"""Smoke test: T5 text encoding under GGUF pipeline.

Builds the Wan22Pipeline (loads all weights including DIT) but only
exercises the T5 encoder — no image-to-video generation. Validates that
the DTYPE=FP16 ↔ BF16 patching lets T5 encode a prompt successfully.

Run:
    docker compose exec -e DIT_QUANT=gguf-Q4_K_M voice-chat \
        python -m tests.component.test_10_t5_encode
"""
from __future__ import annotations

import os
import sys

from tests.component._common import get_logger

log = get_logger("test_10")

DIT_QUANT = os.environ.get("DIT_QUANT", "gguf-Q8_0")
CONFIG_JSON = "/app/configs/lightx2v/wan22_i2v_gguf_5b_turbo.json"
DIT_REPO = "hum-ma/Wan2.2-TI2V-5B-Turbo-GGUF"


def run():
    try:
        from server.video_models.wan22 import Wan22Pipeline
    except ImportError as e:
        log.error("Import failed: %s", e)
        sys.exit(0)

    log.info("Building pipeline (quant=%s) — this loads T5 + DIT weights...", DIT_QUANT)
    pipe = Wan22Pipeline(
        base_repo="Wan-AI/Wan2.2-TI2V-5B",
        dit_repo=DIT_REPO,
        config_json=CONFIG_JSON,
        model_cls="wan2.2",
        resolution=480,
        fps=16,
        dit_quant_scheme=DIT_QUANT,
        t5_quantized=True,
    )
    log.info("Pipeline ready.")

    # Check DTYPE state after init
    from lightx2v.utils.envs import GET_DTYPE
    log.info("GET_DTYPE() = %s (DTYPE env = %s)", GET_DTYPE(), os.environ.get("DTYPE"))

    # Run only the T5 text encoder
    runner = pipe._runner
    prompt = "a person looking at the camera, natural lighting"

    log.info("Running T5 text encoder on prompt: %r", prompt)
    import copy
    input_info = copy.deepcopy(pipe._input_info_template)
    input_info.prompt = prompt

    runner.run_text_encoder(input_info)
    log.info("T5 encode complete.")

    # Inspect output — check all dataclass fields for tensor results
    import torch
    for attr in vars(input_info):
        val = getattr(input_info, attr)
        if isinstance(val, torch.Tensor):
            log.info("  %s: shape=%s dtype=%s device=%s", attr, val.shape, val.dtype, val.device)

    # Verify DTYPE is back to FP16 after T5 runs (if GGUF)
    if DIT_QUANT.startswith("gguf-"):
        current = GET_DTYPE()
        import torch
        expected = torch.float16
        if current == expected:
            log.info("PASS: DTYPE correctly restored to FP16 after T5 encode.")
        else:
            log.error("FAIL: DTYPE is %s after T5, expected %s", current, expected)
            sys.exit(1)

    log.info("PASS: T5 encoding succeeded under %s pipeline.", DIT_QUANT)


if __name__ == "__main__":
    run()