import torch

from lerobot.common.datasets.pusht import PushtExperienceReplay
from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
from rl.torchrl.data.replay_buffers.samplers import PrioritizedSliceSampler

# TODO(rcadene): implement

# dataset_d4rl = D4RLExperienceReplay(
#     dataset_id="maze2d-umaze-v1",
#     split_trajs=False,
#     batch_size=1,
#     sampler=SamplerWithoutReplacement(drop_last=False),
#     prefetch=4,
#     direct_download=True,
# )

# dataset_openx = OpenXExperienceReplay(
#     "cmu_stretch",
#     batch_size=1,
#     num_slices=1,
#     #download="force",
#     streaming=False,
#     root="data",
# )


def make_offline_buffer(cfg, sampler=None):

    overwrite_sampler = sampler is not None

    if not overwrite_sampler:
        # TODO(rcadene): move batch_size outside
        num_traj_per_batch = cfg.policy.batch_size  # // cfg.horizon
        # TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
        # We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
        sampler = PrioritizedSliceSampler(
            max_capacity=100_000,
            alpha=cfg.policy.per_alpha,
            beta=cfg.policy.per_beta,
            num_slices=num_traj_per_batch,
            strict_length=False,
        )

    if cfg.env.name == "simxarm":
        # TODO(rcadene): add PrioritizedSliceSampler inside Simxarm to not have to `sampler.extend(index)` here
        offline_buffer = SimxarmExperienceReplay(
            f"xarm_{cfg.env.task}_medium",
            # download="force",
            download=True,
            streaming=False,
            root="data",
            sampler=sampler,
        )
    elif cfg.env.name == "pusht":
        offline_buffer = PushtExperienceReplay(
            "pusht",
            # download="force",
            download=False,
            streaming=False,
            root="data",
            sampler=sampler,
        )
    else:
        raise ValueError(cfg.env.name)

    if not overwrite_sampler:
        num_steps = len(offline_buffer)
        index = torch.arange(0, num_steps, 1)
        sampler.extend(index)

    return offline_buffer