import torch
import torch.nn as nn
import numpy as np


approx_gelu = lambda: nn.GELU(approximate="tanh")


def get_layernorm(hidden_size: torch.Tensor, eps: float, affine: bool, use_kernel: bool):
    if use_kernel:
        try:
            from apex.normalization import FusedLayerNorm

            return FusedLayerNorm(hidden_size, elementwise_affine=affine, eps=eps)
        except ImportError:
            raise RuntimeError("FusedLayerNorm not available. Please install apex.")
    else:
        return nn.LayerNorm(hidden_size, eps, elementwise_affine=affine)
    

def t2i_modulate(x, shift, scale):
    return x * (1 + scale) + shift


# ===============================================
# Sine/Cosine Positional Embedding Functions
# ===============================================
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py


def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scale=1.0, base_size=None):
    """
    grid_size: int of the grid height and width
    return:
    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
    """
    if not isinstance(grid_size, tuple):
        grid_size = (grid_size, grid_size)

    grid_h = np.arange(grid_size[0], dtype=np.float32) / scale
    grid_w = np.arange(grid_size[1], dtype=np.float32) / scale
    if base_size is not None:
        grid_h *= base_size / grid_size[0]
        grid_w *= base_size / grid_size[1]
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    if cls_token and extra_tokens > 0:
        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
    return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed(embed_dim, length, scale=1.0):
    pos = np.arange(0, length)[..., None] / scale
    return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
    """
    embed_dim: output dimension for each position
    pos: a list of positions to be encoded: size (M,)
    out: (M, D)
    """
    assert embed_dim % 2 == 0
    omega = np.arange(embed_dim // 2, dtype=np.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = np.sin(out)  # (M, D/2)
    emb_cos = np.cos(out)  # (M, D/2)

    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
    return emb