"""Tests for PixelRush node schema (Tier 2: node schema tests)."""
import pathlib

import pytest
import torch


@pytest.mark.unit
class TestPixelRushNodeSchema:
    def test_node_class_exists(self):
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "class PixelRushNode" in content

    def test_node_has_inputs(self):
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        for inp in ["model", "vae", "positive", "negative", "latent_image", "cfg",
                     "num_cascade_stages", "k_timestep", "noise_lambda", "overlap"]:
            assert inp in content, f"PixelRush node should have input: {inp}"

    def test_node_has_output(self):
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "io.Latent.Output" in content

    def test_node_category(self):
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "image/upscaling" in content

    def test_noise_injection_default_slerp(self):
        """Node must expose the noise_injection mode with slerp default."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "noise_injection" in content, (
            "Node must pass noise_injection into PixelRushConfig"
        )
        assert 'default="slerp"' in content, (
            "noise_injection combo default must be 'slerp' (paper default)"
        )

    def test_node_defaults_match_paper(self):
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "default=0.95" in content  # noise_lambda
        assert "default=0.50" in content  # overlap
        assert "default=249" in content  # k_timestep
        assert "default=24.0" in content  # gaussian_sigma
        # gaussian_kernel_size input must be gone (analytic mask has no kernel)
        assert "gaussian_kernel_size" not in content, (
            "gaussian_kernel_size must be removed from the node (analytic mask)"
        )

    def test_sigma_max_allows_paper_default(self):
        """The corrected default sigma=24 must be reachable from the UI."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "max=128.0" in content, (
            "gaussian_sigma max must be 128 (old max=20 blocked the paper default 24)"
        )

    def test_execute_signature_matches_schema(self):
        """Every schema input name must be an execute() parameter and vice
        versa (no drift; catches removed inputs like gaussian_kernel_size)."""
        import re
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        # Schema input names — match calls across line breaks:
        # io.<Type>.Input(\s*"<name>"
        pattern = re.compile(r'io\.\w+\.Input\(\s*"([^"]+)"')
        schema_inputs = set(pattern.findall(content))
        assert schema_inputs, "failed to parse schema inputs"
        assert "refiner_model" in schema_inputs
        sig_start = content.index("def execute(cls,")
        sig_start += len("def execute(cls,")
        sig = content[sig_start:content.index(") -> io.NodeOutput:", sig_start)]
        params = set()
        for chunk in sig.split(","):
            chunk = chunk.strip()
            if "=" in chunk:
                chunk = chunk.split("=")[0].strip()
            if chunk and chunk != "cls":
                params.add(chunk)
        missing = (schema_inputs - params) | (params - schema_inputs)
        assert not missing, (
            f"Schema inputs and execute() params must match exactly; "
            f"schema-only={schema_inputs - params}, exec-only={params - schema_inputs}"
        )

    def test_node_registered_in_extension(self):
        content = (pathlib.Path(__file__).parent.parent / "__init__.py").read_text(encoding="utf-8")
        assert "PixelRush" in content

    def test_imports_pixelrush(self):
        content = (pathlib.Path(__file__).parent.parent / "__init__.py").read_text(encoding="utf-8")
        assert "pixelrush_node" in content or "PixelRushNode" in content


@pytest.mark.unit
class TestPredictEpsConditioningPipeline:
    """Tests for the conditioning pipeline in _make_predict_eps.

    Verifies that the predict_eps adapter uses ComfyUI's canonical
    conditioning pipeline: convert_cond → process_conds → get_area_and_mult → apply_model.
    """

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    def test_uses_convert_cond(self):
        """convert_cond must be called to convert tuple conditioning to dict format."""
        content = self._read_source()
        assert "convert_cond" in content, (
            "_make_predict_eps must call convert_cond to convert tuple conditioning "
            "to dict format before passing to process_conds"
        )

    def test_uses_process_conds(self):
        """process_conds must be called to build model_conds."""
        content = self._read_source()
        assert "process_conds" in content, (
            "_make_predict_eps must call process_conds to build model_conds"
        )

    def test_uses_get_area_and_mult(self):
        """get_area_and_mult must be used instead of manual process() calls."""
        content = self._read_source()
        assert "get_area_and_mult" in content, (
            "_make_predict_eps must use get_area_and_mult to properly process "
            "COND objects (calls process_cond with batch_size and area)"
        )

    def test_does_not_use_manual_process(self):
        """Must not use the incorrect v.process(latent) pattern."""
        content = self._read_source()
        assert "v.process(latent)" not in content, (
            "_make_predict_eps must not use v.process(latent) — COND objects "
            "use process_cond(batch_size, area), not process(latent)"
        )

    def test_does_not_pass_raw_tuples_to_process_conds(self):
        """Must not pass raw positive/negative directly to process_conds."""
        content = self._read_source()
        # The old buggy code passed positive/negative directly:
        # conds_dict = {"positive": positive, "negative": negative}
        # The fixed code converts first:
        # conds_dict = {"positive": pos_converted, "negative": neg_converted}
        assert 'conds_dict = {"positive": positive' not in content, (
            "_make_predict_eps must not pass raw positive/negative tuples to "
            "process_conds — must convert via convert_cond first"
        )

    def test_passes_transformer_options_to_apply_model(self):
        """apply_model requires transformer_options in the conditioning dict."""
        content = self._read_source()
        assert "transformer_options" in content, (
            "_make_predict_eps must include transformer_options in the conditioning "
            "dict passed to apply_model"
        )

    def test_uses_p_input_x_not_raw_latent(self):
        """Should use p.input_x from get_area_and_mult, not raw latent."""
        content = self._read_source()
        assert "p.input_x" in content, (
            "_make_predict_eps should use p.input_x from get_area_and_mult "
            "instead of raw latent (handles area cropping)"
        )

    def test_loads_model_to_gpu(self):
        """Model must be loaded to GPU before calling apply_model."""
        content = self._read_source()
        assert "load_models_gpu" in content, (
            "_make_predict_eps must call load_models_gpu to ensure the model "
            "is on GPU before calling apply_model"
        )

    def test_calls_pre_run(self):
        """pre_run must be called to set current_patcher on the model."""
        content = self._read_source()
        assert "pre_run" in content, (
            "_make_predict_eps must call model.pre_run() to set "
            "current_patcher before apply_hooks is called"
        )

    def test_uses_model_apply_hooks_not_current_patcher(self):
        """Should use model.apply_hooks, not model.model.current_patcher.apply_hooks."""
        content = self._read_source()
        assert "model.apply_hooks" in content, (
            "_make_predict_eps should use model.apply_hooks (ModelPatcher) "
            "directly, not model.model.current_patcher.apply_hooks"
        )

    def test_uses_cond_cat_to_extract_tensors(self):
        """Should use cond_cat to extract tensors from COND objects."""
        content = self._read_source()
        assert "cond_cat" in content, (
            "_make_predict_eps must use cond_cat to extract raw tensors from "
            "COND objects (p.conditioning contains CONDCrossAttn etc., not tensors)"
        )


@pytest.mark.unit
class TestPixelRush5DLatentHandling:
    """Tests for 5D latent handling in PixelRush (Krea2/Qwen/Anima support).

    Verifies that the node correctly handles 3D latent models by:
    - Adding temporal dimension (unsqueeze(2)) before process_latent_in
    - Using process_latent_out/in on 5D tensors in VAE adapters
    - Using repeat_to_batch_size for empty latent channels
    - Unsqeezing/squeezing between 4D (core algorithm) and 5D (model)
    """

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    def test_execute_uses_process_latent_in(self):
        """execute must call process_latent_in to convert initial latent to model format."""
        content = self._read_source()
        assert "process_latent_in" in content, (
            "PixelRush execute must call process_latent_in to convert the initial "
            "latent to model format (the guider normally does this, but PixelRush "
            "calls apply_model directly)"
        )

    def test_execute_adds_temporal_dim_for_3d(self):
        """execute must unsqueeze 4D to 5D for 3D latent models before process_latent_in."""
        content = self._read_source()
        assert "unsqueeze(2)" in content, (
            "PixelRush execute must add temporal dimension (unsqueeze(2)) for 3D "
            "latent models before calling process_latent_in"
        )

    def test_execute_uses_repeat_to_batch_size(self):
        """execute must use repeat_to_batch_size for empty latent channel mismatch."""
        content = self._read_source()
        assert "repeat_to_batch_size" in content, (
            "PixelRush execute must use repeat_to_batch_size for empty latent "
            "channel mismatch (not zero-padding)"
        )

    def test_execute_gets_latent_dimensions(self):
        """execute must read latent_dimensions from model.model.latent_format."""
        content = self._read_source()
        assert "latent_dimensions" in content, (
            "PixelRush execute must read latent_dimensions from the model's "
            "latent_format to detect 3D latent models"
        )

    def test_execute_squeezes_5d_to_4d_for_core(self):
        """execute must squeeze 5D to 4D before passing to pixelrush_cascade."""
        content = self._read_source()
        assert "squeeze(2)" in content, (
            "PixelRush execute must squeeze 5D to 4D before passing to "
            "pixelrush_cascade (core algorithm works in 4D spatial)"
        )

    def test_execute_unsqueezes_4d_to_5d_for_output(self):
        """execute must unsqueeze 4D result back to 5D for 3D latent models."""
        content = self._read_source()
        # The output unsqueeze is separate from the input unsqueeze
        assert content.count("unsqueeze(2)") >= 2, (
            "PixelRush execute must unsqueeze to 5D both for process_latent_in "
            "and for the final output (for 3D latent models)"
        )

    def test_vae_adapters_use_process_latent_out(self):
        """VAE decode adapter must call process_latent_out to convert from model format."""
        content = self._read_source()
        assert "process_latent_out" in content, (
            "PixelRush VAE decode adapter must call process_latent_out to convert "
            "from model latent format to VAE latent format"
        )

    def test_vae_adapters_use_process_latent_in(self):
        """VAE encode adapter must call process_latent_in to convert to model format."""
        content = self._read_source()
        assert "process_latent_in" in content, (
            "PixelRush VAE encode adapter must call process_latent_in to convert "
            "from VAE latent format to model latent format"
        )

    def test_vae_adapters_handle_5d_for_3d_models(self):
        """VAE adapters must handle 5D tensors for 3D latent models."""
        content = self._read_source()
        assert "latent_dim == 3" in content, (
            "PixelRush VAE adapters must check latent_dim == 3 to handle 5D tensors"
        )

    def test_predict_eps_accepts_latent_dimensions(self):
        """_make_predict_eps must accept latent_dimensions parameter."""
        content = self._read_source()
        assert "latent_dimensions" in content, (
            "_make_predict_eps must accept latent_dimensions to know if the model "
            "is 3D latent (for unsqueezing 4D patches to 5D before apply_model)"
        )

    def test_predict_eps_unsqueezes_4d_to_5d(self):
        """predict_eps must unsqueeze 4D patches to 5D for 3D latent models."""
        content = self._read_source()
        assert "is_3d" in content, (
            "predict_eps must track is_3d flag to unsqueeze 4D patches to 5D "
            "before calling apply_model for 3D latent models"
        )

    def test_predict_eps_squeezes_5d_eps_back(self):
        """predict_eps must squeeze 5D eps output back to 4D for the core algorithm."""
        content = self._read_source()
        assert "eps.squeeze(2)" in content or "eps.ndim == 5" in content, (
            "predict_eps must squeeze 5D eps output back to 4D for the core algorithm"
        )


@pytest.mark.unit
class TestPixelRushVAEAdaptersFunctional:
    """Functional tests for VAE adapter 5D latent handling with mock objects."""

    def _make_mock_vae_3d(self):
        """Create a mock 3D VAE (like Qwen2D/WanVAE) for testing."""
        import types

        vae = types.SimpleNamespace()
        vae.latent_dim = 3
        vae.downscale_ratio = 8

        def decode(latent):
            if latent.ndim == 5:
                b, c, t, h, w = latent.shape
                latent = latent.reshape(b * t, c, h, w)
            out = latent[:, :3]
            return out

        def encode(image):
            b = image.shape[0]
            h, w = image.shape[-2], image.shape[-1]
            return torch.randn(b, 16, 1, h, w)

        vae.decode = decode
        vae.encode = encode
        return vae

    def _make_mock_model_3d(self):
        """Create a mock model with 3D latent format (Wan21-like)."""
        import types

        model = types.SimpleNamespace()
        model.model = types.SimpleNamespace()

        model.model.latent_format = types.SimpleNamespace()
        model.model.latent_format.latent_channels = 16
        model.model.latent_format.latent_dimensions = 3

        latents_mean = torch.zeros(1, 16, 1, 1, 1)
        latents_std = torch.ones(1, 16, 1, 1, 1)

        def process_latent_out(latent):
            assert latent.ndim == 5, f"process_latent_out should receive 5D, got {latent.ndim}D"
            return (latent - latents_mean) / latents_std

        def process_latent_in(latent):
            assert latent.ndim == 5, f"process_latent_in should receive 5D, got {latent.ndim}D"
            return latent * latents_std + latents_mean

        model.model.process_latent_out = process_latent_out
        model.model.process_latent_in = process_latent_in

        return model

    def test_vae_decode_5d_calls_process_latent_out_on_5d(self):
        """vae_decode should call process_latent_out on 5D tensor, not 4D."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_vae_adapters

        vae = self._make_mock_vae_3d()
        model = self._make_mock_model_3d()
        device = torch.device("cpu")

        vae_decode, vae_encode = _make_vae_adapters(vae, device, model)

        latent_5d = torch.randn(1, 16, 1, 64, 64)
        result = vae_decode(latent_5d)
        assert result.ndim == 4

    def test_vae_decode_4d_adds_temporal_before_process(self):
        """vae_decode should add temporal dim to 4D before process_latent_out."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_vae_adapters

        vae = self._make_mock_vae_3d()
        model = self._make_mock_model_3d()
        device = torch.device("cpu")

        vae_decode, vae_encode = _make_vae_adapters(vae, device, model)

        latent_4d = torch.randn(1, 16, 64, 64)
        result = vae_decode(latent_4d)
        assert result.ndim == 4

    def test_vae_encode_returns_5d_for_3d_model(self):
        """vae_encode should return 5D latent for 3D latent models."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_vae_adapters

        vae = self._make_mock_vae_3d()
        model = self._make_mock_model_3d()
        device = torch.device("cpu")

        vae_decode, vae_encode = _make_vae_adapters(vae, device, model)

        image = torch.randn(1, 3, 64, 64)
        result = vae_encode(image)
        assert result.ndim == 5, f"Expected 5D output for 3D model, got {result.ndim}D"
        assert result.shape[2] == 1
        assert result.shape[1] == 16

    def test_vae_encode_5d_passes_process_latent_in_on_5d(self):
        """vae_encode should call process_latent_in on 5D tensor."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_vae_adapters

        vae = self._make_mock_vae_3d()
        model = self._make_mock_model_3d()
        device = torch.device("cpu")

        vae_decode, vae_encode = _make_vae_adapters(vae, device, model)

        image = torch.randn(1, 3, 64, 64)
        result = vae_encode(image)
        assert result.ndim == 5

    def test_no_batch_size_corruption(self):
        """Verify 5D handling doesn't corrupt batch dimension (the Krea2 bug)."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        import torch.nn.functional as F

        from src.pixelrush_node import _make_vae_adapters

        vae = self._make_mock_vae_3d()
        model = self._make_mock_model_3d()
        device = torch.device("cpu")

        vae_decode, vae_encode = _make_vae_adapters(vae, device, model)

        latent_5d = torch.randn(1, 16, 1, 64, 64)
        image = vae_decode(latent_5d)
        assert image.shape[0] == 1, f"Batch should be 1, got {image.shape[0]}"

        image_up = F.interpolate(image, size=(128, 128), mode="bicubic", align_corners=False)
        z_up = vae_encode(image_up)
        assert z_up.shape[0] == 1, f"Batch should be 1, got {z_up.shape[0]}"
        assert z_up.ndim == 5, f"Should be 5D, got {z_up.ndim}D"

    def test_cascade_squeezes_5d_to_4d(self):
        """pixelrush_cascade should squeeze 5D vae_encode output to 4D for refine_latent_once."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))

        from src.pixelrush import PixelRushConfig, pixelrush_cascade

        # Track shapes passed to predict_eps
        eps_shapes = []

        def vae_decode(latent):
            if isinstance(latent, dict):
                latent = latent["samples"]
            if latent.ndim == 5:
                latent = latent[:, :, 0]
            return latent[:, :3]  # [B, 3, H, W]

        def vae_encode(image):
            b = image.shape[0]
            h, w = image.shape[-2], image.shape[-1]
            # Return 5D (as the fixed vae_encode adapter does for 3D models)
            return torch.randn(b, 16, 1, h, w)

        def predict_eps(latent, timestep):
            eps_shapes.append(latent.shape)
            return torch.randn_like(latent)

        def alpha_bar_at(timestep):
            return 0.5

        cfg = PixelRushConfig(
            patch_h=32, patch_w=32, overlap=0.5,
            k_timestep=249, noise_lambda=0.95,
            gaussian_sigma=24.0,
        )

        initial_latent = torch.randn(1, 16, 32, 32)
        result = pixelrush_cascade(
            initial_latent=initial_latent,
            num_cascade_stages=1,
            vae_decode=vae_decode,
            vae_encode=vae_encode,
            inversion_eps=predict_eps,
            refiner_eps=predict_eps,
            alpha_bar_at=alpha_bar_at,
            cfg=cfg,
        )

        # Result should be 4D (core algorithm works in 4D)
        assert result.ndim == 4, f"Result should be 4D, got {result.ndim}D"
        # predict_eps should have received 4D patches
        for shape in eps_shapes:
            assert len(shape) == 4, f"predict_eps should receive 4D, got {len(shape)}D shape {shape}"


@pytest.mark.unit
class TestPixelRushProgressBar:
    """Tests for progress bar integration in PixelRush node."""

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    def test_execute_creates_progress_bar(self):
        """execute must create a comfy.utils.ProgressBar."""
        content = self._read_source()
        assert "ProgressBar" in content, (
            "PixelRush execute must create a comfy.utils.ProgressBar for "
            "native ComfyUI progress tracking"
        )

    def test_execute_passes_progress_callback(self):
        """execute must pass progress_callback to pixelrush_cascade."""
        content = self._read_source()
        assert "progress_callback" in content, (
            "PixelRush execute must pass a progress_callback to pixelrush_cascade"
        )

    def test_execute_calls_update_absolute(self):
        """execute must call pbar.update_absolute to update progress."""
        content = self._read_source()
        assert "update_absolute" in content, (
            "PixelRush execute must call pbar.update_absolute to update the progress bar"
        )

    def test_execute_precomputes_total_patches(self):
        """execute must pre-compute total patches across all cascade stages."""
        content = self._read_source()
        assert "total_patches" in content, (
            "PixelRush execute must pre-compute total_patches for the progress bar"
        )

    def test_refine_latent_once_accepts_progress_callback(self):
        """refine_latent_once must accept a progress_callback parameter."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush.py").read_text(encoding="utf-8")
        assert "progress_callback" in content, (
            "refine_latent_once must accept a progress_callback parameter"
        )

    def test_pixelrush_cascade_accepts_progress_callback(self):
        """pixelrush_cascade must accept a progress_callback parameter."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush.py").read_text(encoding="utf-8")
        # The function signature should include progress_callback
        assert "progress_callback" in content, (
            "pixelrush_cascade must accept a progress_callback parameter"
        )

    def test_progress_callback_called_per_patch(self):
        """refine_latent_once should call progress_callback after each patch."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import PixelRushConfig, refine_latent_once

        # Track callback invocations
        callback_calls = []

        def progress_callback(patch_idx, total_patches):
            callback_calls.append((patch_idx, total_patches))

        coarse_latent = torch.randn(1, 4, 64, 64)

        def predict_eps(latent, timestep):
            return torch.randn_like(latent)

        def alpha_bar_at(timestep):
            return 0.5

        cfg = PixelRushConfig(
            patch_h=32, patch_w=32, overlap=0.5,
            k_timestep=249, noise_lambda=0.95,
            gaussian_sigma=24.0,
        )

        refine_latent_once(
            coarse_latent=coarse_latent,
            inversion_eps=predict_eps,
            refiner_eps=predict_eps,
            alpha_bar_at=alpha_bar_at,
            cfg=cfg,
            progress_callback=progress_callback,
        )

        # Should have been called once per patch
        assert len(callback_calls) > 0, "progress_callback should have been called"
        # Last call should have patch_idx == total_patches
        last_idx, last_total = callback_calls[-1]
        assert last_idx == last_total, (
            f"Last callback should have patch_idx == total_patches, "
            f"got {last_idx} != {last_total}"
        )

    def test_cascade_progress_callback_receives_stage_info(self):
        """pixelrush_cascade should pass stage info to progress_callback."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import PixelRushConfig, pixelrush_cascade

        callback_calls = []

        def progress_callback(patch_idx, total_patches, stage, num_stages):
            callback_calls.append((patch_idx, total_patches, stage, num_stages))

        def vae_decode(latent):
            if isinstance(latent, dict):
                latent = latent["samples"]
            return latent[:, :3]

        def vae_encode(image):
            b = image.shape[0]
            h, w = image.shape[-2], image.shape[-1]
            return torch.randn(b, 4, h, w)

        def predict_eps(latent, timestep):
            return torch.randn_like(latent)

        def alpha_bar_at(timestep):
            return 0.5

        cfg = PixelRushConfig(
            patch_h=32, patch_w=32, overlap=0.5,
            k_timestep=249, noise_lambda=0.95,
            gaussian_sigma=24.0,
        )

        initial_latent = torch.randn(1, 4, 32, 32)
        pixelrush_cascade(
            initial_latent=initial_latent,
            num_cascade_stages=2,
            vae_decode=vae_decode,
            vae_encode=vae_encode,
            inversion_eps=predict_eps,
            refiner_eps=predict_eps,
            alpha_bar_at=alpha_bar_at,
            cfg=cfg,
            progress_callback=progress_callback,
        )

        # Should have calls from both stages
        stages_seen = set(call[2] for call in callback_calls)
        assert 0 in stages_seen, "Should have calls from stage 0"
        assert 1 in stages_seen, "Should have calls from stage 1"
        # All calls should have num_stages == 2
        for call in callback_calls:
            assert call[3] == 2, f"num_stages should be 2, got {call[3]}"


@pytest.mark.unit
class TestPixelRushInferenceBugFix:
    """Tests for the inference bug fixes (timestep/sigma conversion, x0→epsilon).

    Bug 1: apply_model returns x0, not epsilon — must convert via (x - x0) / sigma
    Bug 2: timestep used as array index — must use model_sampling.sigma(timestep)
    Bug 3: spherical_lerp uses unit vectors — must use raw vectors to match reference
    """

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    # --- Bug 2: timestep → sigma conversion ---

    def test_predict_eps_uses_model_sampling_sigma(self):
        """predict_eps must use model_sampling.sigma() for timestep conversion."""
        content = self._read_source()
        assert "model_sampling.sigma" in content, (
            "predict_eps must use model_sampling.sigma(timestep) to convert "
            "timestep (0-999) to sigma, not use timestep as array index"
        )

    def test_predict_eps_does_not_use_timestep_as_index(self):
        """predict_eps must not use timestep as an index into sigmas array."""
        content = self._read_source()
        assert "sigmas[timestep]" not in content, (
            "predict_eps must not use sigmas[timestep] — timestep is a value "
            "in 0-999 range, not an index into the sigmas array"
        )

    def test_predict_eps_clamps_sigma_minimum(self):
        """predict_eps must clamp sigma to avoid division by zero at timestep 0."""
        content = self._read_source()
        assert "1e-6" in content or "max(sigma_val" in content, (
            "predict_eps must clamp sigma to a minimum (1e-6) to avoid "
            "division by zero when extracting epsilon at timestep 0"
        )

    def test_alpha_bar_at_uses_model_sampling_sigma(self):
        """alpha_bar_at must use model_sampling.sigma() for timestep conversion."""
        content = self._read_source()
        assert "model_sampling.sigma" in content, (
            "alpha_bar_at must use model_sampling.sigma(timestep) to convert "
            "timestep to sigma before computing alpha_bar"
        )

    def test_alpha_bar_at_does_not_use_index(self):
        """alpha_bar_at must not use timestep as an array index."""
        content = self._read_source()
        assert "alphas_cumprod[timestep]" not in content, (
            "alpha_bar_at must not use alphas_cumprod[timestep] — timestep is "
            "a value in 0-999 range, not an index"
        )

    # --- Bug 1: bypass calculate_denoised to get raw epsilon ---

    def test_predict_eps_bypasses_calculate_denoised(self):
        """predict_eps must bypass calculate_denoised to get raw model output (epsilon).

        apply_model returns calculate_denoised(sigma, model_output, x) = x0,
        which is useless at sigma≈0 (returns x trivially, making eps≈0).
        Instead, predict_eps must call diffusion_model directly to get the raw
        model_output (which IS epsilon for EPS prediction type).
        """
        content = self._read_source()
        assert "diffusion_model(" in content, (
            "predict_eps must call m.diffusion_model() directly to get raw "
            "model output (epsilon), bypassing calculate_denoised"
        )
        assert "calculate_input" in content, (
            "predict_eps must call ms.calculate_input() to scale the input "
            "before feeding to diffusion_model"
        )

    def test_predict_eps_does_not_call_apply_model(self):
        """predict_eps must not call apply_model (it returns x0, not epsilon)."""
        content = self._read_source()
        assert "model.model.apply_model" not in content, (
            "predict_eps must not call apply_model — it returns x0 (via "
            "calculate_denoised), not the raw epsilon we need"
        )

    def test_predict_eps_does_not_divide_by_sigma(self):
        """predict_eps must not divide by sigma (no x0→epsilon conversion needed).

        Since we get the raw model_output (epsilon) directly from diffusion_model,
        there's no need to convert x0→epsilon via (x - x0) / sigma.
        """
        content = self._read_source()
        assert "(p.input_x - x0)" not in content, (
            "predict_eps must not do x0→epsilon conversion — we get raw "
            "epsilon directly from diffusion_model"
        )
        assert "sigma_reshaped" not in content, (
            "predict_eps must not reshape sigma for division — no division "
            "is needed when getting raw epsilon directly"
        )

    # --- Bug 3 fixed per corrected theory: slerp uses RAW vectors ---

    def test_slerp_uses_raw_vectors_and_lerp_fallback(self):
        """slerp must use the corrected standard raw-vector form.

        The corrected-theory reference (pixelrush-correct.txt) uses raw
        a_flat/b_flat in the slerp coefficients with a lerp fallback for
        nearly-collinear vectors — NOT the unit-vector x separate-magnitude
        form the 2026-08-12 fix introduced.
        """
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush.py").read_text(encoding="utf-8")
        assert "sin_omega * a_flat" in content, (
            "slerp direction must use a_flat (raw vector), the corrected-theory form"
        )
        assert "sin_omega * b_flat" in content, (
            "slerp direction must use b_flat (raw vector), the corrected-theory form"
        )
        assert "use_lerp" in content, (
            "slerp must define the collinear lerp fallback (sin_omega < 1e-4)"
        )
        assert "a_unit" not in content, (
            "slerp must not use unit vectors with separate magnitude "
            "interpolation (superseded 2026-08-12 form)"
        )

    def test_slerp_does_not_explode_norm(self):
        """Regression: slerp of two different-magnitude vectors must not square the norm.

        slerp(eps_pred (norm~6), eps_rand (norm~64), 0.95) must yield a result
        whose norm is ~ the interpolated magnitude (~61), NOT ~3900 (squared).
        """
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        import torch

        from src.pixelrush import slerp
        torch.manual_seed(0)
        a = 0.1 * torch.randn(1, 4, 32, 32)   # eps_pred-like (small norm)
        b = torch.randn(1, 4, 32, 32)          # eps_rand-like (large norm)
        out = slerp(a, b, t=0.95)
        out_norm = out.flatten(1).norm(dim=1).item()
        # Interpolated magnitude should be ~ (1-0.95)*||a|| + 0.95*||b||
        expected_mag = 0.05 * a.flatten(1).norm().item() + 0.95 * b.flatten(1).norm().item()
        # Allow 2x tolerance; a squared norm would be ~60x larger.
        assert out_norm < 2.0 * expected_mag, (
            f"slerp exploded the norm: got {out_norm:.1f}, expected ~{expected_mag:.1f} "
            f"(squared-norm bug would give ~{expected_mag**2:.1f})"
        )


@pytest.mark.unit
class TestPixelRushInferenceBugFixFunctional:
    """Functional tests for the inference bug fixes with mock objects."""

    def _make_mock_model_sampling(self):
        """Create a mock model_sampling with sigma() method.

        Simulates ModelSamplingDiscrete.sigma() which maps timestep (0-999)
        to sigma by interpolating in log-space across the sigma schedule.
        The schedule has ~20 entries, but timesteps can be 0-999.
        """
        import types

        ms = types.SimpleNamespace()
        # Mock sigmas array (typical 20-step schedule)
        # Use a small minimum instead of 0 to avoid log(0) = -inf
        ms.sigmas = torch.linspace(0.01, 14.0, 20)
        ms.log_sigmas = ms.sigmas.log()

        def sigma(timestep):
            # ModelSamplingDiscrete.sigma() maps timestep (0-999) to sigma
            # by treating timestep as a continuous index into log_sigmas.
            # timestep=0 → sigmas[0], timestep=999 → sigmas[-1]
            # Scale timestep from 0-999 to 0-(len-1)
            max_ts = 999.0
            t = torch.clamp(timestep.float(), min=0, max=max_ts)
            t_scaled = t * (len(ms.sigmas) - 1) / max_ts
            low_idx = t_scaled.floor().long().clamp(0, len(ms.sigmas) - 1)
            high_idx = t_scaled.ceil().long().clamp(0, len(ms.sigmas) - 1)
            w = t_scaled.frac()
            log_sigma = (1 - w) * ms.log_sigmas[low_idx] + w * ms.log_sigmas[high_idx]
            return log_sigma.exp()

        ms.sigma = sigma
        return ms

    def test_alpha_bar_at_returns_reasonable_values(self):
        """alpha_bar_at should return reasonable values for various timesteps."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        import types

        from src.pixelrush_node import _make_alpha_bar_at

        model = types.SimpleNamespace()
        model.model = types.SimpleNamespace()
        model.model.model_sampling = self._make_mock_model_sampling()
        model.load_device = torch.device("cpu")

        alpha_bar_at = _make_alpha_bar_at(model)

        # timestep=0 should give alpha_bar ≈ 1.0 (clean)
        ab_0 = alpha_bar_at(0)
        assert 0.9 < ab_0 <= 1.0, f"alpha_bar(0) should be ~1.0, got {ab_0}"

        # timestep=249 should give alpha_bar between 0 and 1 (not 0!)
        ab_249 = alpha_bar_at(249)
        assert 0 < ab_249 < 1.0, f"alpha_bar(249) should be in (0, 1), got {ab_249}"

        # timestep=999 should give small alpha_bar (high noise)
        ab_999 = alpha_bar_at(999)
        assert 0 < ab_999 < ab_249, f"alpha_bar(999) should be < alpha_bar(249), got {ab_999}"

        # Different timesteps should give different alpha_bars
        assert ab_0 != ab_249, "Different timesteps should give different alpha_bars"

    def test_alpha_bar_at_249_not_near_zero(self):
        """alpha_bar_at(249) should NOT be near zero (the old bug returned 0)."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        import types

        from src.pixelrush_node import _make_alpha_bar_at

        model = types.SimpleNamespace()
        model.model = types.SimpleNamespace()
        model.model.model_sampling = self._make_mock_model_sampling()
        model.load_device = torch.device("cpu")

        alpha_bar_at = _make_alpha_bar_at(model)
        ab_249 = alpha_bar_at(249)

        # The old bug returned alpha_bar ≈ 0 (using sigmas[-1] which is max sigma)
        # The fix should return a reasonable value
        assert ab_249 > 0.01, (
            f"alpha_bar(249) should be > 0.01, got {ab_249} — "
            "if this is near 0, the timestep is being used as an array index (Bug 2)"
        )

    def test_raw_model_output_is_epsilon(self):
        """Verify that bypassing calculate_denoised gives raw epsilon.

        For EPS models, the raw model_output from diffusion_model IS epsilon.
        calculate_denoised would convert it to x0 via: x0 = x - eps * sigma.
        At sigma≈0, x0 ≈ x (trivially), making (x - x0)/sigma ≈ 0/0 = garbage.
        By bypassing calculate_denoised, we get the true epsilon directly.
        """
        # Simulate: model predicts epsilon directly
        x = torch.randn(1, 4, 8, 8)
        eps_true = torch.randn_like(x)
        sigma = torch.tensor([1.0])

        # The raw model_output IS epsilon (no conversion needed)
        model_output = eps_true  # What diffusion_model returns
        assert torch.equal(model_output, eps_true), (
            "Raw model_output should be epsilon, no conversion needed"
        )

        # Verify calculate_denoised would give x0 (which is NOT what we want)
        sigma_reshaped = sigma.reshape(sigma.shape + (1,) * (x.ndim - sigma.ndim))
        x0 = x - model_output * sigma_reshaped  # This is what apply_model returns
        assert not torch.equal(x0, eps_true), (
            "x0 (from calculate_denoised) should NOT equal epsilon — "
            "this is why we bypass it"
        )

        # At sigma≈0, x0 ≈ x (trivially), making (x - x0)/sigma numerically unstable.
        # The subtraction x - x0 loses precision (two nearly-equal numbers),
        # and dividing by 1e-6 amplifies the error. This is WHY we bypass
        # calculate_denoised and get raw epsilon directly.
        sigma_near_zero = torch.tensor([1e-6])
        sigma_rz = sigma_near_zero.reshape(sigma_near_zero.shape + (1,) * (x.ndim - sigma_near_zero.ndim))
        x0_near_zero = x - eps_true * sigma_rz  # ≈ x since sigma≈0
        eps_via_x0 = (x - x0_near_zero) / sigma_rz  # Numerically unstable!
        # The conversion should NOT match the true epsilon closely —
        # this demonstrates the numerical instability at low sigma.
        max_err = (eps_via_x0 - eps_true).abs().max().item()
        assert max_err > 1e-3, (
            f"At sigma≈0, x0→eps conversion should be numerically unstable "
            f"(max error should be > 1e-3, got {max_err}) — this is why we "
            f"bypass calculate_denoised and get raw epsilon directly"
        )

    def test_ddim_forward_with_correct_alpha_bar(self):
        """DDIM forward with correct alpha_bar should produce partially noised latent."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import ddim_forward_one_step, ddim_reverse_one_step_to_zero

        z_0 = torch.randn(1, 4, 8, 8)
        eps = torch.randn_like(z_0)

        # With correct alpha_bar (e.g., 0.5), z_K should be a mix of z_0 and eps
        alpha_bar = 0.5
        z_K = ddim_forward_one_step(z_0, eps, alpha_bar)
        expected = (0.5 ** 0.5) * z_0 + (0.5 ** 0.5) * eps
        assert torch.allclose(z_K, expected, atol=1e-5), (
            "DDIM forward with alpha_bar=0.5 should produce sqrt(0.5)*z_0 + sqrt(0.5)*eps"
        )

        # Reverse should recover z_0 (approximately, with different eps)
        z_0_hat = ddim_reverse_one_step_to_zero(z_K, eps, alpha_bar)
        assert torch.allclose(z_0_hat, z_0, atol=1e-4), (
            "DDIM reverse with same eps should recover z_0"
        )

    def test_ddim_forward_with_alpha_bar_zero_produces_noise(self):
        """DDIM forward with alpha_bar=0 (old bug) produces pure noise."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import ddim_forward_one_step

        z_0 = torch.randn(1, 4, 8, 8)
        eps = torch.randn_like(z_0)

        # With alpha_bar=0 (the old bug), z_K = 0*z_0 + 1*eps = eps (pure noise!)
        z_K = ddim_forward_one_step(z_0, eps, 0.0)
        assert torch.allclose(z_K, eps, atol=1e-5), (
            "DDIM forward with alpha_bar=0 produces pure noise (this was the bug)"
        )


@pytest.mark.unit
class TestPixelRushPredictionTypeDetection:
    """Tests for prediction-type detection and epsilon conversion.

    The critical bug: FLUX uses CONST (flow-matching) prediction where the
    raw model output is velocity v = eps - x0, NOT epsilon. Treating velocity
    as epsilon produces pure noise. We must detect the prediction type and
    convert correctly.
    """

    def _make_mock_model_sampling(self, prediction_type):
        """Create a mock model_sampling with the given prediction type's MRO."""
        if prediction_type == "const":
            ConstClass = type("CONST", (), {})
            ModelSampling = type("ModelSampling", (ConstClass,), {})
        elif prediction_type == "eps":
            EpsClass = type("EPS", (), {})
            ModelSampling = type("ModelSampling", (EpsClass,), {})
        elif prediction_type == "v_prediction":
            EpsClass = type("EPS", (), {})
            VPClass = type("V_PREDICTION", (EpsClass,), {})
            ModelSampling = type("ModelSampling", (VPClass,), {})
        else:
            raise ValueError(f"Unknown prediction_type: {prediction_type}")
        ms = ModelSampling()
        ms.sigma_data = 1.0
        return ms

    def test_detect_const_prediction_type(self):
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _detect_prediction_type

        ms = self._make_mock_model_sampling("const")
        assert _detect_prediction_type(ms) == "const"

    def test_detect_eps_prediction_type(self):
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _detect_prediction_type

        ms = self._make_mock_model_sampling("eps")
        assert _detect_prediction_type(ms) == "eps"

    def test_detect_v_prediction_type(self):
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _detect_prediction_type

        ms = self._make_mock_model_sampling("v_prediction")
        assert _detect_prediction_type(ms) == "v_prediction"

    def test_const_conversion_velocity_to_epsilon(self):
        """For CONST/flow, raw output is velocity v = eps - x0.

        eps = x_t + v * (1 - sigma)  (stable at sigma≈0)
        """
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_model_output_to_eps

        ms = self._make_mock_model_sampling("const")
        converter = _make_model_output_to_eps(ms, "const")

        x_t = torch.randn(1, 4, 8, 8)
        eps_true = torch.randn_like(x_t)
        sigma = torch.tensor([0.5])

        # velocity v = eps - x0, and x0 = x_t - v*sigma
        # So v = eps - (x_t - v*sigma) → v = (eps - x_t) / (1 - sigma)
        v = (eps_true - x_t) / (1.0 - 0.5)

        eps_converted = converter(v, x_t, sigma)
        assert torch.allclose(eps_converted, eps_true, atol=1e-5), (
            "CONST conversion should recover epsilon from velocity"
        )

    def test_const_conversion_at_sigma_zero(self):
        """At sigma=0, CONST conversion: eps = x_t + v * 1 = x_t + v.

        For a clean image, v ≈ -x_t (since eps≈0), so eps ≈ 0. This is
        stable (no division by sigma).
        """
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_model_output_to_eps

        ms = self._make_mock_model_sampling("const")
        converter = _make_model_output_to_eps(ms, "const")

        x_t = torch.randn(1, 4, 8, 8)
        sigma = torch.tensor([0.0])

        # velocity for clean image: v = eps - x0 = 0 - x_t = -x_t
        v = -x_t
        eps_converted = converter(v, x_t, sigma)
        # eps = x_t + v*1 = x_t - x_t = 0
        assert torch.allclose(eps_converted, torch.zeros_like(x_t), atol=1e-5), (
            "CONST conversion at sigma=0 with clean image should give eps≈0"
        )

    def test_eps_conversion_is_identity(self):
        """For EPS, raw output IS epsilon (identity)."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_model_output_to_eps

        ms = self._make_mock_model_sampling("eps")
        converter = _make_model_output_to_eps(ms, "eps")

        x_t = torch.randn(1, 4, 8, 8)
        model_output = torch.randn_like(x_t)
        sigma = torch.tensor([0.5])

        eps_converted = converter(model_output, x_t, sigma)
        assert torch.equal(eps_converted, model_output), (
            "EPS conversion should be identity (raw output IS epsilon)"
        )

    def test_const_eps_to_x0(self):
        """For CONST/flow, x0 = (x_t - sigma*eps) / (1 - sigma)."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_eps_to_x0

        ms = self._make_mock_model_sampling("const")
        converter = _make_eps_to_x0(ms, "const")

        x_t = torch.randn(1, 4, 8, 8)
        eps = torch.randn_like(x_t)
        sigma = torch.tensor([0.5])

        # x_t = sigma*eps + (1-sigma)*x0 → x0 = (x_t - sigma*eps) / (1-sigma)
        x0_true = (x_t - 0.5 * eps) / (1.0 - 0.5)
        x0_converted = converter(x_t, eps, sigma)
        assert torch.allclose(x0_converted, x0_true, atol=1e-5), (
            "CONST eps_to_x0 should match (x_t - sigma*eps) / (1-sigma)"
        )

    def test_eps_eps_to_x0(self):
        """For EPS, x0 = x_t - sigma*eps."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _make_eps_to_x0

        ms = self._make_mock_model_sampling("eps")
        converter = _make_eps_to_x0(ms, "eps")

        x_t = torch.randn(1, 4, 8, 8)
        eps = torch.randn_like(x_t)
        sigma = torch.tensor([0.5])

        x0_true = x_t - 0.5 * eps
        x0_converted = converter(x_t, eps, sigma)
        assert torch.allclose(x0_converted, x0_true, atol=1e-5), (
            "EPS eps_to_x0 should match x_t - sigma*eps"
        )

    def test_source_uses_prediction_type_detection(self):
        """Source must detect prediction type and convert model output."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "_detect_prediction_type" in content, (
            "predict_eps must detect the model's prediction type"
        )
        assert "_make_model_output_to_eps" in content, (
            "predict_eps must convert raw model output to epsilon using the "
            "prediction type (CONST/flow, EPS, V_PREDICTION, X0)"
        )
        assert "model_output_to_eps(" in content, (
            "run_cond must call model_output_to_eps to convert raw output"
        )

    def test_source_uses_noise_scaling_for_forward(self):
        """Source must use model's noise_scaling for the forward step."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "_make_forward_step" in content, (
            "Node must create a forward_step adapter using noise_scaling"
        )
        assert "ms.noise_scaling" in content, (
            "forward_step must use model_sampling.noise_scaling"
        )

    def test_source_uses_eps_to_x0_for_reverse(self):
        """Source must use eps_to_x0 for the reverse step."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "_make_reverse_step" in content, (
            "Node must create a reverse_step adapter using eps_to_x0"
        )
        assert "_make_eps_to_x0" in content, (
            "reverse_step must use _make_eps_to_x0 (inverse of noise_scaling)"
        )

    def test_refine_latent_once_accepts_adapters(self):
        """refine_latent_once must accept forward_step/reverse_step/sigma_at."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush.py").read_text(encoding="utf-8")
        assert "forward_step:" in content, "refine_latent_once must accept forward_step"
        assert "reverse_step:" in content, "refine_latent_once must accept reverse_step"
        assert "sigma_at:" in content, "refine_latent_once must accept sigma_at"

    def test_pixelrush_cascade_passes_adapters(self):
        """pixelrush_cascade must pass adapters to refine_latent_once."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush.py").read_text(encoding="utf-8")
        assert "forward_step=forward_step" in content, (
            "pixelrush_cascade must pass forward_step to refine_latent_once"
        )
        assert "reverse_step=reverse_step" in content, (
            "pixelrush_cascade must pass reverse_step to refine_latent_once"
        )
        assert "sigma_at=sigma_at" in content, (
            "pixelrush_cascade must pass sigma_at to refine_latent_once"
        )


@pytest.mark.unit
class TestPixelRushKTimestepScaling:
    """Tests for k_timestep scaling to model's native timestep range.

    FLUX/CONST-flow models use 0-1 timestep range; EPS/SD models use 0-999.
    Passing k_timestep=249 (paper default for EPS) to a FLUX model gives
    sigma>1 (invalid), making 1-sigma negative -> pure noise.
    """

    def _make_mock_model(self, timestep_range):
        """Create a mock model with the given timestep range at sigma_max."""
        import types

        model = types.SimpleNamespace()
        model_sampling = types.SimpleNamespace()
        model_sampling.sigma_max = 1.0 if timestep_range == "01" else 14.6
        model_sampling.timestep = lambda sigma: sigma if timestep_range == "01" else sigma * 999.0 / 14.6
        model.model = types.SimpleNamespace()
        model.model.model_sampling = model_sampling
        return model

    def test_scale_k_timestep_flux_01_range(self):
        """For FLUX (0-1 range), k_timestep=249 should scale to ~0.249."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _scale_k_timestep

        model = self._make_mock_model("01")
        scaled = _scale_k_timestep(model, 249)
        assert abs(scaled - 249 / 999.0) < 1e-6, (
            f"FLUX k_timestep should scale 249 -> {249/999.0:.4f}, got {scaled}"
        )

    def test_scale_k_timestep_eps_0999_range(self):
        """For EPS (0-999 range), k_timestep=249 should be unchanged."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _scale_k_timestep

        model = self._make_mock_model("0999")
        scaled = _scale_k_timestep(model, 249)
        assert scaled == 249, (
            f"EPS k_timestep should be unchanged (249), got {scaled}"
        )

    def test_scale_k_timestep_flux_small_value(self):
        """For FLUX, k_timestep=50 should scale to ~0.05."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _scale_k_timestep

        model = self._make_mock_model("01")
        scaled = _scale_k_timestep(model, 50)
        assert abs(scaled - 50 / 999.0) < 1e-6, (
            f"FLUX k_timestep should scale 50 -> {50/999.0:.4f}, got {scaled}"
        )

    def test_source_uses_scale_k_timestep(self):
        """Source must use _scale_k_timestep in execute."""
        content = (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")
        assert "_scale_k_timestep(" in content, (
            "execute must call _scale_k_timestep to scale k_timestep to model range"
        )


@pytest.mark.unit
class TestPipelineSpaceConvention:
    """Plan 2026-09-02 Step 7: the operate_in_vae_space flag is removed.

    The pipeline is ALWAYS VAE-space at the interfaces (ComfyUI LATENT
    convention): execute never pre-converts the initial latent, the VAE
    adapters never apply process_latent_out/in, and predict_eps /
    forward_step / reverse_step own the VAE<->model conversions.
    """

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    @staticmethod
    def _strip_docstrings_and_comments(content):
        import ast
        tree = ast.parse(content)
        lines = content.splitlines()
        for node in ast.walk(tree):
            if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef, ast.Module)):
                doc = ast.get_docstring(node, clean=False)
                if doc:
                    # blank out the docstring lines only
                    for i in range(node.body[0].lineno - 1,
                                   node.body[0].lineno - 1 + doc.count("\n") + 1):
                        lines[i] = ""
        code = "\n".join(lines)
        code = "\n".join(ln.split("#")[0] for ln in code.splitlines())
        return code

    def test_no_operate_in_vae_space_flag(self):
        """The flag must be gone from code (docstrings may mention removal)."""
        for rel in ("src/pixelrush_node.py", "src/pixelrush.py"):
            content = (pathlib.Path(__file__).parent.parent / rel).read_text(encoding="utf-8")
            code = self._strip_docstrings_and_comments(content)
            assert "operate_in_vae_space" not in code, (
                f"{rel} must not reference the removed operate_in_vae_space flag in code"
            )

    def test_execute_never_preconverts_initial_latent(self):
        """execute must pass the initial latent through _prepare_initial_latent
        without process_latent_in (core runs in VAE space)."""
        content = self._read_source()
        assert "initial_latent = _prepare_initial_latent(" in content
        call = content[content.index("initial_latent = _prepare_initial_latent("):]
        call = call[:call.index(")")]
        assert "process_latent_in" not in call, (
            "_prepare_initial_latent must not receive process_latent_in"
        )

    def test_vae_adapters_do_not_convert_space(self):
        """_make_vae_adapters must not apply process_latent_out/in (VAE space
        in, VAE space out); conversions live in predict_eps/forward/reverse."""
        content = self._read_source()
        start = content.index("def _make_vae_adapters")
        end = content.index("def _prepare_initial_latent")
        section = content[start:end]
        code = self._strip_docstrings_and_comments(section)
        assert "process_latent_out(" not in code, (
            "vae_decode must not call process_latent_out (latent is already in VAE space)"
        )
        assert "process_latent_in(" not in code, (
            "vae_encode must not call process_latent_in (latent stays in VAE space)"
        )

    def test_predict_eps_converts_input_via_process_latent_in(self):
        """predict_eps must convert the VAE-space latent to model space for
        the model call (and return model-space eps)."""
        content = self._read_source()
        start = content.index("def _make_predict_eps")
        end = content.index("def _make_forward_step")
        section = content[start:end]
        assert "process_latent_in(latent)" in section, (
            "predict_eps must apply process_latent_in to the input latent"
        )

    def test_forward_reverse_adapters_own_conversion(self):
        """forward/reverse adapters must convert via process_latent_in/out."""
        content = self._read_source()
        start = content.index("def _make_forward_step")
        end = content.index("def _make_sigma_at")
        section = content[start:end]
        assert section.count("process_latent_in(x_0)") >= 1
        assert section.count("process_latent_out(x_k_model)") >= 1
        assert section.count("process_latent_in(x_K)") >= 1
        assert section.count("process_latent_out(x0_model)") >= 1


@pytest.mark.unit
class TestAdapterSpaceConversion:
    """The Step 7 exactness tests: forward/reverse must convert spaces such
    that the MODEL-SPACE view of the noised latent carries eps at full
    model-space scale (SNR matches the timestep sigma).

    Before the fix, VAE-space x was noised with model-space eps directly:
    for SDXL (scale_factor 0.13025) the model then saw
    s*x + s*sigma*eps — noise 7.7x too small for the claimed timestep.
    """

    def _make_adapters(self, scale=0.13025):
        """EPS-model mock with a pure-scaling latent format (SDXL-like)."""
        import sys
        import types
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))

        def noise_scaling(sigma, noise, latent_image):
            sigma_r = sigma.reshape(sigma.shape + (1,) * (latent_image.ndim - sigma.ndim))
            return sigma_r * noise + latent_image

        EpsClass = type("EPS", (), {})
        ModelSampling = type("ModelSampling", (EpsClass,), {})
        ms_instance = ModelSampling()
        ms_instance.noise_scaling = noise_scaling
        ms_instance.timestep = lambda sigma: sigma * 999.0

        model = types.SimpleNamespace()
        model.model = types.SimpleNamespace(model_sampling=ms_instance)

        def process_latent_in(t):
            return t * scale

        def process_latent_out(t):
            return t / scale

        from src.pixelrush_node import _make_forward_step, _make_reverse_step
        forward = _make_forward_step(model, process_latent_in, process_latent_out)
        reverse = _make_reverse_step(model, process_latent_in, process_latent_out)
        return forward, reverse, process_latent_in, scale

    def test_forward_step_produces_model_space_snr(self):
        """process_latent_in(forward(x, eps, sigma)) == s*x + sigma*eps.

        This is the core exactness property: the model-space view of the
        noised latent must carry the eps at full model-space scale.
        """
        forward, _, process_latent_in, s = self._make_adapters()
        x = torch.randn(1, 4, 8, 8)          # VAE space
        eps = torch.randn(1, 4, 8, 8)       # model space
        sigma = torch.tensor([0.5])
        x_k_vae = forward(x, eps, sigma)
        model_view = process_latent_in(x_k_vae)
        expected = s * x + 0.5 * eps         # s*x + sigma*eps
        assert torch.allclose(model_view, expected, atol=1e-5), (
            "forward_step must produce s*x + sigma*eps in model space "
            f"(got max err {(model_view - expected).abs().max():.3e}; the "
            "pre-fix bug gave s*x + s*sigma*eps — noise 7.7x too small)"
        )

    def test_reverse_step_round_trip_identity(self):
        """reverse(forward(x, e, sigma), e, sigma) must return x exactly."""
        forward, reverse, _, _ = self._make_adapters()
        x = torch.randn(2, 4, 8, 8)
        eps = torch.randn(2, 4, 8, 8)
        for sigma_val in (0.1, 0.6, 0.9):
            sigma = torch.tensor([sigma_val])
            x_k = forward(x, eps, sigma)
            x_rec = reverse(x_k, eps, sigma)
            assert torch.allclose(x_rec, x, atol=1e-4), (
                f"round trip failed at sigma={sigma_val}"
            )

    def test_forward_reverse_preserve_vae_space_magnitude(self):
        """With realistic SDXL magnitudes (VAE std ~7.7, eps std ~1), the
        model-space noise/signal ratio of forward output must equal sigma."""
        forward, _, process_latent_in, s = self._make_adapters()
        x = 7.7 * torch.randn(1, 4, 16, 16)   # VAE space
        eps = 1.0 * torch.randn(1, 4, 16, 16)  # model space
        sigma = torch.tensor([0.25])
        x_k_vae = forward(x, eps, sigma)
        model_view = process_latent_in(x_k_vae)
        noise_part = model_view - s * x
        assert torch.allclose(noise_part, 0.25 * eps, atol=1e-4), (
            "noise component in model space must be exactly sigma*eps"
        )
        ratio = noise_part.std() / (s * x).std()
        assert abs(ratio.item() - 0.25) < 0.05, (
            f"noise/signal ratio must match sigma (0.25), got {ratio.item():.3f}"
        )


@pytest.mark.unit
class TestPrepareInitialLatent:
    """Tests for _prepare_initial_latent under the always-VAE convention
    (plan 2026-09-02 Step 7): no space conversion, 3D shape normalization
    only.
    """

    def _import_helper(self):
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush_node import _prepare_initial_latent
        return _prepare_initial_latent

    def test_never_applies_process_latent_in(self):
        """The helper must never scale the latent (space conversions live in
        the adapters)."""
        helper = self._import_helper()
        latent = torch.randn(1, 4, 32, 32)
        out = helper(latent, latent_dimensions=2)
        assert torch.equal(out, latent)

    def test_3d_unsqueezes_4d_to_5d(self):
        """3D latent models: 4D input is unsqueezed to 5D [B, C, 1, H, W]."""
        helper = self._import_helper()
        latent = torch.randn(1, 4, 32, 32)  # 4D
        out = helper(latent, latent_dimensions=3)
        assert tuple(out.shape) == (1, 4, 1, 32, 32)

    def test_3d_5d_passthrough(self):
        """5D input stays 5D unchanged."""
        helper = self._import_helper()
        latent = torch.randn(1, 4, 1, 32, 32)
        out = helper(latent, latent_dimensions=3)
        assert torch.equal(out, latent)


@pytest.mark.unit
class TestEmptyConditioningCFG:
    """Plan 2026-09-02 Step 8: empty-negative CFG fix + empty-positive error.

    Previously run_cond("negative") returned zeros for an empty negative
    list, so CFG degenerated to eps = cfg_scale * eps_cond (7x amplification
    at the default). Now an empty negative returns the conditional eps
    unchanged, and an empty positive raises ValueError.
    """

    def _build_predict_eps(self, positive, negative, cfg_scale=7.0):
        """Build a real _make_predict_eps against the conftest comfy stubs.

        The stubbed process_conds returns the conditioning dict verbatim and
        get_area_and_mult returns a minimal object; the mocked
        diffusion_model returns a fixed eps per prompt so CFG is observable.
        """
        import sys
        import types
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))

        import torch as _torch
        import comfy.samplers  # noqa: F401  (conftest stubs)

        # --- model mock with per-prompt eps ---
        POS_EPS = 0.5
        NEG_EPS = 1.5

        class _Diffusion:
            def __call__(self, xc, t, context=None, control=None,
                         transformer_options=None, **kw):
                # context carries which prompt this run is for via extra_conds;
                # simpler: the stub passes c_crossattn=marker tensor
                marker = kw.get("marker", context)
                if marker is not None and float(marker.flatten()[0]) == -1.0:
                    return _torch.full_like(xc, NEG_EPS)
                return _torch.full_like(xc, POS_EPS)

        model = types.SimpleNamespace()
        model.load_device = _torch.device("cpu")
        model.pre_run = lambda: None
        model.apply_hooks = lambda hooks=None: {}
        model.model = types.SimpleNamespace()

        def process_latent_in(t):
            return t

        model.model.process_latent_in = process_latent_in

        ms = types.SimpleNamespace()
        EpsClass = type("EPS", (), {})
        MS = type("ModelSampling", (EpsClass,), {})
        ms_instance = MS()
        ms_instance.sigma = lambda ts: ts
        ms_instance.timestep = lambda sigma: sigma * 999.0
        ms_instance.sigma_data = 1.0

        def calculate_input(sigma, x):
            return x

        ms_instance.calculate_input = calculate_input
        model.model.model_sampling = ms_instance
        model.model.get_dtype_inference = lambda: _torch.float32
        model.model.current_patcher = None
        model.model.process_timestep = lambda t, **kw: t
        model.model.diffusion_model = _Diffusion()

        # Conditioning: list entries are dicts whose c_crossattn marker
        # selects the prompt (1.0 pos, -1.0 neg); empty list = empty cond.
        def mk(marker):
            return {"c_crossattn": _torch.tensor([[marker]])}

        from src.pixelrush_node import _make_predict_eps
        return _make_predict_eps(
            model,
            [mk(1.0)] if positive else [],
            [mk(-1.0)] if negative else [],
            cfg_scale,
            latent_dimensions=2,
        ), POS_EPS, NEG_EPS

    def test_empty_negative_returns_cond_eps(self):
        """Empty negative + cfg=7.0: returned eps must equal eps_cond
        EXACTLY (before the fix it was 7x eps_cond)."""
        import torch.nn.functional as F
        predict_eps, pos_eps, _ = self._build_predict_eps(
            positive=True, negative=False, cfg_scale=7.0)
        latent = torch.zeros(1, 4, 8, 8)
        eps = predict_eps(latent, timestep=0)
        # diffusion returned constant POS_EPS everywhere -> eps == pos_eps
        assert torch.allclose(eps, torch.full_like(eps, pos_eps), atol=1e-5), (
            f"Empty negative must return eps_cond unchanged; got mean "
            f"{eps.mean().item():.3f}, expected {pos_eps} (pre-fix bug gave "
            f"cfg_scale*eps_cond = {7.0 * pos_eps:.3f})"
        )

    def test_cfg_applied_when_negative_present(self):
        """Non-empty negative + cfg=2.0: eps == eps_uncond + 2*(eps_cond - eps_uncond)."""
        predict_eps, pos_eps, neg_eps = self._build_predict_eps(
            positive=True, negative=True, cfg_scale=2.0)
        latent = torch.zeros(1, 4, 8, 8)
        eps = predict_eps(latent, timestep=0)
        expected = neg_eps + 2.0 * (pos_eps - neg_eps)
        assert torch.allclose(eps, torch.full_like(eps, expected), atol=1e-5), (
            f"CFG must apply when negative is present; got mean {eps.mean().item():.3f}, "
            f"expected {expected:.3f}"
        )

    def test_empty_positive_raises(self):
        """Empty positive must raise ValueError with a clear message."""
        predict_eps, _, _ = self._build_predict_eps(
            positive=False, negative=True, cfg_scale=7.0)
        latent = torch.zeros(1, 4, 8, 8)
        with pytest.raises(ValueError, match="positive conditioning"):
            predict_eps(latent, timestep=0)


@pytest.mark.unit
class TestRefinerModelInput:
    """Plan 2026-09-02 Step 9: optional separate refiner model (G4).

    Paper setup: SDXL base generator + SDXL-Turbo (ADD-distilled) refiner.
    When refiner_model is provided it drives the K-timestep refinement; the
    base model drives the 0-timestep inversion. When absent, the base model
    is reused for both (an intentional choice the corrected theory allows).
    """

    def _read_source(self):
        return (pathlib.Path(__file__).parent.parent / "src" / "pixelrush_node.py").read_text(encoding="utf-8")

    def test_schema_has_optional_refiner_model(self):
        content = self._read_source()
        assert 'io.Model.Input(' in content
        assert '"refiner_model"' in content
        assert "optional=True" in content, (
            "refiner_model input must be optional (base model reused by default)"
        )

    def test_execute_buys_distinct_adapters(self):
        """execute must build inversion_eps from the base model and
        refiner_eps from refiner_model when provided (or reuse inversion_eps
        when not)."""
        content = self._read_source()
        assert "inversion_eps = _make_predict_eps(" in content
        assert "refiner_eps = _make_predict_eps(" in content, (
            "execute must build a separate refiner_eps from refiner_model"
        )
        assert "refiner_model is not None and refiner_model is not model" in content, (
            "refiner eps must only be rebuilt when a distinct refiner model is given"
        )
        # The cascade must receive both adapters
        assert "inversion_eps=inversion_eps" in content
        assert "refiner_eps=refiner_eps" in content

    def test_refiner_model_none_uses_base_for_both(self):
        """No refiner_model: all model calls hit the base model object."""
        import sys
        import types
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import PixelRushConfig, pixelrush_cascade

        calls = []

        def make_eps(tag):
            def eps_fn(latent, timestep):
                calls.append((tag, timestep))
                return torch.zeros_like(latent)
            return eps_fn

        # Simulate the execute() wiring with refiner_model=None
        base_eps = make_eps("base")
        refiner_model = None
        if refiner_model is not None and refiner_model is not object():
            refiner_eps = make_eps("refiner")
        else:
            refiner_eps = base_eps

        def vae_decode(z):
            return z[:, :3] if z.shape[1] >= 3 else z

        def vae_encode(x):
            return x[:, :4] if x.shape[1] >= 4 else x

        cfg = PixelRushConfig(patch_h=32, patch_w=32, overlap=0.5,
                              k_timestep=249, noise_lambda=0.95,
                              noise_injection="additive")
        pixelrush_cascade(
            torch.randn(1, 4, 32, 32), num_cascade_stages=1,
            vae_decode=vae_decode, vae_encode=vae_encode,
            inversion_eps=base_eps, refiner_eps=refiner_eps,
            alpha_bar_at=lambda t: 0.8, cfg=cfg,
        )
        tags = {c[0] for c in calls}
        assert tags == {"base"}, (
            f"Without refiner_model only the base adapter may run; got {tags}"
        )

    def test_refiner_model_provided_uses_refiner_at_k(self):
        """Distinct refiner: base model called once (t=0) and refiner once
        (t=K) per patch — the G4 acceptance test."""
        import sys
        sys.path.insert(0, str(pathlib.Path(__file__).parent.parent))
        from src.pixelrush import PixelRushConfig, refine_latent_once

        base_calls, refiner_calls = [], []

        def base_eps(latent, timestep):
            base_calls.append(timestep)
            return torch.zeros_like(latent)

        def refiner_eps_fn(latent, timestep):
            refiner_calls.append(timestep)
            return 0.1 * torch.ones_like(latent)

        cfg = PixelRushConfig(patch_h=32, patch_w=32, overlap=0.5,
                              k_timestep=249, noise_lambda=0.95,
                              noise_injection="additive")
        refine_latent_once(
            torch.randn(1, 4, 32, 32),
            base_eps, refiner_eps_fn,
            lambda t: 0.8, cfg,
        )
        assert base_calls == [0], (
            f"Base model must be called exactly once at t=0; got {base_calls}"
        )
        assert refiner_calls == [249], (
            f"Refiner must be called exactly once at t=K=249; got {refiner_calls}"
        )

    def test_refiner_receives_same_conditioning(self):
        """execute must pass the same positive/negative to both adapters."""
        content = self._read_source()
        start = content.index("inversion_eps = _make_predict_eps(")
        end = content.index("alpha_bar_at = _make_alpha_bar_at")
        section = content[start:end]
        assert section.count("positive, negative, cfg, latent_dimensions") == 2, (
            "Both inversion and refiner adapters must receive the same "
            "positive/negative/cfg/latent_dimensions"
        )