#    This file may have been modified by Flash-VStream Authors (Flash-VStream Modifications”). All Flash-VStream Modifications are Copyright 2024 Flash-VStream Authors. 
# ------------------------------------------------------------------------
# Based on https://github.com/haotian-liu/LLaVA. Below is the original copyright:
#    Copyright 2023 Haotian Liu
#
#    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
import torch.nn.functional as F
from typing import List, Optional, Tuple, Union
from transformers import AutoConfig, AutoModelForCausalLM, \
                         LlamaConfig, LlamaModel, LlamaForCausalLM
from transformers.modeling_outputs import CausalLMOutputWithPast
from flash_vstream.model.vstream_arch import VStreamMetaModel, VStreamMetaForCausalLM


class VStreamConfig(LlamaConfig):
    model_type = "vstream"


class VStreamLlamaModel(VStreamMetaModel, LlamaModel):
    config_class = VStreamConfig

    def __init__(self, config: LlamaConfig):
        super(VStreamLlamaModel, self).__init__(config)


class VStreamLlamaForCausalLM(VStreamMetaForCausalLM, LlamaForCausalLM):
    config_class = VStreamConfig

    def __init__(self, config):
        super(VStreamLlamaForCausalLM, self).__init__(config)
        self.model = VStreamLlamaModel(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 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,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = True,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        features: Optional[torch.FloatTensor] = None,
        return_dict: Optional[bool] = None,
        cache_position=None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if inputs_embeds is None:
            if self.use_video_streaming_mode:
                (
                    input_ids,
                    position_ids,
                    attention_mask,
                    past_key_values,
                    inputs_embeds,
                    labels
                ) = self.prepare_inputs_labels_for_multimodal_streaming(
                    input_ids,
                    position_ids,
                    attention_mask,
                    past_key_values,
                    labels,
                )
            else:
                (
                    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,
                    features,
                )
        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

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

AutoConfig.register("vstream", VStreamConfig)
AutoModelForCausalLM.register(VStreamConfig, VStreamLlamaForCausalLM)