import math

import torch
import torch.nn as nn
from torch.nn import functional as f
from transformers import PreTrainedModel
from transformers.activations import ACT2FN

from language_config import BigBrainLanguageConfig


def _make_casual_mask(size: int) -> torch.Tensor:
    return torch.tril(torch.ones(size, size))


class RootMeanSquareNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_eps = eps

    def forward(self, x: torch.Tensor):
        variance = x.pow(2).mean(-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_eps)
        return self.weight * x


class MultiLayerPerceptron(nn.Module):
    def __init__(self, config: BigBrainLanguageConfig):
        super().__init__()
        self.config = config
        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class RotaryPositionalEmbedding(nn.Module):
    def __init__(self, dim: int, base: int = 10000):
        super().__init__()
        self.dim = dim
        self.base = base
        self.cos = None
        self.sin = None

    def _build_cache(self, x: torch.Tensor):
        if self.cos is not None and x.shape[0] <= self.cos.shape[0]:
            return

        seq_len = x.shape[0]
        theta = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim)).to(x.device)
        seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device)
        idx_theta = torch.einsum('a,b->ab', seq_idx, theta)
        idx_theta = torch.cat([idx_theta, idx_theta], dim=1)

        self.cos = idx_theta.cos()[:, None, None, :]
        self.sin = idx_theta.sin()[:, None, None, :]

    def _neg_half(self, x: torch.Tensor):
        d_2 = self.dim // 2
        return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1)

    def forward(self, x: torch.Tensor):
        self._build_cache(x)
        x_rope, x_pass = x[..., :self.dim], x[..., self.dim:]
        neg_half_x = self._neg_half(x_rope)
        x_rope = (x_rope * self.cos[:x.shape[0]]) + (neg_half_x * self.sin[:x.shape[0]])
        return torch.cat((x_rope, x_pass), dim=-1)


class RotaryMultiHeadAttention(nn.Module):
    def __init__(self, config: BigBrainLanguageConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.hidden_size // config.num_attention_heads

        if (self.head_dim * config.num_attention_heads) != config.hidden_size:
            raise ValueError('num_embedd must be evenly divisible by num_heads')

        self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.rope_e = RotaryPositionalEmbedding(self.head_dim, config.rope_theta)

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

    def _reshape(self, tensor: torch.Tensor, batch_size: int, seq_len: int):
        return tensor.transpose(1, 2).contiguous().reshape(batch_size, seq_len, self.hidden_size)

    def forward(self, states: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
        batch_size, seq_len, _ = states.size()

        q_states = self.rope_e(self._shape(self.q_proj(states), batch_size, seq_len))
        k_states = self.rope_e(self._shape(self.k_proj(states), batch_size, seq_len))
        v_states = self._shape(self.v_proj(states), batch_size, seq_len)

        attn_weights = torch.matmul(q_states, k_states.transpose(2, 3)) / math.sqrt(self.head_dim)
        attn_weights = torch.clamp(attn_weights, min=-1024.0, max=1024.0)

        if mask is not None:
            attn_weights = attn_weights.masked_fill(mask == 0, float('-inf'))

        attn_weights = f.softmax(attn_weights, dim=-1)
        attn_outputs = torch.matmul(attn_weights, v_states)
        return self._reshape(attn_outputs, batch_size, seq_len)


class BigBrainDecoderLayer(nn.Module):
    def __init__(self, config: BigBrainLanguageConfig):
        super().__init__()
        self.config = config
        self.self_attn = RotaryMultiHeadAttention(config)
        self.feed_forward = MultiLayerPerceptron(config)
        self.input_norm = RootMeanSquareNorm(config.hidden_size, config.layer_norm_eps)
        self.attn_norm = RootMeanSquareNorm(config.hidden_size, config.layer_norm_eps)
        self.register_buffer('attn_mask', _make_casual_mask(config.max_position_embeddings))

    def forward(self, x: torch.Tensor):
        batch_size, seq_len, _ = x.size()
        mask = self.attn_mask[:seq_len, :seq_len]
        x = x + self.self_attn(self.input_norm(x), mask)
        x = x + self.feed_forward(self.attn_norm(x))
        return x


class BigBrainLanguageModel(PreTrainedModel):
    config_class = BigBrainLanguageConfig
    base_model_prefix = 'big-brain-lm'

    def __init__(self, config: BigBrainLanguageConfig):
        super().__init__(config)
        self.config = config
        self.tok_embed = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
        self.layers = nn.ModuleList([BigBrainDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = RootMeanSquareNorm(config.hidden_size, config.layer_norm_eps)
        self.linear = nn.Linear(config.hidden_size, config.vocab_size)
        self.post_init()

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def forward(self, input_ids: torch.Tensor, target_ids: torch.Tensor = None):
        hidden_states = self.tok_embed(input_ids)
        for decoder_layer in self.layers:
            hidden_states = decoder_layer(hidden_states)
        hidden_states = self.norm(hidden_states)
        hidden_states = self.linear(hidden_states)

        if target_ids is None:
            return hidden_states, None

        b, t, c = hidden_states.size()
        loss = f.cross_entropy(hidden_states.view(b * t, c), target_ids.view(b * t))
        return hidden_states, loss