from xvla.models.processing_xvla import XVLAProcessor
from xvla.models.modeling_xvla import XVLA
from xvla.models.configuration_xvla import XVLAConfig
import torch
import random
import numpy as np
from PIL import Image
from lerobot.policies.factory import make_policy
from lerobot.configs.policies import PreTrainedConfig
from lerobot.envs.factory import make_env_config
cfg = XVLAConfig.from_pretrained("/raid/jade/models/xvla-libero")
model = XVLA.from_pretrained("/raid/jade/models/xvla-libero")
model.eval()
model.to("cuda")
processor = XVLAProcessor.from_pretrained("/raid/jade/models/xvla-libero")
# /raid/jade/models/xvla-libero
# seet seed
torch.manual_seed(42)
random.seed(42)
np.random.seed(42)

def make_random_pil_images(num_images=3, H=480, W=640):
    images = []
    for _ in range(num_images):
        # Random RGB image
        arr = np.random.randint(0, 256, (H, W, 3), dtype=np.uint8)
        img = Image.fromarray(arr)
        images.append(img)
    return images

# Example:
images = make_random_pil_images()
language_instruction = "This is a random image"
# Multimodal preprocessing by processor
inputs = processor(images, language_instruction)
if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
    raise ValueError("Processor did not return the expected keys.")

proprio = torch.randn(1, 20)
domain_id = torch.tensor([int(0)], dtype=torch.long)

# Align to model's device/dtype
device = model.device
dtype = next(model.parameters()).dtype

def to_model(t: torch.Tensor) -> torch.Tensor:
    if not isinstance(t, torch.Tensor):
        t = torch.as_tensor(t)
    # cast floats to model dtype, keep integral/bool as-is
    return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)

inputs = {k: to_model(v) for k, v in inputs.items()}
inputs.update({
    "proprio": to_model(proprio),
    "domain_id": domain_id.to(device),
})

# Inference
action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()


#### now for lerobot model #####################################################

cfg = PreTrainedConfig.from_pretrained("/raid/jade/models/xvla-libero-og_migrated")
env_cfg = make_env_config("libero", task="libero_spatial")
cfg.pretrained_path = "/raid/jade/models/xvla-libero-og_migrated"
policy = make_policy(cfg=cfg, env_cfg=env_cfg)
policy.eval()
policy.to("cuda")

action_1 = policy.model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
breakpoint()