from __future__ import annotations

from pathlib import Path

import numpy as np

from repixelizer.baselines import naive_resize_baseline
from repixelizer.metrics import coherence_breakdown, foreground_reconstruction_error
from repixelizer.pipeline import _select_phase_candidate, run_pipeline
from repixelizer.io import nearest_resize
from repixelizer.synthetic import fake_pixelize, make_emblem, make_sprite
from repixelizer.types import InferenceCandidate, InferenceResult


def test_pipeline_writes_output_and_diagnostics(tmp_path: Path) -> None:
    source = make_emblem(32, 32)
    fake = fake_pixelize(source, upscale=10, phase_x=0.15, phase_y=0.25, blur_radius=0.5)
    input_path = tmp_path / "input.png"
    output_path = tmp_path / "output.png"
    diagnostics_dir = tmp_path / "diagnostics"

    from repixelizer.io import save_rgba

    save_rgba(input_path, fake)
    result = run_pipeline(input_path, output_path, diagnostics_dir=diagnostics_dir, steps=24)
    assert output_path.exists()
    assert (diagnostics_dir / "run.json").exists()
    assert (diagnostics_dir / "output-preview.png").exists()
    assert result.output_rgba.shape[0] == result.inference.target_height
    assert result.output_rgba.shape[1] == result.inference.target_width


def test_pipeline_beats_naive_on_roundtrip_fidelity_for_synthetic_emblem(tmp_path: Path) -> None:
    source = make_emblem(32, 32)
    fake = fake_pixelize(
        source,
        upscale=12,
        phase_x=0.2,
        phase_y=0.35,
        blur_radius=0.65,
        warp_strength=0.28,
        warp_detail=6,
        seed=5,
    )
    input_path = tmp_path / "input.png"
    output_path = tmp_path / "output.png"

    from repixelizer.io import save_rgba

    save_rgba(input_path, fake)
    result = run_pipeline(input_path, output_path, steps=32)
    naive = naive_resize_baseline(fake, width=result.inference.target_width, height=result.inference.target_height)
    optimized_preview = nearest_resize(result.output_rgba, width=source.shape[1], height=source.shape[0])
    naive_preview = nearest_resize(naive, width=source.shape[1], height=source.shape[0])
    optimized_error = foreground_reconstruction_error(optimized_preview, source)
    naive_error = foreground_reconstruction_error(naive_preview, source)
    assert optimized_error <= naive_error


def test_pipeline_preserves_transparency_for_sprite(tmp_path: Path) -> None:
    source = make_sprite(24, 24)
    fake = fake_pixelize(source, upscale=9, phase_x=0.1, phase_y=0.22, blur_radius=0.45)
    input_path = tmp_path / "input.png"
    output_path = tmp_path / "output.png"

    from repixelizer.io import save_rgba

    save_rgba(input_path, fake)
    result = run_pipeline(input_path, output_path, steps=20)
    alpha = result.output_rgba[..., 3]
    assert alpha.min() < 0.05
    assert alpha.max() > 0.9
    partial = alpha[(alpha > 0.05) & (alpha < 0.95)]
    assert partial.size == 0


def test_pipeline_can_strip_checkerboard_background(tmp_path: Path) -> None:
    source = make_sprite(24, 24)
    checker = np.ones_like(source)
    checker[..., 3] = 1.0
    for y in range(checker.shape[0]):
        for x in range(checker.shape[1]):
            tile = 0.97 if ((x // 4) + (y // 4)) % 2 == 0 else 0.91
            checker[y, x, :3] = tile
    alpha = source[..., 3:4]
    opaque_input = checker.copy()
    opaque_input[..., :3] = source[..., :3] * alpha + checker[..., :3] * (1.0 - alpha)

    input_path = tmp_path / "input.png"
    output_path = tmp_path / "output.png"

    from repixelizer.io import save_rgba

    save_rgba(input_path, opaque_input)
    result = run_pipeline(input_path, output_path, target_size=24, steps=0, strip_background=True)
    assert result.source_rgba[..., 3].min() < 0.05
    assert result.output_rgba[..., 3].min() < 0.05
    assert result.output_rgba[..., 3].max() > 0.9


def test_pipeline_can_emit_initialized_output_without_optimizer_steps(tmp_path: Path) -> None:
    source = make_emblem(24, 24)
    fake = fake_pixelize(source, upscale=10, phase_x=0.15, phase_y=0.2, blur_radius=0.6, warp_strength=0.2, warp_detail=5)
    input_path = tmp_path / "input.png"
    output_path = tmp_path / "output.png"

    from repixelizer.io import save_rgba

    save_rgba(input_path, fake)
    result = run_pipeline(input_path, output_path, steps=0)
    assert output_path.exists()
    assert result.output_rgba.shape[0] == result.inference.target_height
    assert result.output_rgba.shape[1] == result.inference.target_width


def test_phase_rerank_can_override_low_confidence_inference_pick(monkeypatch) -> None:
    source = np.zeros((2, 2, 4), dtype=np.float32)
    source[0, 0] = np.asarray([1.0, 0.0, 0.0, 1.0], dtype=np.float32)
    candidate_a = InferenceCandidate(target_width=2, target_height=2, phase_x=-0.4, phase_y=0.2, score=0.91, breakdown={})
    candidate_b = InferenceCandidate(target_width=2, target_height=2, phase_x=0.4, phase_y=0.2, score=0.909, breakdown={})
    inference = InferenceResult(
        target_width=2,
        target_height=2,
        phase_x=candidate_a.phase_x,
        phase_y=candidate_a.phase_y,
        confidence=0.1,
        top_candidates=[candidate_a, candidate_b],
    )
    outputs = {
        (candidate_a.phase_x, candidate_a.phase_y): np.ones_like(source),
        (candidate_b.phase_x, candidate_b.phase_y): source.copy(),
    }

    class DummyArtifacts:
        def __init__(self, rgba: np.ndarray) -> None:
            self.target_rgba = rgba

    def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
        assert steps == 0
        return DummyArtifacts(outputs[(inference.phase_x, inference.phase_y)])

    monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)

    selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
    assert selected.phase_x == candidate_b.phase_x
    assert selected.phase_y == candidate_b.phase_y


def test_phase_rerank_can_override_to_better_size_candidate(monkeypatch) -> None:
    source = np.zeros((16, 16, 4), dtype=np.float32)
    candidate_a = InferenceCandidate(target_width=16, target_height=16, phase_x=0.0, phase_y=0.0, score=0.91, breakdown={})
    candidate_b = InferenceCandidate(target_width=18, target_height=18, phase_x=0.0, phase_y=0.0, score=0.88, breakdown={})
    inference = InferenceResult(
        target_width=16,
        target_height=16,
        phase_x=0.0,
        phase_y=0.0,
        confidence=0.0,
        top_candidates=[candidate_a, candidate_b],
    )

    class DummyArtifacts:
        def __init__(self, rgba: np.ndarray) -> None:
            self.target_rgba = rgba

    def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
        rgba = np.zeros((inference.target_height, inference.target_width, 4), dtype=np.float32)
        return DummyArtifacts(rgba)

    def fake_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
        if target_width == 16:
            return {"score": 0.10}
        return {"score": 0.01}

    monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
    monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)

    selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
    assert selected.target_width == candidate_b.target_width
    assert selected.target_height == candidate_b.target_height


def test_phase_rerank_rejects_large_size_jump(monkeypatch) -> None:
    source = np.zeros((16, 16, 4), dtype=np.float32)
    candidate_a = InferenceCandidate(target_width=16, target_height=16, phase_x=0.0, phase_y=0.0, score=0.91, breakdown={})
    candidate_b = InferenceCandidate(target_width=24, target_height=24, phase_x=0.0, phase_y=0.0, score=0.88, breakdown={})
    inference = InferenceResult(
        target_width=16,
        target_height=16,
        phase_x=0.0,
        phase_y=0.0,
        confidence=0.0,
        top_candidates=[candidate_a, candidate_b],
    )

    class DummyArtifacts:
        def __init__(self, rgba: np.ndarray) -> None:
            self.target_rgba = rgba

    def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
        rgba = np.zeros((inference.target_height, inference.target_width, 4), dtype=np.float32)
        return DummyArtifacts(rgba)

    def fake_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
        if target_width == 16:
            return {"score": 0.10}
        return {"score": 0.01}

    monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
    monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)

    selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
    assert selected.target_width == candidate_a.target_width
    assert selected.target_height == candidate_a.target_height


def test_phase_rerank_can_prefer_better_line_metrics(monkeypatch) -> None:
    source = np.zeros((4, 4, 4), dtype=np.float32)
    candidate_a = InferenceCandidate(target_width=2, target_height=2, phase_x=-0.2, phase_y=0.0, score=0.91, breakdown={})
    candidate_b = InferenceCandidate(target_width=2, target_height=2, phase_x=0.2, phase_y=0.0, score=0.909, breakdown={})
    inference = InferenceResult(
        target_width=2,
        target_height=2,
        phase_x=candidate_a.phase_x,
        phase_y=candidate_a.phase_y,
        confidence=0.0,
        top_candidates=[candidate_a, candidate_b],
    )

    class DummyArtifacts:
        def __init__(self, rgba: np.ndarray) -> None:
            self.target_rgba = rgba

    def fake_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
        return {"score": 0.02}

    def fake_edge_position(preview, source_rgba):
        return 0.05

    def fake_wobble(preview, source_rgba):
        return 1.2 if np.mean(preview) < 0.01 else 0.8

    def fake_concentration(rgba):
        return 0.15 if np.mean(rgba) < 0.01 else 0.28

    outputs = {
        candidate_a.phase_x: np.zeros((2, 2, 4), dtype=np.float32),
        candidate_b.phase_x: np.ones((2, 2, 4), dtype=np.float32),
    }

    def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
        return DummyArtifacts(outputs[inference.phase_x])

    monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
    monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)
    monkeypatch.setattr("repixelizer.pipeline.foreground_edge_position_error", fake_edge_position)
    monkeypatch.setattr("repixelizer.pipeline.foreground_stroke_wobble_error", fake_wobble)
    monkeypatch.setattr("repixelizer.pipeline.foreground_edge_concentration", fake_concentration)

    selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
    assert selected.phase_x == candidate_b.phase_x
    assert selected.phase_y == candidate_b.phase_y