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# coding=utf-8
# Copyright 2025 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Callable
from typing import Optional, Union

import torch
import torch.nn as nn

from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPastAndCrossAttentions,
    Seq2SeqLMOutput,
    Seq2SeqModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.generic import OutputRecorder, check_model_inputs
from .configuration_t5gemma import T5GemmaConfig, T5GemmaModuleConfig


logger = logging.get_logger(__name__)


class T5GemmaRMSNorm(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 T5Gemma 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 T5GemmaMLP(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_activation]
        self.dropout = nn.Dropout(config.dropout_rate)

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


class T5GemmaRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: T5GemmaConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = inv_freq

    @staticmethod
    def compute_default_rope_parameters(
        config: Optional[T5GemmaConfig] = None,
        device: Optional["torch.device"] = None,
        seq_len: Optional[int] = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @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)


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],
    dropout: float = 0.0,
    scaling: Optional[float] = None,
    softcap: Optional[float] = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:
    if scaling is None:
        scaling = module.head_dim**-0.5

    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 softcap is not None:
        attn_weights = attn_weights / softcap
        attn_weights = torch.tanh(attn_weights)
        attn_weights = attn_weights * softcap
    if attention_mask is not None:  # no matter the length, we just slice it
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    # upcast attention to fp32
    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 T5GemmaSelfAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: T5GemmaModuleConfig, layer_idx: int):
        super().__init__()
        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
        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 = config.query_pre_attn_scalar**-0.5
        self.attention_dropout = self.config.attention_dropout
        # Required by flash attention: encoder selfattention is non-causal
        self.is_causal = config.is_decoder

        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
        )
        self.attn_logit_softcapping = self.config.attn_logit_softcapping
        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_values: 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_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
        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=self.attention_dropout if self.training else 0.0,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            softcap=self.attn_logit_softcapping,
            **kwargs,
        )

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


class T5GemmaCrossAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: T5GemmaModuleConfig, 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 = config.query_pre_attn_scalar**-0.5
        self.attention_dropout = self.config.attention_dropout
        self.is_causal = False

        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.cross_attention_hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.cross_attention_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
        )
        self.attn_logit_softcapping = self.config.attn_logit_softcapping

        if config.cross_attention_hidden_size is None:
            raise ValueError("Cross-attention needs cross_attention_hidden_size to be specified.")

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        encoder_hidden_states: Optional[torch.Tensor],
        past_key_values: Optional[Cache] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        if encoder_hidden_states is None:
            raise ValueError("Encoder hidden state is required for cross attention.")

        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)

        if past_key_values is not None:
            is_updated = past_key_values.is_updated.get(self.layer_idx)
            curr_past_key_values = past_key_values.cross_attention_cache

        if past_key_values is None or not is_updated:
            encoder_input_shape = encoder_hidden_states.shape[:-1]
            encoder_hidden_shape = (*encoder_input_shape, -1, self.head_dim)
            key_states = self.k_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)
            value_states = self.v_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)

            if past_key_values is not None:
                key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx)
                past_key_values.is_updated[self.layer_idx] = True
        else:
            key_states = curr_past_key_values.layers[self.layer_idx].keys
            value_states = curr_past_key_values.layers[self.layer_idx].values

        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=self.attention_dropout if self.training else 0.0,
            scaling=self.scaling,
            sliding_window=None,
            softcap=self.attn_logit_softcapping,
            **kwargs,
        )

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


class T5GemmaEncoderLayer(GradientCheckpointingLayer):
    """Encoder sub-layer."""

    def __init__(self, config, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.config = config
        self.layer_idx = layer_idx
        self.attention_type = config.layer_types[layer_idx]

        self.self_attn = T5GemmaSelfAttention(
            config=config,
            layer_idx=layer_idx,
        )
        self.pre_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.mlp = T5GemmaMLP(config)
        self.pre_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> tuple[torch.FloatTensor,]:
        residual = hidden_states
        hidden_states = self.pre_self_attn_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=None,
            **kwargs,
        )
        hidden_states = self.post_self_attn_layernorm(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)
        return hidden_states


class T5GemmaDecoderLayer(T5GemmaEncoderLayer):
    """Decoder sub-layer: an extra cross-attention layer."""

    def __init__(self, config, layer_idx: int):
        super().__init__(config, layer_idx)
        self.cross_attn = T5GemmaCrossAttention(config=config, layer_idx=layer_idx)
        self.pre_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[EncoderDecoderCache] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> torch.FloatTensor:
        residual = hidden_states
        hidden_states = self.pre_self_attn_layernorm(hidden_states)
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values.self_attention_cache if past_key_values is not None else None,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = self.post_self_attn_layernorm(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)

        residual = hidden_states
        hidden_states = self.pre_cross_attn_layernorm(hidden_states)
        hidden_states, _ = self.cross_attn(
            hidden_states=hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            **kwargs,
        )
        hidden_states = self.post_cross_attn_layernorm(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)
        return hidden_states


class T5GemmaClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    def __init__(self, hidden_size: int, num_labels: int, classifier_dropout_rate: float = 0.0):
        super().__init__()
        self.dropout = nn.Dropout(p=classifier_dropout_rate)
        self.out_proj = nn.Linear(hidden_size, num_labels)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.out_proj(hidden_states)
        return hidden_states


class T5GemmaLMHead(nn.Module):
    """Head for language modeling (generation) tasks."""

    def __init__(self, hidden_size: int, vocab_size: int, bias: bool = False):
        super().__init__()
        self.out_proj = nn.Linear(hidden_size, vocab_size, bias=bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        logits = self.out_proj(hidden_states)
        return logits


@auto_docstring
class T5GemmaPreTrainedModel(PreTrainedModel):
    config: T5GemmaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["T5GemmaEncoderLayer", "T5GemmaDecoderLayer"]
    _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": T5GemmaDecoderLayer,
        "attentions": [
            OutputRecorder(T5GemmaSelfAttention, index=1, layer_name="self_attn"),
            OutputRecorder(T5GemmaSelfAttention, index=1, layer_name="cross_attn"),
            OutputRecorder(T5GemmaCrossAttention, index=1, layer_name="cross_attn"),
        ],
    }

    def _init_weights(self, module):
        # TODO: support initialization for encoders and decoders separately(?)
        super()._init_weights(module)
        std = self.config.initializer_range
        if isinstance(module, T5GemmaClassificationHead):
            scale = module.out_proj.weight.shape[0] ** -0.5
            module.out_proj.weight.data.normal_(mean=0.0, std=std * scale)
            if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
                module.out_proj.bias.data.zero_()
        elif isinstance(module, T5GemmaLMHead):
            if not self.config.tie_word_embeddings:
                scale = module.out_proj.weight.shape[0] ** -0.5
                module.out_proj.weight.data.normal_(mean=0.0, std=std * scale)
        # We initialize with 0s to be 1 centered as the RMSNorm here does (1 + weight)
        elif "RMSNorm" in module.__class__.__name__:
            module.weight.data.zero_()

    def _shift_right(self, input_ids):
        """
        Shifts input_ids to the right, prepends the decoder_start_token_id, and handles
        pad_token_id replacement for labels that were -100.
        This is a common preparation step for decoder inputs in sequence-to-sequence models.
        """
        decoder_start_token_id = self.config.decoder.bos_token_id
        pad_token_id = self.config.decoder.pad_token_id

        if decoder_start_token_id is None:
            raise ValueError("self.model.config.decoder.bos_token_id has to be defined. ")

        # shift inputs to the right
        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
        shifted_input_ids[..., 0] = decoder_start_token_id

        if pad_token_id is None:
            raise ValueError("self.model.config.decoder.pad_token_id has to be defined.")

        # Is this T5 specific?
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

        return shifted_input_ids


def bidirectional_mask_function(attention_mask: Optional[torch.Tensor]) -> Callable:
    """
    This creates bidirectional attention mask.
    """

    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
        if attention_mask is None:
            return torch.ones((), dtype=torch.bool)
        return attention_mask[batch_idx, kv_idx].to(torch.bool)

    return inner_mask


def sliding_window_bidirectional_mask_function(sliding_window: int) -> Callable:
    """
    This creates bidirectional attention mask with sliding window.
    """

    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
        return (q_idx - sliding_window < kv_idx) & (kv_idx < q_idx + sliding_window)

    return inner_mask


def make_default_2d_attention_mask(
    token_ids: Optional[torch.LongTensor],
    hidden_states: torch.Tensor,
    pad_token_id: Optional[int],
) -> torch.Tensor:
    """Construct the default attention mask."""
    if token_ids is not None:
        if pad_token_id is None:
            raise ValueError("`pad_token_id` is required for padding information.")
        attention_mask = (token_ids != pad_token_id).to(hidden_states.device, torch.long)
    else:
        attention_mask = torch.ones(
            (hidden_states.shape[0], hidden_states.shape[1]), device=hidden_states.device, dtype=torch.long
        )
    return attention_mask


class T5GemmaEncoder(T5GemmaPreTrainedModel):
    _can_record_outputs = {
        "attentions": T5GemmaSelfAttention,
        "hidden_states": T5GemmaEncoderLayer,
    }

    def __init__(self, config):
        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.norm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False

        self.layers = nn.ModuleList(
            [T5GemmaEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.dropout = nn.Dropout(config.dropout_rate)
        self.rotary_emb = T5GemmaRotaryEmbedding(config=config)

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

    @check_model_inputs()
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutput:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        # As we want to pass `past_key_values=None` explicitly everywhere, we need to pop them from kwargs if present
        kwargs.pop("past_key_values", None)

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

        cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)

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

        if attention_mask is None:
            attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id)

        if not isinstance(self_attn_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": None,
                "position_ids": position_ids,
            }
            self_attn_mask_mapping = {
                "full_attention": create_causal_mask(
                    **mask_kwargs,
                    or_mask_function=bidirectional_mask_function(attention_mask),
                ),
                "sliding_attention": create_sliding_window_causal_mask(
                    **mask_kwargs,
                    or_mask_function=sliding_window_bidirectional_mask_function(self.config.sliding_window),
                    and_mask_function=bidirectional_mask_function(attention_mask),
                ),
            }

        hidden_states = inputs_embeds
        normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
        hidden_states = hidden_states * normalizer
        hidden_states = self.dropout(hidden_states)

        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for layer_module in self.layers[: self.config.num_hidden_layers]:
            hidden_states = layer_module(
                hidden_states,
                position_embeddings,
                self_attn_mask_mapping[layer_module.attention_type],
                position_ids,
                **kwargs,
            )
        hidden_states = self.norm(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
        )


class T5GemmaDecoder(T5GemmaEncoder):
    _can_record_outputs = {
        "attentions": OutputRecorder(T5GemmaSelfAttention, index=1),
        "cross_attentions": OutputRecorder(T5GemmaCrossAttention, index=1),
        "hidden_states": T5GemmaDecoderLayer,
    }

    def __init__(self, config):
        super().__init__(config)
        self.layers = nn.ModuleList(
            [T5GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.rotary_emb = T5GemmaRotaryEmbedding(config=config)

        self.post_init()

    @check_model_inputs()
    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[EncoderDecoderCache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPastAndCrossAttentions:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
        if encoder_hidden_states is None:
            raise ValueError("`encoder_hidden_states` must be given in decoder")

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

        if not self.training and use_cache and past_key_values is None:
            past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
        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)

        if attention_mask is None and past_key_values is None:
            attention_mask = make_default_2d_attention_mask(input_ids, inputs_embeds, self.config.pad_token_id)

        if not isinstance(self_attn_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values.self_attention_cache if past_key_values is not None else None,
                "position_ids": position_ids,
            }
            self_attn_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        if not isinstance(cross_attn_mask_mapping := encoder_attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "input_embeds": encoder_hidden_states,
                "attention_mask": encoder_attention_mask,
                "cache_position": cache_position,
                "past_key_values": None,
                "position_ids": None,
            }
            cross_attn_mask_mapping = {
                "full_attention": create_causal_mask(
                    **mask_kwargs,
                    or_mask_function=bidirectional_mask_function(encoder_attention_mask),
                ),
            }

        hidden_states = inputs_embeds
        normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
        hidden_states = hidden_states * normalizer
        hidden_states = self.dropout(hidden_states)

        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for layer_module in self.layers[: self.config.num_hidden_layers]:
            hidden_states = layer_module(
                hidden_states,
                position_embeddings,
                self_attn_mask_mapping[layer_module.attention_type],
                position_ids,
                past_key_values,
                use_cache,
                cache_position,
                encoder_hidden_states,
                cross_attn_mask_mapping["full_attention"],
                **kwargs,
            )
        hidden_states = self.norm(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


@auto_docstring
class T5GemmaModel(T5GemmaPreTrainedModel):
    def __init__(self, config: T5GemmaConfig):
        super().__init__(config)

        if not config.is_encoder_decoder:
            raise ValueError("T5GemmaModel only support encoder-decoder modeling. Use `T5GemmaEncoderModel` instead.")

        self.encoder = T5GemmaEncoder(config.encoder)
        self.decoder = T5GemmaDecoder(config.decoder)

        self.post_init()

    def get_encoder(self):
        return self.encoder

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        return self.encoder.set_input_embeddings(new_embeddings)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        decoder_position_ids: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[BaseModelOutput] = None,
        past_key_values: Optional[EncoderDecoderCache] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        decoder_inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Seq2SeqModelOutput:
        r"""
        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
            config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        """
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                **kwargs,
            )

        encoder_hidden_states = encoder_outputs.last_hidden_state

        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            position_ids=decoder_position_ids,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=attention_mask,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        return Seq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states
            if kwargs.get("output_hidden_states", False)
            else (decoder_outputs.last_hidden_state,),
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


@auto_docstring
class T5GemmaEncoderModel(T5GemmaPreTrainedModel):
    def __init__(self, config: T5GemmaConfig):
        super().__init__(config)

        if config.is_encoder_decoder:
            raise ValueError("T5GemmaEncoderModel only supports encoder-only model. Use `T5GemmaModel` instead.")

        self.encoder = T5GemmaEncoder(config.encoder)
        self.post_init()

    def get_input_embeddings(self):
        return self.encoder.get_input_embeddings()

    def set_input_embeddings(self, new_embeddings):
        return self.encoder.set_input_embeddings(new_embeddings)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutput:
        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )
        return encoder_outputs


class T5GemmaForConditionalGeneration(T5GemmaPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["model.decoder.embed_tokens.weight", "lm_head.out_proj.weight"]
    _tp_plan = {"lm_head.out_proj": "colwise_rep"}
    _pp_plan = {"lm_head.out_proj": (["hidden_states"], ["logits"])}

    def __init__(self, config: T5GemmaConfig):
        config.is_encoder_decoder = True
        super().__init__(config)

        self.model = T5GemmaModel(config)
        self.vocab_size = config.decoder.vocab_size
        self.lm_head = T5GemmaLMHead(config.decoder.hidden_size, self.vocab_size)
        self.loss_type = "ForMaskedLM"

        self.post_init()

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

    def get_output_embeddings(self):
        return self.lm_head.out_proj

    def _tie_weights(self):
        # Decoder input and output embeddings are tied.
        if self.config.tie_word_embeddings:
            self._tie_embedding_weights(self.lm_head.out_proj, self.get_decoder().get_input_embeddings())

    def get_encoder(self):
        return self.model.encoder

    def get_decoder(self):
        return self.model.decoder

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        decoder_position_ids: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[BaseModelOutput] = None,
        past_key_values: Optional[EncoderDecoderCache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_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],
    ) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        r"""
        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
            config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        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]`.
        """

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        decoder_outputs: Seq2SeqModelOutput = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            decoder_position_ids=decoder_position_ids,
            encoder_outputs=encoder_outputs,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = decoder_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, :])
        decoder_config = self.get_decoder().config
        if decoder_config.final_logit_softcapping is not None:
            logits = logits / decoder_config.final_logit_softcapping
            logits = torch.tanh(logits)
            logits = logits * decoder_config.final_logit_softcapping

        loss = None
        if labels is not None:
            # Input has right-shifted so we directly perform masked lm loss
            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)

        return Seq2SeqLMOutput(
            loss=loss,
            logits=logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.decoder_hidden_states,
            decoder_attentions=decoder_outputs.decoder_attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=decoder_outputs.encoder_last_hidden_state,
            encoder_hidden_states=decoder_outputs.encoder_hidden_states,
            encoder_attentions=decoder_outputs.encoder_attentions,
        )

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return self._shift_right(labels)


@auto_docstring
class T5GemmaForSequenceClassification(T5GemmaPreTrainedModel):
    def __init__(self, config: T5GemmaConfig, is_encoder_decoder: Optional[bool] = None):
        r"""
        is_encoder_decoder (`Optional`, *optional*):
            Whether use encoder_decoder for sequence classification. When set to False, only encoder is used.
        """
        if is_encoder_decoder is not None:
            config.is_encoder_decoder = is_encoder_decoder
        super().__init__(config)
        self.num_labels = config.num_labels

        if config.is_encoder_decoder:
            self.model = T5GemmaModel(config)
        else:
            self.model = T5GemmaEncoderModel(config)

        hidden_size = config.encoder.hidden_size
        if config.is_encoder_decoder:
            hidden_size = config.decoder.hidden_size

        classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
        self.score = T5GemmaClassificationHead(hidden_size, self.num_labels, classifier_dropout)
        self.post_init()

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

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

    @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,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.Tensor] = None,
        decoder_position_ids: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[BaseModelOutput] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> SequenceClassifierOutput:
        r"""
        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
            config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        if self.config.is_encoder_decoder and (input_ids is None and inputs_embeds is not None):
            raise NotImplementedError(
                f"Passing input embeddings is currently not supported for {self.__class__.__name__} in encoder-decoder mode."
            )

        # Following T5, we automatically creates decoder_input_ids from input_ids if no decoder_input_ids are provided
        if self.config.is_encoder_decoder and (decoder_input_ids is None and decoder_inputs_embeds is None):
            if input_ids is None:
                raise ValueError(
                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
                    "passed, `input_ids` cannot be `None`. Please pass either "
                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
                )
            decoder_input_ids = self._shift_right(input_ids)

        if self.config.is_encoder_decoder:
            outputs: Seq2SeqModelOutput = self.model(
                input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
                decoder_position_ids=decoder_position_ids,
                encoder_outputs=encoder_outputs,
                inputs_embeds=inputs_embeds,
                decoder_inputs_embeds=decoder_inputs_embeds,
                use_cache=False,
                **kwargs,
            )
            last_hidden_state = outputs.last_hidden_state
            hidden_states = outputs.decoder_hidden_states
            attentions = outputs.decoder_attentions
        else:
            outputs: BaseModelOutput = self.model(
                input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                **kwargs,
            )
            last_hidden_state = outputs.last_hidden_state
            hidden_states = outputs.hidden_states
            attentions = outputs.attentions

        logits = self.score(last_hidden_state)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            last_non_pad_token = -1
        elif input_ids is not None:
            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)

            if self.config.is_encoder_decoder:
                last_non_pad_token += 1  # due to the right shift.
                last_non_pad_token = torch.clamp(last_non_pad_token, max=decoder_input_ids.shape[-1] - 1)
        else:
            last_non_pad_token = -1
            logger.warning_once(
                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
            )

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]

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

        return SequenceClassifierOutput(
            loss=loss,
            logits=pooled_logits,
            hidden_states=hidden_states,
            attentions=attentions,
        )


@auto_docstring
class T5GemmaForTokenClassification(T5GemmaPreTrainedModel):
    def __init__(self, config: T5GemmaConfig, is_encoder_decoder: Optional[bool] = None):
        r"""
        is_encoder_decoder (`Optional`, *optional*):
            Whether use encoder_decoder for token classification. When set to False, only encoder is used.
        """
        if is_encoder_decoder is not None:
            config.is_encoder_decoder = is_encoder_decoder
        super().__init__(config)
        self.num_labels = config.num_labels

        if config.is_encoder_decoder:
            self.model = T5GemmaModel(config)
        else:
            self.model = T5GemmaEncoderModel(config)

        hidden_size = config.encoder.hidden_size
        if config.is_encoder_decoder:
            hidden_size = config.decoder.hidden_size

        classifier_dropout = getattr(config, "classifier_dropout_rate", 0.1)
        self.score = T5GemmaClassificationHead(hidden_size, self.num_labels, classifier_dropout)

        self.post_init()

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

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

    @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,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.Tensor] = None,
        decoder_position_ids: Optional[torch.LongTensor] = None,
        encoder_outputs: Optional[BaseModelOutput] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> TokenClassifierOutput:
        r"""
        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, decoder_sequence_length)`, *optional*):
            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0,
            config.decoder.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """

        if self.config.is_encoder_decoder and (input_ids is None and inputs_embeds is not None):
            raise NotImplementedError(
                f"Passing input embeddings is currently not supported for {self.__class__.__name__} in encoder-decoder mode."
            )

        if self.config.is_encoder_decoder and (decoder_input_ids is None and decoder_inputs_embeds is None):
            if input_ids is None:
                raise ValueError(
                    "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
                    "passed, `input_ids` cannot be `None`. Please pass either "
                    "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
                )
            decoder_input_ids = self._shift_right(input_ids)

        if self.config.is_encoder_decoder:
            outputs: Seq2SeqModelOutput = self.model(
                input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
                decoder_position_ids=decoder_position_ids,
                encoder_outputs=encoder_outputs,
                inputs_embeds=inputs_embeds,
                decoder_inputs_embeds=decoder_inputs_embeds,
                use_cache=False,
                **kwargs,
            )
            last_hidden_state = outputs.last_hidden_state
            hidden_states = outputs.decoder_hidden_states
            attentions = outputs.decoder_attentions
        else:
            outputs: BaseModelOutput = self.model(
                input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                **kwargs,
            )
            last_hidden_state = outputs.last_hidden_state
            hidden_states = outputs.hidden_states
            attentions = outputs.attentions

        logits = self.score(last_hidden_state)

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

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=hidden_states,
            attentions=attentions,
        )


__all__ = [
    "T5GemmaForConditionalGeneration",
    "T5GemmaModel",
    "T5GemmaEncoderModel",
    "T5GemmaPreTrainedModel",
    "T5GemmaForSequenceClassification",
    "T5GemmaForTokenClassification",
]
