# Copyright 2024 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.
"""Inference-only Idefics3 model compatible with HuggingFace weights."""

import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal, Optional, Union

import torch
from torch import nn
from transformers import (BatchFeature, Idefics3Config, Idefics3ImageProcessor,
                          Idefics3Processor)

from aphrodite.config import AphroditeConfig
from aphrodite.common.sequence import IntermediateTensors
from aphrodite.modeling.layers.linear import ReplicatedLinear
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.vocab_parallel_embedding import ParallelLMHead
from aphrodite.modeling.models.module_mapping import MultiModelKeys
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import MULTIMODAL_REGISTRY
from aphrodite.multimodal.inputs import (MultiModalDataDict,
                                         MultiModalFieldConfig,
                                         MultiModalKwargs)
from aphrodite.multimodal.parse import ImageProcessorItems, ImageSize
# yapf conflicts with isort for this block
# yapf: disable
from aphrodite.multimodal.processing import (BaseMultiModalProcessor,
                                             BaseProcessingInfo,
                                             MultiModalDataItems,
                                             PromptReplacement, PromptUpdate,
                                             PromptUpdateDetails)
# yapf: enable
from aphrodite.multimodal.profiling import BaseDummyInputsBuilder
from aphrodite.quantization import QuantizationConfig
from aphrodite.utils.tensor_schema import TensorSchema, TensorShape

# yapf: disable
from .idefics2_vision_model import (
    Idefics2VisionTransformer as Idefics3VisionTransformer)
# yapf: enable
from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
from .llama import LlamaModel
from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
                    merge_multimodal_embeddings)


class Idefics3ImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - bnp: Batch size * number of images * number of patches
        - c: Number of channels (3)
        - h: Height
        - w: Width
    """
    type: Literal["pixel_values"]
    pixel_values: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
    pixel_attention_mask: torch.Tensor
    num_patches: Annotated[torch.Tensor, TensorShape("bn")]


class Idefics3ImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - f: Image feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """
    type: Literal["image_embeds"]
    data: Annotated[torch.Tensor, TensorShape("bn", "f", "h")]


ImageInputs = Union[Idefics3ImagePixelInputs, Idefics3ImageEmbeddingInputs]


class Idefics3ProcessingInfo(BaseProcessingInfo):

    def get_hf_processor(self, **kwargs: object) -> Idefics3Processor:
        return self.ctx.get_hf_processor(Idefics3Processor, **kwargs)

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None}

    def _resize_output_size(self,
                            *,
                            height: int,
                            width: int,
                            max_len: Optional[int] = None,
                            min_len: int = 1,
                            max_size: Optional[int] = None) -> tuple[int, int]:
        # Set default value for max_len if not provided
        max_len = max(height, width) if max_len is None else max_len
        aspect_ratio = width / height

        # Handle the maximum size constraint
        if max_size is not None:
            max_len = min(max_len, max_size)

        # Adjust dimensions according to the aspect ratio
        if width >= height:
            width = max_len
            height = int(width / aspect_ratio)
        else:
            height = max_len
            width = int(height * aspect_ratio)

        # Ensure both width and height are even (if needed)
        height += height % 2
        width += width % 2

        # Ensure dimensions are not smaller than the minimum length
        height = max(height, min_len)
        width = max(width, min_len)

        return height, width

    def _get_resize_output_image_size(
        self,
        *,
        image_width: int,
        image_height: int,
        resolution_max_side: int,
    ) -> tuple[int, int]:
        hf_processor = self.get_hf_processor()
        image_processor: Idefics3ImageProcessor = hf_processor.image_processor
        max_image_size = image_processor.size['longest_edge']
        if resolution_max_side > max_image_size:
            raise ValueError(
                "`resolution_max_side` cannot be larger than `max_image_size`")

        height, width = image_height, image_width

        # Find the output size, when rescaling the longest edge to max_len and
        # preserving the aspect ratio
        height, width = self._resize_output_size(height=height,
                                                 width=width,
                                                 max_len=resolution_max_side)
        return height, width

    def _get_image_feature_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[Idefics3Processor],
    ) -> tuple[int, int]:
        if processor is None:
            processor = self.get_hf_processor()

        image_processor: Idefics3ImageProcessor = processor.image_processor

        max_image_size = image_processor.max_image_size['longest_edge']
        size = image_processor.size['longest_edge']
        assert size % max_image_size == 0, (
            "`longest_edge` in image_processor's `size` must be divisible by "
            "`longest_edge` in `max_image_size`, this may be caused by "
            "incorrect mm_kwargs override.")

        resized_height, resized_width = self._get_resize_output_image_size(
            image_width=image_width,
            image_height=image_height,
            resolution_max_side=size,
        )
        if resized_height > max_image_size or resized_width > max_image_size:
            grid_h = math.ceil(resized_height / max_image_size)
            grid_w = math.ceil(resized_width / max_image_size)
        else:
            grid_h = grid_w = 0
        return grid_w, grid_h

    def get_num_patches(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[Idefics3Processor],
    ) -> int:
        grid_w, grid_h = self._get_image_feature_grid_size(
            image_width=image_width,
            image_height=image_height,
            processor=processor,
        )

        return grid_w * grid_h + 1

    def _get_image_token(
            self,
            processor: Optional[Idefics3Processor]) -> tuple[str, str, str]:
        if processor is None:
            processor = self.get_hf_processor()

        image_token = processor.image_token
        fake_image_token = processor.fake_image_token
        global_image_token = processor.global_image_tag
        return image_token, fake_image_token, global_image_token

    def get_image_repl(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[Idefics3Processor],
    ) -> str:
        if processor is None:
            processor = self.get_hf_processor()

        image_token, fake_image_token, global_img_token = self._get_image_token(
            processor)
        image_seq_len = processor.image_seq_len
        grid_placeholder = "<row_{n_h}_col_{n_w}>"

        p_img = image_token * image_seq_len
        global_img_placeholder = fake_image_token + global_img_token + p_img
        tile_img_placeholder = fake_image_token + grid_placeholder + p_img

        grid_w, grid_h = self._get_image_feature_grid_size(
            image_width=image_width,
            image_height=image_height,
            processor=processor,
        )
        if grid_w == 0 and grid_h == 0:
            return global_img_placeholder + fake_image_token

        tiles_placeholder = list[str]()
        for i in range(grid_h):
            for j in range(grid_w):
                placeholder_per_tile = tile_img_placeholder.format(n_h=i + 1,
                                                                   n_w=j + 1)
                tiles_placeholder.append(placeholder_per_tile)
                # Add line break if it is the last tile in the row
                if j == grid_w - 1:
                    tiles_placeholder.append("\n")

        return "".join([
            *tiles_placeholder,
            "\n",
            global_img_placeholder,
            fake_image_token,
        ])

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[Idefics3Processor],
    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()

        num_patches = self.get_num_patches(
            image_width=image_width,
            image_height=image_height,
            processor=processor,
        )

        return num_patches * processor.image_seq_len

    def get_image_size_with_most_features(self) -> ImageSize:
        processor = self.get_hf_processor()
        image_processor: Idefics3ImageProcessor = processor.image_processor

        return ImageSize(
            width=image_processor.size["longest_edge"],
            height=image_processor.size["longest_edge"],
        )


class Idefics3DummyInputsBuilder(BaseDummyInputsBuilder[Idefics3ProcessingInfo]
                                 ):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        processor = self.info.get_hf_processor()
        image_token, _, _ = self.info._get_image_token(processor)

        return image_token * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        hf_processor = self.info.get_hf_processor()
        image_processor: Idefics3ImageProcessor = hf_processor.image_processor
        longest_edge = image_processor.max_image_size['longest_edge']

        return {
            "image":
            self._get_dummy_images(width=longest_edge,
                                   height=longest_edge,
                                   num_images=num_images)
        }


class Idefics3MultiModalProcessor(
        BaseMultiModalProcessor[Idefics3ProcessingInfo]):

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        # Text-only input not supported in composite processor
        if not (images := mm_data.get("images", [])):
            prompt_ids = self.info.get_tokenizer().encode(prompt)
            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        processed_outputs = super()._call_hf_processor(
            prompt,
            mm_data,
            mm_kwargs,
            tok_kwargs,
        )

        parsed_images = (self._get_data_parser().parse_mm_data({
            "image": images
        }).get_items("image", ImageProcessorItems))
        image_sizes = [
            parsed_images.get_image_size(i) for i in range(len(parsed_images))
        ]
        hf_processor = self.info.get_hf_processor(**mm_kwargs)

        num_patches = [
            self.info.get_num_patches(
                image_width=size.width,
                image_height=size.height,
                processor=hf_processor,
            ) for size in image_sizes
        ]
        processed_outputs["num_patches"] = torch.tensor(num_patches)

        # Remove the extra batch dimension
        processed_outputs["pixel_values"].squeeze_(0)
        processed_outputs["pixel_attention_mask"].squeeze_(0)

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        num_patches = hf_inputs.get("num_patches", torch.empty(0))

        return dict(
            pixel_values=MultiModalFieldConfig.flat_from_sizes(
                "image", num_patches),
            pixel_attention_mask=MultiModalFieldConfig.flat_from_sizes(
                "image", num_patches),
            image_embeds=MultiModalFieldConfig.batched("image"),
            num_patches=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
        image_token, _, _ = self.info._get_image_token(hf_processor)

        def get_replacement_idefics3(item_idx: int) -> PromptUpdateDetails:
            images = mm_items.get_items("image", ImageProcessorItems)

            image_size = images.get_image_size(item_idx)

            image_repl = self.info.get_image_repl(
                image_width=image_size.width,
                image_height=image_size.height,
                processor=hf_processor,
            )

            return PromptUpdateDetails.select_text(
                image_repl,
                embed_text=image_token,
            )

        return [
            PromptReplacement(
                modality="image",
                target=image_token,
                replacement=get_replacement_idefics3,
            )
        ]


class Idefics3SimpleMLP(nn.Module):

    def __init__(
        self,
        config: Idefics3Config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        input_size = config.vision_config.hidden_size * (config.scale_factor**
                                                         2)
        output_size = config.text_config.hidden_size
        self.proj = ReplicatedLinear(
            input_size,
            output_size,
            bias=False,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "proj"),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        out, _ = self.proj(x)
        return out


class Idefics3Connector(nn.Module):

    def __init__(
        self,
        config: Idefics3Config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.scale_factor = config.scale_factor
        self.modality_projection = Idefics3SimpleMLP(
            config,
            quant_config,
            prefix=maybe_prefix(prefix, "modality_projection"),
        )

    def pixel_shuffle(self,
                      x: torch.Tensor,
                      scale_factor: int = 2) -> torch.Tensor:
        bsz, seq, embed_dim = x.size()
        height = width = int(seq**0.5)
        x = x.view(bsz, height, width, embed_dim)
        x = x.view(bsz, height, int(width / scale_factor),
                   embed_dim * scale_factor)
        x = x.permute(0, 2, 1, 3)
        x = x.reshape(
            bsz,
            int(width / scale_factor),
            int(height / scale_factor),
            embed_dim * (scale_factor**2),
        )
        x = x.permute(0, 2, 1, 3)
        x = x.reshape(bsz, int(seq / (scale_factor**2)),
                      embed_dim * (scale_factor**2))
        return x

    def forward(self, image_hidden_states: torch.Tensor) -> torch.Tensor:
        image_hidden_states = self.pixel_shuffle(image_hidden_states,
                                                 self.scale_factor)
        image_hidden_states = self.modality_projection(image_hidden_states)
        return image_hidden_states


class Idefics3Model(nn.Module):

    def __init__(self, *, aphrodite_config: AphroditeConfig, prefix: str = ""):
        super().__init__()

        config: Idefics3Config = aphrodite_config.model_config.hf_config
        quant_config = aphrodite_config.quant_config

        self.config = config
        self.vocab_size = self.config.text_config.vocab_size
        self.vision_model = Idefics3VisionTransformer(
            config.vision_config,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "vision_model"))
        self.connector = Idefics3Connector(
            config,
            quant_config,
            prefix=maybe_prefix(prefix, "connector"),
        )
        self.text_model = LlamaModel(
            aphrodite_config=aphrodite_config.with_hf_config(config.text_config),
            prefix=maybe_prefix(prefix, "text_model"),
        )

        self.image_seq_len = int(
            ((config.vision_config.image_size //
              config.vision_config.patch_size)**2) / (config.scale_factor**2))
        self.image_token_id = self.config.image_token_id

    def image_pixels_to_features(
        self,
        pixel_values: torch.Tensor,
        pixel_attention_mask: torch.Tensor,
    ) -> torch.Tensor:
        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        pixel_values = pixel_values.to(
            dtype=self.vision_model.embeddings.patch_embedding.weight.dtype
        )  # fp16 compatibility

        # Remove padding images - padding images are full 0.
        nb_values_per_image = pixel_values.shape[1:].numel()
        real_images_inds = (pixel_values == 0.0).sum(
            dim=(-1, -2, -3)) != nb_values_per_image
        pixel_values = pixel_values[real_images_inds].contiguous()

        # Handle the vision attention mask
        # Remove padding images from the mask
        pixel_attention_mask = pixel_attention_mask[
            real_images_inds].contiguous()

        patch_size = self.config.vision_config.patch_size
        patches_subgrid = pixel_attention_mask.unfold(dimension=1,
                                                      size=patch_size,
                                                      step=patch_size)
        patches_subgrid = patches_subgrid.unfold(dimension=2,
                                                 size=patch_size,
                                                 step=patch_size)
        patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()

        # Get sequence from the vision encoder
        image_hidden_states = self.vision_model(
            pixel_values=pixel_values,
            patch_attention_mask=patch_attention_mask,
        )

        return image_hidden_states

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
    ) -> torch.Tensor:
        return self.text_model.get_input_embeddings(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:

        hidden_states = self.text_model(
            input_ids,
            positions,
            intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states


@MULTIMODAL_REGISTRY.register_processor(
    Idefics3MultiModalProcessor,
    info=Idefics3ProcessingInfo,
    dummy_inputs=Idefics3DummyInputsBuilder)
class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
                                       SupportsLoRA):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<image>"

        raise ValueError("Only image modality is supported")

    def __init__(self, *, aphrodite_config: AphroditeConfig, prefix: str = ""):
        super().__init__()

        config = aphrodite_config.model_config.hf_config
        quant_config = aphrodite_config.quant_config
        multimodal_config = aphrodite_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config

        self.model = Idefics3Model(aphrodite_config=aphrodite_config,
                                   prefix=maybe_prefix(prefix, "model"))
        self.image_token_id = self.config.image_token_id

        self.lm_head = ParallelLMHead(
            config.text_config.vocab_size,
            config.text_config.hidden_size,
            quant_config=quant_config,
        )
        if self.config.text_config.tie_word_embeddings:
            self.lm_head.weight = self.model.text_model.wte.weight
        self.logits_processor = LogitsProcessor(config.text_config.vocab_size)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[ImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if image_embeds is not None:
            if not isinstance(image_embeds, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image embeddings. "
                                 f"Got type: {type(image_embeds)}")

            return Idefics3ImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds, concat=True),
            )

        if pixel_values is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

            pixel_attention_mask = kwargs.pop("pixel_attention_mask")
            if not isinstance(pixel_attention_mask, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel_attention_mask. "
                                 f"Got type: {type(pixel_attention_mask)}")

            num_patches = kwargs.pop("num_patches")
            if not isinstance(num_patches, (torch.Tensor, list)):
                raise ValueError("Incorrect type of num_patches. "
                                 f"Got type: {type(num_patches)}")

            expected_h = expected_w = self.config.vision_config.image_size
            return Idefics3ImagePixelInputs(
                type="pixel_values",
                pixel_values=flatten_bn(pixel_values, concat=True),
                pixel_attention_mask=flatten_bn(pixel_attention_mask,
                                                concat=True),
                num_patches=flatten_bn(num_patches, concat=True),
                resolve_bindings={
                    "h": expected_h,
                    "w": expected_w
                },
            )

        raise AssertionError("This line should be unreachable.")

    def _process_image_pixels(
            self, inputs: Idefics3ImagePixelInputs) -> torch.Tensor:
        pixel_values = inputs["pixel_values"]
        pixel_attention_mask = inputs["pixel_attention_mask"]

        return self.model.image_pixels_to_features(
            pixel_values,
            pixel_attention_mask=pixel_attention_mask,
        )

    def _process_image_input(
        self,
        image_input: ImageInputs,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        if image_input["type"] == "image_embeds":
            return image_input["data"]

        image_features = self._process_image_pixels(image_input)
        image_features = self.model.connector(image_features)

        num_patches = image_input["num_patches"]
        return [
            e.flatten(0, 1) for e in image_features.split(num_patches.tolist())
        ]

    def get_language_model(self) -> torch.nn.Module:
        return self.model

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                multimodal_embeddings,
                self.config.image_token_id,
            )
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None

        hidden_states = self.model.text_model(input_ids,
                                              positions,
                                              intermediate_tensors,
                                              inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="model.text_model",
            connector="model.connector",
            tower_model="model.vision_model")
