from abc import ABC, abstractmethod
from dataclasses import dataclass
from importlib import import_module
from functools import wraps
from typing import Any, Callable, TypeVar, TypedDict, get_type_hints, get_args

T = TypeVar("T")
F = TypeVar("F", bound=Callable)  # Type for functions


class Backend(ABC):
    """Abstract backend interface for quantization operations."""
    
    @abstractmethod
    def clip(self, x: T, min_val: float, max_val: float) -> T: ...
    
    @abstractmethod
    def abs(self, x: T) -> T: ...
    
    @abstractmethod
    def sign(self, x: T) -> T: ...
    
    @abstractmethod
    def log1p(self, x: T) -> T: ...
    
    @abstractmethod
    def tanh(self, x: T) -> T: ...
    
    @abstractmethod
    def atanh(self, x: T) -> T: ...
    
    @abstractmethod
    def sigmoid(self, x: T) -> T: ...
    
    @abstractmethod
    def logit(self, x: T) -> T: ...
    
    @abstractmethod
    def to_uint8(self, x: T) -> T: ...
    
    @abstractmethod
    def to_float32(self, x: T) -> T: ...
    
    @abstractmethod
    def normal_cdf(self, x: T) -> T: ...
    
    @abstractmethod
    def normal_ppf(self, x: T) -> T: ...


class NumpyBackend(Backend):
    def __init__(self, numpy: str = "numpy", scipy: str = "scipy"):
        self.np = import_module(numpy)
        self.sp = import_module(scipy)
    
    def clip(self, x: T, min_val: float, max_val: float) -> T:
        return self.np.clip(x, min_val, max_val)
    
    def abs(self, x: T) -> T:
        return self.np.abs(x)
    
    def sign(self, x: T) -> T:
        return self.np.sign(x)
    
    def log1p(self, x: T) -> T:
        return self.np.log1p(x)
    
    def tanh(self, x: T) -> T:
        return self.np.tanh(x)
    
    def atanh(self, x: T) -> T:
        return self.np.arctanh(x)
    
    def sigmoid(self, x: T) -> T:
        return self.sp.special.expit(x)
    
    def logit(self, x: T) -> T:
        return self.sp.special.logit(x)
    
    def to_uint8(self, x: T) -> T:
        return self.np.uint8(x)
    
    def to_float32(self, x: T) -> T:
        return self.np.float32(x)
    
    def normal_cdf(self, x: T) -> T:
        return self.sp.stats.norm.cdf(x).astype(x.dtype)  # scipy upcasts this op. 
    
    def normal_ppf(self, x: T) -> T:
        return self.sp.stats.norm.ppf(x).astype(x.dtype)  # scipy upcasts this op. 


class JaxBackend(NumpyBackend):
    def __init__(self):
        super().__init__(numpy="jax.numpy", scipy="jax.scipy")


class TorchBackend(Backend):
    def __init__(self):
        self.torch = import_module("torch")
        self.normal = import_module("torch.distributions").Normal(0, 1)

    def clip(self, x: T, min_val: float, max_val: float) -> T:
        return self.torch.clamp(x, min_val, max_val)

    def abs(self, x: T) -> T:
        return self.torch.abs(x)

    def sign(self, x: T) -> T:
        return self.torch.sign(x)

    def log1p(self, x: T) -> T:
        if isinstance(x, (int, float)):
            # torch.log1p doesn't accept non-tensors.
            x = self.torch.full((), x, dtype=self.torch.float32)
        return self.torch.log1p(x)

    def tanh(self, x: T) -> T:
        return self.torch.tanh(x)

    def atanh(self, x: T) -> T:
        return self.torch.atanh(x)

    def sigmoid(self, x: T) -> T:
        return self.torch.sigmoid(x)

    def logit(self, x: T) -> T:
        return self.torch.logit(x)

    def to_uint8(self, x: T) -> T:
        return x.to(self.torch.uint8)

    def to_float32(self, x: T) -> T:
        return x.to(self.torch.float32)

    def normal_cdf(self, x: T) -> T:
        return self.normal.cdf(x)

    def normal_ppf(self, x: T) -> T:
        return self.normal.icdf(x)


class ClassNameToBackendSingletons(TypedDict):
    ArrayImpl: JaxBackend | None
    DynamicJaxprTracer: JaxBackend | None
    ndarray: NumpyBackend | None
    Tensor: TorchBackend | None


_backend_singletons: ClassNameToBackendSingletons = {
    "ArrayImpl": None, 
    "DynamicJaxprTracer": None,
    "ndarray": None, 
    "Tensor": None,
}


def make_backend(classname: str) -> Backend:
    backend_hints = get_type_hints(ClassNameToBackendSingletons)
    backend_class = get_args(backend_hints[classname])[0]
    return backend_class()


def get_backend(x: Any) -> Backend:
    """
    Get the appropriate backend based on the type of x.
    Lazily imports the backend modules.
    """
    classname = x.__class__.__name__
    try:
        backend = _backend_singletons[classname]
    except KeyError as exc:
        raise NotImplementedError(f"backend for {type(x)} not implemented") from exc
    if backend is None:
        backend = make_backend(classname)
        _backend_singletons[classname] = backend
    return backend


def with_backend(func: F) -> F:
    """
    Decorator that extracts the backend from the first argument
    and passes it as a second argument to the wrapped function.
    """
    @wraps(func)
    def wrapper(self: Any, x: T) -> Any:
        backend = get_backend(x)
        return func(self, x, backend)
    
    return wrapper


@dataclass(frozen=True, kw_only=True)
class QuantizationType(ABC):
    scale: float = 1.0
    
    @abstractmethod
    def nonlinearity(self, x: T, backend: Backend) -> T: ...
    
    @abstractmethod
    def inv_nonlinearity(self, x: T, backend: Backend) -> T: ...

    @with_backend
    def quantize(self, x: T, backend: Backend) -> T:
        x = x * self.scale
        x = self.nonlinearity(x)         # [-1, 1)
        x = x * 128 + 128                # [0, 256)
        x = backend.to_uint8(x)          # [0, 255]
        return x

    @with_backend
    def dequantize(self, x: T, backend: Backend) -> T:        
        x = backend.to_float32(x)        # [0, 255]
        x = x + 0.5                      # [0.5, 255.5]
        x = x / 128 - 1                  # (-1, 1)
        x = self.inv_nonlinearity(x)
        x = x / self.scale
        return x


def descendents(cls: type) -> set[type]:
    """Gets all subclasses of the given class recursively."""
    return set(cls.__subclasses__()).union(*map(descendents, cls.__subclasses__()))


class Normal(QuantizationType):

    @with_backend
    def nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.normal_cdf(x) * 2 - 1
    
    @with_backend
    def inv_nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.normal_ppf((x + 1) / 2)


class Linear(QuantizationType):
    threshold: float = 3.5
    eps: float = 1e-6
    
    @with_backend
    def nonlinearity(self, x: T, backend: Backend) -> T:
        x = backend.clip(x, -self.threshold, self.threshold - self.eps)
        return x / self.threshold
    
    def inv_nonlinearity(self, x: T) -> T:
        return x * self.threshold


class MuLaw(Linear):
    mu: float = 255.
    
    @with_backend
    def nonlinearity(self, x: T, backend: Backend) -> T:
        x = super().nonlinearity(x)
        x_abs = backend.abs(x)
        sign_x = backend.sign(x)
        log_mu = backend.log1p(self.mu)
        log_term = backend.log1p(self.mu * x_abs) / log_mu
        return sign_x * log_term
    
    @with_backend
    def inv_nonlinearity(self, x: T, backend: Backend) -> T:
        x_abs = backend.abs(x)
        sign_x = backend.sign(x)
        numerator = (1 + self.mu) ** x_abs - 1
        x = sign_x * numerator / self.mu
        return super().inv_nonlinearity(x)


class Tanh(QuantizationType):

    @with_backend
    def nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.tanh(x)
    
    @with_backend
    def inv_nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.atanh(x)


class Sigmoid(QuantizationType):

    @with_backend
    def nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.sigmoid(x) * 2 - 1
    
    @with_backend
    def inv_nonlinearity(self, x: T, backend: Backend) -> T:
        return backend.logit((x + 1) / 2)


optimized_for_sdxl = Normal(scale=0.7)
quantization_types = tuple(sorted(list(descendents(QuantizationType)), key=lambda q: q.__name__))


__all__ = [
    "QuantizationType",
    "Normal",
    "Linear",
    "MuLaw",
    "Tanh",
    "Sigmoid",
    "optimized_for_sdxl",
    "quantization_types",
]