"""Augmentation registry and pipeline utilities."""

from __future__ import annotations

import os
import random
from functools import partial
from typing import Any, Callable, Mapping, MutableMapping, Sequence, Tuple

import numpy as np
from PIL import Image, ImageEnhance

_DEFAULT_RNG = np.random.default_rng(int.from_bytes(os.urandom(8), "little"))


def _make_rng() -> np.random.Generator:
    return _DEFAULT_RNG


try:
    Resampling = Image.Resampling  # Pillow>=9.1
except AttributeError:  # pragma: no cover

    class Resampling:
        NEAREST = Image.NEAREST
        BILINEAR = Image.BILINEAR
        BICUBIC = Image.BICUBIC
        LANCZOS = Image.LANCZOS


try:
    Transform = Image.Transform  # Pillow>=9.1
except AttributeError:  # pragma: no cover - compatibility

    class Transform:
        AFFINE = Image.AFFINE


from .image_ops import random_horizontal_flip
from .image_ops_cv import (
    add_gaussian_noise,
    adjust_brightness_contrast,
    gaussian_blur,
    motion_blur,
    random_brightness_contrast,
    random_crop_and_resize,
    random_gaussian_noise,
    random_rotation,
)

try:  # optional torchvision support
    import torchvision.transforms as T

    _TORCHVISION_AVAILABLE = True
except ImportError:  # pragma: no cover - optional dependency
    T = None
    _TORCHVISION_AVAILABLE = False

try:  # diffusion support
    from diffusers import StableDiffusionImg2ImgPipeline, StableDiffusionPipeline

    _DIFFUSERS_AVAILABLE = True
except ImportError:  # pragma: no cover
    StableDiffusionPipeline = None
    StableDiffusionImg2ImgPipeline = None
    _DIFFUSERS_AVAILABLE = False


AugmentationFn = Callable[[Any], Any]


class AugmentationRegistry:
    """Registry for augmentation factories/functions."""

    def __init__(self) -> None:
        self._store: MutableMapping[str, tuple[Callable[..., AugmentationFn], bool]] = {}

    def register(
        self,
        name: str,
        factory: Callable[..., AugmentationFn],
        *,
        override: bool = False,
        is_factory: bool = True,
    ) -> None:
        if not override and name in self._store:
            raise KeyError(f"Augmentation {name!r} already registered")
        self._store[name] = (factory, is_factory)

    def build(self, name: str, **kwargs) -> AugmentationFn:
        factory, is_factory = self._store[name]
        if is_factory:
            return factory(**kwargs)
        if kwargs:
            return partial(factory, **kwargs)  # type: ignore[arg-type]
        return factory  # type: ignore[return-value]

    def get(self, name: str) -> AugmentationFn:
        return self.build(name)

    def list_available(self) -> Sequence[str]:
        return sorted(self._store.keys())


AUGMENTATION_REGISTRY = AugmentationRegistry()


def register_augmentation(
    name: str,
    factory: Callable[..., AugmentationFn],
    *,
    override: bool = False,
    is_factory: bool = True,
) -> None:
    AUGMENTATION_REGISTRY.register(name, factory, override=override, is_factory=is_factory)


def list_augmentations() -> Sequence[str]:
    return AUGMENTATION_REGISTRY.list_available()


def _wrap_pil_function(func: Callable[..., Image.Image]) -> Callable[..., AugmentationFn]:
    def factory(**kwargs) -> AugmentationFn:
        def augmentation(data):
            is_pil = isinstance(data, Image.Image)
            img = data if is_pil else Image.fromarray(data)
            result = func(img, **kwargs)
            return result if is_pil else np.asarray(result)

        return augmentation

    return factory


def _wrap_numpy_function(func: Callable[..., np.ndarray]) -> Callable[..., AugmentationFn]:
    def factory(**kwargs) -> AugmentationFn:
        def augmentation(data):
            is_pil = isinstance(data, Image.Image)
            array = np.asarray(data) if is_pil else data
            result = func(array, **kwargs)
            return result if not is_pil else Image.fromarray(result)

        return augmentation

    return factory


def _wrap_torchvision_transform(
    transform_cls: Callable, *, to_pil: bool = True
) -> Callable[..., AugmentationFn]:
    del to_pil
    if not _TORCHVISION_AVAILABLE:
        raise ImportError("torchvision is required for this augmentation")

    def factory(**kwargs) -> AugmentationFn:
        transform = transform_cls(**kwargs)

        def augmentation(data):
            is_pil = isinstance(data, Image.Image)
            if not is_pil:
                img = Image.fromarray(data)
            else:
                img = data
            result = transform(img)
            if isinstance(result, Image.Image):
                return result if is_pil else np.asarray(result)
            # torchvision transforms may return tensors
            np_result = np.asarray(np.clip(np.array(result), 0, 255), dtype=np.uint8)
            return Image.fromarray(np_result) if is_pil else np_result

        return augmentation

    return factory


def random_cutout(image: np.ndarray, max_frac: float = 0.4, fill: int = 0) -> np.ndarray:
    """Apply random cutout mask to image."""

    rng = _make_rng()
    if image.ndim == 2:
        h, w = image.shape
        c = None
    else:
        h, w, c = image.shape
    cut_h = int(h * rng.uniform(0.05, max_frac))
    cut_w = int(w * rng.uniform(0.05, max_frac))
    top = int(rng.integers(0, max(1, h - cut_h + 1)))
    left = int(rng.integers(0, max(1, w - cut_w + 1)))
    out = image.copy()
    if c is None:
        out[top : top + cut_h, left : left + cut_w] = fill
    else:
        out[top : top + cut_h, left : left + cut_w, :] = fill
    return out


def mixup_batch(
    images: np.ndarray, labels: np.ndarray, alpha: float = 1.0
) -> Tuple[np.ndarray, np.ndarray, float]:
    """Perform mixup on batch of images and labels."""

    if alpha <= 0:
        return images, labels, 1.0
    rng = _make_rng()
    lam = float(rng.beta(alpha, alpha))
    indices = rng.permutation(images.shape[0])
    mixed_images = lam * images + (1 - lam) * images[indices]
    mixed_labels = lam * labels + (1 - lam) * labels[indices]
    return mixed_images, mixed_labels, lam


def cutmix_batch(
    images: np.ndarray, labels: np.ndarray, alpha: float = 1.0
) -> Tuple[np.ndarray, np.ndarray, float]:
    """Perform CutMix augmentation on batch."""

    if alpha <= 0:
        return images, labels, 1.0
    rng = _make_rng()
    lam = float(rng.beta(alpha, alpha))
    indices = rng.permutation(images.shape[0])
    shuffled_images = images[indices]
    shuffled_labels = labels[indices]

    if images.ndim != 4:
        raise ValueError("cutmix_batch expects images with shape (N, C, H, W) or (N, H, W, C)")
    if images.shape[1] <= 4:  # assume NCHW
        channels_first = True
        h, w = images.shape[2], images.shape[3]
    else:  # assume NHWC
        channels_first = False
        h, w = images.shape[1], images.shape[2]

    cut_ratio = np.sqrt(1.0 - lam)
    cut_h = int(h * cut_ratio)
    cut_w = int(w * cut_ratio)

    cy = int(rng.integers(0, h))
    cx = int(rng.integers(0, w))

    y1 = np.clip(cy - cut_h // 2, 0, h)
    y2 = np.clip(cy + cut_h // 2, 0, h)
    x1 = np.clip(cx - cut_w // 2, 0, w)
    x2 = np.clip(cx + cut_w // 2, 0, w)

    mixed_images = images.copy()
    if channels_first:
        mixed_images[:, :, y1:y2, x1:x2] = shuffled_images[:, :, y1:y2, x1:x2]
    else:
        mixed_images[:, y1:y2, x1:x2, :] = shuffled_images[:, y1:y2, x1:x2, :]

    lam_adjusted = 1 - ((y2 - y1) * (x2 - x1) / (h * w))
    mixed_labels = lam_adjusted * labels + (1 - lam_adjusted) * shuffled_labels
    return mixed_images, mixed_labels, lam_adjusted


def _enhance(image: Image.Image, op: str, magnitude: float) -> Image.Image:
    enhancer_map = {
        "brightness": ImageEnhance.Brightness,
        "contrast": ImageEnhance.Contrast,
        "color": ImageEnhance.Color,
        "sharpness": ImageEnhance.Sharpness,
    }
    enhancer_cls = enhancer_map[op]
    enhancer = enhancer_cls(image)
    return enhancer.enhance(magnitude)


def trivial_augment(image: Image.Image, magnitude: float = 0.5) -> Image.Image:
    """Single-operation TrivialAugment."""

    rng = _make_rng()
    ops = [
        lambda img, mag: img.rotate(int(rng.uniform(-30, 30)), resample=Image.BILINEAR),
        lambda img, mag: img.transform(
            img.size,
            Transform.AFFINE,
            (1, mag * rng.uniform(-0.3, 0.3), 0, 0, 1, 0),
            resample=Resampling.BILINEAR,
        ),
        lambda img, mag: _enhance(img, "brightness", 1 + (rng.uniform(-1, 1) * mag)),
        lambda img, mag: _enhance(img, "contrast", 1 + (rng.uniform(-1, 1) * mag)),
        lambda img, mag: _enhance(img, "color", 1 + (rng.uniform(-1, 1) * mag)),
        lambda img, mag: _enhance(img, "sharpness", 1 + (rng.uniform(-1, 1) * mag)),
    ]
    operation = random_horizontal_flip if rng.random() < 0.1 else random.choice(ops)  # NOSONAR - non-crypto RNG (data augmentation)
    if operation is random_horizontal_flip:
        return operation(image)
    return operation(image, magnitude)


def color_jitter(
    image: Image.Image | np.ndarray,
    brightness: float = 0.4,
    contrast: float = 0.4,
    saturation: float = 0.4,
    hue: float = 0.1,
) -> Image.Image | np.ndarray:
    if _TORCHVISION_AVAILABLE:
        transform = T.ColorJitter(
            brightness=brightness, contrast=contrast, saturation=saturation, hue=hue
        )
        is_pil = isinstance(image, Image.Image)
        img = image if is_pil else Image.fromarray(image)
        result = transform(img)
        return result if is_pil else np.asarray(result)

    is_pil = isinstance(image, Image.Image)
    img = image if is_pil else Image.fromarray(image)
    rng = _make_rng()

    def jitter(enhance_cls, magnitude):
        if magnitude <= 0:
            return img
        factor = 1 + rng.uniform(-magnitude, magnitude)
        enhancer = enhance_cls(img)
        return enhancer.enhance(max(0, factor))

    img = jitter(ImageEnhance.Brightness, brightness)
    img = jitter(ImageEnhance.Contrast, contrast)
    img = jitter(ImageEnhance.Color, saturation)
    if not is_pil:
        return np.asarray(img)
    return img


def _autoaugment_policy(name: str):
    if not _TORCHVISION_AVAILABLE:
        raise ImportError("torchvision is required for AutoAugment")
    name = name.lower()
    mapping = {
        "imagenet": T.AutoAugmentPolicy.IMAGENET,
        "cifar10": T.AutoAugmentPolicy.CIFAR10,
        "svhn": T.AutoAugmentPolicy.SVHN,
    }
    if name not in mapping:
        raise ValueError(f"Unsupported AutoAugment policy {name!r}")
    return mapping[name]


def auto_augment(
    image: Image.Image | np.ndarray, policy: str = "imagenet"
) -> Image.Image | np.ndarray:
    if not _TORCHVISION_AVAILABLE:
        raise ImportError("torchvision is required for AutoAugment")
    transform = T.AutoAugment(policy=_autoaugment_policy(policy))
    is_pil = isinstance(image, Image.Image)
    img = image if is_pil else Image.fromarray(image)
    result = transform(img)
    return result if is_pil else np.asarray(result)


def rand_augment(
    image: Image.Image | np.ndarray, num_ops: int = 2, magnitude: int = 9
) -> Image.Image | np.ndarray:
    if not _TORCHVISION_AVAILABLE:
        raise ImportError("torchvision is required for RandAugment")
    transform = T.RandAugment(num_ops=num_ops, magnitude=magnitude)
    is_pil = isinstance(image, Image.Image)
    img = image if is_pil else Image.fromarray(image)
    result = transform(img)
    return result if is_pil else np.asarray(result)


def resolve_augmentation(spec: Any, registry: AugmentationRegistry | None = None) -> AugmentationFn:
    registry = registry or AUGMENTATION_REGISTRY
    if callable(spec) and not isinstance(spec, str):
        return spec  # already augmentation
    if isinstance(spec, str):
        return registry.get(spec)
    if isinstance(spec, Mapping):
        name = spec.get("name")
        if name is None:
            raise ValueError("Augmentation spec dict must contain 'name'")
        kwargs = spec.get("kwargs", {})
        return registry.build(name, **kwargs)
    raise TypeError(f"Unsupported augmentation spec: {spec!r}")


def build_augmentation_pipeline(
    augmentations: Sequence[Any],
    *,
    return_type: str = "same",
    registry: AugmentationRegistry | None = None,
) -> AugmentationFn:
    registry = registry or AUGMENTATION_REGISTRY
    resolved = [resolve_augmentation(spec, registry=registry) for spec in augmentations]

    def pipeline(data):
        initial_pil = isinstance(data, Image.Image)
        current = data
        for aug in resolved:
            current = aug(current)

        if return_type == "pil":
            if isinstance(current, np.ndarray):
                current = Image.fromarray(current)
        elif return_type == "numpy":
            if isinstance(current, Image.Image):
                current = np.asarray(current)
        elif return_type == "same":
            if initial_pil and isinstance(current, np.ndarray):
                current = Image.fromarray(current)
            elif not initial_pil and isinstance(current, Image.Image):
                current = np.asarray(current)
        else:
            raise ValueError("return_type must be one of {'same','pil','numpy'}")
        return current

    return pipeline


def _register_default_augmentations() -> None:
    register_augmentation("random_horizontal_flip", _wrap_pil_function(random_horizontal_flip))
    register_augmentation("random_rotation", _wrap_numpy_function(random_rotation))
    register_augmentation("random_crop_resize", _wrap_numpy_function(random_crop_and_resize))
    register_augmentation("gaussian_noise", _wrap_numpy_function(add_gaussian_noise))
    register_augmentation("random_gaussian_noise", _wrap_numpy_function(random_gaussian_noise))
    register_augmentation("brightness_contrast", _wrap_numpy_function(adjust_brightness_contrast))
    register_augmentation(
        "random_brightness_contrast", _wrap_numpy_function(random_brightness_contrast)
    )
    register_augmentation("gaussian_blur", _wrap_numpy_function(gaussian_blur))
    register_augmentation("motion_blur", _wrap_numpy_function(motion_blur))
    register_augmentation("random_cutout", _wrap_numpy_function(random_cutout))
    register_augmentation("trivial_augment", _wrap_pil_function(trivial_augment))
    register_augmentation("color_jitter", _wrap_pil_function(color_jitter))
    if _TORCHVISION_AVAILABLE:
        register_augmentation("auto_augment", _wrap_pil_function(auto_augment), override=True)
        register_augmentation("rand_augment", _wrap_pil_function(rand_augment), override=True)


_register_default_augmentations()


class DiffusionAugmentor:
    """Wrapper around Stable Diffusion pipelines for data augmentation."""

    def __init__(
        self,
        model: str = "runwayml/stable-diffusion-v1-5",
        *,
        device: str | None = None,
        pipeline: str = "txt2img",
        dtype: str | None = "float16",
        **kwargs,
    ) -> None:
        if not _DIFFUSERS_AVAILABLE:
            raise ImportError("diffusers is required for DiffusionAugmentor")

        # Industrial safety default: do not implicitly download weights.
        # Callers must opt in by setting local_files_only=False explicitly.
        kwargs.setdefault("local_files_only", True)

        self.pipeline_type = pipeline
        self.device = device

        if pipeline == "txt2img":
            self.pipe = StableDiffusionPipeline.from_pretrained(
                model, torch_dtype=_resolve_torch_dtype(dtype), **kwargs
            )
        elif pipeline == "img2img":
            if StableDiffusionImg2ImgPipeline is None:
                raise ImportError("StableDiffusionImg2ImgPipeline not available; update diffusers")
            self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
                model, torch_dtype=_resolve_torch_dtype(dtype), **kwargs
            )
        else:
            raise ValueError("pipeline must be 'txt2img' or 'img2img'")

        if device is not None:
            self.pipe.to(device)
        self.pipe.safety_checker = None

    def generate(
        self,
        prompt: str,
        *,
        num_images: int = 1,
        guidance_scale: float = 7.5,
        height: int | None = None,
        width: int | None = None,
        **kwargs,
    ) -> list[Image.Image]:
        if self.pipeline_type != "txt2img":
            raise RuntimeError("generate is only available for txt2img pipeline")
        result = self.pipe(
            prompt=prompt,
            num_images_per_prompt=num_images,
            guidance_scale=guidance_scale,
            height=height,
            width=width,
            **kwargs,
        )
        return result.images

    def augment(
        self,
        image: Image.Image,
        prompt: str,
        *,
        strength: float = 0.75,
        guidance_scale: float = 7.5,
        num_images: int = 1,
        **kwargs,
    ) -> list[Image.Image]:
        if self.pipeline_type != "img2img":
            raise RuntimeError("augment requires img2img pipeline")
        result = self.pipe(
            prompt=prompt,
            image=image,
            strength=strength,
            guidance_scale=guidance_scale,
            num_images_per_prompt=num_images,
            **kwargs,
        )
        return result.images

    def __call__(self, *args, **kwargs):
        if self.pipeline_type == "txt2img":
            return self.generate(*args, **kwargs)
        return self.augment(*args, **kwargs)


def _resolve_torch_dtype(dtype: str | None):
    if dtype is None:
        return None
    import torch

    mapping = {
        "float16": torch.float16,
        "float32": torch.float32,
        "bfloat16": torch.bfloat16,
    }
    if isinstance(dtype, str):
        return mapping.get(dtype.lower(), torch.float16)
    return dtype


__all__ = [
    "AUGMENTATION_REGISTRY",
    "register_augmentation",
    "list_augmentations",
    "resolve_augmentation",
    "build_augmentation_pipeline",
    "mixup_batch",
    "cutmix_batch",
    "random_cutout",
    "trivial_augment",
    "color_jitter",
    "auto_augment",
    "rand_augment",
    "DiffusionAugmentor",
]
