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#           This file was automatically generated from src/transformers/models/qwen2/modular_qwen2.py.
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from typing import Callable, Optional, Union

import torch
from torch import nn
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.generation import GenerationMixin
from transformers.integrations import use_kernel_forward_from_hub
from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_layers import (
    GenericForQuestionAnswering,
    GenericForSequenceClassification,
    GenericForTokenClassification,
    GradientCheckpointingLayer,
)
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 TransformersKwargs, auto_docstring, can_return_tuple
from transformers.utils.generic import check_model_inputs
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config

from utils import topn_mean, masked_topk, apply_rotary_pos_emb_, last_token_relevance, masked_mini_filled_tensor


class Qwen2MLP(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):
        # Original FFN
        # down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        # return down_proj
        #
        # Revised for AdaRes
        relevance = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
        return self.down_proj(relevance), relevance


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.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    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:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    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: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, 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)

    if value is not None:
        value_states = repeat_kv(value, module.num_key_value_groups)
        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose(1, 2).contiguous()
    else: attn_output = None

    return attn_output, attn_weights


class Qwen2Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Qwen2Config, 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=True)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
        self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None

    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: Unpack[FlashAttentionKwargs],
    ) -> 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).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_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":
            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,
            sliding_window=self.sliding_window,  # main diff with Llama
            **kwargs,
        )

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

    def adares_attn(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        context_length: int,
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # print(f'context_length:{context_length},z_query_length:{z_query_length}')
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2).to(hidden_states.device)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2).to(hidden_states.device)
        # value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2).to(hidden_states.device)

        cos, sin = position_embeddings
        cos = cos.to(hidden_states.device)
        sin = sin.to(hidden_states.device)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        # if past_key_values 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
        
        attention_interface: Callable = eager_attention_forward

        _, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            None,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )
        relevance = attn_weights.sum(dim = 1)
        return relevance[:,context_length:,:context_length]

@use_kernel_forward_from_hub("RMSNorm")
class Qwen2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Qwen2RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class Qwen2DecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Qwen2Config, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)

        self.mlp = Qwen2MLP(config)
        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.attention_type = config.layer_types[layer_idx]

    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,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            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, beta = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states, beta

    def adares_layer(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:

        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = hidden_states.to(residual.device)
        attention_residual = hidden_states
        hidden_states = residual + hidden_states
        # Fully Connected
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, _ = self.mlp(hidden_states)
        # Trust contextual information
        hidden_states = hidden_states.to(residual.device)
        hidden_states = kwargs['lambda_fn'](attention_residual, hidden_states,
                        kwargs['alpha'], kwargs['beta']
        ) + residual
        return hidden_states

@auto_docstring
class Qwen2PreTrainedModel(PreTrainedModel):
    config: Qwen2Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Qwen2DecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": Qwen2DecoderLayer,
        "attentions": Qwen2Attention,
    }


class Qwen2RotaryEmbedding(nn.Module):
    def __init__(self, config: Qwen2Config, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
            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  # power user: used with advanced RoPE types (e.g. dynamic rope)
    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)


@auto_docstring
class Qwen2Model(Qwen2PreTrainedModel):
    def __init__(self, config: Qwen2Config):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.num_layers = config.num_hidden_layers
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
        self.gradient_checkpointing = False
        self.has_sliding_layers = "sliding_attention" in self.config.layer_types

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

    @check_model_inputs
    @auto_docstring
    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,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        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)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
            }
            # The sliding window alternating layers are not always activated depending on the config
            if self.has_sliding_layers:
                causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)

        hidden_states = inputs_embeds

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

        #begin processing context
        if kwargs['H'] is not None and kwargs['context'] is not None:
            # Context information processing
            context_hidden_states: torch.Tensor = self.embed_tokens(kwargs["context"]["input_ids"])
            context_attention_mask = kwargs["context"]["attention_mask"]
            context_length = context_hidden_states.size(1)
            context_past_key_values = DynamicCache()

            context_past_seen_tokens = context_past_key_values.get_seq_length() if context_past_key_values is not None else 0
            context_cache_position: torch.Tensor = torch.arange(
                context_past_seen_tokens, context_past_seen_tokens + context_hidden_states.shape[1], device=context_hidden_states.device
            )
            context_position_ids = context_cache_position.unsqueeze(0)
            context_mask_kwargs = {
                "config": self.config,
                "input_embeds": context_hidden_states,
                "attention_mask": context_attention_mask,
                "cache_position": context_cache_position,
                "past_key_values": context_past_key_values,
                "position_ids": context_position_ids,
            }
            # Create the masks
            context_causal_mask_mapping = {
                "full_attention": create_causal_mask(**context_mask_kwargs),
            }
            # The sliding window alternating layers are not always activated depending on the config
            if self.has_sliding_layers:
                context_causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**context_mask_kwargs)
            context_position_embeddings = self.rotary_emb(context_hidden_states, context_position_ids)

        if kwargs['H'] is not None and kwargs["z_query"] is not None:
            # Query information processing
            z_query_hidden_states: torch.Tensor = self.embed_tokens(kwargs["z_query"]["input_ids"])
            z_query_attention_mask = kwargs["z_query"]["attention_mask"]
            z_query_length = kwargs["z_query"]["attention_mask"].sum(1)

            z_query_past_key_values = DynamicCache()
            z_query_past_seen_tokens = z_query_past_key_values.get_seq_length() if z_query_past_key_values is not None else 0
            z_query_cache_position: torch.Tensor = torch.arange(
                z_query_past_seen_tokens, z_query_past_seen_tokens + z_query_hidden_states.shape[1], device=z_query_hidden_states.device
            )
            z_query_position_ids = z_query_cache_position.unsqueeze(0)
            z_query_mask_kwargs = {
                "config": self.config,
                "input_embeds": z_query_hidden_states,
                "attention_mask": z_query_attention_mask,
                "cache_position": z_query_cache_position,
                "past_key_values": z_query_past_key_values,
                "position_ids": z_query_position_ids,
            }
            # Create the masks
            z_query_causal_mask_mapping = {
                "full_attention": create_causal_mask(**z_query_mask_kwargs),
            }
            # The sliding window alternating layers are not always activated depending on the config
            if self.has_sliding_layers:
                z_query_causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**z_query_mask_kwargs)
            z_query_position_embeddings = self.rotary_emb(z_query_hidden_states, z_query_position_ids)

        if kwargs['H'] is not None and kwargs["context"] is not None and kwargs["z_query"] is not None:
            assert context_hidden_states is not None, 'No context feature is produced'
            assert z_query_hidden_states is not None, 'No query feature is produced'
            cat_feats_attention_mask = torch.cat((context_attention_mask, z_query_attention_mask), dim=1)

        for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            # 1. Alpha Estimation
            if kwargs['H'] is not None and kwargs['context'] is not None and kwargs['z_query'] is not None:
                cat_feats = torch.cat((context_hidden_states, z_query_hidden_states), dim=1)
                cat_feats_cache_position: torch.Tensor = torch.arange(
                    0, cat_feats.shape[1], device=cat_feats.device
                )
                cat_feats_position_ids = cat_feats_cache_position.unsqueeze(0)
                cat_feats_mask_kwargs = {
                    "config": self.config,
                    "input_embeds": cat_feats,
                    "attention_mask": cat_feats_attention_mask,
                    "cache_position": cat_feats_cache_position,
                    "past_key_values": None,
                    "position_ids": cat_feats_position_ids,
                }
                cat_feats_causal_mask_mapping = {
                    "full_attention": create_causal_mask(**cat_feats_mask_kwargs),
                }
                if self.has_sliding_layers:
                    cat_feats_causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**cat_feats_mask_kwargs)
                cat_feats_position_embeddings = self.rotary_emb(cat_feats, cat_feats_position_ids)
                alpha = decoder_layer.self_attn.adares_attn(hidden_states=cat_feats,
                                                            attention_mask=masked_mini_filled_tensor(cat_feats_causal_mask_mapping[decoder_layer.attention_type], cat_feats.dtype),
                                                            context_length=context_length,
                                                            position_embeddings=cat_feats_position_embeddings,)
            # 2. Processing the context information forward pass
            if kwargs['H'] is not None and kwargs['context'] is not None:
                    context_hidden_states, _ = decoder_layer(
                    context_hidden_states,
                    attention_mask=context_causal_mask_mapping[decoder_layer.attention_type],
                    position_ids=context_position_ids,
                    past_key_values=context_past_key_values,
                    cache_position=context_cache_position,
                    position_embeddings=context_position_embeddings,
                    **kwargs,
                )
            # 3. Processing the query information forward pass, and obtain the beta
            if kwargs['H'] is not None and kwargs['z_query'] is not None:
                    z_query_hidden_states, beta = decoder_layer(
                    z_query_hidden_states,
                    attention_mask=z_query_causal_mask_mapping[decoder_layer.attention_type],
                    position_ids=z_query_position_ids,
                    past_key_values=z_query_past_key_values,
                    cache_position=z_query_cache_position,
                    position_embeddings=z_query_position_embeddings,
                    **kwargs,
                )
            # 4. AdaRes layer or not   
            if kwargs['H'] is not None and layer_idx in kwargs['H']\
               and kwargs['context'] is not None and kwargs['z_query'] is not None:
                    alpha = masked_topk(alpha, kwargs["top_n"], mask=~kwargs["z_query"]["attention_mask"].bool(), mx_reduction='mean', topk_reduction='mean')
                    beta = masked_topk(beta, kwargs["top_n"], mask=~kwargs["z_query"]["attention_mask"].bool(), mx_reduction='mean', topk_reduction='mean')
                    hidden_states = decoder_layer.adares_layer(
                        hidden_states,
                        attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                        position_ids=position_ids,
                        past_key_values=past_key_values,
                        cache_position=cache_position,
                        position_embeddings=position_embeddings,
                        alpha = alpha,
                        beta = beta,
                        **kwargs,
                    )
            else:
                hidden_states, _ = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                    position_ids=position_ids,
                    past_key_values=past_key_values,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                    **kwargs,
                )
        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


@auto_docstring
class Qwen2ForCausalLM(Qwen2PreTrainedModel, GenerationMixin):
    _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 = Qwen2Model(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 set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @can_return_tuple
    @auto_docstring
    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,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutputWithPast:
        r"""
        Example:

        ```python
        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM

        >>> model = Qwen2ForCausalLM.from_pretrained("meta-qwen2/Qwen2-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-qwen2/Qwen2-2-7b-hf")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        outputs: BaseModelOutputWithPast = 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,
            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,
        )


class Qwen2ForSequenceClassification(GenericForSequenceClassification, Qwen2PreTrainedModel):
    pass


class Qwen2ForTokenClassification(GenericForTokenClassification, Qwen2PreTrainedModel):
    pass


class Qwen2ForQuestionAnswering(GenericForQuestionAnswering, Qwen2PreTrainedModel):
    base_model_prefix = "transformer"  # For BC, where `transformer` was used instead of `model`


__all__ = [
    "Qwen2PreTrainedModel",
    "Qwen2Model",
    "Qwen2ForCausalLM",
    "Qwen2ForSequenceClassification",
    "Qwen2ForTokenClassification",
    "Qwen2ForQuestionAnswering",
]
