from __future__ import annotations

import random
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 = {}


MAX_LOOP_VALUES = 20
MAX_CARRY_VALUES = MAX_LOOP_VALUES - 2
COLLECTION_MODES = ["auto_batch", "list", "image_batch", "latent_batch", "string_lines"]
ACCUMULATOR_ACTIONS = ["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_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 _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]


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 SxCPAccumulator:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "store_key": ("STRING", {"default": "", "multiline": False}),
                "action": (ACCUMULATOR_ACTIONS, {"default": "replace_by_entry_id"}),
                "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,),
            },
            "hidden": {
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = tuple([ANY_TYPE, "IMAGE", "IMAGE"] + ["IMAGE"] * ACCUMULATOR_IMAGE_GROUPS + ["INT", "STRING"])
    RETURN_NAMES = tuple(
        ["collection", "image_batch", "image_list"]
        + [f"image_batch_{index}" for index in range(1, ACCUMULATOR_IMAGE_GROUPS + 1)]
        + ["count", "status"]
    )
    OUTPUT_IS_LIST = tuple([False, False, True] + [False] * ACCUMULATOR_IMAGE_GROUPS + [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:
        key = str(store_key or "").strip()
        return key or f"node:{unique_id}"

    def _entry_id(self, entry_id: Any, image_index: int, image_count: int) -> str:
        if entry_id is None:
            return ""
        text = str(entry_id).strip()
        if not text:
            return ""
        if image_count <= 1:
            return text
        return f"{text}:{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, skip_empty: bool) -> 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, 0, 1), "image": None, "value": value}]
        image_count = len(images)
        return [
            {
                "id": self._entry_id(entry_id, index, image_count),
                "image": image_item,
                "value": self._value_for_image(value, index, image_count),
            }
            for index, image_item in enumerate(images)
        ]

    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:
            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:
        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"
        batch_state = "all images batched" if image_batch is not None else "mixed sizes or no image batch"
        return (
            f"key={key}; entries={len(store)}; image_entries={len(images)}; "
            f"formats={shape_text}; 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,
        unique_id=None,
    ):
        key = self._store_key(store_key, unique_id)
        action = action if action in ACCUMULATOR_ACTIONS else "replace_by_entry_id"
        store = _ACCUMULATOR_STORES.setdefault(key, [])

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

        if action not in ("clear", "read"):
            entries = self._entry_records(image, value, entry_id, bool(skip_empty))
            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])


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,
    "SxCPAccumulator": SxCPAccumulator,
    "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",
    "SxCPAccumulator": "SxCP Accumulator",
    "SxCPLoopIntAdd": "SxCP Loop Int Add",
    "SxCPLoopLessThan": "SxCP Loop Less Than",
    "SxCPLoopLessThanOrEqual": "SxCP Loop Less Than Or Equal",
}