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# Copyright 2025 Deepseek AI and The HuggingFace 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 dataclasses import dataclass
from typing import Optional, Union

import torch
import torch.nn as nn

from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...modeling_outputs import ModelOutput
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ..auto import AutoModel
from .configuration_deepseek_vl_hybrid import DeepseekVLHybridConfig


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for DeepseekVLHybrid model's outputs that may also contain a past key/values (to speed up sequential decoding).
    """
)
class DeepseekVLHybridBaseModelOutputWithPast(ModelOutput):
    r"""
    last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
        Sequence of hidden-states at the output of the last layer of the model.

        If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
        hidden_size)` is output.
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
        `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
        input) to speed up sequential decoding.
    image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
        Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
        sequence_length, hidden_size)`.

        image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
    """

    last_hidden_state: Optional[torch.FloatTensor] = None
    past_key_values: Optional[Cache] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[tuple[torch.FloatTensor]] = None


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for DeepseekVLHybrid causal language model (or autoregressive) outputs.
    """
)
class DeepseekVLHybridCausalLMOutputWithPast(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
        `past_key_values` input) to speed up sequential decoding.
    image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
        Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
        sequence_length, hidden_size)`.

        image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
    """

    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    past_key_values: Optional[Cache] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None
    image_hidden_states: Optional[tuple[torch.FloatTensor]] = None


class DeepseekVLHybridLayerNorm(nn.LayerNorm):
    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
    """

    def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs):
        super().__init__(normalized_shape, eps=eps, **kwargs)
        if data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError(f"Unsupported data format: {data_format}")
        self.data_format = data_format

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        """
        Args:
            features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels)
        """
        if self.data_format == "channels_first":
            features = features.permute(0, 2, 3, 1)
            features = super().forward(features)
            features = features.permute(0, 3, 1, 2)
        else:
            features = super().forward(features)
        return features


class DeepseekVLSamVisionNeck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
        self.layer_norm1 = DeepseekVLHybridLayerNorm(config.output_channels, data_format="channels_first")
        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
        self.layer_norm2 = DeepseekVLHybridLayerNorm(config.output_channels, data_format="channels_first")

    def forward(self, hidden_states):
        hidden_states = hidden_states.permute(0, 3, 1, 2)
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm1(hidden_states)

        hidden_states = self.conv2(hidden_states)
        hidden_states = self.layer_norm2(hidden_states)
        return hidden_states


class DeepseekVLSamVisionProj(nn.Module):
    def __init__(self, config, output_size: int = 24):
        super().__init__()
        self.config = config
        self.output_size = output_size

        self.conv1 = nn.Conv2d(
            config.output_channels, config.output_channels * 2, kernel_size=3, stride=2, padding=1, bias=False
        )
        self.conv2 = nn.Conv2d(
            config.output_channels * 2, config.output_channels * 4, kernel_size=3, stride=2, padding=1, bias=False
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        # interpolate Sam encodings to match Siglip encodings
        features = torch.nn.functional.interpolate(
            features,
            size=(4 * self.output_size, 4 * self.output_size),
            mode="bilinear",
            align_corners=False,
        )
        features = self.conv1(features)
        features = self.conv2(features)
        return features


class DeepseekVLHybridAligner(nn.Module):
    def __init__(self, config: DeepseekVLHybridConfig):
        super().__init__()

        in_channels = config.vision_config.hidden_size
        high_res_in_channels = config.high_res_vision_config.output_channels * 4
        out_channels = config.text_config.hidden_size

        self.vision_proj = nn.Linear(in_channels, out_channels // 2)
        self.high_res_vision_proj = nn.Linear(high_res_in_channels, out_channels // 2)

        self.act = nn.GELU()
        self.proj = nn.Linear(out_channels, out_channels)

    def forward(
        self,
        vision_encodings: torch.Tensor,
        high_res_vision_encodings: torch.Tensor,
    ) -> torch.Tensor:
        vision_encodings = self.vision_proj(vision_encodings)
        high_res_vision_encodings = self.high_res_vision_proj(high_res_vision_encodings)

        encodings = torch.concat([high_res_vision_encodings, vision_encodings], dim=-1)
        encodings = self.act(encodings)
        encodings = self.proj(encodings)

        return encodings


@auto_docstring
class DeepseekVLHybridPreTrainedModel(PreTrainedModel):
    config: DeepseekVLHybridConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LlamaDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values", "causal_mask"]
    _supports_flash_attn = True
    _supports_sdpa = True

    _can_compile_fullgraph = True
    _supports_param_buffer_assignment = False

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.text_config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Conv2d):
            nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, DeepseekVLHybridLayerNorm):
            module.weight.data.fill_(1.0)
            module.bias.data.zero_()
        elif isinstance(module, DeepseekVLHybridModel):
            module.high_res_vision_alpha.data.zero_()


DEEPSEEK_VL_COMMON_CUSTOM_ARGS = r"""
    high_res_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size), *optional*):
        The tensors corresponding to the input images. Pixel values can be obtained using
        [`AutoImageProcessor`].
"""


@auto_docstring
class DeepseekVLHybridModel(DeepseekVLHybridPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.output_size = config.vision_config.image_size // config.vision_config.patch_size
        self.global_attn_index = config.high_res_vision_config.global_attn_indexes[0]

        self.high_res_vision_model = AutoModel.from_config(config.high_res_vision_config)
        self.high_res_vision_neck = DeepseekVLSamVisionNeck(config.high_res_vision_config)
        self.high_res_vision_proj = DeepseekVLSamVisionProj(
            config.high_res_vision_config, output_size=self.output_size
        )
        self.high_res_vision_alpha = nn.Parameter(torch.zeros(1))
        self.config = config

        self.vision_model = AutoModel.from_config(config.vision_config)
        self.aligner = DeepseekVLHybridAligner(config)

        self.language_model = AutoModel.from_config(config=config.text_config)

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

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

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

    def get_image_features(self, pixel_values, high_res_pixel_values):
        vision_encodings = self.get_low_res_image_features(pixel_values)
        high_res_vision_encodings = self.get_high_res_image_features(high_res_pixel_values)
        images_embeds = self.aligner(vision_encodings, high_res_vision_encodings)
        return images_embeds

    def get_placeholder_mask(
        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
    ):
        """
        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
        equal to the length of multimodal features. If the lengths are different, an error is raised.
        """
        if input_ids is None:
            special_image_mask = inputs_embeds == self.get_input_embeddings()(
                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_image_mask = special_image_mask.all(-1)
        else:
            special_image_mask = input_ids == self.config.image_token_id

        n_image_tokens = special_image_mask.sum()
        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
        if inputs_embeds[special_image_mask].numel() != image_features.numel():
            n_image_features = image_features.shape[0] * image_features.shape[1]
            raise ValueError(
                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
            )
        return special_image_mask

    @can_return_tuple
    @auto_docstring(custom_args=DEEPSEEK_VL_COMMON_CUSTOM_ARGS)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        high_res_pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs,
    ):
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if pixel_values is not None and high_res_pixel_values is None:
            raise ValueError("Both pixel_values and high_res_pixel_values should be specified at the same time")

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

        if pixel_values is not None:
            if input_ids is None:
                image_attention_mask = inputs_embeds == self.get_input_embeddings()(
                    torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
                )
                image_attention_mask = image_attention_mask.all(-1)
            else:
                image_attention_mask = input_ids == self.config.image_token_id

            image_attention_mask = image_attention_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
            image_embeds = self.get_image_features(pixel_values, high_res_pixel_values)
            image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1])
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(image_attention_mask, image_features)

        lm_output = self.language_model(
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        return DeepseekVLHybridBaseModelOutputWithPast(
            last_hidden_state=lm_output.last_hidden_state,
            past_key_values=lm_output.past_key_values,
            hidden_states=lm_output.hidden_states,
            attentions=lm_output.attentions,
            image_hidden_states=image_embeds if pixel_values is not None else None,
        )

    def get_low_res_image_features(self, pixel_values):
        output = self.vision_model(pixel_values)
        output = output[0]
        return output

    def get_high_res_image_features(self, pixel_values):
        output = self.high_res_vision_model(
            pixel_values=pixel_values,
            output_hidden_states=True,
            return_dict=True,
        )
        last_hidden_state = output.last_hidden_state
        last_hidden_state = self.high_res_vision_proj(last_hidden_state)

        hidden_states = output.hidden_states
        global_hidden_state = hidden_states[self.global_attn_index + 1]  # +1 for embedding layer
        global_hidden_state = self.high_res_vision_neck(global_hidden_state)
        global_hidden_state = self.high_res_vision_proj(global_hidden_state)

        output = last_hidden_state + global_hidden_state * self.high_res_vision_alpha

        # batch_size, hidden_size, height, width -> batch_size, seq_len, hidden_size
        output = output.permute(0, 2, 3, 1)
        output = output.reshape(output.shape[0], -1, output.shape[-1])

        return output


class DeepseekVLHybridForConditionalGeneration(DeepseekVLHybridPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["model.language_model.embed_tokens.weight", "lm_head.weight"]
    _can_compile_fullgraph = True

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

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

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

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

    def prepare_embeddings_for_image_generation(self) -> torch.Tensor:
        raise AttributeError("Not needed for DeepseekVLHybrid")

    @can_return_tuple
    @auto_docstring(custom_args=DEEPSEEK_VL_COMMON_CUSTOM_ARGS)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        high_res_pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[TransformersKwargs],
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
        """
        outputs = self.model(
            input_ids=input_ids,
            pixel_values=pixel_values,
            high_res_pixel_values=high_res_pixel_values,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

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

        return DeepseekVLHybridCausalLMOutputWithPast(
            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,
        pixel_values=None,
        high_res_pixel_values=None,
        attention_mask=None,
        cache_position=None,
        logits_to_keep=None,
        **kwargs,
    ):
        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        if cache_position[0] == 0:
            # 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
            model_inputs["pixel_values"] = pixel_values
            model_inputs["high_res_pixel_values"] = high_res_pixel_values

        return model_inputs


__all__ = ["DeepseekVLHybridPreTrainedModel", "DeepseekVLHybridModel", "DeepseekVLHybridForConditionalGeneration"]
