import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Tuple, Optional
from einops import rearrange
from .utils import hash_state_dict_keys
try:
    import flash_attn_interface
    FLASH_ATTN_3_AVAILABLE = True
except ModuleNotFoundError:
    FLASH_ATTN_3_AVAILABLE = False

try:
    import flash_attn
    FLASH_ATTN_2_AVAILABLE = True
except ModuleNotFoundError:
    FLASH_ATTN_2_AVAILABLE = False

try:
    from sageattention import sageattn
    SAGE_ATTN_AVAILABLE = True
except ModuleNotFoundError:
    SAGE_ATTN_AVAILABLE = False
    
from visualizer import get_local

@get_local('attn_weight_image')
# This is only for attention weight visualizing.
# Modified from https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
# Efficient implementation equivalent to the following:
def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0,
        is_causal=False, scale=None, enable_gqa=False) -> torch.Tensor:
    L, S = query.size(-2), key.size(-2)
    scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
    attn_bias = torch.zeros(L, S, dtype=query.dtype, device=query.device)
    if is_causal:
        assert attn_mask is None
        temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
        attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
        attn_bias.to(query.dtype)

    if attn_mask is not None:
        if attn_mask.dtype == torch.bool:
            attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
        else:
            attn_bias = attn_mask + attn_bias

    if enable_gqa:
        key = key.repeat_interleave(query.size(-3)//key.size(-3), -3)
        value = value.repeat_interleave(query.size(-3)//value.size(-3), -3)

    # assert L == S, f"query and key should have the same length, but got L:{L} and S:{S}."
    num_frames = 23  # TODO: get_num_frames()
    _, ST, dim = query.shape
    spatial_dim = ST / num_frames
    spatial_dim = int(spatial_dim)
    query_image = rearrange(
        query, "B (T S) C -> (B S) T C", T=num_frames, S=spatial_dim, C=dim
    )
    if L == S:
        key_image = rearrange(key, "B (T S) C -> (B S) T C", T=num_frames, S=spatial_dim, C=dim)
    else:
        key_image = key.repeat([spatial_dim, 1, 1])
    attn_weight_image = query_image @ key_image.transpose(-2, -1) * scale_factor
    attn_weight_image = torch.softmax(attn_weight_image, dim=-2)
    # attn_weight_image = torch.dropout(attn_weight_image, dropout_p, train=True)

    # attn_weight = query @ key.transpose(-2, -1) * scale_factor
    # attn_weight += attn_bias
    # attn_weight = torch.softmax(attn_weight, dim=-1)
    # attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
    return attn_weight_image # attn_weight @ value


def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False):
    if compatibility_mode:
        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
        x = F.scaled_dot_product_attention(q, k, v)
        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
    elif FLASH_ATTN_3_AVAILABLE:
        q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
        k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
        v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
        x = flash_attn_interface.flash_attn_func(q, k, v)
        if isinstance(x,tuple):
            x = x[0]
        x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
    elif FLASH_ATTN_2_AVAILABLE:
        q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
        k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
        v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
        x = flash_attn.flash_attn_func(q, k, v)
        x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
    elif SAGE_ATTN_AVAILABLE:
        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
        x = sageattn(q, k, v)
        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
    else:
        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
        x = F.scaled_dot_product_attention(q, k, v)
        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
    return x


def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
    return (x * (1 + scale) + shift)


def sinusoidal_embedding_1d(dim, position):
    sinusoid = torch.outer(position.type(torch.float64), torch.pow(
        10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    return x.to(position.dtype)


def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0):
    # 3d rope precompute
    f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta)
    h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
    w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
    return f_freqs_cis, h_freqs_cis, w_freqs_cis


def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
    # 1d rope precompute
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)
                   [: (dim // 2)].double() / dim))
    freqs = torch.outer(torch.arange(end, device=freqs.device), freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    return freqs_cis


def rope_apply(x, freqs, num_heads):
    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
        x.shape[0], x.shape[1], x.shape[2], -1, 2))
    x_out = torch.view_as_real(x_out * freqs).flatten(2)
    return x_out.to(x.dtype)


class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)

    def forward(self, x):
        dtype = x.dtype
        return self.norm(x.float()).to(dtype) * self.weight


class AttentionModule(nn.Module):
    def __init__(self, num_heads):
        super().__init__()
        self.num_heads = num_heads
        
    def forward(self, q, k, v):
        x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads)
        return x


class SelfAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads

        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = RMSNorm(dim, eps=eps)
        self.norm_k = RMSNorm(dim, eps=eps)
        
        self.attn = AttentionModule(self.num_heads)

    def forward(self, x, freqs):
        q = self.norm_q(self.q(x))
        k = self.norm_k(self.k(x))
        v = self.v(x)
        q = rope_apply(q, freqs, self.num_heads)
        k = rope_apply(k, freqs, self.num_heads)
        x = self.attn(q, k, v)

        # attn_weight_image = scaled_dot_product_attention(
        #     q.to(torch.float32), 
        #     k.to(torch.float32), 
        #     v.to(torch.float32)
        # )

        return self.o(x)


class CrossAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_image_input: bool = False):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads

        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = RMSNorm(dim, eps=eps)
        self.norm_k = RMSNorm(dim, eps=eps)
        self.has_image_input = has_image_input
        if has_image_input:
            self.k_img = nn.Linear(dim, dim)
            self.v_img = nn.Linear(dim, dim)
            self.norm_k_img = RMSNorm(dim, eps=eps)
            
        self.attn = AttentionModule(self.num_heads)

    def forward(self, x: torch.Tensor, y: torch.Tensor):
        if self.has_image_input:
            # img = y[:, :257]
            # ctx = y[:, 257:]
            img = y[:, :514]  # TODO: Fix this
            ctx = y[:, 514:]
        else:
            ctx = y
        q = self.norm_q(self.q(x))
        k = self.norm_k(self.k(ctx))
        v = self.v(ctx)
        x = self.attn(q, k, v)
        if self.has_image_input:
            k_img = self.norm_k_img(self.k_img(img))
            v_img = self.v_img(img)
            y = flash_attention(q, k_img, v_img, num_heads=self.num_heads)
            x = x + y
        return self.o(x)


class GateModule(nn.Module):
    def __init__(self,):
        super().__init__()

    def forward(self, x, gate, residual):
        return x + gate * residual

class DiTBlock(nn.Module):
    def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.ffn_dim = ffn_dim

        self.self_attn = SelfAttention(dim, num_heads, eps)
        self.cross_attn = CrossAttention(
            dim, num_heads, eps, has_image_input=has_image_input)
        self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
        self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
        self.norm3 = nn.LayerNorm(dim, eps=eps)
        self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
            approximate='tanh'), nn.Linear(ffn_dim, dim))
        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
        self.gate = GateModule()

    def forward(self, x, context, t_mod, freqs):
        # msa: multi-head self-attention  mlp: multi-layer perceptron
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1)
        input_x = modulate(self.norm1(x), shift_msa, scale_msa)
        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
        x = x + self.cross_attn(self.norm3(x), context)
        input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
        x = self.gate(x, gate_mlp, self.ffn(input_x))
        return x


class MLP(torch.nn.Module):
    def __init__(self, in_dim, out_dim, has_pos_emb=False):
        super().__init__()
        self.proj = torch.nn.Sequential(
            nn.LayerNorm(in_dim),
            nn.Linear(in_dim, in_dim),
            nn.GELU(),
            nn.Linear(in_dim, out_dim),
            nn.LayerNorm(out_dim)
        )
        self.has_pos_emb = has_pos_emb
        if has_pos_emb:
            self.emb_pos = torch.nn.Parameter(torch.zeros((1, 514, 1280)))

    def forward(self, x):
        if self.has_pos_emb:
            x = x + self.emb_pos.to(dtype=x.dtype, device=x.device)
        return self.proj(x)


class Head(nn.Module):
    def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int, int], eps: float):
        super().__init__()
        self.dim = dim
        self.patch_size = patch_size
        self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
        self.head = nn.Linear(dim, out_dim * math.prod(patch_size))
        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)

    def forward(self, x, t_mod):
        shift, scale = (self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(2, dim=1)
        x = (self.head(self.norm(x) * (1 + scale) + shift))
        return x


class WanModel(torch.nn.Module):
    def __init__(
        self,
        dim: int,
        in_dim: int,
        ffn_dim: int,
        out_dim: int,
        text_dim: int,
        freq_dim: int,
        eps: float,
        patch_size: Tuple[int, int, int],
        num_heads: int,
        num_layers: int,
        has_image_input: bool,
        has_image_pos_emb: bool = False,
    ):
        super().__init__()
        self.dim = dim
        self.freq_dim = freq_dim
        self.has_image_input = has_image_input
        self.patch_size = patch_size

        self.patch_embedding = nn.Conv3d(
            in_dim, dim, kernel_size=patch_size, stride=patch_size)
        self.text_embedding = nn.Sequential(
            nn.Linear(text_dim, dim),
            nn.GELU(approximate='tanh'),
            nn.Linear(dim, dim)
        )
        self.time_embedding = nn.Sequential(
            nn.Linear(freq_dim, dim),
            nn.SiLU(),
            nn.Linear(dim, dim)
        )
        self.time_projection = nn.Sequential(
            nn.SiLU(), nn.Linear(dim, dim * 6))
        self.blocks = nn.ModuleList([
            DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps)
            for _ in range(num_layers)
        ])
        self.head = Head(dim, out_dim, patch_size, eps)
        head_dim = dim // num_heads
        self.freqs = precompute_freqs_cis_3d(head_dim)

        if has_image_input:
            self.img_emb = MLP(1280, dim, has_pos_emb=has_image_pos_emb)  # clip_feature_dim = 1280
        self.has_image_pos_emb = has_image_pos_emb

    def patchify(self, x: torch.Tensor):
        x = self.patch_embedding(x)
        grid_size = x.shape[2:]
        x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
        return x, grid_size  # x, grid_size: (f, h, w)

    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
        return rearrange(
            x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
            f=grid_size[0], h=grid_size[1], w=grid_size[2], 
            x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2]
        )

    def forward(self,
                x: torch.Tensor,
                timestep: torch.Tensor,
                context: torch.Tensor,
                clip_feature: Optional[torch.Tensor] = None,
                y: Optional[torch.Tensor] = None,
                use_gradient_checkpointing: bool = False,
                use_gradient_checkpointing_offload: bool = False,
                **kwargs,
                ):
        t = self.time_embedding(
            sinusoidal_embedding_1d(self.freq_dim, timestep))
        t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
        context = self.text_embedding(context)
        
        if self.has_image_input:
            x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
            clip_embdding = self.img_emb(clip_feature)
            context = torch.cat([clip_embdding, context], dim=1)
        
        x, (f, h, w) = self.patchify(x)
        
        freqs = torch.cat([
            self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
            self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
            self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
        ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
        
        def create_custom_forward(module):
            def custom_forward(*inputs):
                return module(*inputs)
            return custom_forward

        for block in self.blocks:
            if self.training and use_gradient_checkpointing:
                if use_gradient_checkpointing_offload:
                    with torch.autograd.graph.save_on_cpu():
                        x = torch.utils.checkpoint.checkpoint(
                            create_custom_forward(block),
                            x, context, t_mod, freqs,
                            use_reentrant=False,
                        )
                else:
                    x = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        x, context, t_mod, freqs,
                        use_reentrant=False,
                    )
            else:
                x = block(x, context, t_mod, freqs)

        x = self.head(x, t)
        x = self.unpatchify(x, (f, h, w))
        return x

    @staticmethod
    def state_dict_converter():
        return WanModelStateDictConverter()
    
    
class WanModelStateDictConverter:
    def __init__(self):
        pass

    def from_diffusers(self, state_dict):
        rename_dict = {
            "blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight",
            "blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight",
            "blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias",
            "blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight",
            "blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias",
            "blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight",
            "blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias",
            "blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight",
            "blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias",
            "blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight",
            "blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight",
            "blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight",
            "blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias",
            "blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight",
            "blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias",
            "blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight",
            "blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias",
            "blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight",
            "blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias",
            "blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight",
            "blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias",
            "blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight",
            "blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias",
            "blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight",
            "blocks.0.norm2.bias": "blocks.0.norm3.bias",
            "blocks.0.norm2.weight": "blocks.0.norm3.weight",
            "blocks.0.scale_shift_table": "blocks.0.modulation",
            "condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias",
            "condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight",
            "condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias",
            "condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight",
            "condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias",
            "condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight",
            "condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias",
            "condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight",
            "condition_embedder.time_proj.bias": "time_projection.1.bias",
            "condition_embedder.time_proj.weight": "time_projection.1.weight",
            "patch_embedding.bias": "patch_embedding.bias",
            "patch_embedding.weight": "patch_embedding.weight",
            "scale_shift_table": "head.modulation",
            "proj_out.bias": "head.head.bias",
            "proj_out.weight": "head.head.weight",
        }
        state_dict_ = {}
        for name, param in state_dict.items():
            if name in rename_dict:
                state_dict_[rename_dict[name]] = param
            else:
                name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:])
                if name_ in rename_dict:
                    name_ = rename_dict[name_]
                    name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:])
                    state_dict_[name_] = param
        if hash_state_dict_keys(state_dict) == "cb104773c6c2cb6df4f9529ad5c60d0b":
            config = {
                "model_type": "t2v",
                "patch_size": (1, 2, 2),
                "text_len": 512,
                "in_dim": 16,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "window_size": (-1, -1),
                "qk_norm": True,
                "cross_attn_norm": True,
                "eps": 1e-6,
            }
        else:
            config = {}
        return state_dict_, config
    
    def from_civitai(self, state_dict):
        state_dict = {name: param for name, param in state_dict.items() if not name.startswith("vace")}
        if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814":
            config = {
                "has_image_input": False,
                "patch_size": [1, 2, 2],
                "in_dim": 16,
                "dim": 1536,
                "ffn_dim": 8960,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 12,
                "num_layers": 30,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70":
            config = {
                "has_image_input": False,
                "patch_size": [1, 2, 2],
                "in_dim": 16,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 36,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "6d6ccde6845b95ad9114ab993d917893":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 36,
                "dim": 1536,
                "ffn_dim": 8960,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 12,
                "num_layers": 30,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 36,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "349723183fc063b2bfc10bb2835cf677":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 48,
                "dim": 1536,
                "ffn_dim": 8960,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 12,
                "num_layers": 30,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "efa44cddf936c70abd0ea28b6cbe946c":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 48,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "eps": 1e-6
            }
        elif hash_state_dict_keys(state_dict) == "3ef3b1f8e1dab83d5b71fd7b617f859f":
            config = {
                "has_image_input": True,
                "patch_size": [1, 2, 2],
                "in_dim": 36,
                "dim": 5120,
                "ffn_dim": 13824,
                "freq_dim": 256,
                "text_dim": 4096,
                "out_dim": 16,
                "num_heads": 40,
                "num_layers": 40,
                "eps": 1e-6,
                "has_image_pos_emb": True
            }
        else:
            config = {}
        return state_dict, config
