"""Test model: forward pass, generation, save/load."""

import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

import numpy as np
from singularity_llm.config import get_model_config, HardwareTier
from singularity_llm.model.model import SingularityLLM
from singularity_llm.model.tokenizer import BPETokenizer


def test_model_forward():
    """Test model forward pass."""
    cfg = get_model_config(HardwareTier.MOBILE)
    cfg.vocab_size = 256
    model = SingularityLLM(config=cfg)

    token_ids = np.array([[1, 5, 10, 15, 20]], dtype=np.int64)
    logits = model.forward(token_ids)

    assert logits.shape == (1, 5, 256), f"Wrong shape: {logits.shape}"
    print(f"  Logits shape: {logits.shape}")
    print(f"  Param count: {model.param_count:,}")


def test_model_generate():
    """Test text generation."""
    cfg = get_model_config(HardwareTier.MOBILE)
    cfg.vocab_size = 256
    model = SingularityLLM(config=cfg)

    output = model.generate("Hello", max_tokens=10, temperature=0.7)
    assert isinstance(output, str), f"Expected str, got {type(output)}"
    assert len(output) > 0, "Empty output"
    print(f"  Generated: {repr(output[:50])}")


def test_model_generate_stream():
    """Test streaming generation."""
    cfg = get_model_config(HardwareTier.MOBILE)
    cfg.vocab_size = 256
    model = SingularityLLM(config=cfg)

    chunks = list(model.generate_stream("Hello", max_tokens=10, temperature=0.7))
    assert len(chunks) > 0, "No chunks generated"
    print(f"  Chunks: {len(chunks)}")


def test_model_with_tokenizer():
    """Test model with trained tokenizer."""
    tok = BPETokenizer(vocab_size=256)
    tok.train("Hello world! This is a test. Hello world again. The quick brown fox jumps.")

    cfg = get_model_config(HardwareTier.MOBILE)
    cfg.vocab_size = 256
    model = SingularityLLM(config=cfg, tokenizer=tok)

    output = model.generate("Hello", max_tokens=10, temperature=0.7)
    assert isinstance(output, str)
    print(f"  Generated with tokenizer: {repr(output[:50])}")


def test_model_truncation():
    """Test that long inputs are truncated to max_seq_len."""
    cfg = get_model_config(HardwareTier.MOBILE)
    cfg.vocab_size = 256
    cfg.max_seq_len = 32
    model = SingularityLLM(config=cfg)

    # Input longer than max_seq_len
    long_input = np.array([[i for i in range(100)]], dtype=np.int64)
    logits = model.forward(long_input)
    assert logits.shape[1] == 32, f"Should truncate to 32, got {logits.shape[1]}"
    print(f"  Truncated to: {logits.shape[1]}")


if __name__ == "__main__":
    print("Running model tests...")
    test_model_forward()
    print("  ✓ test_model_forward")
    test_model_generate()
    print("  ✓ test_model_generate")
    test_model_generate_stream()
    print("  ✓ test_model_generate_stream")
    test_model_with_tokenizer()
    print("  ✓ test_model_with_tokenizer")
    test_model_truncation()
    print("  ✓ test_model_truncation")
    print("\nAll model tests passed!")