from typing import Optional

import torch
import torch.nn.functional as F
from diffusers.models.attention import Attention
from diffusers.models.embeddings import apply_rotary_emb

from .fuser import (get_sequence_parallel_rank,
                    get_sequence_parallel_world_size, get_sp_group,
                    init_distributed_environment, initialize_model_parallel,
                    xFuserLongContextAttention)

class CogVideoXMultiGPUsAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
    query and key vectors, but does not include spatial normalization.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        text_seq_length = encoder_hidden_states.size(1)

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
            if not attn.is_cross_attention:
                key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)

        img_q = query[:, :, text_seq_length:].transpose(1, 2)
        txt_q = query[:, :, :text_seq_length].transpose(1, 2)
        img_k = key[:, :, text_seq_length:].transpose(1, 2)
        txt_k = key[:, :, :text_seq_length].transpose(1, 2)
        img_v = value[:, :, text_seq_length:].transpose(1, 2)
        txt_v = value[:, :, :text_seq_length].transpose(1, 2)

        hidden_states = xFuserLongContextAttention()(
            None,
            img_q, img_k, img_v, dropout_p=0.0, causal=False,
            joint_tensor_query=txt_q,
            joint_tensor_key=txt_k,
            joint_tensor_value=txt_v,
            joint_strategy='front',
        )

        hidden_states = hidden_states.flatten(2, 3)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        encoder_hidden_states, hidden_states = hidden_states.split(
            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
        )
        return hidden_states, encoder_hidden_states