from typing import Optional, Union
import torch
from .layers_registry import norms
norms.register(name='layernorm', func=torch.nn.LayerNorm)

def _cast_if_autocast_enabled(tensor: torch.Tensor) -> torch.Tensor:
    if torch.is_autocast_enabled():
        if tensor.device.type == 'cuda':
            dtype = torch.get_autocast_gpu_dtype()
        elif tensor.device.type == 'cpu':
            dtype = torch.get_autocast_cpu_dtype()
        else:
            raise NotImplementedError()
        return tensor.to(dtype=dtype)
    return tensor

@norms.register_class('low_precision_layernorm')
class LPLayerNorm(torch.nn.LayerNorm):

    def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, elementwise_affine: bool=True, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None):
        super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        module_device = x.device
        downcast_x = _cast_if_autocast_enabled(x)
        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
        downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
        with torch.autocast(enabled=False, device_type=module_device.type):
            return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)

def rms_norm(x: torch.Tensor, weight: Optional[torch.Tensor]=None, eps: float=1e-05) -> torch.Tensor:
    output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
    if weight is not None:
        return output * weight
    return output

@norms.register_class('rmsnorm')
class RMSNorm(torch.nn.Module):

    def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None):
        super().__init__()
        self.eps = eps
        if weight:
            self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
        else:
            self.register_parameter('weight', None)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)

@norms.register_class('low_precision_rmsnorm')
class LPRMSNorm(RMSNorm):

    def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None):
        super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        downcast_x = _cast_if_autocast_enabled(x)
        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
        with torch.autocast(enabled=False, device_type=x.device.type):
            return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)

@norms.register_class('triton_rmsnorm')
class TritonRMSNorm(torch.nn.Module):

    def __init__(self, normalized_shape: Union[int, list[int], torch.Size], eps: float=1e-05, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None):
        super().__init__()
        self.eps = eps
        try:
            from flash_attn.ops.triton.layer_norm import rms_norm_fn
        except ImportError:
            raise ImportError('triton_rms_norm requires Flash Attention to be installed. ' + 'Please pip install flash-attn.')
        if not isinstance(normalized_shape, int):
            raise ValueError('TritonRMSNorm only supports 1D tensors')
        self.rms_norm_fn = rms_norm_fn
        self.weight = torch.nn.Parameter(torch.ones(normalized_shape, device=device, dtype=dtype))

    def forward(self, x: torch.Tensor):
        return self.rms_norm_fn(x, self.weight, None, residual=None, eps=self.eps, dropout_p=0.0, prenorm=False, residual_in_fp32=False)