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# Copyright 2026 Illuin Technology and contributors, and The 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 dataclasses import dataclass

import torch
from torch import nn

from ... import initialization as init
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring
from ...utils.generic import can_return_tuple
from ..auto.modeling_auto import AutoModel
from .configuration_colmodernvbert import ColModernVBertConfig


@auto_docstring
class ColModernVBertPreTrainedModel(PreTrainedModel):
    config: ColModernVBertConfig
    base_model_prefix = "model"
    input_modalities = ("image", "text")
    _no_split_modules = []
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True

    @torch.no_grad()
    def _init_weights(self, module):
        std = (
            self.config.initializer_range
            if hasattr(self.config, "initializer_range")
            else self.config.vlm_config.text_config.initializer_range
        )

        if isinstance(module, (nn.Linear, nn.Conv2d)):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            init.normal_(module.weight, mean=0.0, std=std)
            # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
            if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
                init.zeros_(module.weight[module.padding_idx])


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for ColModernVBert embeddings output.
    """
)
class ColModernVBertForRetrievalOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
        The embeddings of the model.
    image_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True` and `pixel_values` are provided):
        Tuple of `torch.FloatTensor` (one for the output of the image modality projection + one for the output of each layer) of shape
        `(batch_size, num_channels, image_size, image_size)`.
        Hidden-states of the image encoder at the output of each layer plus the initial modality projection outputs.
    """

    loss: torch.FloatTensor | None = None
    embeddings: torch.Tensor | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    image_hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None


@auto_docstring(
    custom_intro="""
    Following the ColPali approach, ColModernVBert leverages VLMs to construct efficient multi-vector embeddings directly
    from document images (“screenshots”) for document retrieval. The model is trained to maximize the similarity
    between these document embeddings and the corresponding query embeddings, using the late interaction method
    introduced in ColBERT.

    Using ColModernVBert removes the need for potentially complex and brittle layout recognition and OCR pipelines with
    a single model that can take into account both the textual and visual content (layout, charts, ...) of a document.

    ColModernVBert is trained on top of ModernVBert, and was introduced in the following paper:
    [*ModernVBERT: Towards Smaller Visual Document Retrievers*](https://arxiv.org/abs/2510.01149).

    ColModernVBert is part of the ColVision model family, which was introduced with ColPali in the following paper:
    [*ColPali: Efficient Document Retrieval with Vision Language Models*](https://huggingface.co/papers/2407.01449).
    """
)
class ColModernVBertForRetrieval(ColModernVBertPreTrainedModel):
    _checkpoint_conversion_mapping = {}

    def __init__(self, config: ColModernVBertConfig):
        super().__init__(config)
        self.config = config
        self.vocab_size = config.vlm_config.text_config.vocab_size
        self.vlm = AutoModel.from_config(config.vlm_config)

        self.embedding_dim = self.config.embedding_dim
        self.embedding_proj_layer = nn.Linear(
            self.config.vlm_config.text_config.hidden_size,
            self.embedding_dim,
        )

        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ColModernVBertForRetrievalOutput:
        vlm_output = self.vlm(
            input_ids=input_ids,
            attention_mask=attention_mask,
            pixel_values=pixel_values,
            **kwargs,
        )

        last_hidden_states = vlm_output[0]  # (batch_size, sequence_length, hidden_size)
        proj_dtype = self.embedding_proj_layer.weight.dtype
        embeddings = self.embedding_proj_layer(last_hidden_states.to(proj_dtype))  # (batch_size, sequence_length, dim)

        # L2 normalization
        embeddings = embeddings / embeddings.norm(dim=-1, keepdim=True)  # (batch_size, sequence_length, dim)

        if attention_mask is not None:
            attention_mask = attention_mask.to(dtype=embeddings.dtype, device=embeddings.device)
            embeddings = embeddings * attention_mask.unsqueeze(-1)  # (batch_size, sequence_length, dim)

        return ColModernVBertForRetrievalOutput(
            embeddings=embeddings,
            hidden_states=vlm_output.hidden_states,
            attentions=vlm_output.attentions,
            image_hidden_states=vlm_output.image_hidden_states,
        )

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

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

    def get_output_embeddings(self):
        return self.vlm.get_output_embeddings()

    def set_output_embeddings(self, new_embeddings):
        self.vlm.set_output_embeddings(new_embeddings)

    def resize_token_embeddings(
        self,
        new_num_tokens: int | None = None,
        pad_to_multiple_of: int | None = None,
        mean_resizing: bool = True,
    ) -> nn.Embedding:
        model_embeds = self.vlm.resize_token_embeddings(
            new_num_tokens=new_num_tokens,
            pad_to_multiple_of=pad_to_multiple_of,
            mean_resizing=mean_resizing,
        )

        self.config.vlm_config.text_config.vocab_size = model_embeds.num_embeddings
        self.config.vlm_config.vocab_size = model_embeds.num_embeddings
        self.vlm.vocab_size = model_embeds.num_embeddings
        self.vocab_size = model_embeds.num_embeddings

        return model_embeds


__all__ = ["ColModernVBertForRetrieval", "ColModernVBertPreTrainedModel"]