import math
from typing import Union

from tinygrad import Tensor, nn, dtypes
from tinygrad.helpers import prod, argfix, Context, TRAINING
from tinygrad.nn.state import get_parameters
from extra.models.unet import UNetModel

# rejection sampling truncated randn
def rand_truncn(*shape, dtype=None, truncstds=2, **kwargs) -> Tensor:
  CNT=8
  x = Tensor.randn(*(*shape, CNT), dtype=dtype, **kwargs)
  ctr = Tensor.arange(CNT).reshape((1,) * len(x.shape[:-1]) + (CNT,)).expand(x.shape)
  take = (x.abs() <= truncstds).where(ctr, CNT).min(axis=-1, keepdim=True)  # set to 0 if no good samples
  return (ctr == take).where(x, 0).sum(axis=-1)

# https://github.com/keras-team/keras/blob/v2.15.0/keras/initializers/initializers.py#L1026-L1065
def he_normal(*shape, a: float = 0.00, **kwargs) -> Tensor:
  std = math.sqrt(2.0 / (1 + a ** 2)) / math.sqrt(prod(argfix(*shape)[1:])) / 0.87962566103423978
  return std * rand_truncn(*shape, **kwargs)

# Stable Diffusion v2 training uses default torch gelu, which doesn't use tanh approximation
def gelu_erf(x:Tensor) -> Tensor:
  return 0.5 * x * (1.0 + (x / 1.4142135623730951).erf())

class Conv2dHeNormal(nn.Conv2d):
  def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
    self.in_channels, self.out_channels = in_channels, out_channels  # for testing
    self.weight = he_normal(out_channels, in_channels//groups, *self.kernel_size, a=0.0, dtype=dtypes.float32)
    if bias: self.bias = self.bias.cast(dtypes.float32)
  def __call__(self, x: Tensor):
    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
                    padding=self.padding, stride=self.stride, dilation=self.dilation, groups=self.groups)

class Linear(nn.Linear):
  def __init__(self, in_features, out_features, bias=True):
    super().__init__(in_features, out_features, bias=bias)
    self.weight = Tensor.normal((out_features, in_features), mean=0.0, std=0.01, dtype=dtypes.float32)
    if bias: self.bias = Tensor.zeros(out_features, dtype=dtypes.float32)
  def __call__(self, x:Tensor):
    return x.linear(self.weight.cast(dtypes.default_float).transpose(), self.bias.cast(dtypes.default_float) if self.bias is not None else None)

class LinearBert(nn.Linear):
  def __init__(self, in_features, out_features, bias=True, std=0.02):
    self.weight = std * rand_truncn(out_features, in_features, dtype=dtypes.float32)
    self.bias = Tensor.zeros(out_features, dtype=dtypes.float32) if bias else None

  def __call__(self, x:Tensor):
    return x.cast(dtypes.default_float).linear(self.weight.cast(dtypes.default_float).transpose(), self.bias.cast(dtypes.default_float) if self.bias is not None else None)

class EmbeddingBert(nn.Embedding):
  def __init__(self, vocab_size:int, embed_size:int, std=0.02):
    self.vocab_sz, self.embed_sz = vocab_size, embed_size
    self.weight = std * rand_truncn(vocab_size, embed_size, dtype=dtypes.float32)

  def __call__(self, idx:Tensor) -> Tensor:
    if idx.numel() == 0: return Tensor.empty(idx.shape+(self.embed_sz,), dtype=self.weight.dtype, device=self.weight.device)
    arange_shp, weight_shp, big_shp = (1, 1, self.vocab_sz, 1), (1, 1, self.vocab_sz, self.embed_sz), idx.shape+(self.vocab_sz, self.embed_sz,)
    if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz).reshape(arange_shp)
    arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1,)).expand(big_shp), self.weight.cast(dtypes.default_float).reshape(weight_shp).expand(big_shp)
    return (arange == idx).where(vals, 0).sum(2, dtype=vals.dtype)

class LayerNormBert:
  def __init__(self, normalized_shape:Union[int, tuple[int, ...]], eps:float=1e-12, elementwise_affine:bool=True):
    self.normalized_shape = (normalized_shape,) if isinstance(normalized_shape, int) else tuple(normalized_shape)
    self.axis, self.eps, self.elementwise_affine = tuple(-1-i for i in range(len(self.normalized_shape))), eps, elementwise_affine
    self.weight, self.bias = (Tensor.ones(*self.normalized_shape, dtype=dtypes.float32), Tensor.zeros(*self.normalized_shape, dtype=dtypes.float32)) if elementwise_affine else (None, None)

  def __call__(self, x:Tensor):
    assert self.normalized_shape == x.shape[-len(self.normalized_shape):], f"last dimensions of {x.shape} must match {self.normalized_shape}"
    xn = x.cast(dtypes.float32).layernorm(eps=self.eps, axis=self.axis).cast(x.dtype)
    if not self.elementwise_affine: return xn
    return (xn * self.weight.cast(dtypes.default_float) + self.bias.cast(dtypes.default_float))

class FrozenBatchNorm2dRetinaNet(nn.BatchNorm2d):
  def __init__(self, sz:int, eps=1e-5, affine=True, track_running_stats=True, momentum=0.1):
    self.eps, self.track_running_stats, self.momentum = eps, track_running_stats, momentum

    self.weight = Tensor.ones(sz, dtype=dtypes.float32).is_param_(False) if affine else None
    self.bias = Tensor.zeros(sz, dtype=dtypes.float32).is_param_(False) if affine else None

    if track_running_stats: self.running_mean, self.running_var = Tensor.zeros(sz, dtype=dtypes.float32).is_param_(False), Tensor.ones(sz, dtype=dtypes.float32).is_param_(False)
    self.num_batches_tracked = Tensor.zeros(1, dtype=dtypes.long).is_param_(False)

  def __call__(self, x:Tensor) -> Tensor:
    batch_mean, batch_var = super().calc_stats(x.cast(dtypes.float32))
    if self.track_running_stats and TRAINING:
      self.running_mean.assign((1-self.momentum) * self.running_mean + self.momentum * batch_mean.detach().cast(self.running_mean.dtype))
      self.running_var.assign((1-self.momentum) * self.running_var + self.momentum * x.numel()/(x.numel()-x.shape[1]) * batch_var.detach().cast(self.running_var.dtype))
      self.num_batches_tracked += 1
    return x.cast(dtypes.float32).batchnorm(self.weight, self.bias, batch_mean, batch_var.add(self.eps).rsqrt()).cast(x.dtype)

class Conv2dNormalRetinaNet(nn.Conv2d):
  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
               bias:bool=True, prior_prob:float|None=None):
    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
    self.weight = Tensor.normal(*self.weight.shape, std=0.01, dtype=dtypes.float32)
    if bias:
      if prior_prob:
        prior_prob = Tensor(prior_prob, device=self.bias.device, dtype=dtypes.float32).expand(*self.bias.shape)
        self.bias = -(((1 - prior_prob) / prior_prob).log())
      else: self.bias = Tensor.zeros_like(self.bias, dtype=dtypes.float32)

  def __call__(self, x:Tensor) -> Tensor:
    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
                    groups=self.groups, stride=self.stride, padding=self.padding)

class Conv2dKaimingUniformRetinaNet(nn.Conv2d):
  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
               bias:bool=True):
    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
    self.weight = Tensor.kaiming_uniform(*self.weight.shape, a=1, dtype=dtypes.float32)
    if bias: self.bias = Tensor.zeros_like(self.bias, dtype=dtypes.float32)

  def __call__(self, x:Tensor) -> Tensor:
    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
                    groups=self.groups, stride=self.stride, padding=self.padding)

class Conv2dRetinaNet(nn.Conv2d):
  def __init__(self, in_channels:int, out_channels:int, kernel_size:int|tuple[int, ...],
               stride:int=1, padding:int|tuple[int, ...]|str=0, dilation:int=1, groups:int=1,
               bias:bool=True):
    super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
    scale = 1 / math.sqrt(in_channels * prod(self.kernel_size))
    self.weight = Tensor.uniform(out_channels, in_channels//groups, *self.kernel_size, low=-scale, high=scale, dtype=dtypes.float32)
    self.bias: Tensor|None = Tensor.uniform(out_channels, low=-scale, high=scale, dtype=dtypes.float32) if bias else None

  def __call__(self, x:Tensor) -> Tensor:
    return x.conv2d(self.weight.cast(dtypes.default_float), self.bias.cast(dtypes.default_float) if self.bias is not None else None,
                    groups=self.groups, stride=self.stride, dilation=self.dilation, padding=self.padding)

# copy torch AMP: isolate mixed precision to just the below autocast ops, instead of using dtypes.default_float which affects all new Tensors
class AutocastLinear(nn.Linear):
  cast_dtype=dtypes.bfloat16 # enable monkeypatching of the mixed precision dtype
  def __call__(self, x:Tensor) -> Tensor:
    dtype = type(self).cast_dtype
    return x.cast(dtype).linear(self.weight.cast(dtype).transpose(), self.bias.cast(dtype) if self.bias is not None else None)

class AutocastConv2d(nn.Conv2d):
  cast_dtype=dtypes.bfloat16
  def __call__(self, x:Tensor) -> Tensor:
    dtype = type(self).cast_dtype
    return x.cast(dtype).conv2d(self.weight.cast(dtype), self.bias.cast(dtype), self.groups, self.stride, self.dilation, self.padding)

# copy torch AMP: upcast to float32 before GroupNorm and LayerNorm
class AutocastGroupNorm(nn.GroupNorm):
  def __call__(self, x:Tensor) -> Tensor:
    return super().__call__(x.cast(dtypes.float32))

class AutocastLayerNorm(nn.LayerNorm):
  def __call__(self, x:Tensor) -> Tensor:
    return super().__call__(x.cast(dtypes.float32))

def zero_module(module):
  for p in get_parameters(module): p.assign(Tensor.zeros_like(p).contiguous())

# Stable Diffusion mlperf reference doesn't call scaled_dot_product_attention
# copy torch AMP: upcast to float32 before softmax on CUDA
def attn_f32_softmax(q:Tensor, k:Tensor, v:Tensor) -> Tensor:
  return (q.matmul(k.transpose(-2,-1), dtype=dtypes.float32) / math.sqrt(q.shape[-1])).softmax(-1).cast(q.dtype) @ v

def init_stable_diffusion(version:str, pretrained:str, devices:list[str]):
  from examples.stable_diffusion import StableDiffusion
  from tinygrad.nn.state import safe_load, safe_save, load_state_dict, get_state_dict
  from tempfile import TemporaryDirectory
  model = StableDiffusion(version=version, pretrained=pretrained)
  unet:UNetModel = model.model.diffusion_model

  # this prevents extra consumption of memory, enabling much larger BS
  Tensor.realize(*get_parameters(unet))
  with TemporaryDirectory(prefix="unet_init") as tmp:
    safe_save(get_state_dict(unet), init_fn:=f"{tmp}/init_model.safetensors")
    load_state_dict(unet, safe_load(init_fn))

  sqrt_alphas_cumprod = model.alphas_cumprod.sqrt().realize()
  sqrt_one_minus_alphas_cumprod = (1 - model.alphas_cumprod).sqrt().realize()

  if len(devices) > 1:
    to_move = [sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod]
    if version == "v2-mlperf-train": to_move += get_parameters(unet) + get_parameters(model.cond_stage_model)
    for p in to_move:
      p.to_(devices)
    with Context(BEAM=0):
      Tensor.realize(*to_move)

  return model, unet, sqrt_alphas_cumprod, sqrt_one_minus_alphas_cumprod