# 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
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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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import copy
import warnings
from collections.abc import Callable
from typing import Any, Optional, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PretrainedConfig, layer_type_validation
from ...masking_utils import create_causal_mask, create_masks_for_generate, create_sliding_window_causal_mask
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast
from ...modeling_rope_utils import rope_config_validation
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
from ...utils.deprecation import deprecate_kwarg
from ..gemma2.configuration_gemma2 import Gemma2Config
from ..gemma2.modeling_gemma2 import (
    Gemma2Attention,
    Gemma2ForCausalLM,
    Gemma2MLP,
    Gemma2Model,
    Gemma2PreTrainedModel,
    Gemma2RMSNorm,
    Gemma2RotaryEmbedding,
    apply_rotary_pos_emb,
    eager_attention_forward,
)
from ..paligemma.modeling_paligemma import (
    PaligemmaCausalLMOutputWithPast,
    PaliGemmaForConditionalGeneration,
    PaliGemmaModel,
    PaligemmaModelOutputWithPast,
)
from ..siglip import SiglipVisionConfig


logger = logging.get_logger(__name__)


class Gemma3TextConfig(Gemma2Config, PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Gemma3TextModel`]. It is used to instantiate an Gemma3Text
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Gemma3Text-7B.
    e.g. [google/gemma3_text-7b](https://huggingface.co/google/gemma3_text-7b)
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.
    Args:
        vocab_size (`int`, *optional*, defaults to 262208):
            Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Gemma3TextModel`]
        hidden_size (`int`, *optional*, defaults to 2304):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 9216):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 26):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 4):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 256):
            The attention head dimension.
        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
            if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
        max_position_embeddings (`int`, *optional*, defaults to 131072):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 1):
            End of stream token id.
        bos_token_id (`int`, *optional*, defaults to 2):
            Beginning of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 1000000.0):
            The base period of the RoPE embeddings.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        query_pre_attn_scalar (`float`, *optional*, defaults to 256):
            Scaling factor used on the attention scores
        sliding_window (`int`, *optional*, defaults to 4096):
            In Gemma3Text, every other layer uses sliding window attention. This is the size of the sliding window.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        final_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the logits.
        attn_logit_softcapping (`float`, *optional*):
            Scaling factor when applying tanh softcapping on the attention scores.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings used in global attention. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
        rope_local_base_freq (float, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings for local attention.

    ```python
    >>> from transformers import Gemma3TextModel, Gemma3TextConfig
    >>> # Initializing a Gemma3Text gemma3_text-7b style configuration
    >>> configuration = Gemma3TextConfig()
    >>> # Initializing a model from the gemma3_text-7b style configuration
    >>> model = Gemma3TextModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "gemma3_text"

    def __init__(
        self,
        vocab_size=262_208,
        hidden_size=2304,
        intermediate_size=9216,
        num_hidden_layers=26,
        num_attention_heads=8,
        num_key_value_heads=4,
        head_dim=256,
        hidden_activation="gelu_pytorch_tanh",
        max_position_embeddings=131_072,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        bos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=1_000_000.0,
        attention_bias=False,
        attention_dropout=0.0,
        query_pre_attn_scalar=256,
        sliding_window=4096,
        layer_types=None,
        final_logit_softcapping=None,
        attn_logit_softcapping=None,
        rope_scaling=None,
        rope_local_base_freq=10_000.0,
        **kwargs,
    ):
        PretrainedConfig.__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.head_dim = head_dim
        self.num_key_value_heads = num_key_value_heads
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.hidden_activation = hidden_activation
        self.query_pre_attn_scalar = query_pre_attn_scalar
        self.sliding_window = sliding_window
        self.final_logit_softcapping = final_logit_softcapping
        self.attn_logit_softcapping = attn_logit_softcapping
        self.layer_types = layer_types

        self.rope_local_base_freq = rope_local_base_freq
        self.rope_scaling = rope_scaling
        rope_config_validation(self)

        # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
        self._sliding_window_pattern = kwargs.get("sliding_window_pattern", 6)

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if bool((i + 1) % self._sliding_window_pattern) else "full_attention"
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types)

    @property
    def sliding_window_pattern(self):
        warnings.warn(
            "The `sliding_window_pattern` attribute is deprecated and will be removed in v4.55.0.",
            FutureWarning,
        )
        return self._sliding_window_pattern

    @sliding_window_pattern.setter
    def sliding_window_pattern(self, value):
        self._sliding_window_pattern = value


class Gemma3Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Gemma3ForConditionalGeneration`]. It is used to instantiate an
    Gemma3ForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the PaliGemma-2B.

    e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b)

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        text_config (`Union[Gemma3TextConfig, dict]`, *optional*):
            The config object of the text backbone.
        vision_config (`Union[AutoConfig, dict]`,  *optional*):
            Custom vision config or dict.
        mm_tokens_per_image (`int`, *optional*, defaults to 256):
            The number of tokens per image embedding.
        boi_token_index (`int`, *optional*, defaults to 255999):
            The begin-of-image token index to wrap the image prompt.
        eoi_token_index (`int`, *optional*, defaults to 256000):
            The end-of-image token index to wrap the image prompt.
        image_token_index (`int`, *optional*, defaults to 262144):
            The image token index to encode the image prompt.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.


    Example:

    ```python
    >>> from transformers import Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig

    >>> # Initializing a Siglip-like vision config
    >>> vision_config = SiglipVisionConfig()

    >>> # Initializing a Gemma3 Text config
    >>> text_config = Gemma3TextConfig()

    >>> # Initializing a Gemma3 gemma-3-4b style configuration
    >>> configuration = Gemma3Config(vision_config, text_config)

    >>> # Initializing a model from the gemma-3-4b style configuration
    >>> model = Gemma3TextConfig(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "gemma3"
    attribute_map = {
        "image_token_id": "image_token_index",
        "boi_token_id": "boi_token_index",
        "eoi_token_id": "eoi_token_index",
    }
    sub_configs = {
        "text_config": Gemma3TextConfig,
        "vision_config": SiglipVisionConfig,
    }

    def __init__(
        self,
        text_config: Optional[Union[Gemma3TextConfig, dict[str, Any]]] = None,
        vision_config: Optional[Union[SiglipVisionConfig, dict[str, Any]]] = None,
        mm_tokens_per_image: int = 256,
        boi_token_index: int = 255_999,
        eoi_token_index: int = 256_000,
        image_token_index: int = 262_144,
        initializer_range: float = 0.02,
        **kwargs,
    ):
        if text_config is None:
            text_config = Gemma3TextConfig()
            logger.info("text_config is None, using default Gemma3TextConfig text config.")
        elif isinstance(text_config, dict):
            text_config = Gemma3TextConfig(**text_config)

        if isinstance(vision_config, dict):
            vision_config = SiglipVisionConfig(**vision_config)
        elif vision_config is None:
            vision_config = SiglipVisionConfig()
            logger.info("vision_config is None, using default SiglipVisionConfig vision config.")

        self.text_config = text_config
        self.vision_config = vision_config
        self.mm_tokens_per_image = mm_tokens_per_image
        self.boi_token_index = boi_token_index
        self.eoi_token_index = eoi_token_index
        self.image_token_index = image_token_index
        self.initializer_range = initializer_range

        super().__init__(**kwargs)


class Gemma3ModelOutputWithPast(PaligemmaModelOutputWithPast):
    pass


class Gemma3CausalLMOutputWithPast(PaligemmaCausalLMOutputWithPast):
    pass


class Gemma3TextScaledWordEmbedding(nn.Embedding):
    """
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)

    def forward(self, input_ids: torch.Tensor):
        return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)


class Gemma3MLP(Gemma2MLP):
    def __init__(self, config: Gemma3TextConfig):
        super().__init__(config)


class Gemma3RMSNorm(Gemma2RMSNorm):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()


class Gemma3RotaryEmbedding(Gemma2RotaryEmbedding):
    def __init__(self, config: Gemma3TextConfig, device=None):
        super().__init__(config)


# Weird way to inherit but otherwise the sliding window gets defined first and can't access `is_sliding`
class Gemma3Attention(Gemma2Attention):
    def __init__(self, config: Gemma3TextConfig, layer_idx: int):
        self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"

        super().__init__()
        self.sliding_window = config.sliding_window if self.is_sliding else None

        self.q_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        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)

        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

        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,
            **kwargs,
        )

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


class Gemma3DecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Gemma3TextConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.attention_type = config.layer_types[layer_idx]
        self.self_attn = Gemma3Attention(config=config, layer_idx=layer_idx)
        self.mlp = Gemma3MLP(config)
        self.input_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.pre_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = Gemma3RMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings_global: torch.Tensor,
        position_embeddings_local: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # apply global RoPE to non-sliding layer only
        if self.self_attn.is_sliding:
            position_embeddings = position_embeddings_local
        else:
            position_embeddings = position_embeddings_global

        hidden_states, self_attn_weights = 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,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + 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 + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


GEMMA3_START_DOCSTRING = None


class Gemma3PreTrainedModel(Gemma2PreTrainedModel):
    base_model_prefix = ""
    _no_split_modules = [
        "Gemma3DecoderLayer",
        "SiglipVisionEmbeddings",
        "SiglipEncoderLayer",
        "SiglipMultiheadAttentionPoolingHead",
    ]

    def _init_weights(self, module):
        Gemma2PreTrainedModel._init_weights(module)
        if isinstance(module, Gemma3MultiModalProjector):
            module.mm_input_projection_weight.data.zero_()


class Gemma3TextModel(Gemma2Model):
    config: Gemma3TextConfig

    def __init__(self, config: Gemma3TextConfig):
        super().__init__(config)

        # Gemma3 downcasts the below to bfloat16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
        self.embed_tokens = Gemma3TextScaledWordEmbedding(
            config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
        )

        # TODO: raushan fix this after RoPE refactor. For now we hack it by reassigning thetas
        # when we want to create a local RoPE layer. Config defaults should hold values for global RoPE
        config = copy.deepcopy(config)
        config.rope_theta = config.rope_local_base_freq
        config.rope_scaling = {"rope_type": "default"}
        self.rotary_emb_local = Gemma3RotaryEmbedding(config=config)

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

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

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

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

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

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

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

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        # embed positions
        hidden_states = inputs_embeds

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

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

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

            layer_outputs = decoder_layer(
                hidden_states,
                position_embeddings_global=position_embeddings_global,
                position_embeddings_local=position_embeddings_local,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

            hidden_states = layer_outputs[0]

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

        hidden_states = self.norm(hidden_states)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class Gemma3ForCausalLM(Gemma2ForCausalLM):
    config: Gemma3TextConfig
    base_model_prefix = "language_model"

    def __init__(self, config: Gemma3TextConfig):
        super().__init__(config)
        self.model = Gemma3TextModel(config)


class Gemma3MultiModalProjector(nn.Module):
    def __init__(self, config: Gemma3Config):
        super().__init__()

        self.mm_input_projection_weight = nn.Parameter(
            torch.zeros(config.vision_config.hidden_size, config.text_config.hidden_size)
        )

        self.mm_soft_emb_norm = Gemma3RMSNorm(
            config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps
        )

        self.patches_per_image = int(config.vision_config.image_size // config.vision_config.patch_size)
        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
        self.kernel_size = self.patches_per_image // self.tokens_per_side
        self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)

    def forward(self, vision_outputs: torch.Tensor):
        batch_size, _, seq_length = vision_outputs.shape

        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
            batch_size, seq_length, self.patches_per_image, self.patches_per_image
        )
        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()

        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)

        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)

        projected_vision_outputs = torch.matmul(normed_vision_outputs, self.mm_input_projection_weight)
        return projected_vision_outputs.type_as(vision_outputs)


def token_type_ids_mask_function(
    token_type_ids: Optional[torch.Tensor],
    image_group_ids: Optional[torch.Tensor],
    tokens_per_image: int,
) -> Optional[Callable]:
    """
    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
    not start and end indices.
    """
    # Do not return an additional mask in this case
    if token_type_ids is None:
        return None

    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
        # If it's 1 for both query and key/value, we are in an image block
        # NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
        # Since vmap doesn't support `if statement` we workaround it with `torch.where`
        safe_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
        token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_idx]
        token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)

        image_group_ids_at_kv_idx = image_group_ids[batch_idx, safe_idx]
        image_group_ids_at_kv_idx = torch.where(kv_idx < image_group_ids.shape[1], image_group_ids_at_kv_idx, -1)

        is_image_block = (token_type_ids[batch_idx, q_idx] == 1) & (token_type_ids_at_kv_idx == 1)
        same_image_block = image_group_ids[batch_idx, q_idx] == image_group_ids_at_kv_idx

        # This is bidirectional attention whenever we are dealing with image tokens
        return is_image_block & same_image_block

    return inner_mask


class Gemma3Model(PaliGemmaModel):
    # we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
    accepts_loss_kwargs = False

    def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
        """
        Projects the last hidden state from the vision model into language model space.

        Args:
            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
               The tensors corresponding to the input images.
        Returns:
            image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
        """
        vision_outputs = self.vision_tower(pixel_values=pixel_values).last_hidden_state
        image_features = self.multi_modal_projector(vision_outputs)
        return image_features

    def _update_causal_mask(self, **super_kwargs):
        raise AttributeError("We don't want to inherit it")

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **lm_kwargs,
    ) -> Union[tuple, Gemma3ModelOutputWithPast]:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # Replace image id woth PAD if the image token if OOV, to avoid index-errors
        if input_ids is not None and self.config.image_token_id >= self.vocab_size:
            special_image_mask = input_ids == self.config.image_token_id
            llm_input_ids = input_ids.clone()
            llm_input_ids[special_image_mask] = 0
        else:
            llm_input_ids = input_ids

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(llm_input_ids)

        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
            )

        # Merge text and images
        if pixel_values is not None:
            image_features = self.get_image_features(pixel_values)
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            special_image_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_features
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config.get_text_config(),
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            if token_type_ids is not None and inputs_embeds.shape[1] != 1:
                # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`

                # First find where a new image block starts: 1 if image and previous not image
                # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
                is_image = (token_type_ids == 1).to(cache_position.device)
                new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
                image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
                image_group_ids = torch.where(
                    is_image, image_group_ids, torch.full_like(token_type_ids, -1, device=is_image.device)
                )
                mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
                    token_type_ids.to(cache_position.device), image_group_ids, self.config.mm_tokens_per_image
                )

            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        outputs = self.language_model(
            attention_mask=causal_mask_mapping,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=True,
            cache_position=cache_position,
            **lm_kwargs,
        )

        return Gemma3ModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values if use_cache else None,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
        )


class Gemma3ForConditionalGeneration(PaliGemmaForConditionalGeneration):
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        pixel_values: torch.FloatTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **lm_kwargs,
    ) -> Union[tuple, Gemma3CausalLMOutputWithPast]:
        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.text_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.text_config.vocab_size]`.

        Example:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, Gemma3ForConditionalGeneration

        >>> model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma-3-4b-it")
        >>> processor = AutoProcessor.from_pretrained("google/gemma-3-4b-it")

        >>> messages = [
        ...     {
        ...         "role": "system",
        ...         "content": [
        ...             {"type": "text", "text": "You are a helpful assistant."}
        ...         ]
        ...     },
        ...     {
        ...         "role": "user", "content": [
        ...             {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
        ...             {"type": "text", "text": "Where is the cat standing?"},
        ...         ]
        ...     },
        ... ]

        >>> inputs = processor.apply_chat_template(
        ...     messages,
        ...     tokenize=True,
        ...     return_dict=True,
        ...     return_tensors="pt",
        ...     add_generation_prompt=True
        ... )
        >>> # Generate
        >>> generate_ids = model.generate(**inputs)
        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "user\nYou are a helpful assistant.\n\n\n\n\n\nWhere is the cat standing?\nmodel\nBased on the image, the cat is standing in a snowy area, likely outdoors. It appears to"
        ```
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            labels=labels,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            **lm_kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            shift_logits = logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            if attention_mask is not None:
                # we use the input attention mask to shift the logits and labels, because it is 2D.
                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
            else:
                shift_logits = shift_logits.contiguous()
                shift_labels = shift_labels.contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()

            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
            flat_labels = shift_labels.view(-1).to(shift_logits.device)
            loss = loss_fct(flat_logits, flat_labels)

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

        return Gemma3CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=outputs.image_hidden_states,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        pixel_values=None,
        attention_mask=None,
        token_type_ids=None,
        use_cache=True,
        logits_to_keep=None,
        labels=None,
        **kwargs,
    ):
        # Overwritten -- custom `position_ids` and `pixel_values` handling
        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            cache_position=cache_position,
            use_cache=use_cache,
            logits_to_keep=logits_to_keep,
            token_type_ids=token_type_ids,
            **kwargs,
        )

        # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
        # Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
        if cache_position[0] == 0:
            model_inputs["pixel_values"] = pixel_values

        return model_inputs

    def _prepare_4d_causal_attention_mask_with_cache_position(self, **super_kwargs):
        raise AttributeError("We don't want to inherit it")

    @staticmethod
    def create_masks_for_generate(
        config: PretrainedConfig,
        input_embeds: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        cache_position: torch.Tensor,
        past_key_values: Optional[Cache],
        position_ids: Optional[torch.Tensor],
        token_type_ids: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> dict:
        # Prepare mask arguments
        mask_kwargs = {
            "config": config.get_text_config(),
            "input_embeds": input_embeds,
            "attention_mask": attention_mask,
            "cache_position": cache_position,
            "past_key_values": past_key_values,
            "position_ids": position_ids,
        }
        # Add the token type ids mask for generate as well
        if token_type_ids is not None and input_embeds.shape[1] != 1:
            # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`

            # First find where a new image block starts: 1 if image and previous not image
            # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
            is_image = (token_type_ids == 1).to(cache_position.device)
            new_image_start = is_image & ~nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
            image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
            image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
            mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
                token_type_ids.to(cache_position.device), image_group_ids, config.mm_tokens_per_image
            )

        return create_masks_for_generate(**mask_kwargs)


class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = Gemma3Model(config)
        self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)

        # Initialize weights and apply final processing
        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: torch.LongTensor = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> SequenceClassifierOutputWithPast:
        r"""
        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).
        """

        transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            pixel_values=pixel_values,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            token_type_ids=token_type_ids,
            use_cache=use_cache,
            **kwargs,
        )
        hidden_states = transformer_outputs.last_hidden_state
        logits = self.score(hidden_states)

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

        if self.config.text_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.text_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.text_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)
        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 SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


__all__ = [
    "Gemma3Config",
    "Gemma3TextConfig",
    "Gemma3PreTrainedModel",  # noqa: F822
    "Gemma3TextModel",
    "Gemma3ForCausalLM",
    "Gemma3ForConditionalGeneration",
    "Gemma3Model",
    "Gemma3ForSequenceClassification",
]
