# 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.


import torch
import torch.nn as nn
from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import (
    PreTokenizedInput,
    TextInput,
)
from ...utils import (
    auto_docstring,
    logging,
)
from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel
from ..idefics.modeling_idefics import IdeficsBaseModelOutputWithPast, IdeficsCausalLMOutputWithPast
from ..janus.image_processing_janus import JanusImageProcessor
from ..janus.image_processing_pil_janus import JanusImageProcessorPil
from ..janus.modeling_janus import JanusForConditionalGeneration, JanusModel, JanusPreTrainedModel


logger = logging.get_logger(__name__)


@auto_docstring(checkpoint="deepseek-community/deepseek-vl-1.3b-chat")
@strict
class DeepseekVLConfig(PreTrainedConfig):
    r"""
    Example:

    ```python
    >>> from transformers import DeepseekVLConfig, DeepseekVLModel

    >>> # Initializing a DeepseekVL deepseek-community/deepseek-vl-1.3b-chat style configuration
    >>> configuration = DeepseekVLConfig()

    >>> # Initializing a model (with random weights) from the deepseek-community/deepseek-vl-1.3b-chat style configuration
    >>> model = DeepseekVLModel(configuration)

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

    model_type = "deepseek_vl"
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}

    text_config: dict | PreTrainedConfig | None = None
    vision_config: dict | PreTrainedConfig | None = None
    image_token_id: int = 100015
    tie_word_embeddings: bool = True

    def __post_init__(self, **kwargs):
        if self.text_config is None:
            self.text_config = {}
            logger.info("`text_config` is `None`. Initializing the `LlamaConfig` with default values.")
        if isinstance(self.text_config, dict):
            self.text_config["model_type"] = self.text_config.get("model_type", "llama")
            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)

        if self.vision_config is None:
            self.vision_config = {}
            logger.info("`vision_config` is `None`. Initializing the `SiglipVisionConfig` with default values.")
        if isinstance(self.vision_config, dict):
            self.vision_config["model_type"] = self.vision_config.get("model_type", "siglip_vision_model")
            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)

        super().__post_init__(**kwargs)


class DeepseekVLBaseModelOutputWithPast(IdeficsBaseModelOutputWithPast):
    pass


class DeepseekVLCausalLMOutputWithPast(IdeficsCausalLMOutputWithPast):
    pass


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

        in_features = config.vision_config.hidden_size
        out_features = config.text_config.hidden_size

        self.linear1 = nn.Linear(in_features, out_features)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(out_features, out_features)

    def forward(self, vision_encodings: torch.Tensor) -> torch.Tensor:
        x = self.linear1(vision_encodings)
        x = self.activation(x)
        x = self.linear2(x)
        return x


class DeepseekVLPreTrainedModel(JanusPreTrainedModel):
    _no_split_modules = ["LlamaDecoderLayer"]

    def _init_weights(self, module):
        raise AttributeError("No need to inherit!")


@auto_docstring
class DeepseekVLModel(JanusModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config

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

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

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

        del self.vqmodel
        del self.generation_embeddings
        del self.generation_aligner
        del self.generation_head


class DeepseekVLForConditionalGeneration(JanusForConditionalGeneration):
    output_modalities = ("text",)

    def prepare_embeddings_for_image_generation(self):
        raise AttributeError("Not needed for DeepseekVL")

    def decode_image_tokens(self):
        raise AttributeError("Not needed for DeepseekVL")

    def generate(self):
        raise AttributeError("Not needed for DeepseekVL")


class DeepseekVLImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    min_size (`int`, *optional*, defaults to 14):
        The minimum allowed size for the resized image. Ensures that neither the height nor width
        falls below this value after resizing.
    """

    min_size: int


class DeepseekVLImageProcessorPil(JanusImageProcessorPil):
    def postprocess(self):
        raise AttributeError("Not needed for DeepseekVL")

    def unnormalize(self):
        raise AttributeError("Not needed for DeepseekVL")


class DeepseekVLImageProcessor(JanusImageProcessor):
    def postprocess(self):
        raise AttributeError("Not needed for DeepseekVL")


class DeepseekVLProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {"padding": False},
        "common_kwargs": {"return_tensors": "pt"},
    }


@auto_docstring
class DeepseekVLProcessor(ProcessorMixin):
    def __init__(
        self,
        image_processor,
        tokenizer,
        chat_template=None,
        num_image_tokens=576,
    ):
        r"""
        num_image_tokens (`int`, *optional*, defaults to 576):
            The number of special image tokens used as placeholders for visual content in text sequences.
        """
        self.image_token = tokenizer.image_token
        self.num_image_tokens = num_image_tokens

        super().__init__(image_processor, tokenizer, chat_template=chat_template)

    @auto_docstring
    def __call__(
        self,
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
        images: ImageInput | None = None,
        **kwargs: Unpack[DeepseekVLProcessorKwargs],
    ) -> BatchFeature:
        r"""
        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
        """
        output_kwargs = self._merge_kwargs(
            DeepseekVLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
        )
        if text is None and images is None:
            raise ValueError("You must specify either text or images.")

        if text is not None:
            if isinstance(text, str):
                text = [text]
            elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
                raise ValueError("Invalid input text. Please provide a string, or a list of strings")

        prompt_strings = []
        one_img_tokens = self.image_token * self.num_image_tokens
        for prompt in text:
            prompt = prompt.replace(self.image_token, one_img_tokens)
            prompt_strings.append(prompt)

        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])

        # process images if pixel_values are provided
        if images is not None:
            data["pixel_values"] = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]

        return BatchFeature(data=data)

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


__all__ = [
    "DeepseekVLConfig",
    "DeepseekVLPreTrainedModel",
    "DeepseekVLModel",
    "DeepseekVLForConditionalGeneration",
    "DeepseekVLImageProcessor",
    "DeepseekVLImageProcessorPil",
    "DeepseekVLProcessor",
]
