# modeling_olmoe.py - Extended version of OLMo for custom training

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, Dict, Optional, Tuple, Union, Any
# Import necessary components from transformers
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
# from transformers.modeling_layers import GradientCheckpointingLayer
from torch.utils.checkpoint import checkpoint
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
# from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import LossKwargs, is_torch_flex_attn_available, logging
from transformers import OlmoConfig

# Import flex attention components if available
if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask
    # from transformers.integrations.flex_attention import make_flex_block_causal_mask

from functools import partial
# Define GradientCheckpointingLayer since it's missing
class GradientCheckpointingLayer(nn.Module):
    gradient_checkpointing = False
    def __call__(self, *args, **kwargs):
        # Use checkpoint on `forward` when enabled
        if self.gradient_checkpointing and self.training:
            return checkpoint(self.forward, *args, **kwargs)
        return super().__call__(*args, **kwargs)

    def forward(self, *args, **kwargs):
        # To be implemented by subclasses
        raise NotImplementedError("Subclasses must implement `forward`")

import math
import functools

# Define our own dynamic_rope_update decorator and ROPE_INIT_FUNCTIONS
def dynamic_rope_update(func):
    """
    Decorator for updating RoPE embeddings when using RoPE scaling strategies.
    """
    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        # Only dynamic scaling needs to modify the positional encodings
        if self.rope_type == "dynamic" and hasattr(self, "original_max_seq_len"):
            if self.config.rope_scaling is None:
                return func(self, *args, **kwargs)
            # Extract max_position_embeddings from the actual model
            current_ctx_len = kwargs.get("position_ids", None)
            if current_ctx_len is not None:
                # position_ids shape is [batch_size, seq_len]
                current_ctx_len = current_ctx_len.shape[-1]

            # If we're inside a context window we've seen before, we don't have to change anything
            if current_ctx_len is not None and current_ctx_len <= self.max_seq_len_cached:
                return func(self, *args, **kwargs)

            current_ctx_len = self.config.max_position_embeddings if current_ctx_len is None else current_ctx_len
            scaling_factor = self.config.rope_scaling["factor"]
            
            self.max_seq_len_cached = min(
                int(self.original_max_seq_len * scaling_factor), 
                self.config.rope_scaling.get("max_position_embeddings", float("inf"))
            )
            
            # Reset the cached maximum position embeddings to the new value
            power = 0.0 if scaling_factor <= 1.0 else -0.5
            self.inv_freq = self.original_inv_freq * (scaling_factor ** power)
        
        return func(self, *args, **kwargs)
    
    return wrapper

def get_default_rope_init(config, device=None):
    """
    Default initialization for rotary position embeddings.
    """
    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
    inv_freq = 1.0 / (config.rope_theta ** (torch.arange(0, head_dim, 2).float().to(device) / head_dim))
    return inv_freq, None
    
def get_linear_rope_init(config, device=None):
    """
    Linear initialization for dynamic scaling rotary position embeddings.
    """
    base = get_default_rope_init(config, device)[0]
    scaling_factor = config.rope_scaling["factor"]
    
    # Scale the base frequencies
    return base / scaling_factor, scaling_factor

def get_dynamic_rope_init(config, device=None):
    """
    Dynamic initialization for dynamic scaling rotary position embeddings (NTK approach).
    """
    head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
    scaling_factor = config.rope_scaling["factor"]
    
    # Adjust the base frequencies by a power of the scaling factor
    power = 0.0 if scaling_factor <= 1.0 else -0.5
    inv_freq = 1.0 / (config.rope_theta ** 
                     (torch.arange(0, head_dim, 2).float().to(device) / head_dim))
    inv_freq = inv_freq * (scaling_factor ** power)
    
    return inv_freq, scaling_factor

# Define the dictionary of RoPE initialization functions
ROPE_INIT_FUNCTIONS = {
    "default": get_default_rope_init,
    "linear": get_linear_rope_init,
    "dynamic": get_dynamic_rope_init,
}

def can_return_tuple(inputs):
    # Copied logic from the original source
    return getattr(inputs, "return_tuple", False) if hasattr(inputs, "return_tuple") else False

# Start Modeling Code
logger = logging.get_logger(__name__)

# Core OLMo components (reused from original implementation)
class OlmoLayerNorm(nn.Module):
    """LayerNorm but with no learnable weight or bias."""

    def __init__(self, hidden_size: int) -> None:
        super().__init__()
        self.normalized_shape = (hidden_size,)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_dtype = hidden_states.dtype
        return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to(
            orig_dtype
        )


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

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


# Helper functions for rotary position embeddings
def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors."""
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    Repeats key/value states for grouped queries attention.
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    """Default eager implementation of multi-head attention"""
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


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

    def __init__(self, config: OlmoConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        if self.config.clip_qkv is not None:
            query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
            key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
            value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)

        query_states = query_states.view(hidden_shape).transpose(1, 2)
        key_states = key_states.view(hidden_shape).transpose(1, 2)
        value_states = value_states.view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class OlmoDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: OlmoConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx)

        self.mlp = OlmoMLP(config)
        self.input_layernorm = OlmoLayerNorm(config.hidden_size)
        self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


class OlmoRotaryEmbedding(nn.Module):
    def __init__(self, config: OlmoConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    @torch.no_grad()
    @dynamic_rope_update
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


# Base model classes
class OlmoEPreTrainedModel(PreTrainedModel):
    """Base class for OlmoE models with additional extensibility features"""
    
    config_class = OlmoConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["OlmoDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_attention_backend = True

    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_()


class OlmoEModel(OlmoEPreTrainedModel):
    """Extended OLMo base model with additional customization points"""
    
    def __init__(self, config: OlmoConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = OlmoLayerNorm(config.hidden_size)
        self.rotary_emb = OlmoRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def _update_causal_mask(
        self,
        attention_mask: Union[torch.Tensor, "BlockMask"],
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool = False,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and (attention_mask == 0.0).any():
                return attention_mask
            return None
        # if self.config._attn_implementation == "flex_attention":
        #     if isinstance(attention_mask, torch.Tensor):
        #         attention_mask = make_flex_block_causal_mask(attention_mask)
        #     return attention_mask

        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False

        if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype = input_tensor.dtype
        sequence_length = input_tensor.shape[1]
        if using_compilable_cache:
            target_length = past_key_values.get_max_cache_shape()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type in ["cuda", "xpu", "npu"]
            and not output_attentions
        ):
            min_dtype = torch.finfo(dtype).min
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):
        """Creates a causal 4D mask."""
        if attention_mask is not None and attention_mask.dim() == 4:
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask
        
    @can_return_tuple
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **flash_attn_kwargs,
    ) -> BaseModelOutputWithPast:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        if not isinstance(past_key_values, (type(None), Cache)):
            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_value=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **flash_attn_kwargs,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...


class OlmoEForCausalLM(OlmoEPreTrainedModel, GenerationMixin):
    """OLMo Causal Language Model with extensions for custom training"""
    
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = OlmoEModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model
        
    @can_return_tuple
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs,
    ) -> CausalLMOutputWithPast:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        # Get model outputs
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


# Example of custom model extensions you can create:

class OlmoEWithAdaptersMLP(OlmoMLP):
    """An extended MLP with adapters for parameter-efficient fine-tuning"""
    
    def __init__(self, config):
        super().__init__(config)
        # Example adapter dimensions (typically much smaller than original dims)
        adapter_size = getattr(config, "adapter_size", 64)
        
        # Add adapter layers
        self.down_adapter = nn.Sequential(
            nn.Linear(self.hidden_size, adapter_size, bias=False),
            nn.ReLU(),
            nn.Linear(adapter_size, self.hidden_size, bias=False),
        )
        
        # Initialize adapter layers with small weights
        self.down_adapter[0].weight.data.normal_(mean=0.0, std=0.01)
        self.down_adapter[2].weight.data.normal_(mean=0.0, std=0.01)
        
    def forward(self, x):
        # Original MLP computation
        mlp_output = super().forward(x)
        
        # Add adapter path with residual connection
        adapter_output = self.down_adapter(x)
        return mlp_output + adapter_output


class OlmoEWithAdaptersDecoderLayer(OlmoDecoderLayer):
    """OLMo decoder layer with adapters for efficient fine-tuning"""
    
    def __init__(self, config, layer_idx):
        # Replace the standard MLP with an adapter-based MLP
        super().__init__(config, layer_idx)
        self.mlp = OlmoEWithAdaptersMLP(config)


class OlmoEWithAdaptersModel(OlmoEModel):
    """OLMo model with adapter layers"""
    
    def __init__(self, config):
        super().__init__(config)
        # Replace all layers with adapter-based layers
        self.layers = nn.ModuleList(
            [OlmoEWithAdaptersDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        
        # Initialize weights
        self.post_init()


class OlmoEWithAdaptersForCausalLM(OlmoEForCausalLM):
    """OLMo for causal language modeling with adapters"""
    
    def __init__(self, config, adapters_config: Optional[Dict[str, Any]] = None):
        super().__init__(config)
        self.adapters_config = adapters_config

        # Initialize the model with adapters using the config
        self.model = OlmoEWithAdaptersModel(config)
        
        # Initialize weights
        self.post_init()
    
    def freeze_base_model(self):
        """Freeze all parameters except adapters for efficient fine-tuning"""
        for param in self.model.embed_tokens.parameters():
            param.requires_grad = False

        for layer in self.model.layers:
            for name, param in layer.self_attn.named_parameters():
                param.requires_grad = False

            for name, param in layer.mlp.named_parameters():
                if "down_adapter" not in name:
                    param.requires_grad = False

            for param in layer.input_layernorm.parameters():
                param.requires_grad = False
            for param in layer.post_attention_layernorm.parameters():
                param.requires_grad = False

        for param in self.model.norm.parameters():
            param.requires_grad = False

        # Uncomment to freeze LM head
        # for param in self.lm_head.parameters():
        #     param.requires_grad = False

    def get_trainable_parameters(self):
        """Return only trainable parameters for optimizer"""
        return [p for p in self.parameters() if p.requires_grad]

    @classmethod
    def from_config_and_adapters(
        cls,
        config,
        adapters_config: Optional[Dict[str, Any]] = None,
    ) -> "OlmoEWithAdaptersForCausalLM":
        """Optional factory method, if you want to keep this pattern."""
        return cls(config=config, adapters_config=adapters_config)