# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0

# This file is modified from https://github.com/haotian-liu/LLaVA/

from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModelForCausalLM, MixtralConfig, MixtralForCausalLM, MixtralModel
from transformers.modeling_outputs import CausalLMOutputWithPast

from ..llava_arch import LlavaMetaForCausalLM, LlavaMetaModel


class LlavaMixtralConfig(MixtralConfig):
    model_type = "llava_mixtral"
    pretraining_tp = 1


class LlavaMixtralModel(MixtralModel, LlavaMetaModel):
    config_class = LlavaMixtralConfig

    def __init__(self, config: MixtralConfig):
        super().__init__(config)


class LlavaMixtralForCausalLM(MixtralForCausalLM, LlavaMetaForCausalLM):
    config_class = LlavaMixtralConfig

    def __init__(self, config):
        super(MixtralForCausalLM, self).__init__(config)
        self.model = LlavaMixtralModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def get_model(self):
        return self.model

    def get_lm_head(self):
        return self.lm_head

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        seqlens_in_batch: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels,
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids, position_ids, attention_mask, past_key_values, labels, images
            )
        if self.training:
            (
                _,
                new_position_ids,
                new_attention_mask,
                _,
                new_inputs_embeds,
                new_labels,
                sorted_seqlens_in_batch,
            ) = self.repack_multimodal_data(
                input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels
            )
            if sorted_seqlens_in_batch is None:
                sorted_seqlens_in_batch = seqlens_in_batch
            new_input_ids = None
            past_key_values = None
        else:
            new_attention_mask = attention_mask
            new_position_ids = position_ids
            new_inputs_embeds = inputs_embeds
            new_labels = labels
            sorted_seqlens_in_batch = attention_mask.sum(-1).int()
            new_input_ids = input_ids

        outputs = super().forward(
            input_ids=new_input_ids,
            attention_mask=new_attention_mask,
            position_ids=new_position_ids,
            past_key_values=past_key_values,
            inputs_embeds=new_inputs_embeds,
            labels=new_labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            seqlens_in_batch=sorted_seqlens_in_batch,
        )
        return outputs

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        _inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        if images is not None:
            _inputs["images"] = images
        return _inputs


AutoConfig.register("llava_mixtral", LlavaMixtralConfig)
AutoModelForCausalLM.register(LlavaMixtralConfig, LlavaMixtralForCausalLM)