# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Copyright 2025 The vLLM team.
# Copyright 2025 IBM.
#
# 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 IBM/NASA Prithvi Geospatial model."""

from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Optional, Union

import torch
import torch.nn as nn
from transformers import BatchFeature

from vllm.config import VllmConfig
from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.utils import AutoWeightsLoader
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (ImageItem, ModalityData,
                                    MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalInputs, MultiModalKwargsItems,
                                    PlaceholderRange)
from vllm.multimodal.parse import (DictEmbeddingItems, ModalityDataItems,
                                   MultiModalDataItems, MultiModalDataParser)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors

from .interfaces import (IsAttentionFree, MultiModalEmbeddings,
                         SupportsMultiModal)
from .interfaces_base import default_pooling_type


def _prithvi_field_config(hf_inputs: Mapping[str, torch.Tensor]):
    # This model receives in input a multi-dimensional tensor representing
    # a single image patch and therefore it is not to be split
    # into multiple elements, but rather to be considered a single one.
    # Hence, the decision of using a MultiModalSharedField.
    # The expected shape is (num_channels, width, height).

    # This model however allows the user to also submit multiple image
    # patches as a batch, adding a further dimension to the above shape.
    # At this stage we only support submitting one patch per request and
    # batching is achieved via vLLM batching.
    # TODO (christian-pinto): enable support for multi patch requests
    # in tandem with vLLM batching.
    return dict(
        pixel_values=MultiModalFieldConfig.shared(batch_size=1,
                                                  modality="image"),
        location_coords=MultiModalFieldConfig.shared(batch_size=1,
                                                     modality="image"),
    )


class PrithviGeoSpatialMAEMultiModalDataParser(MultiModalDataParser):

    def _parse_image_data(
        self,
        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
    ) -> Optional[ModalityDataItems[Any, Any]]:
        if isinstance(data, dict):
            return DictEmbeddingItems(
                data,
                modality="image",
                required_fields={"pixel_values", "location_coords"},
                fields_factory=_prithvi_field_config,
            )

        return super()._parse_image_data(data)


class PrithviGeoSpatialMAEProcessingInfo(BaseProcessingInfo):

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


class PrithviGeoSpatialMAEInputBuilder(
        BaseDummyInputsBuilder[PrithviGeoSpatialMAEProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        # This model input is fixed and is in the form of a torch Tensor.
        # The size of pixel_values might change in the cases where we resize
        # the input but never exceeds the dimensions below.
        image_data = {
            "pixel_values": torch.full((6, 512, 512), 1.0,
                                       dtype=torch.float16),
            "location_coords": torch.full((1, 2), 1.0, dtype=torch.float16),
        }

        return {"image": image_data}


class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):

    def _get_data_parser(self) -> MultiModalDataParser:
        return PrithviGeoSpatialMAEMultiModalDataParser()

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _prithvi_field_config(hf_inputs)

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        return []

    def apply(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Optional[Mapping[str, object]] = None,
        mm_hash_overrides: Optional[dict[str, list[str]]] = None,
    ) -> MultiModalInputs:
        if "image" in mm_data:
            image_data = mm_data["image"]
        else:
            image_data = mm_data
            mm_data = {"image": mm_data}

        mm_items = self._to_mm_items(mm_data)
        tokenization_kwargs = tokenization_kwargs or {}
        mm_hashes = (mm_hash_overrides if mm_hash_overrides is not None else
                     self._hash_mm_items(mm_items, hf_processor_mm_kwargs,
                                         tokenization_kwargs))
        mm_placeholders = {"image": [PlaceholderRange(offset=0, length=0)]}

        mm_processed_data = BatchFeature(image_data)

        mm_kwargs = MultiModalKwargsItems.from_hf_inputs(
            mm_processed_data,
            self._get_mm_fields_config(mm_processed_data,
                                       hf_processor_mm_kwargs),
        )

        return MultiModalInputs(
            type="multimodal",
            prompt=prompt,
            prompt_token_ids=[1],
            mm_kwargs=mm_kwargs,
            mm_hashes=mm_hashes,
            mm_placeholders=mm_placeholders,
        )


@default_pooling_type("All")
@MULTIMODAL_REGISTRY.register_processor(
    PrithviGeoSpatialMAEMultiModalProcessor,
    info=PrithviGeoSpatialMAEProcessingInfo,
    dummy_inputs=PrithviGeoSpatialMAEInputBuilder,
)
class PrithviGeoSpatialMAE(nn.Module, IsAttentionFree, SupportsMultiModal):
    """Prithvi Masked Autoencoder"""

    supports_multimodal_raw_input_only = True
    is_pooling_model = True

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

    def _instantiate_model(self, config: dict) -> Optional[nn.Module]:
        # We might be able/need to support different tasks with this same model
        if config["task_args"]["task"] == "SemanticSegmentationTask":
            from terratorch.cli_tools import SemanticSegmentationTask

            task = SemanticSegmentationTask(
                config["model_args"],
                config["task_args"]["model_factory"],
                loss=config["task_args"]["loss"],
                lr=config["task_args"]["lr"],
                ignore_index=config["task_args"]["ignore_index"],
                optimizer=config["task_args"]["optimizer"],
                optimizer_hparams=config["optimizer_params"],
                scheduler=config["task_args"]["scheduler"],
                scheduler_hparams=config["scheduler_params"],
                plot_on_val=config["task_args"]["plot_on_val"],
                freeze_decoder=config["task_args"]["freeze_decoder"],
                freeze_backbone=config["task_args"]["freeze_backbone"],
            )

            return task.model
        else:
            return None

    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        # the actual model is dynamically instantiated using terratorch
        # allowing us to perform changes to the model architecture
        # at startup time (e.g., change the model decoder class.)
        self.model = self._instantiate_model(
            vllm_config.model_config.hf_config.to_dict()["pretrained_cfg"])
        if self.model is None:
            raise ValueError(
                "Unsupported task. "
                "Only SemanticSegmentationTask is supported for now "
                "by PrithviGeospatialMAE.")

        pooler_config = vllm_config.model_config.pooler_config
        assert pooler_config is not None

        self.pooler = DispatchPooler(
            {"encode": Pooler.for_encode(pooler_config)}, )

    def _parse_and_validate_multimodal_data(
            self, **kwargs) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        pixel_values = kwargs.pop("pixel_values", None)
        if not isinstance(pixel_values, torch.Tensor):
            raise ValueError(f"Incorrect type of pixel_values. "
                             f"Got type: {type(pixel_values)}")

        location_coords = kwargs.pop("location_coords", None)
        if not isinstance(location_coords, torch.Tensor):
            raise ValueError(f"Incorrect type of location_coords. "
                             f"Got type: {type(location_coords)}")
        location_coords = torch.unbind(location_coords, dim=0)[0]
        if location_coords.shape == torch.Size([0]):
            location_coords = None

        return pixel_values, location_coords

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        # We do not really use any input tokens and therefore no embeddings
        # to be calculated. However, due to the mandatory token ids in
        # the input prompt we pass one token and the size of the dummy
        # embedding tensors must reflect that.
        return torch.empty((input_ids.shape[0], 0))

    def forward(
        self,
        input_ids: Optional[torch.Tensor],
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ):
        pixel_values, location_coords = (
            self._parse_and_validate_multimodal_data(**kwargs))
        model_output = self.model(pixel_values,
                                  location_coords=location_coords)

        return model_output.output

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        params_list = []
        model_buffers = dict(self.named_buffers())
        loaded_buffers = []
        for key, value in weights:
            if key == "state_dict":
                weights_to_parse = value
                for name, weight in weights_to_parse.items():
                    if "pos_embed" in name:
                        continue

                    if "_timm_module." in name:
                        name = name.replace("_timm_module.", "")

                    # this model requires a couple of buffers to be loaded
                    # that are not loadable with the AutoWeightsLoader
                    if name in model_buffers:
                        if "_timm_module." in name:
                            name = name.replace("_timm_module.", "")
                        buffer = model_buffers[name]
                        weight_loader = getattr(buffer, "weight_loader",
                                                default_weight_loader)
                        weight_loader(buffer, weight)
                        loaded_buffers.append(name)
                    else:
                        params_list.append((name, weight))
                break

        # Load the remaining model parameters
        loader = AutoWeightsLoader(self)
        autoloaded_weights = loader.load_weights(params_list)

        return autoloaded_weights.union(set(loaded_buffers))
