from __future__ import annotations

import json
import os
import random
import re
from typing import Any

try:
    from comfy_execution.graph import ExecutionBlocker
    from comfy_execution.graph_utils import GraphBuilder, is_link
except Exception:  # Allows local syntax/import checks outside ComfyUI.
    ExecutionBlocker = None
    GraphBuilder = None

    def is_link(value: Any) -> bool:
        return isinstance(value, list) and len(value) == 2

try:
    from nodes import NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS
except Exception:
    ALL_NODE_CLASS_MAPPINGS = {}

try:
    import folder_paths
    import numpy as np
    from PIL import Image
    from PIL.PngImagePlugin import PngInfo
    from comfy.cli_args import args
except Exception:
    folder_paths = None
    np = None
    Image = None
    PngInfo = None

    class _ArgsFallback:
        disable_metadata = True

    args = _ArgsFallback()


MAX_LOOP_VALUES = 20
MAX_CARRY_VALUES = MAX_LOOP_VALUES - 2
MAX_SWITCH_INPUTS = 64
COLLECTION_MODES = ["auto_batch", "list", "image_batch", "latent_batch", "string_lines"]
ACCUMULATOR_ACTIONS = ["append_variant", "replace_by_entry_id", "append", "clear_then_append", "clear", "read"]
ACCUMULATOR_IMAGE_BATCH_MODES = ["same_size_only", "resize_to_first"]
ACCUMULATOR_IMAGE_GROUPS = 4
ACCUMULATOR_PREVIEW_DELETE_ACTIONS = ["none", "delete_entry_id", "delete_index", "clear"]
INDEX_SWITCH_MODES = ["pick_input", "route_output"]
INDEX_SWITCH_BASES = ["one_based", "zero_based"]
INDEX_SWITCH_MISSING_BEHAVIORS = ["fallback", "none", "clamp", "wrap"]

_ACCUMULATOR_STORES: dict[str, list[dict[str, Any]]] = {}


class AnyType(str):
    def __ne__(self, _other: object) -> bool:
        return False


ANY_TYPE = AnyType("*")


def _require_graph_builder() -> None:
    if GraphBuilder is None:
        raise RuntimeError("SxCP loop nodes require ComfyUI's comfy_execution GraphBuilder.")


def _execution_blocker() -> Any:
    return ExecutionBlocker(None) if ExecutionBlocker is not None else None


def _torch_cat(first: Any, second: Any) -> Any | None:
    try:
        import torch
    except Exception:
        return None
    if torch.is_tensor(first) and torch.is_tensor(second):
        return torch.cat((first, second), dim=0)
    return None


def _latent_cat(first: Any, second: Any) -> Any | None:
    if not isinstance(first, dict) or not isinstance(second, dict):
        return None
    if "samples" not in first or "samples" not in second:
        return None
    samples = _torch_cat(first["samples"], second["samples"])
    if samples is None:
        return None
    merged = dict(second)
    merged["samples"] = samples
    return merged


def _torch_cat_many(values: list[Any]) -> Any | None:
    if not values:
        return None
    result = values[0]
    for value in values[1:]:
        result = _torch_cat(result, value)
        if result is None:
            return None
    return result


def _is_image_tensor(value: Any) -> bool:
    try:
        import torch
    except Exception:
        return False
    return torch.is_tensor(value) and len(value.shape) == 4


def _image_shape(value: Any) -> tuple[int, ...] | None:
    if not _is_image_tensor(value):
        return None
    return tuple(int(part) for part in value.shape[1:])


def _split_image_value(value: Any) -> list[Any]:
    if value is None:
        return []
    if isinstance(value, (list, tuple)):
        images: list[Any] = []
        for item in value:
            images.extend(_split_image_value(item))
        return images
    if not _is_image_tensor(value):
        return []
    if int(value.shape[0]) <= 1:
        return [value]
    return [value[index : index + 1] for index in range(int(value.shape[0]))]


def _resize_image_to_shape(image: Any, shape: tuple[int, ...]) -> Any | None:
    if not _is_image_tensor(image):
        return None
    try:
        import comfy.utils
    except Exception:
        return None
    height, width = int(shape[0]), int(shape[1])
    return comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)


def _image_batch_from_images(images: list[Any], mode: str = "same_size_only") -> Any | None:
    if not images:
        return None
    mode = mode if mode in ACCUMULATOR_IMAGE_BATCH_MODES else "same_size_only"
    first_shape = _image_shape(images[0])
    if first_shape is None:
        return None
    normalized = []
    for image in images:
        if _image_shape(image) != first_shape:
            if mode != "resize_to_first":
                return None
            image = _resize_image_to_shape(image, first_shape)
            if image is None:
                return None
        normalized.append(image)
    return _torch_cat_many(normalized)


def _group_image_batches(images: list[Any]) -> list[Any]:
    grouped: dict[tuple[int, ...], list[Any]] = {}
    order: list[tuple[int, ...]] = []
    for image in images:
        shape = _image_shape(image)
        if shape is None:
            continue
        if shape not in grouped:
            grouped[shape] = []
            order.append(shape)
        grouped[shape].append(image)
    batches = [_torch_cat_many(grouped[shape]) for shape in order]
    return [batch for batch in batches if batch is not None]


def _accumulator_store_key(store_key: str, unique_id: Any = None) -> str:
    key = str(store_key or "").strip()
    if key:
        return key
    return f"node:{unique_id}"


def _entry_value_summary(value: Any) -> str:
    if value is None:
        return ""
    text = str(value)
    text = re.sub(r"\s+", " ", text).strip()
    return text[:160]


def _metadata_copy(value: Any) -> Any:
    try:
        return json.loads(json.dumps(value))
    except Exception:
        return value


def _entry_metadata(prompt: Any, extra_pnginfo: Any) -> dict[str, Any]:
    return {
        "prompt": _metadata_copy(prompt),
        "extra_pnginfo": _metadata_copy(extra_pnginfo) if isinstance(extra_pnginfo, dict) else {},
    }


def _entry_infos(store: list[dict[str, Any]]) -> list[dict[str, Any]]:
    entries = []
    for index, entry in enumerate(store, start=1):
        image = entry.get("image")
        shape = _image_shape(image)
        has_metadata = "prompt" in entry or "extra_pnginfo" in entry
        entries.append(
            {
                "index": index,
                "id": str(entry.get("id") or ""),
                "has_image": image is not None,
                "has_metadata": has_metadata,
                "shape": list(shape) if shape is not None else [],
                "value": _entry_value_summary(entry.get("value")),
            }
        )
    return entries


def _accumulator_status(key: str, store: list[dict[str, Any]]) -> str:
    images = [entry.get("image") for entry in store if entry.get("image") is not None]
    shapes = []
    for image in images:
        shape = _image_shape(image)
        if shape is not None and shape not in shapes:
            shapes.append(shape)
    shape_text = ", ".join(f"{shape[1]}x{shape[0]}" for shape in shapes) or "no images"
    return f"key={key}; entries={len(store)}; image_entries={len(images)}; formats={shape_text}"


def accumulator_list_entries(store_key: str) -> dict[str, Any]:
    key = str(store_key or "").strip()
    if not key:
        raise ValueError("store_key is required for accumulator preview actions")
    store = _ACCUMULATOR_STORES.setdefault(key, [])
    return {
        "store_key": key,
        "entries": _entry_infos(store),
        "count": len(store),
        "status": _accumulator_status(key, store),
    }


def accumulator_delete_entries(
    store_key: str,
    entry_id: str = "",
    index: int = 0,
    clear: bool = False,
) -> dict[str, Any]:
    key = str(store_key or "").strip()
    if not key:
        raise ValueError("store_key is required for accumulator preview actions")
    store = _ACCUMULATOR_STORES.setdefault(key, [])
    removed = 0
    if clear:
        removed = len(store)
        store.clear()
    else:
        entry_id = str(entry_id or "").strip()
        if entry_id:
            before = len(store)
            store[:] = [entry for entry in store if str(entry.get("id") or "") != entry_id]
            removed = before - len(store)
        elif int(index) > 0:
            zero_index = int(index) - 1
            if zero_index < len(store):
                del store[zero_index]
                removed = 1
        else:
            raise ValueError("entry_id or 1-based index is required")
    result = accumulator_list_entries(key)
    result["removed"] = removed
    return result


def _require_image_saving() -> None:
    if folder_paths is None or np is None or Image is None:
        raise RuntimeError("Image preview/save helpers require ComfyUI image dependencies.")


def _metadata(prompt: Any, extra_pnginfo: Any) -> Any:
    if args.disable_metadata or PngInfo is None:
        return None
    metadata = PngInfo()
    if prompt is not None:
        metadata.add_text("prompt", json.dumps(prompt))
    if isinstance(extra_pnginfo, dict):
        for key, value in extra_pnginfo.items():
            metadata.add_text(key, json.dumps(value))
    return metadata


def _metadata_for_entry(entry: dict[str, Any], fallback_prompt: Any, fallback_extra_pnginfo: Any) -> Any:
    prompt = entry["prompt"] if "prompt" in entry else fallback_prompt
    extra_pnginfo = entry["extra_pnginfo"] if "extra_pnginfo" in entry else fallback_extra_pnginfo
    return _metadata(prompt, extra_pnginfo)


def _image_to_pil(image: Any) -> Any:
    _require_image_saving()
    tensor = image
    try:
        if len(tensor.shape) == 4:
            tensor = tensor[0]
    except Exception:
        pass
    image_data = 255.0 * tensor.cpu().numpy()
    return Image.fromarray(np.clip(image_data, 0, 255).astype(np.uint8))


def _safe_filename_prefix(prefix: str, default: str = "sxcp_accumulator") -> str:
    text = str(prefix or "").strip() or default
    text = os.path.basename(text)
    text = re.sub(r"[^A-Za-z0-9._-]+", "_", text).strip("._")
    return text or default


def _next_save_counter(folder: str, prefix: str) -> int:
    pattern = re.compile(rf"^{re.escape(prefix)}_(\d+)_\.png$")
    counter = 1
    try:
        for filename in os.listdir(folder):
            match = pattern.match(filename)
            if match:
                counter = max(counter, int(match.group(1)) + 1)
    except FileNotFoundError:
        pass
    return counter


def _preview_image_results(
    entries: list[dict[str, Any]],
    preview_limit: int,
    prompt: Any,
    extra_pnginfo: Any,
) -> list[dict[str, str]]:
    image_entries = [entry for entry in entries if entry.get("image") is not None]
    if not image_entries:
        return []
    _require_image_saving()
    output_dir = folder_paths.get_temp_directory()
    preview_entries = image_entries[: max(1, int(preview_limit))]
    first_shape = _image_shape(preview_entries[0].get("image"))
    height, width = (first_shape[0], first_shape[1]) if first_shape else (512, 512)
    prefix = "SxCPAccumulatorPreview_temp_" + "".join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5))
    full_output_folder, filename, counter, subfolder, _filename_prefix = folder_paths.get_save_image_path(prefix, output_dir, width, height)
    results = []
    for entry in preview_entries:
        image = entry.get("image")
        metadata = _metadata_for_entry(entry, prompt, extra_pnginfo)
        file = f"{filename}_{counter:05}_.png"
        _image_to_pil(image).save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=1)
        results.append({"filename": file, "subfolder": subfolder, "type": "temp"})
        counter += 1
    return results


def _resolve_save_folder(save_path: str) -> str:
    _require_image_saving()
    raw_path = os.path.expanduser(str(save_path or "").strip())
    if not raw_path:
        raw_path = "sxcp_accumulator"
    if os.path.isabs(raw_path):
        return raw_path
    return os.path.join(folder_paths.get_output_directory(), raw_path)


def _save_images_to_folder(
    entries: list[dict[str, Any]],
    save_path: str,
    filename_prefix: str,
    prompt: Any,
    extra_pnginfo: Any,
) -> list[str]:
    image_entries = [entry for entry in entries if entry.get("image") is not None]
    if not image_entries:
        return []
    folder = _resolve_save_folder(save_path)
    os.makedirs(folder, exist_ok=True)
    prefix = _safe_filename_prefix(filename_prefix)
    counter = _next_save_counter(folder, prefix)
    saved_paths = []
    for entry in image_entries:
        image = entry.get("image")
        metadata = _metadata_for_entry(entry, prompt, extra_pnginfo)
        file = f"{prefix}_{counter:05}_.png"
        path = os.path.join(folder, file)
        _image_to_pil(image).save(path, pnginfo=metadata, compress_level=4)
        saved_paths.append(path)
        counter += 1
    return saved_paths


def _as_list(collection: Any) -> list[Any]:
    if collection is None:
        return []
    return list(collection) if isinstance(collection, list) else [collection]


def append_collected_value(collection: Any, value: Any, mode: str = "auto_batch", skip_none: bool = True) -> Any:
    if value is None and skip_none:
        return collection
    mode = mode if mode in COLLECTION_MODES else "auto_batch"
    if mode == "string_lines":
        value_text = "" if value is None else str(value)
        if not collection:
            return value_text
        return f"{collection}\n{value_text}"
    if mode == "list":
        return _as_list(collection) + [value]
    if collection is None:
        return value
    if mode in ("auto_batch", "image_batch"):
        tensor_batch = _torch_cat(collection, value)
        if tensor_batch is not None:
            return tensor_batch
        if mode == "image_batch":
            return _as_list(collection) + [value]
    if mode in ("auto_batch", "latent_batch"):
        latent_batch = _latent_cat(collection, value)
        if latent_batch is not None:
            return latent_batch
        if mode == "latent_batch":
            return _as_list(collection) + [value]
    return _as_list(collection) + [value]


def _switch_available_indices(kwargs: dict[str, Any]) -> list[int]:
    indices = []
    for key in kwargs:
        match = re.match(r"^input_(\d+)$", str(key))
        if match:
            indices.append(int(match.group(1)))
    return sorted(set(indices))


def _switch_requested_index(index: Any, index_base: str) -> int:
    requested = int(index)
    return requested + 1 if index_base == "zero_based" else requested


def _switch_resolved_index(requested: int, available: list[int], missing_behavior: str) -> int | None:
    if requested in available:
        return requested
    if missing_behavior in ("fallback", "none") or not available:
        return None
    if missing_behavior == "wrap":
        return available[(requested - 1) % len(available)]
    if requested <= available[0]:
        return available[0]
    if requested >= available[-1]:
        return available[-1]
    lower = [value for value in available if value <= requested]
    return lower[-1] if lower else available[0]


def _switch_status(requested: int, selected: int | None, used_fallback: bool, available: list[int]) -> str:
    available_text = ",".join(str(index) for index in available) or "none"
    if used_fallback:
        return f"requested=input_{requested}; selected=fallback; available={available_text}"
    if selected is None:
        return f"requested=input_{requested}; selected=none; available={available_text}"
    return f"requested=input_{requested}; selected=input_{selected}; available={available_text}"


class SxCPWhileLoopStart:
    @classmethod
    def INPUT_TYPES(cls):
        inputs = {
            "required": {
                "condition": ("BOOLEAN", {"default": True}),
            },
            "optional": {},
        }
        for index in range(MAX_LOOP_VALUES):
            inputs["optional"][f"initial_value{index}"] = (ANY_TYPE,)
        return inputs

    RETURN_TYPES = tuple(["FLOW_CONTROL"] + [ANY_TYPE] * MAX_LOOP_VALUES)
    RETURN_NAMES = tuple(["flow"] + [f"value{index}" for index in range(MAX_LOOP_VALUES)])
    FUNCTION = "open"
    CATEGORY = "prompt_builder/loop"

    def open(self, condition, **kwargs):
        values = []
        for index in range(MAX_LOOP_VALUES):
            values.append(kwargs.get(f"initial_value{index}") if condition else _execution_blocker())
        return tuple(["stub"] + values)


class SxCPWhileLoopEnd:
    @classmethod
    def INPUT_TYPES(cls):
        inputs = {
            "required": {
                "flow": ("FLOW_CONTROL", {"rawLink": True}),
                "condition": ("BOOLEAN", {}),
            },
            "optional": {},
            "hidden": {
                "dynprompt": "DYNPROMPT",
                "unique_id": "UNIQUE_ID",
                "extra_pnginfo": "EXTRA_PNGINFO",
            },
        }
        for index in range(MAX_LOOP_VALUES):
            inputs["optional"][f"initial_value{index}"] = (ANY_TYPE,)
        return inputs

    RETURN_TYPES = tuple([ANY_TYPE] * MAX_LOOP_VALUES)
    RETURN_NAMES = tuple([f"value{index}" for index in range(MAX_LOOP_VALUES)])
    FUNCTION = "close"
    CATEGORY = "prompt_builder/loop"

    def _explore_dependencies(self, node_id: str, dynprompt: Any, upstream: dict[str, list[str]], parent_ids: list[str]) -> None:
        node_info = dynprompt.get_node(node_id)
        if "inputs" not in node_info:
            return
        for value in node_info["inputs"].values():
            if not is_link(value):
                continue
            parent_id = value[0]
            display_id = dynprompt.get_display_node_id(parent_id)
            display_node = dynprompt.get_node(display_id)
            class_type = display_node["class_type"]
            if class_type not in ("SxCPForLoopEnd", "SxCPWhileLoopEnd"):
                parent_ids.append(display_id)
            if parent_id not in upstream:
                upstream[parent_id] = []
                self._explore_dependencies(parent_id, dynprompt, upstream, parent_ids)
            upstream[parent_id].append(node_id)

    def _explore_output_nodes(
        self,
        dynprompt: Any,
        upstream: dict[str, list[str]],
        output_nodes: dict[str, Any],
        parent_ids: list[str],
    ) -> None:
        for parent_id in upstream:
            display_id = dynprompt.get_display_node_id(parent_id)
            for output_id, link in output_nodes.items():
                linked_id = link[0]
                if linked_id in parent_ids and display_id == linked_id and output_id not in upstream[parent_id]:
                    if "." in parent_id:
                        parts = parent_id.split(".")
                        parts[-1] = output_id
                        upstream[parent_id].append(".".join(parts))
                    else:
                        upstream[parent_id].append(output_id)

    def _collect_contained(self, node_id: str, upstream: dict[str, list[str]], contained: dict[str, bool]) -> None:
        if node_id not in upstream:
            return
        for child_id in upstream[node_id]:
            if child_id in contained:
                continue
            contained[child_id] = True
            self._collect_contained(child_id, upstream, contained)

    def close(self, flow, condition, dynprompt=None, unique_id=None, **kwargs):
        if not condition:
            return tuple(kwargs.get(f"initial_value{index}") for index in range(MAX_LOOP_VALUES))

        _require_graph_builder()
        upstream: dict[str, list[str]] = {}
        parent_ids: list[str] = []
        self._explore_dependencies(unique_id, dynprompt, upstream, parent_ids)
        parent_ids = list(set(parent_ids))

        output_nodes = {}
        for node_id, node in dynprompt.get_original_prompt().items():
            if "inputs" not in node:
                continue
            class_def = ALL_NODE_CLASS_MAPPINGS.get(node["class_type"])
            if not class_def or not getattr(class_def, "OUTPUT_NODE", False):
                continue
            for value in node["inputs"].values():
                if is_link(value):
                    output_nodes[node_id] = value

        graph = GraphBuilder()
        self._explore_output_nodes(dynprompt, upstream, output_nodes, parent_ids)
        contained: dict[str, bool] = {}
        open_node = flow[0]
        self._collect_contained(open_node, upstream, contained)
        contained[unique_id] = True
        contained[open_node] = True

        for node_id in contained:
            original_node = dynprompt.get_node(node_id)
            node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id)
            node.set_override_display_id(node_id)
        for node_id in contained:
            original_node = dynprompt.get_node(node_id)
            node = graph.lookup_node("Recurse" if node_id == unique_id else node_id)
            for key, value in original_node["inputs"].items():
                if is_link(value) and value[0] in contained:
                    parent = graph.lookup_node(value[0])
                    node.set_input(key, parent.out(value[1]))
                else:
                    node.set_input(key, value)

        new_open = graph.lookup_node(open_node)
        original_open = dynprompt.get_node(open_node)
        if original_open["class_type"] == "SxCPForLoopStart":
            new_open.set_input("initial_index", kwargs.get("initial_value0"))
            new_open.set_input("initial_collected", kwargs.get("initial_value1"))
            for carry_index in range(1, MAX_CARRY_VALUES + 1):
                new_open.set_input(f"initial_value{carry_index}", kwargs.get(f"initial_value{carry_index + 1}"))
        else:
            for index in range(MAX_LOOP_VALUES):
                new_open.set_input(f"initial_value{index}", kwargs.get(f"initial_value{index}"))
        my_clone = graph.lookup_node("Recurse")
        return {
            "result": tuple(my_clone.out(index) for index in range(MAX_LOOP_VALUES)),
            "expand": graph.finalize(),
        }


class SxCPForLoopStart:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "total": ("INT", {"default": 2, "min": 1, "max": 100000, "step": 1}),
                "skip": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
            },
            "optional": {
                f"initial_value{index}": (ANY_TYPE,) for index in range(1, MAX_CARRY_VALUES + 1)
            },
            "hidden": {
                "initial_index": (ANY_TYPE,),
                "initial_collected": (ANY_TYPE,),
                "prompt": "PROMPT",
                "extra_pnginfo": "EXTRA_PNGINFO",
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = tuple(["FLOW_CONTROL", "INT", ANY_TYPE] + [ANY_TYPE] * MAX_CARRY_VALUES)
    RETURN_NAMES = tuple(["flow", "index", "collected"] + [f"value{index}" for index in range(1, MAX_CARRY_VALUES + 1)])
    FUNCTION = "start"
    CATEGORY = "prompt_builder/loop"

    def start(self, total, skip=0, initial_index=None, initial_collected=None, **kwargs):
        _require_graph_builder()
        total = max(1, int(total))
        skip = max(0, int(skip))
        first_index = skip + 1
        index = first_index if initial_index is None else max(int(initial_index), first_index)
        collected = initial_collected
        initial_values = {
            "initial_value0": index,
            "initial_value1": collected,
        }
        for carry_index in range(1, MAX_CARRY_VALUES + 1):
            initial_values[f"initial_value{carry_index + 1}"] = kwargs.get(f"initial_value{carry_index}")
        graph = GraphBuilder()
        graph.node("SxCPWhileLoopStart", condition=index <= total, **initial_values)
        return {
            "result": tuple(["stub", index, collected] + [kwargs.get(f"initial_value{index}") for index in range(1, MAX_CARRY_VALUES + 1)]),
            "expand": graph.finalize(),
        }


class SxCPLoopAppend:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "mode": (COLLECTION_MODES, {"default": "auto_batch"}),
                "skip_none": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "collection": (ANY_TYPE,),
                "value": (ANY_TYPE,),
            },
        }

    RETURN_TYPES = (ANY_TYPE,)
    RETURN_NAMES = ("collected",)
    FUNCTION = "append"
    CATEGORY = "prompt_builder/loop"

    def append(self, mode, skip_none, collection=None, value=None):
        return (append_collected_value(collection, value, mode=mode, skip_none=skip_none),)


class SxCPIndexSwitch:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "index": ("INT", {"default": 1, "min": -100000, "max": 100000, "step": 1}),
                "mode": (INDEX_SWITCH_MODES, {"default": "pick_input"}),
                "index_base": (INDEX_SWITCH_BASES, {"default": "one_based"}),
                "missing_behavior": (INDEX_SWITCH_MISSING_BEHAVIORS, {"default": "fallback"}),
            },
            "optional": {
                "fallback": (ANY_TYPE, {"lazy": True}),
                "route_value": (ANY_TYPE, {"lazy": True}),
                **{
                    f"input_{index}": (ANY_TYPE, {"lazy": True})
                    for index in range(1, MAX_SWITCH_INPUTS + 1)
                },
            },
        }

    RETURN_TYPES = tuple([ANY_TYPE, "INT", "STRING"] + [ANY_TYPE] * MAX_SWITCH_INPUTS)
    RETURN_NAMES = tuple(["value", "selected_index", "status"] + [f"output_{index}" for index in range(1, MAX_SWITCH_INPUTS + 1)])
    FUNCTION = "switch"
    CATEGORY = "prompt_builder/loop"

    def _input_selection(
        self,
        index: Any,
        index_base: str,
        missing_behavior: str,
        kwargs: dict[str, Any],
    ) -> tuple[int, int | None, list[int]]:
        index_base = index_base if index_base in INDEX_SWITCH_BASES else "one_based"
        missing_behavior = missing_behavior if missing_behavior in INDEX_SWITCH_MISSING_BEHAVIORS else "fallback"
        requested = _switch_requested_index(index, index_base)
        available = _switch_available_indices(kwargs)
        selected = _switch_resolved_index(requested, available, missing_behavior)
        return requested, selected, available

    def _route_selection(self, index: Any, index_base: str, missing_behavior: str) -> tuple[int, int | None]:
        index_base = index_base if index_base in INDEX_SWITCH_BASES else "one_based"
        missing_behavior = missing_behavior if missing_behavior in INDEX_SWITCH_MISSING_BEHAVIORS else "fallback"
        requested = _switch_requested_index(index, index_base)
        if 1 <= requested <= MAX_SWITCH_INPUTS:
            return requested, requested
        if missing_behavior == "wrap":
            return requested, ((requested - 1) % MAX_SWITCH_INPUTS) + 1
        if missing_behavior == "clamp":
            return requested, min(max(requested, 1), MAX_SWITCH_INPUTS)
        return requested, None

    def _blocked_outputs(self) -> list[Any]:
        return [_execution_blocker() for _index in range(MAX_SWITCH_INPUTS)]

    def check_lazy_status(self, index, mode, index_base, missing_behavior, **kwargs):
        mode = mode if mode in INDEX_SWITCH_MODES else "pick_input"
        if mode == "route_output":
            return ["route_value"] if "route_value" in kwargs else []
        requested, selected, _available = self._input_selection(index, index_base, missing_behavior, kwargs)
        selected_name = f"input_{selected}" if selected is not None else f"input_{requested}"
        if selected_name in kwargs:
            return [selected_name]
        if missing_behavior == "fallback" and "fallback" in kwargs:
            return ["fallback"]
        return []

    def switch(self, index, mode, index_base, missing_behavior, **kwargs):
        mode = mode if mode in INDEX_SWITCH_MODES else "pick_input"
        missing_behavior = missing_behavior if missing_behavior in INDEX_SWITCH_MISSING_BEHAVIORS else "fallback"
        if mode == "route_output":
            requested, selected = self._route_selection(index, index_base, missing_behavior)
            value = kwargs.get("route_value")
            outputs = self._blocked_outputs()
            if selected is not None and "route_value" in kwargs:
                outputs[selected - 1] = value
            status = f"mode=route_output; requested=output_{requested}; selected={'none' if selected is None else f'output_{selected}'}; range=1-{MAX_SWITCH_INPUTS}"
            selected_index = selected or 0
            return tuple([value if "route_value" in kwargs else None, selected_index, status] + outputs)

        requested, selected, available = self._input_selection(index, index_base, missing_behavior, kwargs)
        if selected is not None:
            selected_name = f"input_{selected}"
            if selected_name in kwargs:
                value = kwargs.get(selected_name)
                status = f"mode=pick_input; {_switch_status(requested, selected, False, available)}"
                return tuple([value, selected, status] + self._blocked_outputs())
        if missing_behavior == "fallback" and "fallback" in kwargs:
            status = f"mode=pick_input; {_switch_status(requested, None, True, available)}"
            return tuple([kwargs.get("fallback"), 0, status] + self._blocked_outputs())
        status = f"mode=pick_input; {_switch_status(requested, None, False, available)}"
        return tuple([None, 0, status] + self._blocked_outputs())


class SxCPAccumulator:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "store_key": ("STRING", {"default": "", "multiline": False}),
                "action": (ACCUMULATOR_ACTIONS, {"default": "append_variant"}),
                "max_items": ("INT", {"default": 32, "min": 1, "max": 10000, "step": 1}),
                "image_batch_mode": (ACCUMULATOR_IMAGE_BATCH_MODES, {"default": "same_size_only"}),
                "skip_empty": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "image": ("IMAGE",),
                "value": (ANY_TYPE,),
                "entry_id": (ANY_TYPE,),
                "entry_tag": ("STRING", {"default": "", "multiline": False}),
            },
            "hidden": {
                "prompt": "PROMPT",
                "extra_pnginfo": "EXTRA_PNGINFO",
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = tuple([ANY_TYPE, "IMAGE", "IMAGE"] + ["IMAGE"] * ACCUMULATOR_IMAGE_GROUPS + ["INT", "STRING", "STRING"])
    RETURN_NAMES = tuple(
        ["collection", "image_batch", "image_list"]
        + [f"image_batch_{index}" for index in range(1, ACCUMULATOR_IMAGE_GROUPS + 1)]
        + ["count", "status", "store_key"]
    )
    OUTPUT_IS_LIST = tuple([False, False, True] + [False] * ACCUMULATOR_IMAGE_GROUPS + [False, False, False])
    FUNCTION = "accumulate"
    CATEGORY = "prompt_builder/loop"

    @classmethod
    def IS_CHANGED(cls, *args, **kwargs):
        return random.random()

    def _store_key(self, store_key: str, unique_id: Any) -> str:
        return _accumulator_store_key(store_key, unique_id)

    def _entry_id(self, entry_id: Any, entry_tag: str, image_index: int, image_count: int) -> str:
        text = "" if entry_id is None else str(entry_id).strip()
        tag = str(entry_tag or "").strip()
        if text and tag:
            text = f"{text}:{tag}"
        elif tag:
            text = tag
        if image_count <= 1:
            return text
        return f"{text or 'image'}:{image_index + 1}"

    def _value_for_image(self, value: Any, image_index: int, image_count: int) -> Any:
        if image_count <= 1:
            return value
        if isinstance(value, (list, tuple)) and len(value) == image_count:
            return value[image_index]
        return value

    def _entry_records(
        self,
        image: Any,
        value: Any,
        entry_id: Any,
        entry_tag: str,
        skip_empty: bool,
        metadata: dict[str, Any],
    ) -> list[dict[str, Any]]:
        images = _split_image_value(image)
        if not images:
            if value is None and skip_empty:
                return []
            return [{"id": self._entry_id(entry_id, entry_tag, 0, 1), "image": None, "value": value, **metadata}]
        image_count = len(images)
        return [
            {
                "id": self._entry_id(entry_id, entry_tag, index, image_count),
                "image": image_item,
                "value": self._value_for_image(value, index, image_count),
                **metadata,
            }
            for index, image_item in enumerate(images)
        ]

    def _variant_entry_id(self, store: list[dict[str, Any]], base_id: str) -> str:
        base_id = base_id or "entry"
        prefix = f"{base_id}#"
        count = 0
        for existing in store:
            existing_id = str(existing.get("id") or "")
            if existing_id == base_id or existing_id.startswith(prefix):
                count += 1
        return f"{base_id}#{count + 1}"

    def _append_or_replace(self, store: list[dict[str, Any]], entries: list[dict[str, Any]], action: str) -> None:
        replace = action == "replace_by_entry_id"
        for entry in entries:
            if action == "append_variant":
                entry = dict(entry)
                entry["id"] = self._variant_entry_id(store, str(entry.get("id") or ""))
                store.append(entry)
                continue
            entry_id = entry.get("id") or ""
            if replace and entry_id:
                for index, existing in enumerate(store):
                    if existing.get("id") == entry_id:
                        store[index] = entry
                        break
                else:
                    store.append(entry)
            else:
                store.append(entry)

    def _collection(self, store: list[dict[str, Any]]) -> list[Any]:
        collection = []
        for entry in store:
            value = entry.get("value")
            collection.append(value if value is not None else entry.get("image"))
        return collection

    def _status(self, key: str, store: list[dict[str, Any]], image_batch: Any, image_batches: list[Any]) -> str:
        batch_state = "all images batched" if image_batch is not None else "mixed sizes or no image batch"
        return f"{_accumulator_status(key, store)}; grouped_batches={len(image_batches)}; {batch_state}"

    def accumulate(
        self,
        store_key,
        action,
        max_items,
        image_batch_mode,
        skip_empty,
        image=None,
        value=None,
        entry_id=None,
        entry_tag="",
        prompt=None,
        extra_pnginfo=None,
        unique_id=None,
    ):
        key = self._store_key(store_key, unique_id)
        action = action if action in ACCUMULATOR_ACTIONS else "append_variant"
        store = _ACCUMULATOR_STORES.setdefault(key, [])

        if action in ("clear", "clear_then_append"):
            store.clear()

        if action not in ("clear", "read"):
            metadata = _entry_metadata(prompt, extra_pnginfo)
            entries = self._entry_records(image, value, entry_id, entry_tag, bool(skip_empty), metadata)
            self._append_or_replace(store, entries, action)

        max_items = max(1, int(max_items))
        if len(store) > max_items:
            del store[: len(store) - max_items]

        images = [entry["image"] for entry in store if entry.get("image") is not None]
        image_batch = _image_batch_from_images(images, image_batch_mode)
        image_batches = _group_image_batches(images)
        grouped_outputs = image_batches[:ACCUMULATOR_IMAGE_GROUPS]
        grouped_outputs += [None] * (ACCUMULATOR_IMAGE_GROUPS - len(grouped_outputs))
        status = self._status(key, store, image_batch, image_batches)
        return tuple([self._collection(store), image_batch, images] + grouped_outputs + [len(store), status, key])


class SxCPAccumulatorPreview:
    OUTPUT_NODE = True

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "store_key": ("STRING", {"default": "", "multiline": False}),
                "preview_limit": ("INT", {"default": 64, "min": 1, "max": 512, "step": 1}),
                "delete_action": (ACCUMULATOR_PREVIEW_DELETE_ACTIONS, {"default": "none"}),
                "delete_entry_id": ("STRING", {"default": "", "multiline": False}),
                "delete_index": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
                "save_batch": ("BOOLEAN", {"default": False}),
                "finished": ("BOOLEAN", {"default": True}),
                "save_path": ("STRING", {"default": "sxcp_accumulator", "multiline": False}),
                "filename_prefix": ("STRING", {"default": "sxcp_accum", "multiline": False}),
                "clear_after_save": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                "store_key_input": ("STRING", {"forceInput": True}),
            },
            "hidden": {
                "prompt": "PROMPT",
                "extra_pnginfo": "EXTRA_PNGINFO",
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = ("INT", "STRING", "STRING")
    RETURN_NAMES = ("count", "status", "saved_paths_json")
    FUNCTION = "preview"
    CATEGORY = "prompt_builder/loop"

    @classmethod
    def IS_CHANGED(cls, *args, **kwargs):
        return random.random()

    def _delete_from_inputs(self, key: str, delete_action: str, delete_entry_id: str, delete_index: int) -> int:
        action = delete_action if delete_action in ACCUMULATOR_PREVIEW_DELETE_ACTIONS else "none"
        if action == "none":
            return 0
        if action == "clear":
            return int(accumulator_delete_entries(key, clear=True).get("removed", 0))
        if action == "delete_entry_id":
            entry_id = str(delete_entry_id or "").strip()
            if not entry_id:
                return 0
            return int(accumulator_delete_entries(key, entry_id=entry_id).get("removed", 0))
        if action == "delete_index":
            index = int(delete_index)
            if index <= 0:
                return 0
            return int(accumulator_delete_entries(key, index=index).get("removed", 0))
        return 0

    def preview(
        self,
        store_key,
        preview_limit,
        delete_action,
        delete_entry_id,
        delete_index,
        save_batch,
        finished,
        save_path,
        filename_prefix,
        clear_after_save,
        store_key_input=None,
        prompt=None,
        extra_pnginfo=None,
        unique_id=None,
    ):
        key_source = str(store_key_input or "").strip() or store_key
        key = _accumulator_store_key(key_source, unique_id)
        store = _ACCUMULATOR_STORES.setdefault(key, [])
        removed = self._delete_from_inputs(key, delete_action, delete_entry_id, delete_index)
        images = [entry["image"] for entry in store if entry.get("image") is not None]

        saved_paths: list[str] = []
        save_status = ""
        if bool(save_batch) and bool(finished):
            saved_paths = _save_images_to_folder(store, save_path, filename_prefix, prompt, extra_pnginfo)
            save_status = f"; saved={len(saved_paths)}"
            if saved_paths and bool(clear_after_save):
                store.clear()
                images = []
                save_status += "; cleared_after_save"

        preview_images = _preview_image_results(store, preview_limit, prompt, extra_pnginfo)
        entries = _entry_infos(store)
        status = _accumulator_status(key, store)
        if removed:
            status += f"; removed={removed}"
        status += save_status
        saved_json = json.dumps(saved_paths, ensure_ascii=True)
        return {
            "ui": {
                "images": preview_images,
                "entries": entries,
                "status": [status],
                "saved_paths": saved_paths,
                "store_key": [key],
            },
            "result": (len(store), status, saved_json),
        }


class SxCPForLoopEnd:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "flow": ("FLOW_CONTROL", {"rawLink": True}),
                "collection_mode": (COLLECTION_MODES, {"default": "auto_batch"}),
                "skip_none": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "collected": (ANY_TYPE, {"rawLink": True}),
                "collect_value": (ANY_TYPE, {"rawLink": True}),
                **{
                    f"initial_value{index}": (ANY_TYPE, {"rawLink": True})
                    for index in range(1, MAX_CARRY_VALUES + 1)
                },
            },
            "hidden": {
                "dynprompt": "DYNPROMPT",
                "extra_pnginfo": "EXTRA_PNGINFO",
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = tuple([ANY_TYPE] + [ANY_TYPE] * MAX_CARRY_VALUES)
    RETURN_NAMES = tuple(["collected"] + [f"value{index}" for index in range(1, MAX_CARRY_VALUES + 1)])
    FUNCTION = "end"
    CATEGORY = "prompt_builder/loop"

    def end(self, flow, collection_mode, skip_none, dynprompt=None, **kwargs):
        _require_graph_builder()
        graph = GraphBuilder()
        loop_start = flow[0]
        start_node = dynprompt.get_node(loop_start)
        if start_node["class_type"] != "SxCPForLoopStart":
            raise ValueError("SxCP For Loop End must receive flow from SxCP For Loop Start.")
        total = start_node["inputs"]["total"]
        next_index = graph.node("SxCPLoopIntAdd", a=[loop_start, 1], b=1)
        condition = graph.node("SxCPLoopLessThanOrEqual", a=next_index.out(0), b=total)
        collection = kwargs.get("collected") or [loop_start, 2]
        collect_value = kwargs.get("collect_value")
        next_collection = graph.node(
            "SxCPLoopAppend",
            collection=collection,
            value=collect_value,
            mode=collection_mode,
            skip_none=skip_none,
        )
        next_values = {
            "initial_value0": next_index.out(0),
            "initial_value1": next_collection.out(0),
        }
        for carry_index in range(1, MAX_CARRY_VALUES + 1):
            next_values[f"initial_value{carry_index + 1}"] = kwargs.get(f"initial_value{carry_index}")
        while_close = graph.node("SxCPWhileLoopEnd", flow=flow, condition=condition.out(0), **next_values)
        return {
            "result": tuple(while_close.out(index) for index in range(1, MAX_LOOP_VALUES)),
            "expand": graph.finalize(),
        }


class SxCPLoopIntAdd:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "a": ("INT", {"default": 0}),
                "b": ("INT", {"default": 1}),
            }
        }

    RETURN_TYPES = ("INT",)
    RETURN_NAMES = ("int",)
    FUNCTION = "add"
    CATEGORY = "prompt_builder/loop/internal"

    def add(self, a, b):
        return (int(a) + int(b),)


class SxCPLoopLessThan:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "a": ("INT", {"default": 0}),
                "b": ("INT", {"default": 1}),
            }
        }

    RETURN_TYPES = ("BOOLEAN",)
    RETURN_NAMES = ("boolean",)
    FUNCTION = "compare"
    CATEGORY = "prompt_builder/loop/internal"

    def compare(self, a, b):
        return (int(a) < int(b),)


class SxCPLoopLessThanOrEqual:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "a": ("INT", {"default": 0}),
                "b": ("INT", {"default": 0}),
            }
        }

    RETURN_TYPES = ("BOOLEAN",)
    FUNCTION = "compare"
    CATEGORY = "prompt_builder/loop/internal"

    def compare(self, a, b):
        return (int(a) <= int(b),)


LOOP_NODE_CLASS_MAPPINGS = {
    "SxCPWhileLoopStart": SxCPWhileLoopStart,
    "SxCPWhileLoopEnd": SxCPWhileLoopEnd,
    "SxCPForLoopStart": SxCPForLoopStart,
    "SxCPForLoopEnd": SxCPForLoopEnd,
    "SxCPLoopAppend": SxCPLoopAppend,
    "SxCPIndexSwitch": SxCPIndexSwitch,
    "SxCPAccumulator": SxCPAccumulator,
    "SxCPAccumulatorPreview": SxCPAccumulatorPreview,
    "SxCPLoopIntAdd": SxCPLoopIntAdd,
    "SxCPLoopLessThan": SxCPLoopLessThan,
    "SxCPLoopLessThanOrEqual": SxCPLoopLessThanOrEqual,
}

LOOP_NODE_DISPLAY_NAME_MAPPINGS = {
    "SxCPWhileLoopStart": "SxCP While Loop Start",
    "SxCPWhileLoopEnd": "SxCP While Loop End",
    "SxCPForLoopStart": "SxCP For Loop Start",
    "SxCPForLoopEnd": "SxCP For Loop End",
    "SxCPLoopAppend": "SxCP Loop Append",
    "SxCPIndexSwitch": "SxCP Index Switch",
    "SxCPAccumulator": "SxCP Accumulator",
    "SxCPAccumulatorPreview": "SxCP Accumulator Preview",
    "SxCPLoopIntAdd": "SxCP Loop Int Add",
    "SxCPLoopLessThan": "SxCP Loop Less Than",
    "SxCPLoopLessThanOrEqual": "SxCP Loop Less Than Or Equal",
}