import torch
from transformers import LlamaForCausalLM
from .configuration_pruned_llama import LlamaPrunedConfig
import torch.nn as nn


class LlamaPrunedForCausalLM(LlamaForCausalLM):
    config_class = LlamaPrunedConfig

    def __init__(self, config: LlamaPrunedConfig):
        super().__init__(config)

        for i in range(32):
            self.model.layers[i].self_attn.hidden_size = 2048
            self.model.layers[i].self_attn.q_proj = nn.Linear(4096, 1024, bias=False)
            self.model.layers[i].self_attn.k_proj = nn.Linear(4096, 256, bias=False)
            self.model.layers[i].self_attn.v_proj = nn.Linear(4096, 256, bias=False)
            self.model.layers[i].self_attn.o_proj = nn.Linear(1024, 4096, bias=False)
            self.model.layers[i].mlp.gate_proj = nn.Linear(4096, 2007, bias=False)
            self.model.layers[i].mlp.up_proj = nn.Linear(4096, 2007, bias=False)
            self.model.layers[i].mlp.down_proj = nn.Linear(2007, 4096, bias=False)

        for layer in self.model.layers:
            layer.self_attn.num_heads = layer.self_attn.q_proj.weight.data.shape[0] // layer.self_attn.head_dim
            layer.self_attn.num_key_value_heads = layer.self_attn.k_proj.weight.data.shape[
                                                      0] // layer.self_attn.head_dim