import os

import pickle

import random

import sys

from typing import List, Optional

import fire

import torch

import transformers

from datasets import load_dataset

import wandb

from torch import nn

from torch.utils.data import Sampler

from transformers.modeling_utils import unwrap_model

from local_config import WANDB_ENTITY

from utils.datacollator import MyDataCollatorForSeq2Seq

from model.lavis.models.blip2_models.modeling_llama_imgemb import LlamaForCausalLM

from peft import (

    LoraConfig,

    get_peft_model,

    get_peft_model_state_dict,

    prepare_model_for_int8_training,

    set_peft_model_state_dict,

)

from transformers import AutoTokenizer, PreTrainedModel

from utils.prompter import Prompter

import logging

logger = logging.getLogger(__name__)

#how are input and instruction put together:

'''

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:

{instruction}

### Response:

or

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:

{instruction}

### Input:

{input}

### Response:

'''

class BalancedSampler(Sampler):

    def __init__(self, true_indices, false_indices):

        self.true_indices = true_indices

        self.false_indices = false_indices

        self.num_samples = 2 * min(len(self.true_indices), len(self.false_indices))

    def __iter__(self):

        # Randomly sample from true_indices

        sampled_true_indices = random.sample(self.true_indices, len(self.false_indices))

        # Merge and shuffle the two lists of indices

        indices = sampled_true_indices + self.false_indices

        random.shuffle(indices)

        return iter(indices)

    def __len__(self):

        return self.num_samples

class InstructTrainer(transformers.Trainer):

    def __init__(self, *args, rep_idxs=None, inst_idxs=None, **kwargs):

        super().__init__(*args, **kwargs)

        self.rep_idxs = rep_idxs

        self.inst_idxs = inst_idxs

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:

        return BalancedSampler(self.rep_idxs, self.inst_idxs)

WEIGHTS_NAME = "pytorch_model.bin"

WEIGHTS_NAME_FINAL = "adapter_model.bin"

TRAINING_ARGS_NAME = "training_args.bin"

class ImgTrainer(transformers.Trainer): #also save img projector

    def _save(self, output_dir: Optional[str] = None, state_dict=None):

        # If we are executing this function, we are the process zero, so we don't check for that.

        output_dir = output_dir if output_dir is not None else self.args.output_dir

        os.makedirs(output_dir, exist_ok=True)

        logger.info(f"Saving model checkpoint to {output_dir}")

        # Save a trained model and configuration using `save_pretrained()`.

        # They can then be reloaded using `from_pretrained()`

        if not isinstance(self.model, PreTrainedModel):

            if state_dict is None:

                state_dict = self.model.state_dict()

                base_state_dict = self.model.base_model.state_dict()

                if 'model.model.img_proj_layer.weight' in base_state_dict:

                    state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']

                    state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']

            if isinstance(unwrap_model(self.model), PreTrainedModel):

                unwrap_model(self.model).save_pretrained(

                    output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors

                )

            else:

                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")

                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))

        else:

            self.model.save_pretrained(

                output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors

            )

        if self.tokenizer is not None:

            self.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model

        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

def save_pretrained(model, save_directory, **kwargs):

    r"""

    This function saves the adapter model and the adapter configuration files to a directory, so that it can be

    reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]

    method.

    Args:

        save_directory (`str`):

            Directory where the adapter model and configuration files will be saved (will be created if it does not

            exist).

        kwargs (additional keyword arguments, *optional*):

            Additional keyword arguments passed along to the `push_to_hub` method.

    """

    if os.path.isfile(save_directory):

        raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")

    os.makedirs(save_directory, exist_ok=True)

    # save only the trainable weights

    output_state_dict = get_peft_model_state_dict(model, kwargs.get("state_dict", None))

    base_state_dict = model.base_model.state_dict()

    if 'model.model.img_proj_layer.weight' in base_state_dict:

        output_state_dict['base_model.model.model.img_proj_layer.weight'] = base_state_dict['model.model.img_proj_layer.weight']

        output_state_dict['base_model.model.model.img_proj_layer.bias'] = base_state_dict['model.model.img_proj_layer.bias']

    torch.save(output_state_dict, os.path.join(save_directory, WEIGHTS_NAME_FINAL))

    inference_mode = model.peft_config.inference_mode

    model.peft_config.inference_mode = True

    model.peft_config.save_pretrained(save_directory)

    model.peft_config.inference_mode = inference_mode

def train(

    # model/data params

    base_model: str = "",  # the only required argument

    lora_weights: str = None,

    data_path: str = "yahma/alpaca-cleaned",

    output_dir: str = "./lora-cxr",

    # training hyperparams

    batch_size: int = 128,

    micro_batch_size: int = 2,

    num_epochs: int = 10,

    learning_rate: float = 3e-4,

    cutoff_len: int = 1024, #256 -> need much more with examples in prompt (1024), 512 for without examples but long IG labels

    val_set_size: int = 5,

    # lora hyperparams

    lora_r: int = 8,

    lora_alpha: int = 16,

    lora_dropout: float = 0.05,

    lora_target_modules: List[str] = [ #default is for llama models

        "q_proj",

        "v_proj",

    ],

    # llm hyperparams

    train_on_inputs: bool = False,  # if False, masks out inputs in loss

    add_eos_token: bool = False,

    group_by_length: bool = False,  # faster, but produces an odd training loss curve

    # wandb params

    wandb_project: str = "lora_training",

    wandb_run_name: str = "lora_mimic_cxr",

    wandb_entity: str = WANDB_ENTITY,

    wandb_watch: str = "",  # options: false | gradients | all

    wandb_log_model: str = "",  # options: false | true

    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter

    prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.

    use_embs=False,

    use_instruct_data=False

):

    if int(os.environ.get("LOCAL_RANK", 0)) == 0:

        print(

            f"Training Alpaca-LoRA model with params:\n"

            f"base_model: {base_model}\n"

            f"lora_weights: {lora_weights}\n"

            f"data_path: {data_path}\n"

            f"output_dir: {output_dir}\n"

            f"batch_size: {batch_size}\n"

            f"micro_batch_size: {micro_batch_size}\n"

            f"num_epochs: {num_epochs}\n"

            f"learning_rate: {learning_rate}\n"

            f"cutoff_len: {cutoff_len}\n"

            f"val_set_size: {val_set_size}\n"

            f"lora_r: {lora_r}\n"

            f"lora_alpha: {lora_alpha}\n"

            f"lora_dropout: {lora_dropout}\n"

            f"lora_target_modules: {lora_target_modules}\n"

            f"train_on_inputs: {train_on_inputs}\n"

            f"add_eos_token: {add_eos_token}\n"

            f"group_by_length: {group_by_length}\n"

            f"wandb_project: {wandb_project}\n"

            f"wandb_run_name: {wandb_run_name}\n"

            f"wandb_entity: {wandb_entity}\n"

            f"wandb_watch: {wandb_watch}\n"

            f"wandb_log_model: {wandb_log_model}\n"

            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"

            f"prompt template: {prompt_template_name}\n"

        )

    assert (

        base_model

    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"

    gradient_accumulation_steps = batch_size // micro_batch_size

    prompter = Prompter(prompt_template_name)

    device_map = "auto"

    world_size = int(os.environ.get("WORLD_SIZE", 1))

    ddp = world_size != 1

    if ddp:

        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}

        gradient_accumulation_steps = gradient_accumulation_steps // world_size

    # Check if parameter passed or if set within environ

    use_wandb = len(wandb_project) > 0 or (

        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0

    )

    # Only overwrite environ if wandb param passed

    if len(wandb_project) > 0:

        os.environ["WANDB_PROJECT"] = wandb_project

    if len(wandb_watch) > 0:

        os.environ["WANDB_WATCH"] = wandb_watch

    if len(wandb_log_model) > 0:

        os.environ["WANDB_LOG_MODEL"] = wandb_log_model

    if base_model == 'vicuna_v13':

        model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-13b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)

        tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3", use_fast=False, truncation_side="right", padding_side="right")

    else: #7b

        model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3", torch_dtype=torch.float16, device_map='auto', load_in_8bit=False)

        tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3", use_fast=False, truncation_side="right", padding_side="right")

    tokenizer.pad_token = tokenizer.unk_token

    if use_embs:

        model.base_model.img_proj_layer = nn.Linear(768, model.base_model.config.hidden_size).to(model.base_model.device)

    # add special token to tokenizer

    tokenizer.add_special_tokens({"additional_special_tokens": ["<IMG>"]})

    model.resize_token_embeddings(len(tokenizer))

    def tokenize(prompt, add_eos_token=True):

        # there's probably a way to do this with the tokenizer settings

        # but again, gotta move fast

        result = tokenizer(

            prompt,

            truncation=True,

            max_length=cutoff_len,

            padding=False,

            return_tensors=None,

        )

        if (

            result["input_ids"][-1] != tokenizer.eos_token_id

            and len(result["input_ids"]) < cutoff_len

            and add_eos_token

        ):

            result["input_ids"].append(tokenizer.eos_token_id)

            result["attention_mask"].append(1)

        result["labels"] = result["input_ids"].copy()

        return result

    def generate_and_tokenize_prompt(data_point):

        full_prompt = prompter.generate_prompt(

            data_point["instruction"],

            data_point["input"],

            data_point["output"],

        )

        tokenized_full_prompt = tokenize(full_prompt)

        if not train_on_inputs:

            user_prompt = prompter.generate_prompt(

                data_point["instruction"], data_point["input"]

            )

            tokenized_user_prompt = tokenize(

                user_prompt, add_eos_token=add_eos_token

            )

            user_prompt_len = len(tokenized_user_prompt["input_ids"])

            if add_eos_token:

                user_prompt_len -= 1

            tokenized_full_prompt["labels"] = [

                -100

            ] * user_prompt_len + tokenized_full_prompt["labels"][

                user_prompt_len:

            ]  # could be sped up, probably

        return tokenized_full_prompt

    model = prepare_model_for_int8_training(model)

    config = LoraConfig(

        r=lora_r,

        lora_alpha=lora_alpha,

        target_modules=lora_target_modules,

        lora_dropout=lora_dropout,

        bias="none",

        task_type="CAUSAL_LM",

    )

    model = get_peft_model(model, config) #this sets requires_grad for all params to False

    # unfreeze the img_proj_layer

    model.model.base_model.img_proj_layer.weight.requires_grad = True

    model.model.base_model.img_proj_layer.bias.requires_grad = True

    print("Loading data from ", data_path)

    if data_path.endswith(".json") or data_path.endswith(".jsonl"):

        data = load_dataset("json", data_files=data_path)

    else:

        data = load_dataset(data_path)

    if resume_from_checkpoint:

        # Check the available weights and load them

        checkpoint_name = os.path.join(

            resume_from_checkpoint, "pytorch_model.bin"

        )  # Full checkpoint

        if not os.path.exists(checkpoint_name):

            checkpoint_name = os.path.join(

                resume_from_checkpoint, "adapter_model.bin"

            )  # only LoRA model - LoRA config above has to fit

            resume_from_checkpoint = (

                False  # So the trainer won't try loading its state

            )

        # The two files above have a different name depending on how they were saved, but are actually the same.

        if os.path.exists(checkpoint_name):

            print(f"Restarting from {checkpoint_name}")

            adapters_weights = torch.load(checkpoint_name)

            set_peft_model_state_dict(model, adapters_weights)

        else:

            print(f"Checkpoint {checkpoint_name} not found")

    model.print_trainable_parameters()  # Be more transparent about the % of trainable params.

    if val_set_size > 0:

        train_val = data["train"].train_test_split(

            test_size=val_set_size, shuffle=True, seed=42

        )

        train_data = (

            train_val["train"].shuffle().map(generate_and_tokenize_prompt)

        )

        val_data = (

            train_val["test"].shuffle().map(generate_and_tokenize_prompt)

        )

    else:

        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)

        val_data = None

    if use_instruct_data:

        report_indices = [i for i, item in enumerate(train_data) if item['is_report']][:5]

        instruct_indices = [i for i, item in enumerate(train_data) if not item['is_report']][:5]

    if not ddp and torch.cuda.device_count() > 1:

        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available

        model.is_parallelizable = True

        model.model_parallel = True

    wandb.init(

        project=wandb_project,

        entity=wandb_entity,

        name=wandb_run_name

    )

    if use_instruct_data:

        trainer = InstructTrainer(

            model=model,

            train_dataset=train_data,

            eval_dataset=val_data,

            rep_idxs = report_indices,

            inst_idxs = instruct_indices,

            args=transformers.TrainingArguments(

                per_device_train_batch_size=micro_batch_size,

                gradient_accumulation_steps=gradient_accumulation_steps,

                warmup_steps=100,

                num_train_epochs=num_epochs,

                learning_rate=learning_rate,

                fp16=True,

                logging_steps=10,

                optim="adamw_torch",

                evaluation_strategy="steps" if val_set_size > 0 else "no",

                save_strategy="steps",

                eval_steps=200 if val_set_size > 0 else None,

                save_steps=200,

                output_dir=output_dir,

                save_total_limit=None,

                load_best_model_at_end=True if val_set_size > 0 else False,

                ddp_find_unused_parameters=False if ddp else None,

                group_by_length=group_by_length,

                report_to="wandb" if use_wandb else None,

                run_name=wandb_run_name if use_wandb else None,

                max_steps=-1,

                dataloader_num_workers=8,

                remove_unused_columns=False if use_embs else True,

            ),

            data_collator=MyDataCollatorForSeq2Seq(

                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True

            ) if use_embs else

            transformers.DataCollatorForSeq2Seq(

                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True

            ),

        )

    else:

        trainer = ImgTrainer(

            model=model,

            train_dataset=train_data,

            eval_dataset=val_data,

            args=transformers.TrainingArguments(

                per_device_train_batch_size=micro_batch_size,

                gradient_accumulation_steps=gradient_accumulation_steps,

                warmup_steps=100,

                num_train_epochs=num_epochs,

                learning_rate=learning_rate,

                fp16=True,

                logging_steps=10,

                optim="adamw_torch",

                evaluation_strategy="steps" if val_set_size > 0 else "no",

                save_strategy="steps",

                eval_steps=400 if val_set_size > 0 else None,

                save_steps=400,

                output_dir=output_dir,

                save_total_limit=None,

                load_best_model_at_end=True if val_set_size > 0 else False,

                ddp_find_unused_parameters=False if ddp else None,

                group_by_length=group_by_length,

                report_to="wandb" if use_wandb else None,

                run_name=wandb_run_name if use_wandb else None,

                max_steps=-1,

                dataloader_num_workers=8,

                remove_unused_columns=False if use_embs else True

            ),

            data_collator=MyDataCollatorForSeq2Seq(

                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True

            ) if use_embs else

            transformers.DataCollatorForSeq2Seq(

                tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True

            ),

        )

    model.config.use_cache = False

    old_state_dict = model.state_dict

    model.state_dict = (

        lambda self, *_, **__: get_peft_model_state_dict(

            self, old_state_dict()

        )

    ).__get__(model, type(model))

    if torch.__version__ >= "2" and sys.platform != "win32":

        model = torch.compile(model)

    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

    save_pretrained(model, output_dir)

    print(

        "\n If there's a warning about missing keys above, please disregard :)"

    )

if __name__ == "__main__":

    fire.Fire(train)