from functools import partial

import torch

from lightx2v.common.transformer_infer.transformer_infer import BaseTransformerInfer
from lightx2v.utils.envs import *

from .utils import apply_rotary_emb, apply_rotary_emb_chunk, compute_freqs, compute_freqs_dist


class WanTransformerInfer(BaseTransformerInfer):
    def __init__(self, config):
        self.config = config
        self.task = config.task
        self.attention_type = config.get("attention_type", "flash_attn2")
        self.blocks_num = config.num_layers
        self.phases_num = 4
        self.num_heads = config.num_heads
        self.head_dim = config.dim // config.num_heads
        self.window_size = config.get("window_size", (-1, -1))
        self.parallel_attention = None
        if config.get("rotary_chunk", False):
            chunk_size = config.get("rotary_chunk_size", 100)
            self.apply_rotary_emb_func = partial(apply_rotary_emb_chunk, chunk_size=chunk_size)
        else:
            self.apply_rotary_emb_func = apply_rotary_emb
        self.clean_cuda_cache = self.config.get("clean_cuda_cache", False)
        self.mask_map = None
        self.infer_dtype = GET_DTYPE()
        self.sensitive_layer_dtype = GET_SENSITIVE_DTYPE()

        if self.config["seq_parallel"]:
            self.seq_p_group = self.config.get("device_mesh").get_group(mesh_dim="seq_p")
        else:
            self.seq_p_group = None
        self.infer_func = self.infer_without_offload

    def _calculate_q_k_len(self, q, k_lens):
        q_lens = torch.tensor([q.size(0)], dtype=torch.int32, device=q.device)
        cu_seqlens_q = torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32)
        cu_seqlens_k = torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32)
        return cu_seqlens_q, cu_seqlens_k

    def compute_freqs(self, q, grid_sizes, freqs):
        if self.config["seq_parallel"]:
            freqs_i = compute_freqs_dist(q.size(0), q.size(2) // 2, grid_sizes, freqs, self.seq_p_group)
        else:
            freqs_i = compute_freqs(q.size(2) // 2, grid_sizes, freqs)
        return freqs_i

    def infer(self, weights, pre_infer_out):
        x = self.infer_main_blocks(weights, pre_infer_out)
        return self.infer_non_blocks(weights, x, pre_infer_out.embed)

    def infer_main_blocks(self, weights, pre_infer_out):
        x = self.infer_func(weights, pre_infer_out.x, pre_infer_out)
        return x

    def infer_non_blocks(self, weights, x, e):
        if e.dim() == 2:
            modulation = weights.head_modulation.tensor  # 1, 2, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
        elif e.dim() == 3:  # For Diffustion forcing
            modulation = weights.head_modulation.tensor.unsqueeze(2)  # 1, 2, seq, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
            e = [ei.squeeze(1) for ei in e]

        x = weights.norm.apply(x)

        if self.sensitive_layer_dtype != self.infer_dtype:
            x = x.to(self.sensitive_layer_dtype)
        x.mul_(1 + e[1].squeeze()).add_(e[0].squeeze())
        if self.sensitive_layer_dtype != self.infer_dtype:
            x = x.to(self.infer_dtype)

        x = weights.head.apply(x)

        if self.clean_cuda_cache:
            del e
            torch.cuda.empty_cache()
        return x

    def infer_without_offload(self, weights, x, pre_infer_out):
        for block_idx in range(self.blocks_num):
            self.block_idx = block_idx
            x = self.infer_block(weights.blocks[block_idx], x, pre_infer_out)
        return x

    def infer_block(self, weights, x, pre_infer_out):
        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = self.infer_modulation(
            weights.compute_phases[0],
            pre_infer_out.embed0,
        )
        y_out = self.infer_self_attn(
            weights.compute_phases[1],
            pre_infer_out.grid_sizes,
            x,
            pre_infer_out.seq_lens,
            pre_infer_out.freqs,
            shift_msa,
            scale_msa,
        )
        x, attn_out = self.infer_cross_attn(weights.compute_phases[2], x, pre_infer_out.context, y_out, gate_msa)
        y = self.infer_ffn(weights.compute_phases[3], x, attn_out, c_shift_msa, c_scale_msa)
        x = self.post_process(x, y, c_gate_msa, pre_infer_out)
        return x

    def infer_modulation(self, weights, embed0):
        if embed0.dim() == 3 and embed0.shape[2] == 1:
            modulation = weights.modulation.tensor.unsqueeze(2)
            embed0 = (modulation + embed0).chunk(6, dim=1)
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = [ei.squeeze(1) for ei in embed0]
        else:
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (weights.modulation.tensor + embed0).chunk(6, dim=1)

        if self.clean_cuda_cache:
            del embed0
            torch.cuda.empty_cache()

        return shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa

    def infer_self_attn(self, weights, grid_sizes, x, seq_lens, freqs, shift_msa, scale_msa):
        if hasattr(weights, "smooth_norm1_weight"):
            norm1_weight = (1 + scale_msa.squeeze()) * weights.smooth_norm1_weight.tensor
            norm1_bias = shift_msa.squeeze() * weights.smooth_norm1_bias.tensor
        else:
            norm1_weight = 1 + scale_msa.squeeze()
            norm1_bias = shift_msa.squeeze()

        norm1_out = weights.norm1.apply(x)

        if self.sensitive_layer_dtype != self.infer_dtype:
            norm1_out = norm1_out.to(self.sensitive_layer_dtype)

        norm1_out.mul_(norm1_weight).add_(norm1_bias)

        if self.sensitive_layer_dtype != self.infer_dtype:
            norm1_out = norm1_out.to(self.infer_dtype)

        s, n, d = *norm1_out.shape[:1], self.num_heads, self.head_dim

        q = weights.self_attn_norm_q.apply(weights.self_attn_q.apply(norm1_out)).view(s, n, d)
        k = weights.self_attn_norm_k.apply(weights.self_attn_k.apply(norm1_out)).view(s, n, d)
        v = weights.self_attn_v.apply(norm1_out).view(s, n, d)

        freqs_i = self.compute_freqs(q, grid_sizes, freqs)

        q = self.apply_rotary_emb_func(q, freqs_i)
        k = self.apply_rotary_emb_func(k, freqs_i)

        k_lens = torch.empty_like(seq_lens).fill_(freqs_i.size(0))
        cu_seqlens_q, cu_seqlens_k = self._calculate_q_k_len(q, k_lens=k_lens)

        if self.clean_cuda_cache:
            del freqs_i, norm1_out, norm1_weight, norm1_bias
            torch.cuda.empty_cache()

        if self.config["seq_parallel"]:
            attn_out = weights.self_attn_1_parallel.apply(
                q=q,
                k=k,
                v=v,
                img_qkv_len=q.shape[0],
                cu_seqlens_qkv=cu_seqlens_q,
                attention_module=weights.self_attn_1,
                seq_p_group=self.seq_p_group,
            )
        else:
            attn_out = weights.self_attn_1.apply(
                q=q,
                k=k,
                v=v,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_k,
                max_seqlen_q=q.size(0),
                max_seqlen_kv=k.size(0),
                model_cls=self.config["model_cls"],
                mask_map=self.mask_map,
            )

        y = weights.self_attn_o.apply(attn_out)

        if self.clean_cuda_cache:
            del q, k, v, attn_out
            torch.cuda.empty_cache()

        return y

    def infer_cross_attn(self, weights, x, context, y_out, gate_msa):
        if self.sensitive_layer_dtype != self.infer_dtype:
            x = x.to(self.sensitive_layer_dtype) + y_out.to(self.sensitive_layer_dtype) * gate_msa.squeeze()
        else:
            x.add_(y_out * gate_msa.squeeze())

        norm3_out = weights.norm3.apply(x)
        if self.task in ["i2v", "flf2v"] and self.config.get("use_image_encoder", True):
            context_img = context[:257]
            context = context[257:]
        else:
            context_img = None

        if self.sensitive_layer_dtype != self.infer_dtype:
            context = context.to(self.infer_dtype)
            if self.task in ["i2v", "flf2v"] and self.config.get("use_image_encoder", True):
                context_img = context_img.to(self.infer_dtype)

        n, d = self.num_heads, self.head_dim

        q = weights.cross_attn_norm_q.apply(weights.cross_attn_q.apply(norm3_out)).view(-1, n, d)
        k = weights.cross_attn_norm_k.apply(weights.cross_attn_k.apply(context)).view(-1, n, d)
        v = weights.cross_attn_v.apply(context).view(-1, n, d)
        cu_seqlens_q, cu_seqlens_k = self._calculate_q_k_len(
            q,
            k_lens=torch.tensor([k.size(0)], dtype=torch.int32, device=k.device),
        )
        attn_out = weights.cross_attn_1.apply(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_k,
            max_seqlen_q=q.size(0),
            max_seqlen_kv=k.size(0),
            model_cls=self.config["model_cls"],
        )

        if self.task in ["i2v", "flf2v"] and self.config.get("use_image_encoder", True) and context_img is not None:
            k_img = weights.cross_attn_norm_k_img.apply(weights.cross_attn_k_img.apply(context_img)).view(-1, n, d)
            v_img = weights.cross_attn_v_img.apply(context_img).view(-1, n, d)

            cu_seqlens_q, cu_seqlens_k = self._calculate_q_k_len(
                q,
                k_lens=torch.tensor([k_img.size(0)], dtype=torch.int32, device=k.device),
            )
            img_attn_out = weights.cross_attn_2.apply(
                q=q,
                k=k_img,
                v=v_img,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_k,
                max_seqlen_q=q.size(0),
                max_seqlen_kv=k_img.size(0),
                model_cls=self.config["model_cls"],
            )
            attn_out.add_(img_attn_out)

            if self.clean_cuda_cache:
                del k_img, v_img, img_attn_out
                torch.cuda.empty_cache()

        attn_out = weights.cross_attn_o.apply(attn_out)

        if self.clean_cuda_cache:
            del q, k, v, norm3_out, context, context_img
            torch.cuda.empty_cache()
        return x, attn_out

    def infer_ffn(self, weights, x, attn_out, c_shift_msa, c_scale_msa):
        x.add_(attn_out)

        if self.clean_cuda_cache:
            del attn_out
            torch.cuda.empty_cache()

        if hasattr(weights, "smooth_norm2_weight"):
            norm2_weight = (1 + c_scale_msa.squeeze()) * weights.smooth_norm2_weight.tensor
            norm2_bias = c_shift_msa.squeeze() * weights.smooth_norm2_bias.tensor
        else:
            norm2_weight = 1 + c_scale_msa.squeeze()
            norm2_bias = c_shift_msa.squeeze()

        norm2_out = weights.norm2.apply(x)
        if self.sensitive_layer_dtype != self.infer_dtype:
            norm2_out = norm2_out.to(self.sensitive_layer_dtype)
        norm2_out.mul_(norm2_weight).add_(norm2_bias)
        if self.sensitive_layer_dtype != self.infer_dtype:
            norm2_out = norm2_out.to(self.infer_dtype)

        y = weights.ffn_0.apply(norm2_out)
        if self.clean_cuda_cache:
            del norm2_out, x, norm2_weight, norm2_bias
            torch.cuda.empty_cache()
        y = torch.nn.functional.gelu(y, approximate="tanh")
        if self.clean_cuda_cache:
            torch.cuda.empty_cache()
        y = weights.ffn_2.apply(y)

        return y

    def post_process(self, x, y, c_gate_msa, pre_infer_out):
        if self.sensitive_layer_dtype != self.infer_dtype:
            x = x.to(self.sensitive_layer_dtype) + y.to(self.sensitive_layer_dtype) * c_gate_msa.squeeze()
        else:
            x.add_(y * c_gate_msa.squeeze())

        if self.clean_cuda_cache:
            del y, c_gate_msa
            torch.cuda.empty_cache()
        return x
