from typing import List, Optional, Tuple

import torch
from torch import nn

import transformers
from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRotaryEmbedding, apply_rotary_pos_emb

from einops import rearrange

from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input

class LlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        config: LlamaConfig,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        num_heads = config.num_attention_heads
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = self.hidden_size // num_heads

        if (self.head_dim * num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {num_heads}).")
        self.q_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.k_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.v_proj = nn.Linear(
            hidden_size,
            num_heads * self.head_dim,
            bias=False,
        )
        self.o_proj = nn.Linear(
            num_heads * self.head_dim,
            hidden_size,
            bias=False,
        )
        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads,
                           self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
               Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel
        
        attention_mask: [bsz, q_len]
        """
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states).view(
            bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(
            bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(
            bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        # [bsz, q_len, nh, hd]
        # [bsz, nh, q_len, hd]

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states,
                                                        key_states,
                                                        cos,
                                                        sin,
                                                        position_ids)
        # [bsz, nh, t, hd]
        assert not output_attentions, "output_attentions is not supported"
        assert not use_cache, "use_cache is not supported"
        assert past_key_value is None, "past_key_value is not supported"

        # Flash attention codes from
        # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py

        # transform the data into the format required by flash attention
        qkv = torch.stack([query_states, key_states, value_states], dim=2) # [bsz, nh, 3, q_len, hd]
        qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
        # We have disabled _prepare_decoder_attention_mask in LlamaModel
        # the attention_mask should be the same as the key_padding_mask
        key_padding_mask = attention_mask


        if key_padding_mask is None:
            qkv = rearrange(qkv, 'b s ... -> (b s) ...')
            max_s = q_len
            cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
                                    device=qkv.device)
            output = flash_attn_unpadded_qkvpacked_func(
                qkv, cu_q_lens, max_s, 0.0,
                softmax_scale=None, causal=True
            )
            output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
        else:
            nheads = qkv.shape[-2]
            x = rearrange(qkv, 'b s three h d -> b s (three h d)')
            x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
            x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
            output_unpad = flash_attn_unpadded_qkvpacked_func(
                x_unpad, cu_q_lens, max_s, 0.0,
                softmax_scale=None, causal=True
            )
            output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
                                        indices, bsz, q_len),
                            'b s (h d) -> b s h d', h=nheads)
        return self.o_proj(rearrange(output,
                                     'b s h d -> b s (h d)')), None, None


# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
                                    inputs_embeds, past_key_values_length):
    # [bsz, seq_len]
    return attention_mask


def replace_llama_attn_with_flash_attn():
    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
    transformers.models.llama.modeling_llama.LlamaAttention = LlamaAttention