n")
+
+    # Get expected image dimensions
+    resize_size = get_image_resize_size(cfg)
+
+    # Start evaluation
+    total_episodes, total_successes = 0, 0
+    extra_waits_dict = {}
+    for task_id in tqdm.tqdm(range(num_tasks_in_suite)):
+        # Get task
+        task = task_suite.get_task(task_id)
+
+        # Get default LIBERO initial states
+        initial_states = task_suite.get_task_init_states(task_id)
+
+        # Initialize LIBERO environment and task description
+        env, task_description = get_libero_env(task, cfg.model_family, resolution=256)
+
+        # Start episodes
+        task_episodes, task_successes = 0, 0
+        extra_waits = []
+        for episode_idx in tqdm.tqdm(range(cfg.num_trials_per_task)):
+            print(f"\nTask: {task_description}")
+            log_file.write(f"\nTask: {task_description}\n")
+
+            # Reset environment
+            env.reset()
+
+            # Set initial states
+            obs = env.set_init_state(initial_states[episode_idx])
+            
+            # Setup
+            t = 0
+            replay_images = []
+            if cfg.task_suite_name == "libero_spatial":
+                max_steps = 220  # longest training demo has 193 steps
+            elif cfg.task_suite_name == "libero_object":
+                max_steps = 280  # longest training demo has 254 steps
+            elif cfg.task_suite_name == "libero_goal":
+                max_steps = 300  # longest training demo has 270 steps
+            elif cfg.task_suite_name == "libero_10":
+                max_steps = 520  # longest training demo has 505 steps
+            elif cfg.task_suite_name == "libero_90":
+                max_steps = 400  # longest training demo has 373 steps
+
+            t = 0
+            while t < cfg.num_steps_wait:
+                # try:
+                # IMPORTANT: Do nothing for the first few timesteps because the simulator drops objects
+                # and we need to wait for them to fall
+                obs, reward, done, info = env.step(get_libero_dummy_action(cfg.model_family))
+                t += 1
+            
+            # Process observations and task
+            task_texts = [task_description]
+            texts = ['robot ' + task_description]
+            img = get_libero_image(obs, resize_size)
+            img = img[:,::-1,:]
+            batch_data = process_inputs(processor=processor, pixel_values=img, device='cuda')
+
+            horizon = 1
+            batch_data['pixel_values'] = batch_data['pixel_values'].unsqueeze(1).repeat(1,horizon,1,1,1)            
+            # Replicate frames across temporal dimension for initialization purposes
+            # Get the object-centric slot embeddings
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                slot_outputs = get_current_slots(model, batch_data, texts, in_past_tokens=None, device_id='cuda')
+                    
+            # Query model to get several prelim actions
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                actions = get_normalized_actions(model, batch_data, slot_outputs, device_id='cuda')
+                actions = actions.detach().float().cpu().numpy()
+                unnormalized_actions = denormalize_actions(model, cfg.unnorm_key, actions)
+
+            ## Start running the rest of the model here
+            while t < max_steps + cfg.num_steps_wait:
+                # Get preprocessed image
+                img = get_libero_image(obs, resize_size)
+                img = img[:,::-1,:]
+                batch_data = process_inputs(processor=processor, pixel_values=img, device='cuda')
+                # pixel_values    = batch_data['pixel_values']    #  torch.Size([1, 6, 224, 224])
+                # input_ids       = batch_data['input_ids']       #  torch.Size([1, 50])
+                # attention_mask  = batch_data['attention_mask']  # torch.Size([1, 50])
+                # Save preprocessed image for replay video
+                replay_images.append(img)
+
+                # Getting frames across temporal dimension for initialization purposes
+                batch_data['pixel_values'] = batch_data['pixel_values'].unsqueeze(1)
+                # Get the object-centric slot embeddings
+                with torch.autocast("cuda", dtype=torch.bfloat16):
+                    slot_outputs = get_current_slots(model, batch_data, texts, in_past_tokens=None, device_id='cuda')
+                                        
+                # Query model to get several prelim actions
+                with torch.autocast("cuda", dtype=torch.bfloat16):
+                    actions = get_normalized_actions(model, batch_data, slot_outputs, device_id='cuda')
+                    actions = actions.detach().float().cpu().numpy()
+                    unnormalized_actions = denormalize_actions(model, cfg.unnorm_key, actions)
+                    action = unnormalized_actions[0]
+
+                # Normalize gripper action [0,1] -> [-1,+1] because the environment expects the latter
+                action = normalize_gripper_action(action, binarize=True)
+
+                # [OpenVLA] The dataloader flips the sign of the gripper action to align with other datasets
+                # (0 = close, 1 = open), so flip it back (-1 = open, +1 = close) before executing the action
+                # if cfg.model_family == "openvla":
+                #     action = invert_gripper_action(action)
+
+                # Execute action in environment
+                obs, reward, done, info = env.step(action.tolist())
+                t += 1
+                if done:
+                    task_successes += 1
+                    total_successes += 1
+                    break
+
+            task_episodes += 1
+            total_episodes += 1
+
+            # Save a replay video of the episode
+            cv2.destroyAllWindows()
+            save_rollout_video(
+                replay_images, total_episodes, success=done, task_description=task_description, log_file=log_file, saved_dir=cfg.saved_dir
+            )
+            # # Log current results
+            print(f"Success: {done}")
+            print(f"# episodes completed so far: {total_episodes}")
+            print(f"# successes: {total_successes} ({total_successes / total_episodes * 100:.1f}%)")
+            log_file.write(f"Success: {done}\n")
+            log_file.write(f"# episodes completed so far: {total_episodes}\n")
+            log_file.write(f"# successes: {total_successes} ({total_successes / total_episodes * 100:.1f}%)\n")
+            log_file.flush()
+            # # 1/0
+        # 1/0
+            
+        # Log final results
+        print(f"Current task success rate: {float(task_successes) / float(task_episodes)}")
+        print(f"Current total success rate: {float(total_successes) / float(total_episodes)}")
+        log_file.write(f"Current task success rate: {float(task_successes) / float(task_episodes)}\n")
+        log_file.write(f"Current total success rate: {float(total_successes) / float(total_episodes)}\n")
+        log_file.flush()
+
+    # Save local log file
+    log_file.close()
+
+if __name__ == "__main__":
+    eval_libero()