processing_vlarm.py

import collections
import collections.abc
import importlib
import re
import warnings
from abc import abstractmethod
from functools import cached_property
from typing import Dict, List, Optional, Sequence, Tuple, TypeVar

import numpy as np
import PIL.Image
import roma
import torch
import torchvision.transforms.v2
import transformers
import yaml

try:
    from .hf_compat import Configurable, Template
except Exception:
    _hf_compat = importlib.import_module("src.hf_compat")
    Configurable = _hf_compat.Configurable
    Template = _hf_compat.Template

from .common_vlarm import (
    Normalization,
    ResizeMode,
    RoboticsControlPlan,
    RoboticsInput,
    RoboticsOutput,
    RoboticsTarget,
    RotationFormat,
    expand_dims,
)
from .configuration_vlarm import (
    ControlDataIOConfig,
    ControlTokenizerConfig,
    EmptyTokenizerConfig,
    ImageSizeConfig,
    PaliGemmaProcessorConfig,
    RegressionProcessorConfig,
    VLAMProcessorConfig,
    VLARMProcessorConfig,
    VLMProcessorConfig,
)

ControlTokenizerConfigT = TypeVar('ControlTokenizerConfigT', bound=ControlTokenizerConfig)


class ControlTokenizer(Configurable[ControlTokenizerConfigT], Template[ControlTokenizerConfigT]):
    @abstractmethod
    def __call__(self, *args, **kwargs) -> str:
        """Given GT actions and possibly other information, output text control. Gets appened to the prompt"""


class EmptyTokenizer(ControlTokenizer[EmptyTokenizerConfig]):
    """
    Takes the LLM hidden states from `llm_layer_indices` and concatenates them to produce the
    desired result. Includes the hidden states for the image tokens.
    """

    def __init__(self, config, tokenizer: transformers.PreTrainedTokenizerBase) -> None:
        super().__init__(config)
        self.tokenizer = tokenizer

    def __call__(self, *_) -> str:
        return ''


def np_unique(data: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """
    Compute unique elements in data and corresponding indices.

    np.unique returns the values in a sorted order, even if the source is not sorted. Thus, if you simply
    run np.unique on unsorted data, the indices you will get will be invalid.

    """
    (_, indices, inverse) = np.unique(data, return_index=True, return_inverse=True)
    (_, indices_of_first_occurence, inverse_indices, counts) = np.unique(
        indices[inverse], return_index=True, return_inverse=True, return_counts=True
    )
    unique_ids = data[indices_of_first_occurence]
    return unique_ids, indices_of_first_occurence, inverse_indices, counts


def euler_to_rotmat(angles: torch.Tensor) -> torch.Tensor:
    """
    Args:
        angles: Euler angles in radians in the format 'xyz', shape [..., 3]
    Returns:
        torch.Tensor of shape [..., 3, 3] containing rotation matrices
    """
    return roma.euler_to_rotmat(convention='xyz', angles=angles, degrees=False)


def euler_to_unit_quaternion(angles: torch.Tensor) -> torch.Tensor:
    """
    Args:
        angles: Euler angles in radians in the format 'xyz', shape [..., 3]
    Returns:
        torch.Tensor of shape [..., 4] containing unit quaternions
    """
    return roma.euler_to_unitquat(convention='xyz', angles=angles, degrees=False, normalize=True)


def normalize_quaternion(quaternion: torch.Tensor, eps: float = 1e-08) -> torch.Tensor:
    """
    Args:
        quaternion: Unnormalized quaternion, torch.Tensor of shape [..., 4]
        eps: Small constant to prevent division by zero
    Returns:
        torch.Tensor of shape [..., 4] of unit quaternions
    """
    return quaternion / (quaternion.norm(dim=-1, keepdim=True) + eps)


def quaternion_to_euler(quaternion: torch.Tensor) -> torch.Tensor:
    """
    Args:
        quaternion: torch.Tensor of shape [..., 4]; Can be non-normalized
    Returns:
        torch.Tensor of shape [..., 3, 3] containing rotation matrices in SO(3)
    """
    unit_quat = normalize_quaternion(quaternion)
    rotmat = roma.unitquat_to_euler(convention='xyz', quat=unit_quat, as_tuple=False, degrees=False)
    return rotmat


def quaternion_to_rotmat(quaternion: torch.Tensor) -> torch.Tensor:
    """
    Args:
        quaternion: torch.Tensor of shape [..., 4]; Can be non-normalized
    Returns:
        torch.Tensor of shape [..., 3, 3] containing rotation matrices in SO(3)
    """
    unit_quat = normalize_quaternion(quaternion)
    rotmat = roma.unitquat_to_rotmat(unit_quat)
    return rotmat


def is_quaternion(quaternion: torch.Tensor) -> bool:
    return quaternion.shape[-1] == 4


def is_unit_quaternion(
    quaternion: torch.Tensor, epsilon: float = 1e-08, reduction: str = 'none'
) -> torch.Tensor | bool:
    """
    Check if a quternion is normalized or not.
    Args:
        quaternion: torch.Tensor of shape [..., 4]
        tolerance: Tolerance for numerical comparisons
        reduction:
            'none' - returns torch.Tensor of bools with the same batch shape
            'all' - returns a bool, True if ALL quaternions in the batch are normalized
    Returns:
        torch.Tensor with the same batch shape or bool
    """
    assert is_quaternion(quaternion)
    is_norm = torch.isclose(
        quaternion.norm(dim=-1, keepdim=True),
        torch.tensor(1.0, dtype=quaternion.dtype, device=quaternion.device),
        atol=epsilon,
    )
    if reduction == 'none':
        return is_norm
    if reduction == 'all':
        return bool(torch.all(is_norm).item())
    raise ValueError(f'Unknown reduction mode {reduction}')


def quaternion_half_cover(quaternion: torch.Tensor) -> torch.Tensor:
    """
    Flip quaternions so they cover only a half the space. If the q_w is negative, flip the quaternion.
    If q_w is 0, then choose such that the first non-zero component is positive. Note that geometrically,
    this doesn't correspond to a single hemisphere of the unit sphere. Follows
    https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.as_quat.html#scipy.spatial.transform.Rotation.as_quat
    """
    assert is_quaternion(quaternion), quaternion.shape
    with torch.no_grad():
        is_zero = quaternion == 0
        flip_condition = (
            (quaternion[..., -1:] < 0)
            | is_zero[..., -1:] & (quaternion[..., 0:1] < 0)
            | is_zero[..., -1:] & is_zero[..., 0:1] & (quaternion[..., 1:2] < 0)
            | is_zero[..., -1:] & is_zero[..., 0:1] & is_zero[..., 1:2] & (quaternion[..., 2:3] < 0)
        )
    quaternion = torch.where(flip_condition, -quaternion, quaternion)
    return quaternion


def rotmat_as_3x3(rotmat: torch.Tensor) -> torch.Tensor:
    """Convert any rotmat input to [..., 3, 3] shape"""
    if rotmat.shape[-1] == 9:
        return rotmat.reshape(*rotmat.shape[:-1], 3, 3)
    if rotmat.shape[-2:] == torch.Size([3, 3]):
        return rotmat
    raise ValueError(f"Can't convert tensor of shape {rotmat.shape} to a 3x3 rotation matrix")


def rotmat_to_euler(rotmat: torch.Tensor) -> torch.Tensor:
    """
    Args:
        rotmat: Batch of rotation matrices, shape [..., 3, 3]
    Returns:
        Batch of Euler angles in radiant, shape [..., 3]
    """
    rotmat = rotmat_as_3x3(rotmat)
    return roma.rotmat_to_euler(convention='xyz', rotmat=rotmat, as_tuple=False, degrees=False)


def rotmat_to_unit_quaternion(rotmat: torch.Tensor) -> torch.Tensor:
    """
    Args:
        rotmat: Batch of rotation matrices, shape [..., 3, 3]
    Returns:
        Batch of unit quaternions, shape [..., 4]
    """
    rotmat = rotmat_as_3x3(rotmat)
    return roma.rotmat_to_unitquat(rotmat)


def symmetric_orthogonalization(x: torch.Tensor) -> torch.Tensor:
    """
    Maps 9D input vectors onto SO(3) via symmetric orthogonalization.
        - Let SVD(M) = U \Sigma V^T
        - Returned value is SVD+(M) =  U diag(1, 1, det(UV^T)) V^T
        - det(UV^T) ensures that det(SVD+(M)) = 1
        - The return value is a rotation matrix (ortonormal) with the least-squares distance to M

    Args:
        x: Input matrices, not necessarily orthonormal, shape [..., 9] or [..., 3, 3]
    Returns:
        torch.Tensor with the same shape as x, where each inner 3x3 matrix is in SO(3)
    """
    with warnings.catch_warnings():
        warnings.filterwarnings(
            'ignore', message='In CPU autocast, but the target dtype is not supported. Disabling autocast.'
        )
        with torch.autocast(device_type=x.device.type, dtype=torch.float32):
            matrices = x.view(-1, 3, 3)
            matrices = matrices.to(dtype=torch.float32)
            (u, s, v) = torch.svd(matrices)
            vt = torch.transpose(v, 1, 2)
            det = torch.det(torch.matmul(u, vt)).view(-1, 1, 1)
            diag_vt = torch.cat((vt[:, :2, :], vt[:, -1:, :] * det), dim=1)
            result = torch.matmul(u, diag_vt)
            result = result.view(*x.shape)
    result = result.to(dtype=x.dtype)
    return result


def is_rotmat_3x3(rotmat: torch.Tensor) -> bool:
    return rotmat.shape[-2:] == torch.Size([3, 3])


def is_rotmat_9(rotmat: torch.Tensor) -> bool:
    return rotmat.shape[-1] == 9


def is_rotmat(rotmat: torch.Tensor) -> bool:
    """
    Checks if the tensor shape matches that of a rotmat. However, it's not guaranteed the data is a
    valid rotmat. `is_orthonormal_rotmat` performs this additional check.
    NOTE: This might incorrectly return True if the underlying data is euler angles and accidentally
    `rotmat.shape[-2:] == [3, 3]`. This would happen very rarely, but use with caution
    """
    return is_rotmat_3x3(rotmat) or is_rotmat_9(rotmat)


def is_rotmat_orthonormal(
    rotmat: torch.Tensor, epsilon: float = 1e-06, reduction: str = 'none'
) -> torch.Tensor | bool:
    """
    Check if a rotation matrix is orthonormal or not.
    Args:
        rotmat: torch.Tensor of shape [..., 3, 3] or [..., 9]
        epsilon: Tolerance for numerical comparisons. Bigger values allow for more freedom. Generally,
            anything smaller than 1e-6 might incorrectly detect some otrhonormal matrices as not
        reduction:
            'none' - returns torch.Tensor of bools with the same batch shape
            'all' - returns a bool, True is ALL matrices in the batch are orthonormal
    Returns:
        torch.Tensor with the same batch shape or bool
    """
    assert is_rotmat(rotmat)
    rotmat = rotmat_as_3x3(rotmat.to(dtype=torch.float32))
    is_orthonormal = roma.is_orthonormal_matrix(rotmat, epsilon=epsilon)
    if reduction == 'none':
        return is_orthonormal
    if reduction == 'all':
        return bool(torch.all(is_orthonormal).item())
    raise ValueError(f'Unknown reduction mode {reduction}')


def is_orthonormal_rotmat(rotmat: torch.Tensor) -> bool:
    """
    Checks if the tensor shape matches that of a rotmat. If the last dimensions of shape are 3x3,
    also checks if the data is a valid rotmat. This is to avoid a possible clash with euler angles
    when accidentally `rotmat.shape[-2:] == [3, 3]`
    """
    return (
        is_rotmat_9(rotmat)
        or is_rotmat_3x3(rotmat)
        and is_rotmat_orthonormal(rotmat, epsilon=0.02, reduction='all')
    )


def is_euler(euler: torch.Tensor) -> bool:
    return euler.shape[-1] == 3 and not is_orthonormal_rotmat(euler)


def rotation_format_from_tensor(rotation) -> RotationFormat:
    if is_quaternion(rotation):
        return RotationFormat.QUATERNION
    if is_orthonormal_rotmat(rotation):
        return RotationFormat.ROTMAT
    if is_euler(rotation):
        return RotationFormat.EULER
    raise ValueError(f'Tensor shape {rotation.shape} is not a valid rotation format')


def rotmat_as_9(rotmat: torch.Tensor) -> torch.Tensor:
    """Convert any rotmat input to [..., 9] shape"""
    if is_rotmat_9(rotmat):
        return rotmat
    if is_rotmat_3x3(rotmat):
        return rotmat.reshape(*rotmat.shape[:-2], 9)
    raise ValueError(f"Can't convert tensor of shape {rotmat.shape} to a 3x3 rotation matrix")


def convert_rotation(
    rotation: torch.Tensor | np.ndarray,
    output_format: RotationFormat,
    autonorm: bool = True,
    half_cover: bool = True,
) -> torch.Tensor | np.ndarray:
    is_np = isinstance(rotation, np.ndarray)
    if is_np:
        rotation = torch.from_numpy(rotation)
    if is_quaternion(rotation):
        if autonorm and not is_unit_quaternion(rotation, reduction='all'):
            rotation = normalize_quaternion(rotation)
        if output_format == RotationFormat.QUATERNION:
            output = rotation
        elif output_format == RotationFormat.ROTMAT:
            output = rotmat_as_9(quaternion_to_rotmat(rotation))
        elif output_format == RotationFormat.EULER:
            output = quaternion_to_euler(rotation)
        else:
            raise NotImplementedError(f'Unsupported rotation format: {output_format}')
    elif is_orthonormal_rotmat(rotation):
        if autonorm and not is_rotmat_orthonormal(rotation, epsilon=0.01, reduction='all'):
            rotation = symmetric_orthogonalization(rotation)
        if output_format == RotationFormat.QUATERNION:
            output = rotmat_to_unit_quaternion(rotation)
        elif output_format == RotationFormat.ROTMAT:
            output = rotmat_as_9(rotation)
        elif output_format == RotationFormat.EULER:
            output = rotmat_to_euler(rotation)
        else:
            raise NotImplementedError(f'Unsupported rotation format: {output_format}')
    elif is_euler(rotation):
        if output_format == RotationFormat.QUATERNION:
            output = euler_to_unit_quaternion(rotation)
        elif output_format == RotationFormat.ROTMAT:
            output = rotmat_as_9(euler_to_rotmat(rotation))
        elif output_format == RotationFormat.EULER:
            output = rotation
        else:
            raise NotImplementedError(f'Unsupported rotation format: {output_format}')
    else:
        raise ValueError(f'Unknown rotation encoding with shape {rotation.shape}')
    if output_format == RotationFormat.QUATERNION and half_cover:
        output = quaternion_half_cover(output)
    if is_np:
        output = output.numpy()
    return output


def delta_to_world_rotations(rotation_sequence: torch.Tensor, reference_frame: torch.Tensor) -> torch.Tensor:
    """
    Transform a sequence of rotation representations encoded w.r.t. `reference_frame` to encoding w.r.t.
    WORLD frame, where `reference_frame` is provided w.r.t. WORLD frame

    Ex:
        Sequence of points (rotations): R_1, R_2, R_3, R_4
        `rotation_sequence` contains the rotations: R_01, R_02, R_03, R_04, where R_0 is the reference frame
            and R_01 is the pose of R1 frame in reference frame, i.e. R_10 converts from reference frame to R1 frame
        Output: R_W1, R_W2, R_W3, R_W4, where W is the world frame, i.e. the rotation poses of
            R_1, R_2, R_3, R_4 expressed in world frame

    Args:
        rotation_sequence: torch.Tensor of shape [..., S, 9], [..., S, 3, 3] or [..., S, 4], containing
            either rotation matrices (R_01, R_12, R_23, R_34, ...) or quaternions
        reference_frame: torch.Tensor, shape [..., S, 9], [..., S, 3, 3] or [..., S, 4] and the SAME number of BATCH
            dims as `rotation_sequence`. The reference frame, provided w.r.t. WORLD coordinate frame R_W0,1,2,3
    Returns:
        torch.Tensor of shape [..., S, 9], [..., S, 3, 3] or [..., S, 4] containing transformed rotations
            (R_W1, R_W2, R_W3, R_W4, ...)
    """
    assert rotation_sequence.ndim >= 3, rotation_sequence.shape
    rotation_format: RotationFormat = rotation_format_from_tensor(rotation_sequence)
    reference_frame = rotmat_as_3x3(convert_rotation(reference_frame, RotationFormat.ROTMAT))
    rotation_sequence = rotmat_as_3x3(convert_rotation(rotation_sequence, RotationFormat.ROTMAT))
    if reference_frame.ndim != rotation_sequence.ndim:
        raise ValueError(
            f'Cannot broadcast reference_frame of shape {reference_frame.shape} to rotation_sequence of shape {rotation_sequence.shape}. Provide tensors with the same number of batch dimensions'
        )
    R_W0 = reference_frame
    world_rotations = torch.matmul(R_W0, rotation_sequence)
    world_rotations = world_rotations.view(*world_rotations.shape[:-2], 9)
    world_rotations = convert_rotation(world_rotations, rotation_format)
    return world_rotations


def delta_to_relative_rotations(rotation_sequence: torch.Tensor) -> torch.Tensor:
    """
    Transform a sequence of rotation representations encoded w.r.t. the PREVIOUS rotation frame in the
    sequence to the 0-th element preceding the sequence

    Ex:
        `rotation_sequence` contains the rotations: R_01, R_12, R_23, R_34, where R0 is the base frame,
            implicitly encoded in R_01 and R_10 converts from R0 frame to R1 frame
        Output: R_01, R_02, R_03, R_04

    Args:
        rotation_sequence: torch.Tensor of shape [..., S, 9], [..., S, 3, 3] or [..., S, 4], containing
            either rotation matrices (R_01, R_12, R_23, R_34, ...) or quaternions
    Returns:
        torch.Tensor of shape [..., S, 9], [..., S, 3, 3] or [..., S, 4] containing transformed rotations
            (R_01, R_02, R_03, R_04, ...)

    TODO: Can you make it work without for loop
    """
    assert rotation_sequence.ndim >= 3, rotation_sequence.shape
    rotation_format: RotationFormat = rotation_format_from_tensor(rotation_sequence)
    rotation_sequence = convert_rotation(rotation_sequence, RotationFormat.QUATERNION)
    batch_dims = np.arange(rotation_sequence.ndim - 2)
    delta_rotations = torch.cat(
        [rotation_sequence[..., :1, :]]
        + [
            roma.quat_composition(rotation_sequence[..., :i, :].permute(-2, *batch_dims, -1).unsqueeze(-2))
            for i in range(2, rotation_sequence.shape[-2] + 1)
        ],
        dim=-2,
    )
    delta_rotations = convert_rotation(delta_rotations, rotation_format)
    return delta_rotations


def assert_np_hwc_or_hw_image(image: np.ndarray | PIL.Image.Image) -> np.ndarray:
    """Make sure image is of type np.ndarray and HWC format"""
    if isinstance(image, PIL.Image.Image):
        image = np.asarray(image)
    assert isinstance(image, np.ndarray), type(image)
    assert image.ndim in [2, 3], image.shape
    if image.ndim == 3:
        assert image.shape[-1] <= 4, image.shape
    return image


def hw_from_image(image: PIL.Image.Image | np.ndarray) -> tuple[int, int]:
    if isinstance(image, np.ndarray):
        (height, width) = image.shape[:2]
    else:
        (width, height) = image.size
    return height, width


def pad_image(
    image: PIL.Image.Image | np.ndarray,
    target_size: dict[str, int],
    pad_value: tuple[int, int, int] | tuple[float, float, float] | int | float = 0,
) -> PIL.Image.Image | np.ndarray:
    """Pad image adding a symmetric border around the height/width."""
    assert isinstance(image, (PIL.Image.Image, np.ndarray)), type(image)
    (height, width) = hw_from_image(image)
    (target_width, target_height) = (target_size['width'], target_size['height'])
    if width == target_width and height == target_height:
        return image
    assert target_width >= width, f"Can't pad image of width {width} to {target_width}"
    assert target_height >= height, f"Can't pad image of height {height} to {target_height}"
    (horizontal_pad, vertical_pad) = (int((target_width - width) / 2), int((target_height - height) / 2))
    if isinstance(image, np.ndarray):
        padding = ((vertical_pad, vertical_pad), (horizontal_pad, horizontal_pad)) + ((0, 0),) * (
            image.ndim - 2
        )
        image = np.pad(image, padding, mode='constant', constant_values=pad_value)
    else:
        padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
        image = torchvision.transforms.v2.functional.pad(
            image, padding=padding, fill=pad_value, padding_mode='constant'
        )
    return image


def pad_image_to_ratio(
    image: PIL.Image.Image | np.ndarray,
    target_wh_ratio: float,
    pad_value: tuple[int, int, int] | tuple[float, float, float] | int | float = 0,
) -> PIL.Image.Image | np.ndarray:
    """Pad image to a target aspect ratio."""
    (height, width) = hw_from_image(image)
    wh_ratio = width / height
    if target_wh_ratio >= wh_ratio:
        pad_size = {'width': round(height * target_wh_ratio), 'height': height}
    else:
        pad_size = {'width': width, 'height': round(width / target_wh_ratio)}
    image = pad_image(image, target_size=pad_size, pad_value=pad_value)
    return image


def crop_image(
    image: np.ndarray | PIL.Image.Image,
    start_height: int,
    start_width: int,
    target_height: int,
    target_width: int,
) -> np.ndarray | PIL.Image.Image:
    np_image = assert_np_hwc_or_hw_image(image)
    (height, width) = hw_from_image(image)
    assert target_width <= width, f"Can't crop image of width {width} to {target_width}"
    assert target_height <= height, f"Can't crop image of width {height} to {target_height}"
    (start_height, start_width) = (round(start_height), round(start_width))
    (target_height, target_width) = (round(target_height), round(target_width))
    np_image = np_image[
        start_height : start_height + target_height, start_width : start_width + target_width, ...
    ]
    image = PIL.Image.fromarray(np_image) if isinstance(image, PIL.Image.Image) else np_image
    return image


def crop_image_center(
    image: np.ndarray | PIL.Image.Image, target_size: dict[str, int]
) -> np.ndarray | PIL.Image.Image:
    np_image = assert_np_hwc_or_hw_image(image)
    (height, width) = np_image.shape[:2]
    (target_height, target_width) = (target_size['height'], target_size['width'])
    assert target_width <= width, f"Can't crop image of width {width} to {target_width}"
    assert target_height <= height, f"Can't crop image of width {height} to {target_height}"
    top = (height - target_height) // 2
    left = (width - target_width) // 2
    np_image = crop_image(np_image, top, left, target_height, target_width)
    image = PIL.Image.fromarray(np_image) if isinstance(image, PIL.Image.Image) else np_image
    return image


def crop_image_to_ratio(
    image: PIL.Image.Image | np.ndarray, target_wh_ratio: float
) -> PIL.Image.Image | np.ndarray:
    """Pad image to a target aspect ratio."""
    (height, width) = hw_from_image(image)
    wh_ratio = width / height
    if target_wh_ratio >= wh_ratio:
        crop_size = {'width': width, 'height': round(width / target_wh_ratio)}
    else:
        crop_size = {'width': round(height * target_wh_ratio), 'height': height}
    image = crop_image_center(image, target_size=crop_size)
    return image


def crop_and_pad_image_to_ratio(
    image: PIL.Image.Image | np.ndarray,
    target_wh_ratio: float,
    mode: ResizeMode | str,
    pad_value: tuple[int, int, int] | tuple[float, float, float] | int | float = 0,
) -> PIL.Image.Image | np.ndarray:
    """
    Crop and pad an image to a target size depending on the mode.
    It's expected that the source image and target size have different aspect ratios.

    Args:
        image: The image to crop and pad.
        target_size: The target size to crop and pad the image to.
        mode: The mode to use for cropping and padding.
    """
    (height, width) = hw_from_image(image)
    wh_ratio = width / height
    if np.isclose(wh_ratio, target_wh_ratio, rtol=0.01, atol=0.0001):
        return image
    if mode == ResizeMode.SMART:
        aspect_ratio = max(width, height) / min(width, height)
        target_ratio = max(target_wh_ratio, 1 / target_wh_ratio)
        if aspect_ratio == 1:
            if target_ratio >= 4 / 3 - 0.01:
                crop_wh_ratio = 4 / 3 if target_wh_ratio >= 1.0 else 3 / 4
                image = crop_image_to_ratio(image, crop_wh_ratio)
            else:
                pass
        elif aspect_ratio <= 4 / 3 + 0.01:
            if wh_ratio >= 1.0 != (target_wh_ratio >= 1.0):
                image = crop_image_to_ratio(image, 1.0)
        elif wh_ratio >= 1.0 != (target_wh_ratio >= 1.0):
            image = crop_image_to_ratio(image, 1.0)
        elif target_ratio >= 4 / 3 + 0.01:
            pass
        else:
            crop_wh_ratio = 4 / 3 if target_wh_ratio >= 1.0 else 3 / 4
            image = crop_image_to_ratio(image, crop_wh_ratio)
        image = pad_image_to_ratio(image, target_wh_ratio, pad_value=pad_value)
    elif mode == ResizeMode.PAD:
        image = pad_image_to_ratio(image, target_wh_ratio, pad_value=pad_value)
    elif mode == ResizeMode.CROP:
        image = crop_image_to_ratio(image, target_wh_ratio)
    else:
        raise ValueError(f'Mode {mode} not supported')
    return image


def is_single_channel_image(image: np.ndarray | PIL.Image.Image) -> bool:
    if isinstance(image, PIL.Image.Image):
        return image.mode in ['1', 'L', 'LA', 'La', 'P', 'PA', 'F', 'I', 'I;16', 'I;16L', 'I;16B', 'I;16N']
    if isinstance(image, np.ndarray):
        return image.ndim == 2 or image.ndim == 3 and image.shape[2] == 1
    raise ValueError(f'Unsupported image type: {type(image)}')


def is_binary_mask(image: np.ndarray | PIL.Image.Image) -> bool:
    image = np.asarray(image)
    return image.dtype in [np.uint8, np.bool_] and np.max(image) == 1


def resize_image(
    image: PIL.Image.Image | np.ndarray,
    target_size: dict[str, int],
    mode: ResizeMode | str,
    resample: PIL.Image.Resampling | str = 'auto',
    pad_value: tuple[int, int, int] | tuple[float, float, float] | int | float = 0,
) -> PIL.Image.Image | np.ndarray:
    (target_width, target_height) = (target_size['width'], target_size['height'])
    (height, width) = hw_from_image(image)
    if height == target_height and width == target_width:
        return image
    if resample == 'auto':
        if is_single_channel_image(image):
            resample = PIL.Image.Resampling.BILINEAR
        else:
            resample = PIL.Image.Resampling.LANCZOS
    else:
        assert isinstance(resample, PIL.Image.Resampling), resample
        if is_single_channel_image(image) and resample not in [
            PIL.Image.Resampling.BILINEAR,
            PIL.Image.Resampling.BICUBIC,
        ]:
            raise ValueError(
                f'Single channel images must be resized with bilinear or bicubic, but got {resample}'
            )
    if is_bin_mask := is_binary_mask(image):
        image = np.asarray(image).astype(np.uint8) * 255
    if mode == ResizeMode.SMART:
        image = crop_and_pad_image_to_ratio(
            image, target_wh_ratio=target_width / target_height, mode=mode, pad_value=pad_value
        )
    pil_image = PIL.Image.fromarray(image) if isinstance(image, np.ndarray) else image
    if mode in [ResizeMode.NAIVE, ResizeMode.SMART]:
        pil_image = pil_image.resize((target_width, target_height), resample=resample)
    else:
        raise NotImplementedError(f'Mode {mode} not supported')
    image = np.asarray(pil_image) if isinstance(image, np.ndarray) else pil_image
    if is_bin_mask:
        image = image.astype(np.uint8) > 127
    return image


def is_global_norm(norm: Normalization | Dict[str, torch.Tensor | np.ndarray | tuple | list]) -> bool:
    """Return true if norm is NONE or global for all datasets"""
    return norm == Normalization.NONE or isinstance(norm, collections.abc.Mapping)


def is_mean_norm(norm: Normalization | Dict[str, torch.Tensor | np.ndarray | tuple | list]) -> bool:
    """Return true if norm is based on mean and std"""
    return (
        norm == Normalization.MEAN
        or isinstance(norm, collections.abc.Mapping)
        and set(norm.keys()) == {'mean', 'std'}
    )


def _broadcast_shapes(
    value: torch.Tensor, low: torch.Tensor, high: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Broadcast shapes for normalization:
    Args:
        value: torch.Tensor of shape [..., num_components]. The entire shape might be:
            - [num_components]: `value` has no batch dimension
            - [num_datasets, num_components]: `value` contains entries *aligned* with the dataset bounds
                contained in `low` and `high`
            - [num_datasets, ..., num_components]: `value` contains entries *aligned* with the dataset bounds
                contained in `low` and `high`
            - [..., num_components]: `value` contains multiple dimensions. In this case, `low` and `high`
                must be for a single dataset, i.e. `num_datasets = 1`

        low: torch.Tensor, shape [num_datasets, num_components], where `num_datasets` can be 1 when `low`
            contains normalization bounds for a single dataset
        high: torch.Tensor, shape [num_datasets, num_components], where `num_datasets` can be 1 when `high`
            contains normalization bounds for a single dataset
    Returns:
        Tuple of torch.Tensors (low, high), where `low` and `high` have the same number of dimensions as `value`
    """
    assert low.ndim == high.ndim == 2, f'{low.shape} != {high.shape} or ndim != 2'
    assert value.shape[-1] == low.shape[-1] == high.shape[-1], f'{value.shape} != {low.shape} / {high.shape}'
    if value.ndim == low.ndim == high.ndim:
        return low, high
    if value.ndim < low.ndim:
        assert low.ndim == high.ndim == 2, f'{low.shape}, {high.shape}'
        assert low.shape[0] == high.shape[0] == 1, f'{low.shape}, {high.shape}'
        (low, high) = (low.view(-1), high.view(-1))
        return low, high
    if low.shape[0] == high.shape[0] == 1:
        low = expand_dims(low.view(-1), ndim=value.ndim, order=[-1, 1])
        high = expand_dims(high.view(-1), ndim=value.ndim, order=[-1, 1])
    else:
        assert value.shape[0] == low.shape[0] == high.shape[0], f'{value.shape} != {low.shape} / {high.shape}'
        low = expand_dims(low, ndim=value.ndim, order=[1, -1, 1])
        high = expand_dims(high, ndim=value.ndim, order=[1, -1, 1])
    return low, high


def unnormalize_by_moments(value: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
    (mean, std) = _broadcast_shapes(value, mean, std)
    (mean, std) = (mean.to(device=value.device), std.to(device=value.device))
    return value * (std + 1e-08) + mean


def unnormalize_by_bounds(value: torch.Tensor, low: torch.Tensor, high: torch.Tensor) -> torch.Tensor:
    (low, high) = _broadcast_shapes(value, low, high)
    (low, high) = (low.to(device=value.device), high.to(device=value.device))
    return 0.5 * (value + 1) * (high - low) + low


def normalize_gripper_by_bounds(
    value: torch.Tensor, low: torch.Tensor, high: torch.Tensor, binary: bool = True
) -> torch.Tensor:
    """
    If binary, normalize to [0, 1], otherwise normalize to [-1, 1]
    """
    (low, high) = _broadcast_shapes(value, low, high)
    (low, high) = (low.to(device=value.device), high.to(device=value.device))
    if binary:
        return torch.clamp((value - low) / torch.clamp(high - low, min=1e-08), min=0.0, max=1.0)
    return torch.clamp(2 * (value - low) / torch.clamp(high - low, min=1e-08) - 1, min=-1.0, max=1.0)


def normalize_by_moments(value: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
    (mean, std) = _broadcast_shapes(value, mean, std)
    (mean, std) = (mean.to(device=value.device), std.to(device=value.device))
    return (value - mean) / (std + 1e-08)


def normalize_by_bounds(value: torch.Tensor, low: torch.Tensor, high: torch.Tensor) -> torch.Tensor:
    (low, high) = _broadcast_shapes(value, low, high)
    (low, high) = (low.to(device=value.device), high.to(device=value.device))
    return torch.clamp(2 * (value - low) / torch.clamp(high - low, min=1e-08) - 1, min=-1.0, max=1.0)


def invert_gripper(gripper: np.ndarray, low: float, high: float) -> np.ndarray:
    if low < 0.0:
        return np.clip(-gripper, low, high)
    return high - np.clip(gripper, low, high)


GRIPPER_BOUNDS = {
    'austin_buds_dataset': (0.0, 0.08),
    'austin_sailor_dataset': (0.0, 0.08),
    'austin_sirius_dataset': (0.0, 0.08),
    'bc_z': (0.0, 1.0),
    'berkeley_autolab_ur5': (0.0, 1.0),
    'berkeley_cable_routing': (0.0, 1.0),
    'berkeley_fanuc_manipulation': (0.0, 1.0),
    'bridge': (0.0, 1.0),
    'bridge_orig': (0.0, 1.0),
    'bridge_action_lang': (0.0, 1.0),
    'cmu_stretch': (-3.0, 3.0),
    'dlr_edan_shared_control': (0.0, 1.0),
    'droid': (0.0, 1.0),
    'fmb': (0.0, 1.0),
    'fractal20220817_data': (0.0, 1.0),
    'furniture_bench_dataset': (0.0, 0.08),
    'iamlab_cmu_pickup_insert': (0.0, 1.0),
    'jaco_play': (0.0, 1.4),
    'kuka': (0.0, 1.0),
    'language_table': (0.0, 1.0),
    'nyu_franka_play_dataset': (0.0, 1.0),
    'roboset': (0.0, 1.0),
    'roboturk': (0.0, 1.0),
    'stanford_hydra_dataset': (0.0, 0.08),
    'taco_play': (0.0, 0.08),
    'toto': (0.0, 1.0),
    'ucsd_kitchen_dataset': (0.0, 1.0),
    'utaustin_mutex': (0.0, 0.08),
    'viola': (0.0, 0.08),
}


def preprocess_gripper_observation(
    gripper: np.ndarray, dataset_name: str | np.ndarray, binary: bool = True
) -> np.ndarray:
    """
    Preprocess gripper observation depending on dataset. Input is the raw gripper observation from the dataset
    or from the robot and output is normalized continuous value.
        - if `binary`, output is in [0, 1], with 0 = closed and 1 = open.
        - otherwise, output is in [-1, 1], with -1 = closed and 1 = open.

    Dataset-specific gripper observations:
        austin_buds_dataset: continuous; ~[0=closed; 0.08=open] (franka gripper)
        austin_sailor_dataset: continuous; ~[0=closed; 0.08=open] (franka gripper)
        austin_sirius_dataset: continuous; ~[0=closed; 0.08=open] (franka gripper)
        bc_z: continuous; [0=open; 1=closed]
        berkeley_autolab_ur5: binary; [0=open; 1=closed]
        berkeley_cable_routing: constant (closed)
        berkeley_fanuc_manipulation: binary; [0=open; 1=closed]
        bridge: continuous; ~[0=closed; 1=open]
        bridge_orig: continuous; ~[0=closed; 1=open]
        cmu_stretch: continuous; [-3=closed; 3=open]
        dlr_edan_shared_control: missing
        droid: continuous; [0=open, 1=closed]
        fmb: binary; [0=open; 1=closed]
        fractal20220817_data: continuous; [0=open; 1=closed]
        furniture_bench_dataset: continuous; ~[0=closed; 0.08=open] (franka gripper)
        iamlab_cmu_pickup_insert: binary; [0=closed; 1=open]
        jaco_play: continuous; [0=open; 1.4=closed]
        kuka: binary; [0=open; 1=closed]
        language_table: constant (no gripper)
        nyu_franka_play_dataset: missing
        roboset: continuous; [0=open, 1=closed]
        roboturk: continuous; [0=closed, 0.04=open]
        stanford_hydra_dataset: continuous; ~[0=closed; 0.08=open] (franka gripper)
        taco_play: continuous; ~[0=closed; 0.08=open] (franka gripper)
        toto: constant (closed)
        ucsd_kitchen_dataset: missing
        utaustin_mutex: continuous; ~[0=closed; 0.08=open] (franka gripper)
        viola: continuous; ~[0=closed; 0.08=open] (franka gripper)

    """
    if isinstance(dataset_name, np.ndarray):
        assert np.unique(dataset_name).size == 1, dataset_name
        dataset_name = str(dataset_name[0])
    if dataset_name in [
        'berkeley_cable_routing',
        'dlr_edan_shared_control',
        'language_table',
        'nyu_franka_play_dataset',
        'toto',
        'ucsd_kitchen_dataset',
    ]:
        gripper = normalize_gripper_by_bounds(
            torch.from_numpy(gripper),
            low=torch.full(gripper.shape, GRIPPER_BOUNDS[dataset_name][0], dtype=torch.float32),
            high=torch.full(gripper.shape, GRIPPER_BOUNDS[dataset_name][1], dtype=torch.float32),
            binary=binary,
        ).numpy()
    elif dataset_name in [
        'bc_z',
        'berkeley_autolab_ur5',
        'berkeley_fanuc_manipulation',
        'droid',
        'fmb',
        'fractal20220817_data',
        'jaco_play',
        'kuka',
        'roboset',
    ]:
        (low, high) = GRIPPER_BOUNDS[dataset_name]
        gripper = normalize_gripper_by_bounds(
            torch.from_numpy(invert_gripper(gripper, low=low, high=high)),
            low=torch.full(gripper.shape, GRIPPER_BOUNDS[dataset_name][0], dtype=torch.float32),
            high=torch.full(gripper.shape, GRIPPER_BOUNDS[dataset_name][1], dtype=torch.float32),
            binary=binary,
        ).numpy()
    elif dataset_name in [
        'austin_buds_dataset',
        'austin_sailor_dataset',
        'austin_sirius_dataset',
        'bridge',
        'bridge_orig',
        'bridge_action_lang',
        'cmu_stretch',
        'furniture_bench_dataset',
        'iamlab_cmu_pickup_insert',
        'roboturk',
        'stanford_hydra_dataset',
        'taco_play',
        'utaustin_mutex',
        'viola',
    ]:
        (low, high) = GRIPPER_BOUNDS[dataset_name]
        gripper = normalize_gripper_by_bounds(
            torch.from_numpy(gripper),
            low=torch.full(gripper.shape, low, dtype=torch.float32),
            high=torch.full(gripper.shape, high, dtype=torch.float32),
            binary=binary,
        ).numpy()
    else:
        raise NotImplementedError(f'Unknown dataset: {dataset_name}')
    return gripper


def rotation_norm_bounds(
    rotation_norm: Normalization,
    rotation_format: RotationFormat,
    stats: Dict[str, Dict[str, Dict[str, List[float]]]],
    dataset_names: List[str],
) -> Dict[str, Dict[str, torch.Tensor]]:
    if rotation_format == RotationFormat.EULER and rotation_norm != Normalization.NONE:
        if rotation_norm == Normalization.BOUNDS:
            results = {
                dataset_name: {
                    'low': torch.tensor(dataset_stats['euler']['min']),
                    'high': torch.tensor(dataset_stats['euler']['max']),
                }
                for (dataset_name, dataset_stats) in stats.items()
            }
        elif rotation_norm == Normalization.BOUNDS_Q99:
            results = {
                dataset_name: {
                    'low': torch.tensor(dataset_stats['euler']['q01']),
                    'high': torch.tensor(dataset_stats['euler']['q99']),
                }
                for (dataset_name, dataset_stats) in stats.items()
            }
        else:
            raise NotImplementedError(f'Normalization type {rotation_norm} not yet implemented')
    else:
        assert rotation_norm == Normalization.NONE, rotation_norm
        if rotation_format == RotationFormat.EULER:
            rotation_size = 3
        elif rotation_format == RotationFormat.QUATERNION:
            rotation_size = 4
        else:
            rotation_size = 9
        results = {
            dataset_name: {
                'low': -1 * torch.ones(rotation_size, dtype=torch.float32),
                'high': 1 * torch.ones(rotation_size, dtype=torch.float32),
            }
            for dataset_name in dataset_names
        }
    return results


def translation_norm_bounds(
    translation_norm: Normalization | tuple,
    stats: Dict[str, Dict[str, Dict[str, List[float]]]],
    dataset_names: List[str],
) -> Dict[str, Dict[str, torch.Tensor]]:
    if isinstance(translation_norm, Normalization) and translation_norm != Normalization.NONE:
        if translation_norm == Normalization.BOUNDS:
            results = {
                dataset_name: {
                    'low': torch.tensor(dataset_stats['translation']['min']),
                    'high': torch.tensor(dataset_stats['translation']['max']),
                }
                for (dataset_name, dataset_stats) in stats.items()
            }
        elif translation_norm == Normalization.BOUNDS_Q99:
            results = {
                dataset_name: {
                    'low': torch.tensor(dataset_stats['translation']['q01']),
                    'high': torch.tensor(dataset_stats['translation']['q99']),
                }
                for (dataset_name, dataset_stats) in stats.items()
            }
        elif translation_norm == Normalization.MEAN:
            results = {
                dataset_name: {
                    'mean': torch.tensor(dataset_stats['translation']['mean']),
                    'std': torch.tensor(dataset_stats['translation']['std']),
                }
                for (dataset_name, dataset_stats) in stats.items()
            }
        else:
            raise NotImplementedError(f'Normalization type {translation_norm} not yet implemented')
    elif isinstance(translation_norm, Normalization) and translation_norm == Normalization.NONE:
        results = {
            dataset_name: {
                'low': -1 * torch.ones(3, dtype=torch.float32),
                'high': 1 * torch.ones(3, dtype=torch.float32),
            }
            for dataset_name in dataset_names
        }
    else:
        assert isinstance(translation_norm, collections.abc.Mapping), type(translation_norm)
        assert all((len(value) == 3 for value in translation_norm.values())), translation_norm
        assert set(translation_norm.keys()) in ({'low', 'high'}, {'mean', 'std'}), translation_norm
        results = {
            dataset_name: {
                key: torch.tensor(value, dtype=torch.float32) for (key, value) in translation_norm.items()
            }
            for dataset_name in dataset_names
        }
    return results


VLMProcessorConfigT = TypeVar('VLMProcessorConfigT', bound=VLMProcessorConfig)


class VLMProcessor(Configurable[VLMProcessorConfigT], Template[VLMProcessorConfigT]):
    @abstractmethod
    def preprocess_inputs(
        self, chat: List[str], images: Dict[str, List[PIL.Image.Image]]
    ) -> Dict[str, torch.Tensor | Dict[str, torch.Tensor]]:
        ...

    @property
    @abstractmethod
    def tokenizer(self) -> transformers.PreTrainedTokenizerBase:
        pass

    @property
    @abstractmethod
    def image_sizes(self) -> Dict[str, ImageSizeConfig]:
        pass


VLAMProcessorConfigT = TypeVar('VLAMProcessorConfigT', bound=VLAMProcessorConfig)


class VLAMProcessor(Configurable[VLAMProcessorConfigT], Template[VLAMProcessorConfigT]):
    def __init__(self, config: VLAMProcessorConfigT, vlm_processor: VLMProcessor):
        super().__init__(config)
        self.vlm_processor = vlm_processor
        self.control_tokenizer = EmptyTokenizer(
            config=self.config.control_tokenizer_config, tokenizer=self.tokenizer
        )
        self.norm_bounds: Dict[str, Dict[str, Dict[str, torch.Tensor]]] = {
            'obs_translation': self.obs_translation_norm_bounds,
            'obs_rotation': self.obs_rotation_norm_bounds,
            'translation': self.translation_norm_bounds,
            'rotation': self.rotation_norm_bounds,
            'joints': self.joints_norm_bounds,
        }

    @property
    def tokenizer(self) -> transformers.PreTrainedTokenizerBase:
        return self.vlm_processor.tokenizer

    @property
    def image_sizes(self) -> Dict[str, ImageSizeConfig]:
        return self.vlm_processor.image_sizes

    @property
    def camera_names(self) -> List[str]:
        return list(self.vlm_processor.image_sizes.keys())

    @property
    def control_io_config(self) -> ControlDataIOConfig:
        return self.config.control_io_config

    @cached_property
    def rotation_components(self) -> int:
        if self.config.rotation_format == RotationFormat.EULER:
            return 3
        if self.config.rotation_format == RotationFormat.QUATERNION:
            return 4
        if self.config.rotation_format == RotationFormat.ROTMAT:
            return 9
        raise NotImplementedError(self.config.rotation_format)

    @abstractmethod
    def policy_control_plan_from_model_target(
        self, target: RoboticsTarget, dataset_name: np.ndarray
    ) -> RoboticsControlPlan:
        pass

    @abstractmethod
    def policy_control_plan_from_model_output(
        self, model_output: RoboticsOutput, dataset_name: np.ndarray, valid_mask: torch.Tensor
    ) -> RoboticsControlPlan:
        pass

    def resize_image(
        self, camera_name: str, image: PIL.Image.Image | np.ndarray
    ) -> PIL.Image.Image | np.ndarray:
        return resize_image(
            image,
            target_size={
                'width': self.image_sizes[camera_name].width,
                'height': self.image_sizes[camera_name].height,
            },
            mode=self.config.image_resize,
            resample=PIL.Image.Resampling.LANCZOS,
        )

    def preprocess_inputs(
        self,
        chat: List[str],
        images: Dict[str, PIL.Image.Image | List[PIL.Image.Image]],
        ee_pose_translation: np.ndarray,
        ee_pose_rotation: np.ndarray,
        gripper: np.ndarray,
        joints: np.ndarray,
        dataset_name: np.ndarray,
        inference_mode: bool,
        control_target: Optional[RoboticsTarget] = None,
    ) -> Dict[str, torch.Tensor | Dict[str, torch.Tensor]]:
        """
        Preprocess the inputs for a single example
        Args:
            instruction: Language instruction
            images: History of input images with increasing timestamps
            ee_pose_translation: np.ndarray, shape [..., num_past_scalars, 3]
            ee_pose_rotation: np.ndarray, shape [..., num_past_scalars, 3 | 4 | 9]
            joints: np.ndarray, shape  [..., num_past_scalars, <= 7]
            dataset_name: 1D np.ndarray
            inference_mode: If True, prepare the input for inference (e.g. don't include target
                any tokens in the input if relevant). If control_target is available, it should
                still be preprocessed for test dataset comparison
            control_target: RoboticsTarget, each component of shape
                [..., num_control_steps, num_control_components]. Provided only when available, usually
                during training and dataset test
        Returns:
            Dict containing torch.Tensor with inputs
        """
        del control_target
        del inference_mode
        inputs = self.vlm_processor.preprocess_inputs(chat=chat, images=images)
        images: Dict[str, torch.Tensor] = inputs['images']
        input_ids: torch.Tensor = inputs['input_ids'][..., : self.tokenizer.model_max_length]
        target_text_tokens_ids: torch.Tensor = inputs['target_ids'][..., : self.tokenizer.model_max_length]
        attn_mask = torch.ones(input_ids.shape, dtype=torch.bool)
        ee_pose_translation = torch.tensor(ee_pose_translation, dtype=torch.float32)
        ee_pose_rotation = torch.tensor(ee_pose_rotation, dtype=torch.float32)
        ee_pose_rotation = convert_rotation(ee_pose_rotation, self.config.rotation_format, autonorm=True)
        gripper = preprocess_gripper_observation(gripper, dataset_name)
        gripper = torch.tensor(gripper, dtype=torch.float32)
        ee_pose_translation = self.normalize(
            ee_pose_translation, dataset_name=dataset_name, key='obs_translation'
        )
        ee_pose_rotation = self.normalize(ee_pose_rotation, dataset_name=dataset_name, key='obs_rotation')
        joints = torch.tensor(joints, dtype=torch.float32)
        if joints.shape[-1] < 7:
            missing_size = 7 - joints.shape[-1]
            joints = torch.cat([joints, torch.zeros([*joints.shape[:-1], missing_size])], dim=-1)
        joints = self.normalize(joints, dataset_name=dataset_name, key='joints')
        outputs = {
            'images': images,
            'input_ids': input_ids,
            'target_text_tokens_ids': target_text_tokens_ids,
            'attn_mask': attn_mask,
            'ee_pose_translation': ee_pose_translation,
            'ee_pose_rotation': ee_pose_rotation,
            'gripper': gripper,
            'joints': joints,
            'control_tokens_ids': None,
            'target_control_tokens_ids': None,
        }
        return outputs

    def create_input(
        self,
        chat: List[str],
        images: Dict[str, List[PIL.Image.Image]],
        ee_pose_translation: np.ndarray,
        ee_pose_rotation: np.ndarray,
        gripper: np.ndarray,
        joints: np.ndarray,
        dataset_name: np.ndarray,
        inference_mode: bool,
        control_target: Optional[RoboticsTarget] = None,
    ) -> RoboticsInput:
        inputs = self.preprocess_inputs(
            chat=chat,
            images=images,
            ee_pose_translation=ee_pose_translation,
            ee_pose_rotation=ee_pose_rotation,
            gripper=gripper,
            joints=joints,
            dataset_name=dataset_name,
            inference_mode=inference_mode,
            control_target=control_target,
        )
        inputs.pop('target_text_tokens_ids')
        inputs.pop('target_control_tokens_ids')
        return RoboticsInput(**inputs)

    def normalize(self, value: torch.Tensor, dataset_name: np.ndarray, key: str) -> torch.Tensor:
        if is_mean_norm(getattr(self.config, f'{key}_norm')):
            (mean, std) = self._norm_bounds_from_dataset_name(dataset_name, component_key=key)
            output = normalize_by_moments(value, mean=mean, std=std)
        else:
            (low, high) = self._norm_bounds_from_dataset_name(dataset_name, component_key=key)
            output = normalize_by_bounds(value, low=low, high=high)
        return output

    def unnormalize(self, value: torch.Tensor, dataset_name: np.ndarray, key: str) -> torch.Tensor:
        if is_mean_norm(getattr(self.config, f'{key}_norm')):
            (mean, std) = self._norm_bounds_from_dataset_name(dataset_name, component_key=key)
            output = unnormalize_by_moments(value, mean=mean, std=std)
        else:
            (low, high) = self._norm_bounds_from_dataset_name(dataset_name, component_key=key)
            output = unnormalize_by_bounds(value, low=low, high=high)
        return output

    def _norm_bounds_from_dataset_name(
        self, dataset_name: np.ndarray, component_key: str
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Create an array of normalization bounds corresponding to dataset names
        Args:
            dataset_name: Array of shape [B] of dataset names for which to fetch the low and high
                normalization bounds. Note the values can be repeating
            component_key: str. One of 'action', 'translation', 'rotation'. Indicates for which control to
                compute the normalization bounds
        Returns:
            Tuple of low and high bounds or norm and std, each of shape [B, -1]
        """
        norm = getattr(self.config, f'{component_key}_norm')
        if is_mean_norm(norm):
            (stats_key_1, stats_key_2) = ('mean', 'std')
        else:
            (stats_key_1, stats_key_2) = ('low', 'high')
        if component_key == 'joints':
            if not isinstance(norm, collections.abc.Mapping):
                raise NotImplementedError()
            stats = {
                key: torch.from_numpy(np.tile(np.reshape(value, [1, -1]), [len(dataset_name), 1]))
                for (key, value) in self.joints_norm_bounds['ANY'].items()
            }
            return tuple(stats.values())
        component_size = list(list(self.norm_bounds[component_key].values())[0].values())[0].shape[-1]
        if self.dataset_names == ['ANY']:
            stats_1 = self.norm_bounds[component_key]['ANY'][stats_key_1]
            stats_2 = self.norm_bounds[component_key]['ANY'][stats_key_2]
            stats_1 = np.repeat(np.expand_dims(stats_1, axis=0), len(dataset_name), axis=0)
            stats_2 = np.repeat(np.expand_dims(stats_2, axis=0), len(dataset_name), axis=0)
        else:
            (unique_names, _, inverse_indices, _) = np_unique(dataset_name)
            stats_1 = np.zeros([len(unique_names), component_size], dtype=np.float32)
            stats_2 = np.zeros([len(unique_names), component_size], dtype=np.float32)
            for i, ds_name in enumerate(unique_names):
                stats_1[i] = self.norm_bounds[component_key][ds_name][stats_key_1]
                stats_2[i] = self.norm_bounds[component_key][ds_name][stats_key_2]
            stats_1 = stats_1[inverse_indices]
            stats_2 = stats_2[inverse_indices]
        return torch.from_numpy(stats_1), torch.from_numpy(stats_2)

    @cached_property
    def obs_rotation_norm_bounds(self) -> Dict[str, Dict[str, torch.Tensor]]:
        return rotation_norm_bounds(
            rotation_norm=self.config.obs_rotation_norm,
            rotation_format=self.config.rotation_format,
            stats=self._observation_stats,
            dataset_names=self.dataset_names,
        )

    @cached_property
    def obs_translation_norm_bounds(self) -> Dict[str, Dict[str, torch.Tensor]]:
        return translation_norm_bounds(
            translation_norm=self.config.obs_translation_norm,
            stats=self._observation_stats,
            dataset_names=self.dataset_names,
        )

    @cached_property
    def rotation_norm_bounds(self) -> Dict[str, Dict[str, torch.Tensor]]:
        return rotation_norm_bounds(
            rotation_norm=self.config.rotation_norm,
            rotation_format=self.config.rotation_format,
            stats=self._control_stats,
            dataset_names=self.dataset_names,
        )

    @cached_property
    def translation_norm_bounds(self) -> Dict[str, Dict[str, torch.Tensor]]:
        return translation_norm_bounds(
            translation_norm=self.config.translation_norm,
            stats=self._control_stats,
            dataset_names=self.dataset_names,
        )

    @cached_property
    def joints_norm_bounds(self) -> Dict[str, Dict[str, torch.Tensor]]:
        """
        NOTE:
            - Joint values across all joints and all datasets vary in the range [-2pi; 2pi]
            - The effective range of a single joint is in practice one of [-2pi; 0], [-pi; pi], [0; 2pi]
            - It's possible to shift all ranges to [-pi; pi], but it requires careful handling for each joint
        """
        low = torch.tensor(self.config.joints_norm['low'], dtype=torch.float32)
        high = torch.tensor(self.config.joints_norm['high'], dtype=torch.float32)
        results = {'ANY': {'low': low, 'high': high}}
        return results

    @cached_property
    def _observation_stats(self) -> Dict[str, Dict[str, Dict[str, List[float]]]]:
        return {
            'austin_buds_dataset': {
                'euler': {
                    'max': [3.141589641571045, 0.2279592752456665, 0.10982763767242432],
                    'mean': [-0.433798223733902, 0.02660757675766945, 0.01057341042906046],
                    'min': [-3.1415905952453613, -0.17229366302490234, -0.08408939838409424],
                    'q01': [-3.1401021480560303, -0.15158048272132874, -0.07398375868797302],
                    'q99': [3.1401915550231934, 0.1699378788471222, 0.08044551312923431],
                    'std': [3.071873664855957, 0.08403228223323822, 0.04623554274439812],
                },
                'gripper': {
                    'max': [0.07999841868877411],
                    'min': [0.00019240332767367363],
                    'q01': [0.00760263716802001],
                    'q99': [0.07997412234544754],
                },
                'joints': {
                    'max': [
                        0.25105541944503784,
                        1.0239691734313965,
                        0.25514841079711914,
                        0.0,
                        0.05838121101260185,
                        3.0727620124816895,
                        1.1911247968673706,
                    ],
                    'mean': [
                        -0.02248559147119522,
                        0.4224241375923157,
                        0.011533008888363838,
                        -2.040178060531616,
                        -0.004422259051352739,
                        2.440391778945923,
                        0.7626844644546509,
                    ],
                    'min': [
                        -0.46276336908340454,
                        -0.2620261609554291,
                        -0.37377235293388367,
                        -3.010026693344116,
                        -0.015008972026407719,
                        0.0,
                        0.0,
                    ],
                    'q01': [
                        -0.3563080132007599,
                        -0.19165010750293732,
                        -0.29941368103027344,
                        -2.766944408416748,
                        -0.012211786583065987,
                        1.9946393966674805,
                        0.21924567222595215,
                    ],
                    'q99': [
                        0.22295619547367096,
                        0.8841447830200195,
                        0.20571835339069366,
                        -1.339158296585083,
                        0.05605502426624298,
                        2.9369189739227295,
                        1.1475461721420288,
                    ],
                    'std': [
                        0.1684190183877945,
                        0.2238602489233017,
                        0.1185624971985817,
                        0.3182348608970642,
                        0.012190691195428371,
                        0.2673698663711548,
                        0.2611873149871826,
                    ],
                },
                'translation': {
                    'max': [0.7684353590011597, 0.22995209693908691, 0.3893454968929291],
                    'mean': [0.5589713454246521, -0.0022956086322665215, 0.12593945860862732],
                    'min': [0.0, -0.31078848242759705, 0.0],
                    'q01': [0.3499317765235901, -0.2854413390159607, 0.010516085661947727],
                    'q99': [0.7243335843086243, 0.20652863383293152, 0.3218296766281128],
                    'std': [0.08953548222780228, 0.1462203562259674, 0.0769098550081253],
                },
            },
            'austin_sailor_dataset': {
                'euler': {
                    'max': [3.1415910720825195, 0.26857471466064453, 2.4386940002441406],
                    'mean': [0.040416110306978226, 0.013173789717257023, -0.044821564108133316],
                    'min': [-3.141592264175415, -0.21639788150787354, -1.995558738708496],
                    'q01': [-3.1401662826538086, -0.16171419620513916, -1.5918874740600586],
                    'q99': [3.1401755809783936, 0.17527776956558228, 1.3560799360275269],
                    'std': [3.0995969772338867, 0.08136381208896637, 0.6311237812042236],
                },
                'gripper': {
                    'max': [0.07783995568752289],
                    'min': [-8.864999836077914e-05],
                    'q01': [0.0005174533580429852],
                    'q99': [0.07778151333332062],
                },
                'joints': {
                    'max': [
                        0.18366889655590057,
                        1.188208818435669,
                        1.1904715299606323,
                        -1.0253318548202515,
                        0.06765712797641754,
                        2.98008131980896,
                        2.7747786045074463,
                    ],
                    'mean': [
                        -0.4481923580169678,
                        0.428782194852829,
                        0.29826778173446655,
                        -2.1356770992279053,
                        -0.2064618021249771,
                        2.5082974433898926,
                        0.8104547262191772,
                    ],
                    'min': [
                        -1.4425580501556396,
                        -0.34988880157470703,
                        -0.4436887800693512,
                        -2.9587178230285645,
                        -0.9116092324256897,
                        1.7689018249511719,
                        -1.4776484966278076,
                    ],
                    'q01': [
                        -1.0579811334609985,
                        -0.13357718288898468,
                        -0.2518821358680725,
                        -2.6593017578125,
                        -0.6571194529533386,
                        2.072176456451416,
                        -0.5861577391624451,
                    ],
                    'q99': [
                        0.08897759765386581,
                        0.8743473887443542,
                        0.8909343481063843,
                        -1.4748132228851318,
                        -0.010495365597307682,
                        2.859259605407715,
                        2.319192409515381,
                    ],
                    'std': [
                        0.26852160692214966,
                        0.22502659261226654,
                        0.2577133774757385,
                        0.2963367700576782,
                        0.1629231870174408,
                        0.18130357563495636,
                        0.6348837614059448,
                    ],
                },
                'translation': {
                    'max': [0.7488242387771606, 0.2829112708568573, 0.3541720509529114],
                    'mean': [0.5367430448532104, -0.08604215085506439, 0.11135970056056976],
                    'min': [0.3208981454372406, -0.3730051815509796, 0.020952222868800163],
                    'q01': [0.387094110250473, -0.3164229393005371, 0.024492919445037842],
                    'q99': [0.6869593262672424, 0.2086469978094101, 0.2551962733268738],
                    'std': [0.08000300824642181, 0.13984571397304535, 0.056377921253442764],
                },
            },
            'austin_sirius_dataset': {
                'euler': {
                    'max': [3.141592502593994, 0.09964871406555176, 0.3406205177307129],
                    'mean': [1.1875473260879517, -0.018573710694909096, -0.4977009892463684],
                    'min': [-3.141592502593994, -0.14349305629730225, -1.892538070678711],
                    'q01': [-3.140658378601074, -0.12321051210165024, -1.743293285369873],
                    'q99': [3.1407761573791504, 0.07348346710205078, 0.062370821833610535],
                    'std': [2.8533384799957275, 0.03962606564164162, 0.5864803791046143],
                },
                'gripper': {
                    'max': [0.07965432107448578],
                    'min': [0.00016088332631625235],
                    'q01': [0.0334978811442852],
                    'q99': [0.07948359102010727],
                },
                'joints': {
                    'max': [
                        0.5567107200622559,
                        0.3814372420310974,
                        0.36868324875831604,
                        0.0,
                        0.021617505699396133,
                        2.825117588043213,
                        2.8925509452819824,
                    ],
                    'mean': [
                        0.22241303324699402,
                        0.03750193864107132,
                        -0.013105375692248344,
                        -2.428316593170166,
                        -0.017613587900996208,
                        2.4755642414093018,
                        1.4929485321044922,
                    ],
                    'min': [
                        -0.23144784569740295,
                        -0.41377919912338257,
                        -0.3536752760410309,
                        -2.9289300441741943,
                        -0.06330326944589615,
                        0.0,
                        0.0,
                    ],
                    'q01': [
                        -0.10539760440587997,
                        -0.2651134133338928,
                        -0.21157152950763702,
                        -2.831827402114868,
                        -0.04555036500096321,
                        0.0,
                        0.0,
                    ],
                    'q99': [
                        0.48007193207740784,
                        0.3064860999584198,
                        0.2438022643327713,
                        0.0,
                        0.012844694778323174,
                        2.71537446975708,
                        2.51297926902771,
                    ],
                    'std': [
                        0.1297803372144699,
                        0.132253035902977,
                        0.08940940350294113,
                        0.345712274312973,
                        0.015448619611561298,
                        0.3493262231349945,
                        0.632145345211029,
                    ],
                },
                'translation': {
                    'max': [0.5655431151390076, 0.3374997079372406, 0.3071175515651703],
                    'mean': [0.4490734338760376, 0.09406533092260361, 0.17131780087947845],
                    'min': [0.0, -0.16291481256484985, 0.0],
                    'q01': [0.0, -0.11814527958631516, 0.0],
                    'q99': [0.532875120639801, 0.26084619760513306, 0.27225059270858765],
                    'std': [0.07605387270450592, 0.08447220921516418, 0.04798709973692894],
                },
            },
            'bc_z': {
                'euler': {
                    'max': [3.141583088638963, 1.5360414168274534, 3.141576023430307],
                    'mean': [-0.32332700342924814, -0.1255861641435509, -0.07202428898041417],
                    'min': [-3.13973587780783, -1.5322501214383415, -3.1415575429114675],
                    'q01': [-1.0433973645798003, -1.007553367940694, -2.6477418236555788],
                    'q99': [0.8467956620806508, 0.9111507554055364, 2.226032625129908],
                    'std': [0.40307241985577485, 0.4832146677789649, 1.0666829542185445],
                },
                'gripper': {'max': [1.0], 'min': [0.0], 'q01': [0.20000000298023224], 'q99': [1.0]},
                'joints': {
                    'max': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'mean': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'min': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'q01': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'q99': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'std': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                },
                'translation': {
                    'max': [0.6597589254379272, 0.7259413599967957, 1.1217665672302246],
                    'mean': [0.012632723897695541, 0.10061875730752945, 0.7831991910934448],
                    'min': [-0.7181899547576904, -0.3756217360496521, -0.281008243560791],
                    'q01': [-0.3956047296524048, -0.11924505233764648, 0.601338267326355],
                    'q99': [0.332028865814209, 0.3088575601577759, 0.98329097032547],
                    'std': [0.1808762550354004, 0.0960959941148758, 0.08665762096643448],
                },
            },
            'berkeley_autolab_ur5': {
                'euler': {
                    'max': [3.141586857385381, 0.9715026080766722, 1.0041252839605828],
                    'mean': [0.10885329009674993, -0.005823583245937125, 0.01756067034138813],
                    'min': [-3.14158698706268, -0.9912158529884818, -0.8782840134752882],
                    'q01': [-3.1391613317061315, -0.6410961610461836, -0.4513447799408977],
                    'q99': [3.13947692478744, 0.6738775260636495, 0.535202669480506],
                    'std': [3.069331637100682, 0.18171227413203145, 0.17512358924250696],
                },
                'gripper': {'max': [1.0], 'min': [0.0], 'q01': [0.0], 'q99': [1.0]},
                'joints': {
                    'max': [
                        -2.5115966796875,
                        -0.682390034198761,
                        2.6830153465270996,
                        -1.1711143255233765,
                        -0.6423047184944153,
                        4.032349109649658,
                    ],
                    'mean': [
                        -3.2676453590393066,
                        -1.3197712898254395,
                        2.122663736343384,
                        -2.3710596561431885,
                        -1.567111611366272,
                        3.0015013217926025,
                    ],
                    'min': [
                        -4.0775299072265625,
                        -2.2796404361724854,
                        1.1905627250671387,
                        -3.814586877822876,
                        -2.1223204135894775,
                        1.5279699563980103,
                    ],
                    'q01': [
                        -3.839585781097412,
                        -1.8913453817367554,
                        1.5813319683074951,
                        -3.3646113872528076,
                        -1.805822730064392,
                        2.22794246673584,
                    ],
                    'q99': [
                        -2.7322237491607666,
                        -0.8280109763145447,
                        2.5063326358795166,
                        -1.5614854097366333,
                        -1.2497813701629639,
                        3.7013423442840576,
                    ],
                    'std': [
                        0.2746899724006653,
                        0.27345725893974304,
                        0.22793518006801605,
                        0.34690913558006287,
                        0.10438559204339981,
                        0.3388271629810333,
                    ],
                },
                'translation': {
                    'max': [0.6610942482948303, 0.38513994216918945, 0.2049914449453354],
                    'mean': [0.45345133543014526, 0.05642913281917572, -0.0356401689350605],
                    'min': [0.1970997005701065, -0.27643972635269165, -0.20529526472091675],
                    'q01': [0.3020566999912262, -0.21297279000282288, -0.18836002051830292],
                    'q99': [0.6132073998451233, 0.30656182765960693, 0.12212439626455307],
                    'std': [0.07906801998615265, 0.12497302889823914, 0.08053416013717651],
                },
            },
            'berkeley_cable_routing': {
                'euler': {
                    'max': [3.141592264175415, 0.05923163890838623, 3.1243016719818115],
                    'mean': [-0.6730493903160095, 0.010155542753636837, 0.9881726503372192],
                    'min': [-3.141592264175415, -0.10035276412963867, -1.6915032863616943],
                    'q01': [-3.1412672996520996, -0.02922845631837845, -0.6449583172798157],
                    'q99': [3.1412830352783203, 0.039870113134384155, 2.4969191551208496],
                    'std': [3.0588438510894775, 0.01435503363609314, 0.6301024556159973],
                },
                'gripper': {'max': [-1.0], 'min': [-1.0], 'q01': [-1.0], 'q99': [-1.0]},
                'joints': {
                    'max': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'mean': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'min': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'q01': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'q99': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                    'std': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                },
                'translation': {
                    'max': [0.7113381028175354, 0.35645395517349243, 0.23169316351413727],
                    'mean': [0.5643643736839294, 0.00464651919901371, 0.07635799050331116],
                    'min': [0.378939151763916, -0.3244381248950958, 0.02837979607284069],
                    'q01': [0.4641263782978058, -0.2806571424007416, 0.030183622613549232],
                    'q99': [0.6452807784080505, 0.28204888105392456, 0.1557157188653946],
                    'std': [0.0380910187959671, 0.16768066585063934, 0.032017387449741364],
                },
            },
            'berkeley_fanuc_manipulation': {
                'euler': {
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                    'q01': [0.2845426588179575, -0.3288349528691964, -0.0934955147249334],
                    'q99': [0.8773894592247552, 0.28575230587640144, 0.3286392635131121],
                    'std': [0.12719404213832913, 0.1476650170975746, 0.09248325352628932],
                },
            },
            'stanford_hydra_dataset': {
                'euler': {
                    'max': [3.141592357591889, 1.5364991593031068, 3.141222461465462],
                    'mean': [-0.2789889021491306, -0.19247585545209975, 0.34538177027867784],
                    'min': [-3.1415919781981962, -1.5573291068141568, -3.1413972494043216],
                    'q01': [-3.138796116403405, -1.107955818954808, -2.271278880707407],
                    'q99': [3.1386728135975384, 0.915868569686168, 2.4011245578416336],
                    'std': [2.883189482433683, 0.43362190146795254, 1.199450318648319],
                },
                'gripper': {
                    'max': [0.0811397060751915],
                    'min': [-0.0005391233135014772],
                    'q01': [1.3133333141013281e-06],
                    'q99': [0.08100640028715134],
                },
                'joints': {
                    'max': [
                        1.9801124334335327,
                        1.5752310752868652,
                        2.6661651134490967,
                        -0.6274839639663696,
                        2.3508076667785645,
                        3.4612932205200195,
                        2.747223377227783,
                    ],
                    'mean': [
                        -0.052988938987255096,
                        -0.19803331792354584,
                        -0.046565085649490356,
                        -2.395282506942749,
                        0.4740530848503113,
                        2.119305372238159,
                        0.031005585566163063,
                    ],
                    'min': [
                        -2.5843772888183594,
                        -1.287919044494629,
                        -1.9306836128234863,
                        -2.9673542976379395,
                        -1.9528191089630127,
                        0.5680276155471802,
                        -2.74257755279541,
                    ],
                    'q01': [
                        -1.0905369520187378,
                        -0.9110571146011353,
                        -0.9165626764297485,
                        -2.876581907272339,
                        -0.9029546976089478,
                        1.3720064163208008,
                        -2.0172016620635986,
                    ],
                    'q99': [
                        0.6443323493003845,
                        1.0052685737609863,
                        0.9653106927871704,
                        -1.4030946493148804,
                        1.738037109375,
                        2.943267822265625,
                        2.451101541519165,
                    ],
                    'std': [
                        0.3502423167228699,
                        0.41914135217666626,
                        0.39895766973495483,
                        0.31860536336898804,
                        0.5207170248031616,
                        0.35328012704849243,
                        1.2519474029541016,
                    ],
                },
                'translation': {
                    'max': [0.7598075952006731, 0.36410959179102775, 0.597508369211053],
                    'mean': [0.4348564561896346, 0.024563536690147474, 0.2938664975066919],
                    'min': [0.18414022283711137, -0.32311685510034516, 0.017172937739480726],
                    'q01': [0.2373728557200433, -0.26521679456931635, 0.09069013461938308],
                    'q99': [0.712423814640525, 0.25299056815993204, 0.49505407602925094],
                    'std': [0.10465658310507645, 0.12745896408828714, 0.10379447506049931],
                },
            },
            'taco_play': {
                'euler': {
                    'max': [3.1415891647338867, 0.30717557668685913, 2.4624788761138916],
                    'mean': [-0.06622616201639175, -0.17697572708129883, 0.4614097774028778],
                    'min': [-3.1415886878967285, -0.8683895468711853, -1.789320707321167],
                    'q01': [-3.139000654220581, -0.6962019205093384, -1.1729414463043213],
                    'q99': [3.1392500400543213, 0.12436603754758835, 1.8065968751907349],
                    'std': [3.0384926795959473, 0.18692463636398315, 0.6806972622871399],
                },
                'gripper': {
                    'max': [0.08074767142534256],
                    'min': [9.652999870013446e-05],
                    'q01': [0.00015234666352625936],
                    'q99': [0.08067675679922104],
                },
                'joints': {
                    'max': [
                        0.9704633951187134,
                        0.4390600025653839,
                        0.9892600178718567,
                        -1.116769790649414,
                        1.2800638675689697,
                        2.894169330596924,
                        2.627519130706787,
                    ],
                    'mean': [
                        0.14954668283462524,
                        -0.3514331877231598,
                        0.1295831948518753,
                        -2.205498456954956,
                        0.11473798751831055,
                        2.0389058589935303,
                        0.5516068339347839,
                    ],
                    'min': [
                        -0.6495265960693359,
                        -1.2877551317214966,
                        -1.5125423669815063,
                        -2.956650733947754,
                        -1.0317779779434204,
                        1.1109415292739868,
                        -1.8966686725616455,
                    ],
                    'q01': [
                        -0.4596467614173889,
                        -0.9323632121086121,
                        -0.6153853535652161,
                        -2.8417088985443115,
                        -0.3472142219543457,
                        1.428541898727417,
                        -0.5756277441978455,
                    ],
                    'q99': [
                        0.7262046933174133,
                        0.21787810325622559,
                        0.7387099266052246,
                        -1.3517359495162964,
                        0.7581852674484253,
                        2.564530849456787,
                        1.6355704069137573,
                    ],
                    'std': [
                        0.281919926404953,
                        0.2702403962612152,
                        0.3334408402442932,
                        0.412654846906662,
                        0.21815766394138336,
                        0.2808808982372284,
                        0.4732993543148041,
                    ],
                },
                'translation': {
                    'max': [0.7124971151351929, 0.6118948459625244, 0.617118775844574],
                    'mean': [0.37278515100479126, 0.13003520667552948, 0.38341864943504333],
                    'min': [0.07693906873464584, -0.4944135844707489, 0.20030911266803741],
                    'q01': [0.1368357390165329, -0.4297449290752411, 0.20516259968280792],
                    'q99': [0.6700438857078552, 0.5943909883499146, 0.5966404676437378],
                    'std': [0.11509375274181366, 0.2694733738899231, 0.11266136914491653],
                },
            },
            'toto': {
                'euler': {
                    'max': [3.1414193360696108, 1.570402878730552, 3.1415549880169924],
                    'mean': [-1.31124856158284, 0.8107744638901545, 1.042033586691101],
                    'min': [-3.1415814091974665, -1.3210638993411208, -3.141515215393105],
                    'q01': [-2.9459294143675696, -0.6449196412662773, -2.9790265961739872],
                    'q99': [2.824635320375007, 1.4897934376557762, 3.0083314049108925],
                    'std': [1.2205361480020216, 0.45724967725161403, 1.3644152538580079],
                },
                'gripper': {'max': [-1.0], 'min': [-1.0], 'q01': [-1.0], 'q99': [-1.0]},
                'joints': {
                    'max': [
                        1.4926575422286987,
                        0.7715681195259094,
                        1.9506583213806152,
                        -1.6330622434616089,
                        2.804656744003296,
                        2.0850095748901367,
                        1.9661481380462646,
                    ],
                    'mean': [
                        -0.040506213903427124,
                        -0.2537725269794464,
                        0.05389903113245964,
                        -2.5135483741760254,
                        -0.161987766623497,
                        0.8815387487411499,
                        0.0034975146409124136,
                    ],
                    'min': [
                        -1.8345723152160645,
                        -1.7133995294570923,
                        -1.40083646774292,
                        -2.985748767852783,
                        -2.814244270324707,
                        -0.5165338516235352,
                        -3.5424435138702393,
                    ],
                    'q01': [
                        -1.4044159650802612,
                        -1.3978556394577026,
                        -0.8029389381408691,
                        -2.97342586517334,
                        -1.5402026176452637,
                        0.0011001524981111288,
                        -2.9709367752075195,
                    ],
                    'q99': [
                        0.7075999975204468,
                        0.42536962032318115,
                        1.3345959186553955,
                        -1.8281668424606323,
                        2.5240790843963623,
                        2.0835044384002686,
                        1.212519884109497,
                    ],
                    'std': [
                        0.3405684530735016,
                        0.3766334354877472,
                        0.40128207206726074,
                        0.3202400505542755,
                        0.6421889066696167,
                        0.33138400316238403,
                        0.5801302194595337,
                    ],
                },
                'translation': {
                    'max': [0.7777318005382999, 0.5688841397096768, 0.672235403102292],
                    'mean': [0.15078069344893, -0.03012185632654897, 0.35519823701585035],
                    'min': [-0.18532184496861265, -0.6282599632853518, 0.06312318086547261],
                    'q01': [-0.09177927811484686, -0.3571658956454935, 0.21965464356283362],
                    'q99': [0.6757593680120374, 0.2889021640318381, 0.501109413161318],
                    'std': [0.14726563054735392, 0.13472763062259546, 0.059123705378892395],
                },
            },
            'ucsd_kitchen_dataset': {
                'euler': {
                    'max': [3.141592644655991, 0.10471752134072587, 2.0420500160887434],
                    'mean': [-2.349219123990161, -0.5844573008678524, 0.8632842703198627],
                    'min': [-3.141592637838787, -1.5533486027935484, -1.676143206926509],
                    'q01': [-3.1415921825947537, -1.535883315130894, -0.6806766312742327],
                    'q99': [3.1328677560237566, 0.01745363977705911, 1.8326023938995777],
                    'std': [1.756197370631558, 0.482005280180061, 0.7151569996653978],
                },
                'gripper': {'max': [0.0], 'min': [0.0], 'q01': [0.0], 'q99': [0.0]},
                'joints': {
                    'max': [
                        2.4945411682128906,
                        1.0603798627853394,
                        0.5518655776977539,
                        2.71895432472229,
                        1.9269907474517822,
                        2.2231369018554688,
                        0.7844778895378113,
                    ],
                    'mean': [
                        0.35797572135925293,
                        -0.23473882675170898,
                        -0.34185951948165894,
                        0.8982798457145691,
                        0.5780586004257202,
                        1.0351765155792236,
                        -1.2190868854522705,
                    ],
                    'min': [
                        -0.6943671703338623,
                        -1.6351218223571777,
                        -2.0142624378204346,
                        0.00768359424546361,
                        -0.17713193595409393,
                        0.011292487382888794,
                        -2.6592507362365723,
                    ],
                    'q01': [
                        -0.43497809767723083,
                        -1.4843493700027466,
                        -1.7824348211288452,
                        0.0955040231347084,
                        -0.07186625152826309,
                        0.27948257327079773,
                        -2.4006130695343018,
                    ],
                    'q99': [
                        2.25881028175354,
                        0.8190488815307617,
                        0.2994879484176636,
                        2.153681993484497,
                        1.6126199960708618,
                        1.8523634672164917,
                        0.06944511085748672,
                    ],
                    'std': [
                        0.6131777167320251,
                        0.6322475075721741,
                        0.5427954792976379,
                        0.539089024066925,
                        0.43294647336006165,
                        0.3588581085205078,
                        0.6916991472244263,
                    ],
                },
                'translation': {
                    'max': [0.681192194250889, 0.2323359966361606, 0.6159547986373596],
                    'mean': [0.4067338752307897, 0.027551073758015975, 0.313373054289359],
                    'min': [0.13453314224525192, -0.2553313745227843, 0.03515888153950328],
                    'q01': [0.18739915591793913, -0.18234310016370514, 0.048970692427190994],
                    'q99': [0.6410437631246786, 0.20632224240340083, 0.5983893391276117],
                    'std': [0.12446715882142037, 0.08252308025761342, 0.11421020385340024],
                },
            },
            'utaustin_mutex': {
                'euler': {
                    'max': [3.1415927410125732, 0.5760129690170288, 0.6430304050445557],
                    'mean': [1.643947720527649, 0.03388536348938942, -0.31948330998420715],
                    'min': [-3.141592025756836, -0.9390894770622253, -1.8157784938812256],
                    'q01': [-3.1398682594299316, -0.7223533391952515, -1.5468647480010986],
                    'q99': [3.140272855758667, 0.3558599054813385, 0.3583984971046448],
                    'std': [2.2715108394622803, 0.19258549809455872, 0.5275800824165344],
                },
                'gripper': {
                    'max': [0.07569462060928345],
                    'min': [0.00026726332725957036],
                    'q01': [0.0019378233700990677],
                    'q99': [0.07565194368362427],
                },
                'joints': {
                    'max': [
                        0.5831676125526428,
                        0.8024097681045532,
                        1.350082516670227,
                        -1.1145747900009155,
                        0.5345654487609863,
                        3.2108523845672607,
                        2.875584363937378,
                    ],
                    'mean': [
                        -0.09772995859384537,
                        0.05997378006577492,
                        -0.009316973388195038,
                        -2.3428714275360107,
                        -0.48651641607284546,
                        2.2390215396881104,
                        1.3341773748397827,
                    ],
                    'min': [
                        -0.6990927457809448,
                        -0.8311276435852051,
                        -0.5323343276977539,
                        -2.9861886501312256,
                        -2.060809850692749,
                        0.826747477054596,
                        0.05972049757838249,
                    ],
                    'q01': [
                        -0.532222330570221,
                        -0.5078368782997131,
                        -0.3437865376472473,
                        -2.784912586212158,
                        -1.8685580492019653,
                        1.3974121809005737,
                        0.5157660841941833,
                    ],
                    'q99': [
                        0.35958197712898254,
                        0.6339818835258484,
                        0.5833610892295837,
                        -1.612648367881775,
                        0.2780923545360565,
                        2.8907132148742676,
                        2.837320327758789,
                    ],
                    'std': [
                        0.2101736217737198,
                        0.2880096137523651,
                        0.17824982106685638,
                        0.23544654250144958,
                        0.6387099027633667,
                        0.33533376455307007,
                        0.5090957880020142,
                    ],
                },
                'translation': {
                    'max': [0.581696093082428, 0.4334338903427124, 0.6800903081893921],
                    'mean': [0.4331520199775696, -0.10096638649702072, 0.22044037282466888],
                    'min': [0.2454746514558792, -0.502901017665863, 0.008792983368039131],
                    'q01': [0.3217194080352783, -0.4733337163925171, 0.014122226275503635],
                    'q99': [0.5321439504623413, 0.3733823001384735, 0.5785381197929382],
                    'std': [0.04380597919225693, 0.20559938251972198, 0.12614832818508148],
                },
            },
            'viola': {
                'euler': {
                    'max': [3.141592502593994, 0.9555190801620483, 0.40456175804138184],
                    'mean': [-0.7171992063522339, 0.07086259871721268, -0.67817223072052],
                    'min': [-3.141592025756836, -0.4305531978607178, -2.212939739227295],
                    'q01': [-3.1400232315063477, -0.19947049021720886, -1.8703945875167847],
                    'q99': [3.1401824951171875, 0.7830920219421387, 0.19578109681606293],
                    'std': [2.902841567993164, 0.20232577621936798, 0.7306717038154602],
                },
                'gripper': {
                    'max': [0.07748929411172867],
                    'min': [-5.3190000471659005e-05],
                    'q01': [0.00020356666937004775],
                    'q99': [0.07747221738100052],
                },
                'joints': {
                    'max': [
                        0.3366159200668335,
                        0.8983011841773987,
                        0.330204576253891,
                        0.0,
                        1.3329579830169678,
                        3.1103708744049072,
                        2.8318748474121094,
                    ],
                    'mean': [
                        0.027196571230888367,
                        0.2290346622467041,
                        -0.06033482402563095,
                        -2.060992479324341,
                        0.25734394788742065,
                        2.2569527626037598,
                        1.2675725221633911,
                    ],
                    'min': [
                        -0.4073199927806854,
                        -0.23913456499576569,
                        -0.5244941115379333,
                        -2.744713068008423,
                        -0.48249971866607666,
                        0.0,
                        -0.22237232327461243,
                    ],
                    'q01': [
                        -0.26899582147598267,
                        -0.189721941947937,
                        -0.3926161825656891,
                        -2.620305061340332,
                        -0.199931338429451,
                        1.6743353605270386,
                        0.1440846174955368,
                    ],
                    'q99': [
                        0.23294022679328918,
                        0.764011561870575,
                        0.20191603899002075,
                        -1.5629768371582031,
                        1.039290189743042,
                        2.9412121772766113,
                        2.798232078552246,
                    ],
                    'std': [
                        0.10160040855407715,
                        0.21415969729423523,
                        0.14444759488105774,
                        0.27077943086624146,
                        0.3251941502094269,
                        0.338652640581131,
                        0.7529342174530029,
                    ],
                },
                'translation': {
                    'max': [0.6868156790733337, 0.20999883115291595, 0.5037735104560852],
                    'mean': [0.5464076995849609, 0.006265466101467609, 0.23136523365974426],
                    'min': [0.0, -0.33860740065574646, 0.0],
                    'q01': [0.40061360597610474, -0.25196850299835205, 0.010269512422382832],
                    'q99': [0.6458418369293213, 0.17776551842689514, 0.4456312954425812],
                    'std': [0.062116123735904694, 0.12932434678077698, 0.13205111026763916],
                },
            },
        }

    @cached_property
    def _control_stats(self) -> Dict[str, Dict[str, Dict[str, List[float]]]]:
        if is_global_norm(self.config.rotation_norm) and is_global_norm(self.config.translation_norm):
            return {}
        with open(self.config.control_stats_path, 'r') as file:
            stats = yaml.safe_load(file)
            if self.config.delta_controls:
                if self.control_io_config.future_controls_sequence_stride_sec is None:
                    horizon = 0.0
                else:
                    horizon = self.control_io_config.future_controls_sequence_stride_sec
            elif self.control_io_config.future_controls_sequence_stride_sec is None:
                if self.control_io_config.future_controls_sequence_length == 1:
                    horizon = 0.0
                else:
                    raise NotImplementedError()
            else:
                horizon = (
                    self.control_io_config.future_controls_sequence_length
                    * self.control_io_config.future_controls_sequence_stride_sec
                )
            key = f'horizon_{round(horizon, 2)}s'
            if key in stats:
                stats = stats[key]
            else:
                raise ValueError(
                    f'Missing control statistics key {key} for future_controls_sequence_length={self.config.control_io_config.future_controls_sequence_length} future_controls_sequence_stride_sec={self.config.control_io_config.future_controls_sequence_stride_sec}. Available keys: [{stats.keys()}]'
                )
        return stats

    @cached_property
    def dataset_names(self) -> List[str]:
        if (
            is_global_norm(self.config.rotation_norm)
            and is_global_norm(self.config.obs_rotation_norm)
            and is_global_norm(self.config.translation_norm)
            and is_global_norm(self.config.obs_translation_norm)
        ):
            return ['ANY']
        return list(set(self._control_stats.keys()) | set(self._observation_stats.keys()))


def delta_to_relative_translations(translation_sequence: torch.Tensor) -> torch.Tensor:
    """
    Transform a sequence of translation vectors encoded w.r.t. PREVIOUS frame in the sequence to encoding
    w.r.t. the 0-th element preceding the sequence
    Ex:
        Sequence of points: T1, T2, T3, T4
        `translation_sequence` contains the vectors: T0T1, T1T2, T2T3, T3T4, where T0 is the base frame,
        implicitly encoded in T0T1
        Output: T0T1, T0T2, T0T3, T0T4

    Args:
        translation_sequence: torch.Tensor of shape [..., S, 3], containing the translation vectors, where S
            corresponds to the sequence dimension
    Returns:
        torch.Tensor of the same shape as translation_sequence, containing delta translations
    """
    assert translation_sequence.ndim >= 3, translation_sequence.shape
    delta_translations = torch.cumsum(translation_sequence, dim=-2)
    return delta_translations


class RegressionProcessor(VLAMProcessor[RegressionProcessorConfig]):
    def policy_control_plan_from_model_target(
        self, target: RoboticsTarget, dataset_name: np.ndarray
    ) -> RoboticsControlPlan:
        translation_m = self.unnormalize(target.translation, dataset_name=dataset_name, key='translation')
        rotation = self.unnormalize(target.rotation, dataset_name=dataset_name, key='rotation')
        rotmat = convert_rotation(rotation, RotationFormat.ROTMAT)
        gripper_prob = target.gripper
        if self.config.delta_controls:
            translation_m = delta_to_relative_translations(translation_m)
            rotmat = delta_to_relative_rotations(rotmat)
        return RoboticsControlPlan(
            translation_m=translation_m,
            rotmat=rotmat,
            gripper_prob=gripper_prob,
            valid_mask=target.valid_mask,
        )

    def policy_control_plan_from_model_output(
        self, model_output: RoboticsOutput, dataset_name: np.ndarray, valid_mask: torch.Tensor
    ) -> RoboticsControlPlan:
        """Called during inference to create control plan from model output"""
        translation_m = self.unnormalize(
            model_output.translation, dataset_name=dataset_name, key='translation'
        )
        rotation = self.unnormalize(model_output.rotation, dataset_name=dataset_name, key='rotation')
        rotmat = convert_rotation(rotation, RotationFormat.ROTMAT, autonorm=True)
        gripper_prob = torch.sigmoid(model_output.gripper)
        if self.config.delta_controls:
            translation_m = delta_to_relative_translations(translation_m)
            rotmat = delta_to_relative_rotations(rotmat)
        return RoboticsControlPlan(
            translation_m=translation_m, rotmat=rotmat, gripper_prob=gripper_prob, valid_mask=valid_mask
        )


class VLARMProcessor(RegressionProcessor):
    def __init__(self, config: VLARMProcessorConfig, vlm_processor: VLMProcessor):
        Configurable.__init__(self, config)
        self.vlm_processor = vlm_processor
        self.control_tokenizer = EmptyTokenizer(
            config=self.config.control_tokenizer_config, tokenizer=self.tokenizer
        )
        self.norm_bounds: Dict[str, Dict[str, Dict[str, torch.Tensor]]] = {
            'obs_translation': self.obs_translation_norm_bounds,
            'obs_rotation': self.obs_rotation_norm_bounds,
            'translation': self.translation_norm_bounds,
            'rotation': self.rotation_norm_bounds,
            'joints': self.joints_norm_bounds,
        }

    @property
    def dataset_np_names(self) -> np.ndarray:
        return self._dataset_np_names

    def _set_dataset_np_names(self, dataset_np_names: np.ndarray) -> None:
        self._dataset_np_names = dataset_np_names

    def preprocess_inputs(
        self,
        chat: List[str],
        images: Dict[str, List[PIL.Image.Image]],
        ee_pose_translation: np.ndarray,
        ee_pose_rotation: np.ndarray,
        gripper: np.ndarray,
        joints: np.ndarray,
        dataset_name: np.ndarray,
        inference_mode: bool,
        control_target: Optional[RoboticsTarget] = None,
    ) -> Dict[str, torch.Tensor | Dict[str, torch.Tensor]]:
        inputs = self.vlm_processor.preprocess_inputs(chat=chat, images=images)
        images: Dict[str, torch.Tensor] = inputs['images']
        input_ids: torch.Tensor = inputs['input_ids'][..., : self.tokenizer.model_max_length]
        target_text_tokens_ids: torch.Tensor = inputs['target_ids'][..., : self.tokenizer.model_max_length]
        attn_mask = torch.ones(input_ids.shape, dtype=torch.bool)
        ee_pose_translation = torch.tensor(ee_pose_translation, dtype=torch.float32)
        ee_pose_rotation = torch.tensor(ee_pose_rotation, dtype=torch.float32)
        ee_pose_rotation = convert_rotation(ee_pose_rotation, self.config.rotation_format, autonorm=True)
        gripper = preprocess_gripper_observation(gripper, dataset_name)
        gripper = torch.tensor(gripper, dtype=torch.float32)
        ee_pose_translation = self.normalize(
            ee_pose_translation, dataset_name=dataset_name, key='obs_translation'
        )
        ee_pose_rotation = self.normalize(ee_pose_rotation, dataset_name=dataset_name, key='obs_rotation')
        joints = torch.tensor(joints, dtype=torch.float32)
        if joints.shape[-1] < 7:
            missing_size = 7 - joints.shape[-1]
            joints = torch.cat([joints, torch.zeros([*joints.shape[:-1], missing_size])], dim=-1)
        joints = self.normalize(joints, dataset_name=dataset_name, key='joints')
        return {
            'images': images,
            'input_ids': input_ids,
            'target_text_tokens_ids': target_text_tokens_ids,
            'attn_mask': attn_mask,
            'ee_pose_translation': ee_pose_translation,
            'ee_pose_rotation': ee_pose_rotation,
            'gripper': gripper,
            'joints': joints,
            'control_tokens_ids': None,
            'target_control_tokens_ids': None,
        }

    def model_otoi_but_gt_gripper(
        self,
        translation_output: torch.Tensor,
        rotation_output: torch.Tensor,
        reference_translation: torch.Tensor,
        reference_rotation: torch.Tensor,
        gt_gripper: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Called during inference to create control plan from model output"""
        translation_delta = self.unnormalize(
            translation_output, dataset_name=self.dataset_np_names, key='translation'
        )
        rotation_delta = self.unnormalize(rotation_output, dataset_name=self.dataset_np_names, key='rotation')
        translation_reference = self.unnormalize(
            reference_translation, dataset_name=self.dataset_np_names, key='obs_translation'
        )
        rotation_reference = self.unnormalize(
            reference_rotation, dataset_name=self.dataset_np_names, key='obs_rotation'
        )
        translation = translation_reference + translation_delta
        rotation = delta_to_world_rotations(rotation_delta, rotation_reference)
        translation_next = self.normalize(
            translation, dataset_name=self.dataset_np_names, key='obs_translation'
        )
        rotation_next = self.normalize(rotation, dataset_name=self.dataset_np_names, key='obs_rotation')
        gripper_next = gt_gripper
        return translation_next, rotation_next, gripper_next


def make_causal_mask(shape: Sequence[int]) -> torch.Tensor:
    """
    Create a causal attention mask of shape `shape`
    Args:
        shape: Shape of the output mask, the last two dimensions correspond to [query_seq_len, kv_seq_len]
    Returns:
        torch.Tensor of dtype torch.bool. False values indicate that the row (i.e. query) can't attend
            to the corresponding column (i.e. key)

    Example:
        shape = (3, 5) -> Mask the upper triangular part
        [
            [ 1, 0, 0, 0, 0],
            [ 1, 1, 0, 0, 0],
            [ 1, 1, 1, 0, 0]
        ]
    """
    return torch.tril(torch.ones(shape, dtype=torch.bool), diagonal=0)


def enable_full_attn_blocks(attn_mask: torch.Tensor, full_attn: torch.Tensor) -> torch.Tensor:
    """
    Enable full bi-directional attention in `attn_mask` inside specific blocks
    Args:
        attn_mask: Existing attention mask of shape [..., query_seq_len, kv_seq_len] and dtype torch.bool
            where False values indicate disabled attention
        full_attn: torch.Tensor of shape [query_seq_len], dtype torch.bool. Blocks of True values indicate
            positions where full bi-directional attention should be enabled

    Example:
            1, 0, 0, 0, 0, 0, 0, 0,                 1, 1, 1, 0, 0, 0, 0, 0,
            1, 1, 0, 0, 0, 0, 0, 0,                 1, 1, 1, 0, 0, 0, 0, 0,
            1, 1, 1, 0, 0, 0, 0, 0,                 1, 1, 1, 0, 0, 0, 0, 0,
            1, 1, 1, 1, 0, 0, 0, 0,      ->         1, 1, 1, 1, 0, 0, 0, 0,
            1, 1, 1, 1, 1, 0, 0, 0,                 1, 1, 1, 1, 1, 0, 0, 0,
            1, 1, 1, 1, 1, 1, 0, 0,                 1, 1, 1, 1, 1, 1, 1, 1,
            1, 1, 1, 1, 1, 1, 1, 0,                 1, 1, 1, 1, 1, 1, 1, 1,
            1, 1, 1, 1, 1, 1, 1, 1,                 1, 1, 1, 1, 1, 1, 1, 1,

    """
    assert full_attn.dtype == torch.bool, full_attn.dtype
    assert full_attn.ndim == 1, full_attn.shape
    assert full_attn.shape[0] == attn_mask.shape[-2], f'{full_attn.shape[0]}, {attn_mask.shape}'
    if attn_mask.shape[-1] != attn_mask.shape[-2]:
        raise NotImplementedError('Only self-attention supported right now.')
    x = full_attn.view(-1, 1) & full_attn.view(1, -1)
    x = x | make_causal_mask([full_attn.shape[0], full_attn.shape[0]])
    x = torch.cumprod(x, dim=1).to(dtype=torch.bool)
    x = x & x.permute(1, 0)
    mask_positions = torch.sum(x, dim=0) == 1 & ~full_attn
    mask_indices = torch.where(mask_positions)[0]
    x[mask_indices, mask_indices] = 0
    attn_mask = attn_mask | expand_dims(x, ndim=attn_mask.ndim, order=[-1, 1, 1])
    return attn_mask


IGNORE_INDEX = -100


class PaliGemmaProcessor(VLMProcessor[PaliGemmaProcessorConfig]):
    def __init__(
        self,
        config: PaliGemmaProcessorConfig,
        hf_processor: transformers.models.paligemma.processing_paligemma.PaliGemmaProcessor,
        **kwargs,
    ):
        del kwargs
        super().__init__(config)
        self.hf_processor = hf_processor
        self.hf_processor.image_processor.size = dict(self.config.image_sizes['main'].as_json())
        self.hf_processor.image_seq_length = self.config.num_image_tokens['main']
        self.hf_processor.image_processor.image_seq_length = self.config.num_image_tokens['main']
        self.bos_id: int = self.tokenizer.bos_token_id
        self.eos_id: int = self.tokenizer.eos_token_id
        self.sep_token = '\n'
        self.sep_id: int = self.tokenizer(
            self.sep_token, padding=False, add_special_tokens=False, return_attention_mask=False
        )['input_ids'][0]
        self.image_token_id: int = self.tokenizer(
            self.config.image_token, padding=False, add_special_tokens=False, return_attention_mask=False
        )['input_ids'][0]
        self.image_tokens: list[int] = [self.image_token_id] * sum(self.config.num_image_tokens.values())
        self.bbox_pattern = re.compile(
            '\\[(\\d+\\.\\d+),\\s*(\\d+\\.\\d+),\\s*(\\d+\\.\\d+),\\s*(\\d+\\.\\d+)\\]'
        )

    def preprocess_inputs(
        self, chat: List[str], images: Dict[str, List[PIL.Image.Image]]
    ) -> Dict[str, torch.Tensor | Dict[str, torch.Tensor]]:
        """
        Based on PaliGemma paper https://arxiv.org/pdf/2407.07726 and example code at
        https://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma#create_model_inputs
        Chat must be always made of separate messages from user and model, always starting with user

        <image><image> ... <bos><instruction><sep><assistant><sep><instruction><sep><assistant>...<eos>

        Args:
            chat: List[str] of even size where each entry corresponds to a different turn in the conversation
            images: Dict[str, List[PIL.Image.Image]] where different cameras correspond to different keys
                in the Dict and the List corresponds to history of images
        """
        for key, value in images.items():
            if not isinstance(value, list):
                raise TypeError(f'Camera {key} contains values of type {type(value)} instead of list')
        (input_ids, target_ids) = ([], [])
        for i, text in enumerate(chat):
            text = text.replace(self.sep_token, ' ').replace('<image>', '')
            text = self.bbox_pattern.sub(self._bbox_to_loc_tokens, text)
            turn_input_ids: List[int] = self.tokenizer(
                text, padding=False, add_special_tokens=False, return_attention_mask=False
            )['input_ids']
            if i % 2 == 0:
                turn_target_ids = [IGNORE_INDEX] * len(turn_input_ids)
            else:
                turn_target_ids = turn_input_ids
            if i != len(chat) - 1:
                turn_input_ids = turn_input_ids + [self.sep_id]
                turn_target_ids = turn_target_ids + [IGNORE_INDEX]
            input_ids = input_ids + turn_input_ids
            target_ids = target_ids + turn_target_ids
        input_ids = [self.bos_id] + input_ids + [self.eos_id]
        target_ids = [IGNORE_INDEX] + target_ids + [self.eos_id]
        image_tokens = self.image_tokens
        input_ids = image_tokens + input_ids
        target_ids = [IGNORE_INDEX] * len(image_tokens) + target_ids
        input_ids = torch.tensor(input_ids, dtype=torch.int64)
        target_ids = torch.tensor(target_ids, dtype=torch.int64)
        image_tensors: Dict[str, torch.Tensor] = {
            f'{camera_name}.siglip': self.hf_processor.image_processor(
                camera_images, size=self.config.image_sizes[camera_name].as_json(), return_tensors='pt'
            )['pixel_values']
            for (camera_name, camera_images) in images.items()
        }
        attn_mask = make_causal_mask([len(input_ids), len(input_ids)])
        attn_mask = enable_full_attn_blocks(attn_mask, full_attn=target_ids == IGNORE_INDEX)
        return {
            'input_ids': input_ids,
            'target_ids': target_ids,
            'images': image_tensors,
            'attn_mask': attn_mask,
        }

    @property
    def tokenizer(self) -> transformers.PreTrainedTokenizerBase:
        return self.hf_processor.tokenizer

    @staticmethod
    def _bbox_to_loc_tokens(match: str) -> str:
        """
        https://developers.googleblog.com/en/gemma-explained-paligemma-architecture/
        """
        floats = list(map(float, match.groups()))
        transformed = [f'<loc{np.clip(round(num * 1024), 0, 1023):04d}>' for num in floats]
        return f"[{', '.join(transformed)}]"

    @property
    def image_sizes(self) -> Dict[str, ImageSizeConfig]:
        return self.config.image_sizes