# coding=utf-8
# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch RecurrentGemma model."""

import math
from typing import Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithNoAttention, CausalLMOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from ...utils.import_utils import is_torchdynamo_compiling
from .configuration_recurrent_gemma import RecurrentGemmaConfig


logger = logging.get_logger(__name__)
_MAX_SQRT_GRADIENT = 1000.0


# Copied from transformers.models.gemma.modeling_gemma.GemmaRMSNorm with Gemma->RecurrentGemma
class RecurrentGemmaRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.zeros(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float())
        # Llama does x.to(float16) * w whilst RecurrentGemma is (x * w).to(float16)
        # See https://github.com/huggingface/transformers/pull/29402
        output = output * (1.0 + self.weight.float())
        return output.type_as(x)

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


class RecurrentGemmaRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, dim, base=10000, device=None):
        super().__init__()
        self.dim = dim
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)

    @torch.no_grad()
    def forward(self, x, position_ids, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        self.inv_freq.to(x.device)
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 since bfloat16 loses precision on long contexts
        # See https://github.com/huggingface/transformers/pull/29285
        device_type = x.device.type
        device_type = device_type if device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


# Copied from transformers.models.llama.modeling_llama.rotate_half
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)


# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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


# Copied from transformers.models.llama.modeling_llama.repeat_kv
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)


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

    def __init__(self, config: RecurrentGemmaConfig):
        super().__init__()
        self.config = config
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
        self.partial_rotary_factor = config.partial_rotary_factor

        self.q_proj = nn.Linear(self.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_attention_heads * self.head_dim, self.hidden_size, bias=True)
        self.rotary_emb = RecurrentGemmaRotaryEmbedding(
            int(self.partial_rotary_factor * self.head_dim),
            base=config.rope_theta,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        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)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_attention_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb(value_states, position_ids)

        # Partial rotary embedding
        query_rot, query_pass = torch.chunk(query_states, int(1 / self.partial_rotary_factor), dim=-1)
        key_rot, key_pass = torch.chunk(key_states, int(1 / self.partial_rotary_factor), dim=-1)
        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
        query_states = torch.cat((query_rot, query_pass), dim=-1)
        key_states = torch.cat((key_rot, key_pass), dim=-1)

        if use_cache and hasattr(self, "key_states"):
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = self._update_cache(key_states, value_states, **cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        causal_mask = attention_mask
        if attention_mask is not None:
            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states.contiguous(),
            key_states.contiguous(),
            value_states.contiguous(),
            attn_mask=causal_mask,  # pretty much a must for sliding window backend!
            dropout_p=self.attention_dropout if self.training else 0.0,
            scale=self.head_dim**-0.5,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        return attn_output

    def _setup_cache(self, batch_size, device, dtype=None):
        if dtype is None and self.config.dtype is not None:
            dtype = self.config.dtype
        dtype = dtype if dtype is not None else torch.float32
        cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
        self.value_states = torch.zeros(cache_shape, dtype=dtype, device=device)
        self.key_states = torch.zeros(cache_shape, dtype=dtype, device=device)

    @torch.no_grad()
    def _update_cache(self, key_states, value_states, **cache_kwargs):
        """
        torch.compile compatible sliding window.
        Computes the `indices` based on `cache_position >= self.config.attention_window_size - 1`.
        The `to_shift` is only true once we are above attention_window_size. Thus with `attention_window_size==64`:

        indices = (slicing + to_shift[-1].int()-1) % self.config.attention_window_size
        tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
                19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
                37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
                55, 56, 57, 58, 59, 60, 61, 62, 63,  0])

        We overwrite the cache using these, then we always write at cache_position (clamped to `attention_window_size`)
        """
        cache_position = cache_kwargs.get("cache_position")
        if cache_position.shape[0] > self.config.attention_window_size:
            # int indexing -> device sync? in compile, use tensor
            k_out = key_states[:, :, -self.config.attention_window_size :, :]
            v_out = value_states[:, :, -self.config.attention_window_size :, :]
        else:
            slicing = torch.ones(
                self.config.attention_window_size, dtype=torch.long, device=value_states.device
            ).cumsum(0)
            cache_position = cache_position.clamp(0, self.config.attention_window_size - 1)
            to_shift = cache_position >= self.config.attention_window_size - 1
            indices = (slicing + to_shift[-1].int() - 1) % self.config.attention_window_size

            k_out, v_out = self.key_states.to(key_states.device), self.value_states.to(value_states.device)
            k_out = k_out[:, :, indices]
            v_out = v_out[:, :, indices]

            k_out[:, :, cache_position] = key_states.to(k_out.dtype)
            v_out[:, :, cache_position] = value_states.to(v_out.dtype)

        self.key_states, self.value_states = k_out, v_out
        return k_out, v_out


class SqrtBoundDerivative(torch.autograd.Function):
    """Computes a square root with a gradient clipped at `_MAX_SQRT_GRADIENT`."""

    @staticmethod
    def forward(ctx, x: torch.Tensor) -> torch.Tensor:
        """The forward pass, which is a normal `sqrt`."""
        ctx.save_for_backward(x)
        return torch.sqrt(x)

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
        """The backward pass, which clips the `sqrt` gradient."""
        (x,) = ctx.saved_tensors
        clipped_x_times_4 = torch.clip(4.0 * x, min=1 / (_MAX_SQRT_GRADIENT**2))
        return grad_output / torch.sqrt(clipped_x_times_4)


class RecurrentGemmaRglru(nn.Module):
    """A Real-Gated Linear Recurrent Unit (RG-LRU) layer."""

    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.block_width = config.lru_width // self.num_attention_heads

        self.recurrent_param = nn.Parameter(torch.empty([config.lru_width]))
        self.input_gate_weight = nn.Parameter(
            torch.empty([self.num_attention_heads, self.block_width, self.block_width])
        )
        self.input_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))

        self.recurrent_gate_weight = nn.Parameter(
            torch.empty([self.num_attention_heads, self.block_width, self.block_width])
        )
        self.recurrent_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))
        self.recurrent_states = None

    def forward(
        self,
        activations: torch.Tensor,
        position_ids: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        batch_size, seq_len, lru_width = activations.shape
        reset = position_ids[:, :, None] == 0

        reshape_act = activations.reshape(batch_size * seq_len, self.num_attention_heads, self.block_width)
        reshape_act = reshape_act.permute(1, 0, 2)

        res = torch.baddbmm(self.input_gate_bias[:, None, :], reshape_act, self.input_gate_weight)
        input_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))

        res = torch.baddbmm(self.recurrent_gate_bias[:, None, :], reshape_act, self.recurrent_gate_weight)
        recurrent_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))

        # Compute the parameter `A` of the recurrence.
        log_recurrent_gate = -8.0 * recurrent_gate * nn.functional.softplus(self.recurrent_param)
        recurrent_gate = torch.exp(log_recurrent_gate)
        a_square = torch.exp(2 * log_recurrent_gate)

        # Gate the input.
        gated_inputs = activations * input_gate

        # Apply gamma normalization to the input. We need to clip the derivatives of
        # `sqrt` in order to prevent NaNs during training in bfloat16. TODO a bit annoying
        multiplier = 1
        tracing = isinstance(activations, torch.fx.Proxy) or is_torchdynamo_compiling()
        if not torch.jit.is_tracing() and not tracing:
            multiplier = SqrtBoundDerivative.apply(1 - a_square)
        multiplier = reset + ~reset * multiplier
        normalized_x = gated_inputs * multiplier.type(activations.dtype)

        hidden_states, recurrent_states = self._rnn_scan(
            hidden_states=normalized_x,
            recurrent_gate=recurrent_gate,
            reset=reset,
            recurrent_states=self.recurrent_states,
        )
        self.recurrent_states = recurrent_states
        return hidden_states

    # TODO refactor
    def _rnn_scan(
        self,
        hidden_states: torch.Tensor,
        recurrent_gate: torch.Tensor,
        reset: torch.Tensor,
        recurrent_states: Union[torch.Tensor, None],
        acc_dtype: torch.dtype = torch.float32,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Runs the recurrence of a linear RNN.

        Args:
        hidden_states: The input sequence.
        recurrent_gate: The diagonal of the recurrence matrix `A`.
        reset: Indicator of document boundaries, e.g. when to reset the hidden state
            of the RNN.
        recurrent_states: The initial hidden state.
        acc_dtype: The data type for the accumulation.

        Returns:
        The output of the linear recurrence.
        """
        # Multiply `a` by the reset.
        recurrent_gate = recurrent_gate * ~reset

        if hidden_states.shape[1] == 1:
            # Using scan in sampling mode.
            if recurrent_states is None:  # same here, when decoding you always have cache
                return hidden_states, hidden_states[:, 0].type(acc_dtype)

            else:
                contextualized_states = recurrent_gate.type(acc_dtype) * recurrent_states[:, None].to(
                    recurrent_gate.device
                )
                contextualized_states += hidden_states.type(acc_dtype)
                return contextualized_states.type(hidden_states.dtype), contextualized_states[:, -1]

        else:
            # Using scan in linear mode.
            if recurrent_states is None:
                recurrent_states = torch.zeros(hidden_states[:, 0].shape, dtype=acc_dtype, device=hidden_states.device)

            contextualized_states = torch.zeros_like(hidden_states)
            for t in range(hidden_states.shape[1]):
                recurrent_states = recurrent_gate[:, t].type(acc_dtype) * recurrent_states.to(recurrent_gate.device)
                recurrent_states = recurrent_states + hidden_states[:, t].type(acc_dtype)
                contextualized_states[:, t] = recurrent_states.type(hidden_states.dtype)

        return contextualized_states, recurrent_states


class RecurrentGemmaRecurrentBlock(nn.Module):
    """Griffin and Hawk's recurrent block."""

    def __init__(self, config):
        super().__init__()
        self.lru_width = config.lru_width
        self.hidden_size = config.hidden_size
        self.linear_y = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
        self.linear_x = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
        self.linear_out = nn.Linear(in_features=config.lru_width, out_features=config.hidden_size)
        self.conv1d_width = config.conv1d_width
        self.conv_1d = nn.Conv1d(
            config.lru_width,
            config.lru_width,
            kernel_size=config.conv1d_width,
            groups=config.lru_width,
            padding=config.conv1d_width - 1,
        )
        self.rg_lru = RecurrentGemmaRglru(config)
        self.act_fn = ACT2FN[config.hidden_activation]

        self.conv1d_state = None

    def forward(
        self,
        input_states: torch.Tensor,
        position_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        cache_position: torch.Tensor,
        use_cache: bool = True,
    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
        _, seq_len, _ = input_states.shape

        y_branch = self.linear_y(input_states)
        y_branch = self.act_fn(y_branch)

        x_branch = self.linear_x(input_states)
        x_branch = x_branch.transpose(1, 2)

        if use_cache:
            if cache_position.shape[0] != 1:  # prefill
                self.conv1d_state = nn.functional.pad(x_branch, (self.conv1d_width - x_branch.shape[-1] - 1, 0))
                x_branch = self.conv_1d(x_branch)[..., :seq_len]
            else:  # decoding
                conv_state = torch.cat((self.conv1d_state, x_branch), -1)
                x_branch = torch.sum(conv_state * self.conv_1d.weight[:, 0, :], dim=-1) + self.conv_1d.bias
                x_branch = x_branch.unsqueeze(-1)
                self.conv1d_state = conv_state[:, :, 1:]
        else:
            x_branch = self.conv_1d(x_branch)[..., :seq_len]

        x_branch = self.rg_lru(x_branch.transpose(1, 2), position_ids)

        hidden_states = x_branch * y_branch
        hidden_states = self.linear_out(hidden_states)
        return hidden_states

    def _setup_cache(self, batch, device, dtype):
        # recurrent_states always computed in full precision
        self.rg_lru.recurrent_states = torch.zeros((batch, self.lru_width), device=device, dtype=torch.float32)
        self.conv1d_state = torch.zeros((batch, self.hidden_size, self.conv1d_width - 1), device=device, dtype=dtype)


TEMPORAL_BLOCK_CLASSES = {"recurrent": RecurrentGemmaRecurrentBlock, "attention": RecurrentGemmaSdpaAttention}


class RecurrentGemmaMlp(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size // 2
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=True)
        self.act_fn = ACT2FN[config.hidden_activation]

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


class RecurrentGemmaDecoderLayer(GradientCheckpointingLayer):
    """Griffin and Hawk's residual block."""

    def __init__(self, config, layer_idx):
        super().__init__()
        self.temporal_pre_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.temporal_block = TEMPORAL_BLOCK_CLASSES[config.layers_block_type[layer_idx]](config)
        self.channel_pre_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.mlp_block = RecurrentGemmaMlp(config)

    def forward(
        self,
        activations: torch.Tensor,
        position_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        cache_position: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
        raw_activations = activations
        inputs_normalized = self.temporal_pre_norm(raw_activations)  # RMSNorm introduces slight slight differences

        hidden_states = self.temporal_block(
            inputs_normalized, position_ids, attention_mask, cache_position=cache_position, use_cache=use_cache
        )

        residual = hidden_states + raw_activations

        hidden_states = self.channel_pre_norm(residual)
        hidden_states = self.mlp_block(hidden_states)

        hidden_states = hidden_states + residual
        return hidden_states


@auto_docstring
class RecurrentGemmaPreTrainedModel(PreTrainedModel):
    config: RecurrentGemmaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["RecurrentGemmaDecoderLayer"]
    _skip_keys_device_placement = ["cache"]
    _supports_flash_attn = False
    _supports_sdpa = False  # we can't compare with eager for now

    def _init_weights(self, module):
        std = math.sqrt(self.config.w_init_variance_scale / self.config.conv1d_width)
        if isinstance(module, nn.Conv1d):
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.bias)
        elif isinstance(module, RecurrentGemmaSdpaAttention):
            torch.nn.init.normal_(module.q_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            torch.nn.init.normal_(module.k_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            torch.nn.init.normal_(module.v_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))

            std = math.sqrt(self.config.final_w_init_variance_scale / self.config.hidden_size)
            torch.nn.init.normal_(module.o_proj.weight, mean=0.0, std=std)
        elif isinstance(module, RecurrentGemmaRecurrentBlock):
            torch.nn.init.zeros_(module.linear_x.bias)
            torch.nn.init.normal_(module.linear_x.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))

            torch.nn.init.zeros_(module.linear_y.bias)
            torch.nn.init.normal_(module.linear_y.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))

            std = math.sqrt(self.config.final_w_init_variance_scale / self.config.lru_width)
            torch.nn.init.normal_(module.linear_out.weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.linear_out.bias)
        elif isinstance(module, RecurrentGemmaRglru):
            std = math.sqrt(
                self.config.w_init_variance_scale / (self.config.lru_width // self.config.num_attention_heads)
            )
            torch.nn.init.normal_(module.input_gate_weight, mean=0.0, std=std)
            torch.nn.init.normal_(module.recurrent_gate_weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.input_gate_bias)
            torch.nn.init.zeros_(module.recurrent_gate_bias)

            module.recurrent_param.data.uniform_(0.9**2 + 1e-8, 0.999**2 + 1e-8)
            module.recurrent_param.data.log_().mul_(0.5)
            module.recurrent_param.data.neg_().exp_().sub_(1.0).log_()
        elif isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            if getattr(module, "bias", None) is not None:
                torch.nn.init.zeros_(module.bias)
        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_()

        # We initialize with 0s to be 1 centered as the RMSNorm here does (1 + weight)
        elif isinstance(module, RecurrentGemmaRMSNorm):
            module.weight.data.zero_()

    def _setup_cache(self, config, batch, device, dtype):
        layers = getattr(self, "model", self).layers
        for layer in layers:
            layer.temporal_block._setup_cache(batch, device, dtype)

    def reset_cache(self, batch, device, dtype):
        pass


@auto_docstring
class RecurrentGemmaModel(RecurrentGemmaPreTrainedModel):
    def __init__(self, config: RecurrentGemmaConfig):
        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(
            [RecurrentGemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.final_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False

        self.register_buffer(
            "normalizer", torch.tensor(self.config.hidden_size**0.5, dtype=torch.bfloat16), persistent=False
        )
        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithNoAttention]:
        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
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

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

        hidden_states = inputs_embeds

        if use_cache and inputs_embeds.shape[1] != 1:  # TODO let's maybe only call in the `generate`?
            self._setup_cache(self.config, hidden_states.shape[0], hidden_states.device, hidden_states.dtype)

        if cache_position is None:
            cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device)
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

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

        hidden_states = hidden_states * self.normalizer.type(hidden_states.dtype)

        all_hidden_states = () if output_hidden_states else None
        for i, residual_block in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            hidden_states = residual_block(hidden_states, position_ids, causal_mask, cache_position, use_cache)

        hidden_states = self.final_norm(hidden_states)

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

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)

        return BaseModelOutputWithNoAttention(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
        )

    # Ignore copy
    def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        target_length = max(self.config.attention_window_size, sequence_length)

        diagonal = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
        causal_mask = diagonal
        if sequence_length != 1:
            causal_mask = torch.triu(diagonal, diagonal=-1)

        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
        if attention_mask is not None:
            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
            if attention_mask.dim() == 2:
                # Crop the attention mask to the target length.
                attention_mask = attention_mask[:, -target_length:]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)

        if attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"]:
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask


# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->RECURRENTGEMMA,Llama->RecurrentGemma,llama->gemma
@auto_docstring
class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = RecurrentGemmaModel(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()

    @auto_docstring
    # Ignore copy
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        use_cache: Optional[bool] = None,
        **kwargs,
    ) -> Union[tuple, CausalLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

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

        >>> model = RecurrentGemmaForCausalLM.from_pretrained("google/recurrentgemma-2b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-2b")

        >>> prompt = "What is your favorite condiment?"
        >>> 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]
        "What is your favorite condiment?"
        ```"""
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = True
        outputs = self.model(
            input_ids=input_ids,
            position_ids=position_ids,
            cache_position=cache_position,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        # Soft-cap the logits TODO remove if always done.
        # if self.config.logits_soft_cap is not None:
        cap = self.config.logits_soft_cap
        logits = nn.functional.tanh(logits / cap) * cap

        loss = None
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            loss = self.loss_function(
                logits,
                labels,
                vocab_size=self.config.vocab_size,
                **kwargs,
            )

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )


__all__ = ["RecurrentGemmaForCausalLM", "RecurrentGemmaModel", "RecurrentGemmaPreTrainedModel"]