import math
from typing import Optional, Tuple

import torch
import torch.nn.functional as F
import transformers
from torch import nn
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb


def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = 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]
    """
    from einops import rearrange
    try:  # v1
        from flash_attn.flash_attn_interface import \
            flash_attn_unpadded_qkvpacked_func
    except:  # v2
        from flash_attn.flash_attn_interface import \
            flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
    from flash_attn.bert_padding import pad_input, unpad_input

    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]
    assert past_key_value is None, 'past_key_value is not supported'

    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'

    # 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


# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(
        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
    # [bsz, seq_len]
    return attention_mask


def forward_2(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    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)
    )

    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
    )

    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'

    if past_key_value is not None:
        # reuse k, v, self_attention
        key_states = torch.cat([past_key_value[0], key_states], dim=2)
        value_states = torch.cat([past_key_value[1], value_states], dim=2)

    past_key_value = (key_states, value_states) if use_cache else None
    if self.training:
        attn_output = F.scaled_dot_product_attention(
            query_states, key_states, value_states, dropout_p=0.0, is_causal=True
        )
        attn_weights = None
    else:
        attn_weights = torch.matmul(
            query_states, key_states.transpose(2, 3)
        ) / math.sqrt(self.head_dim)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f'Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is'
                f' {attn_weights.size()}'
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(
                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
            )

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(
            attn_weights, dim=-1, dtype=torch.float32
        ).to(query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)

    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
        raise ValueError(
            f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
            f' {attn_output.size()}'
        )

    attn_output = attn_output.transpose(1, 2)
    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def replace_llama_attn_with_flash_attn():
    if hasattr(F, 'scaled_dot_product_attention'):
        transformers.models.llama.modeling_llama.LlamaAttention.forward = forward_2
    else:
        transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
            _prepare_decoder_attention_mask
        )
        transformers.models.llama.modeling_llama.LlamaAttention.forward = forward