#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 Santiago Hincapie-Potes & The HuggingFace 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.
"""
Training a CLIP like dual encoder models using text and vision encoders in the library.

The script can be used to train CLIP like models for languages other than english by using
a text encoder pre-trained in the desired language. Currently this script support the following vision
and text models:
Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip)
Text models: BERT, ROBERTa (https://huggingface.co/models?filter=masked-lm)
"""

import logging
import os
import sys
import time
import getpass
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Optional

import torch
from torch.utils.data import ConcatDataset
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
from tqdm import tqdm

import jax
import jax.numpy as jnp
import numpy as onp
import optax
import transformers
from flax import jax_utils
from flax.jax_utils import unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, shard, shard_prng_key
from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, is_tensorboard_available, set_seed
import wandb

from src.modeling_medclip import FlaxMedCLIP
from src.datasets_medclip import MIMICDataset, ROCODataset

logger = logging.getLogger(__name__)

# Cache the result
has_tensorboard = is_tensorboard_available()
if has_tensorboard:
    try:
        from flax.metrics.tensorboard import SummaryWriter
    except ImportError as ie:
        has_tensorboard = False
        print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")

else:
    print(
        "Unable to display metrics through TensorBoard because the package is not installed: "
        "Please run pip install tensorboard to enable."
    )

@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    text_model_name_or_path: str = field(
        metadata={
            "help": "The text model checkpoint for weights initialization."
            "Don't set if you want to train a model from scratch."
        },
    )
    vision_model_name_or_path: str = field(
        metadata={
            "help": "The vision model checkpoint for weights initialization."
            "Don't set if you want to train a model from scratch."
        },
    )
    from_pt: bool = field(
        default=True,
        metadata={"help": "whether to load the text and vision model using PyTorch checkpoints."},
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    dtype: Optional[str] = field(
        default="float32",
        metadata={
            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    mimic_data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory with that containing the MIMIC-CXD dataset."})
    mimic_train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines file)."}
    )
    mimic_validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
    )
    mimic_mode: Optional[str] = field(default=None, metadata={"help": "longest or docs"})
    roco_data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory with that containing the ROCO dataset."})
    max_seq_length: Optional[int] = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=32,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )

    def __post_init__(self):
        if self.mimic_train_file is None and self.mimic_validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
        else:
            if self.mimic_train_file is not None:
                extension = self.mimic_train_file.split(".")[-1]
                assert extension == "json", "`train_file` should be a json file."
            if self.mimic_validation_file is not None:
                extension = self.mimic_validation_file.split(".")[-1]
                assert extension == "json", "`validation_file` should be a json file."


# We use torchvision for faster image pre-processing.
# We need to ensure faster processing speed as it can become a bottleneck on TPU
class Transform(torch.nn.Module):
    def __init__(self, image_size):
        super().__init__()
        self.transforms = torch.nn.Sequential(
            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
            CenterCrop(image_size),
            ConvertImageDtype(torch.float),
            Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        with torch.no_grad():
            x = self.transforms(x)
        return x

class TrainState(train_state.TrainState):
    dropout_rng: jnp.ndarray

    def replicate(self):
        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))


def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
    summary_writer.scalar("train_time", train_time, step)

    train_metrics = get_metrics(train_metrics)
    for key, vals in train_metrics.items():
        tag = f"train_{key}"
        for i, val in enumerate(vals):
            summary_writer.scalar(tag, val, step - len(vals) + i + 1)

    for metric_name, value in eval_metrics.items():
        summary_writer.scalar(f"eval_{metric_name}", value, step)


def create_learning_rate_fn(
    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
    """Returns a linear warmup, linear_decay learning rate function."""
    steps_per_epoch = train_ds_size // train_batch_size
    num_train_steps = steps_per_epoch * num_train_epochs
    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
    decay_fn = optax.linear_schedule(
        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
    )
    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
    return schedule_fn


def main():
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if jax.process_index() == 0:
        wandb.init(
            entity=getpass.getuser(),
            project='medclip',
            sync_tensorboard=True
        )

        wandb.config.update(model_args)
        wandb.config.update(data_args)
        wandb.config.update(training_args)

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty."
            "Use --overwrite_output_dir to overcome."
        )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    # Setup logging, we only want one process per machine to log things on the screen.
    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
    if jax.process_index() == 0:
        transformers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()

    # Set the verbosity to info of the Transformers logger (on main process only):
    logger.info(f"Training/evaluation parameters {training_args}")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
        )
    elif model_args.text_model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.text_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    model = FlaxMedCLIP.from_text_vision_pretrained(
        model_args.text_model_name_or_path,
        model_args.vision_model_name_or_path,
        seed=training_args.seed,
        dtype=getattr(jnp, model_args.dtype),
        text_from_pt=model_args.from_pt,
        vision_from_pt=model_args.from_pt,
    )
    config = model.config
    # set seed for torch dataloaders
    set_seed(training_args.seed)

    # Initialize torchvision transforms and jit them for faster processing
    preprocess = Transform(config.vision_config.image_size)
    preprocess = torch.jit.script(preprocess)

    _train_datasets = []
    _eval_datasets = []

    if data_args.mimic_data_dir is not None:
        # Initialize the image-text dataset
        _train_datasets.append(
            MIMICDataset(
                data_args.mimic_data_dir,
                data_args.mimic_train_file,
                transform=preprocess,
                mode=data_args.mimic_mode,
            )
        )

        _eval_datasets.append(
            MIMICDataset(
                data_args.mimic_data_dir,
                data_args.mimic_validation_file,
                transform=preprocess,
                mode=data_args.mimic_mode,
            )
        )

    if data_args.roco_data_dir is not None:
        _train_datasets.append(
            ROCODataset(
                data_args.roco_data_dir,
                split="train",
                transform=preprocess,
            )
        )

        _eval_datasets.append(
            ROCODataset(
                data_args.roco_data_dir,
                split="validate",
                transform=preprocess,
            )
        )

    if not _train_datasets or not _eval_datasets:
        raise ValueError
    else:
        train_dataset = ConcatDataset(_train_datasets)
        eval_dataset = ConcatDataset(_eval_datasets)

    # Store some constant
    num_epochs = int(training_args.num_train_epochs)
    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
    steps_per_epoch = len(train_dataset) // train_batch_size
    total_train_steps = steps_per_epoch * num_epochs

    # Use collate function to tokenizer the text and convert the processed images to numpy
    def collate_fn(examples):
        pixel_values = torch.stack([example[0] for example in examples]).permute(0, 2, 3, 1).numpy()
        texts = [example[1] for example in examples]

        inputs = tokenizer(
            texts,
            max_length=data_args.max_seq_length, 
            padding="max_length",
            return_tensors="np",
            truncation=True,
        )

        batch = {
            "pixel_values": pixel_values,
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
        }

        return batch

    # Create data loaders
    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=train_batch_size,
        shuffle=True,
        num_workers=data_args.preprocessing_num_workers,
        persistent_workers=True,
        drop_last=True,
        pin_memory=True,
        collate_fn=collate_fn,
    )

    eval_loader = torch.utils.data.DataLoader(
        eval_dataset,
        batch_size=eval_batch_size,
        shuffle=False,
        num_workers=data_args.preprocessing_num_workers,
        persistent_workers=True,
        drop_last=True,
        pin_memory=True,
        collate_fn=collate_fn,
    )

    # Enable tensorboard only on the master node
    if has_tensorboard and jax.process_index() == 0:
        log_dir = Path(training_args.output_dir).joinpath("logs").as_posix()
        summary_writer = SummaryWriter(log_dir=log_dir)

    # Initialize our training
    rng = jax.random.PRNGKey(training_args.seed)
    rng, dropout_rng = jax.random.split(rng)

    """
    # Create learning rate schedule
    linear_decay_lr_schedule_fn = create_learning_rate_fn(
        len(train_dataset),
        train_batch_size,
        training_args.num_train_epochs,
        training_args.warmup_steps,
        training_args.learning_rate,
    )
    """

    cosine_decay_lr_schedule_fn = optax.cosine_decay_schedule(
        training_args.learning_rate,
        training_args.warmup_steps,
        training_args.learning_rate / 1000,
    )

    # create adam optimizer
    adamw = optax.lamb(
        learning_rate=cosine_decay_lr_schedule_fn, #linear_decay_lr_schedule_fn,
        b1=training_args.adam_beta1,
        b2=training_args.adam_beta2,
        eps=training_args.adam_epsilon,
        weight_decay=training_args.weight_decay,
    )

    # Setup train state
    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)

    def cross_entropy(logits, axis):
        logprobs = jax.nn.log_softmax(logits, axis=axis)
        nll = jnp.diag(logprobs)
        ce = -jnp.mean(nll)
        return ce

    def clip_loss(similarity):
        loss = (cross_entropy(similarity, axis=0) + cross_entropy(similarity, axis=1)) / 2
        return loss

    # Define gradient update step fn
    def train_step(state, batch):
        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)

        def compute_loss(params):
            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
            loss = clip_loss(logits)
            return loss

        grad_fn = jax.value_and_grad(compute_loss)
        loss, grad = grad_fn(state.params)
        grad = jax.lax.pmean(grad, "batch")

        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)

        metrics = {"loss": loss, "learning_rate": cosine_decay_lr_schedule_fn(state.step)}
        metrics = jax.lax.pmean(metrics, axis_name="batch")

        return new_state, metrics

    # Define eval fn
    def eval_step(params, batch):
        logits = model(**batch, params=params, train=False)[0]
        loss = clip_loss(logits)

        # summarize metrics
        metrics = {"loss": loss}
        metrics = jax.lax.pmean(metrics, axis_name="batch")
        return metrics

    # Create parallel version of the train and eval step
    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
    p_eval_step = jax.pmap(eval_step, "batch")

    # Replicate the train state on each device
    state = state.replicate()

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {num_epochs}")
    logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel & distributed) = {train_batch_size}")
    logger.info(f"  Total optimization steps = {total_train_steps}")

    train_time = 0
    # Create sampling rng
    rng, input_rng = jax.random.split(rng)

    #jax.profiler.start_trace(log_dir)
    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
    for epoch in epochs:
        # ======================== Training ================================
        train_start = time.time()

        # Create sampling rng
        rng, input_rng = jax.random.split(rng)
        train_metrics = []

        steps_per_epoch = len(train_dataset) // train_batch_size
        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
        # train
        for batch in train_loader:
            batch = shard(batch)
            state, train_metric = p_train_step(state, batch)
            train_metrics.append(train_metric)

            train_step_progress_bar.update(1)

        train_time += time.time() - train_start

        train_metric = unreplicate(train_metric)

        train_step_progress_bar.close()
        epochs.write(
            f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
        )

        # ======================== Evaluating ==============================
        eval_metrics = []
        eval_steps = len(eval_dataset) // eval_batch_size
        eval_step_progress_bar = tqdm(total=eval_steps, desc="Evaluating...", position=2, leave=False)
        for batch in eval_loader:
            # Model forward
            batch = shard(batch)
            metrics = p_eval_step(state.params, batch)
            eval_metrics.append(metrics)

            eval_step_progress_bar.update(1)

        # normalize eval metrics
        eval_metrics = get_metrics(eval_metrics)

        eval_metrics = jax.tree_map(jnp.mean, eval_metrics)

        # Print metrics and update progress bar
        eval_step_progress_bar.close()
        desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']})"
        epochs.write(desc)
        epochs.desc = desc

        # Save metrics
        if has_tensorboard and jax.process_index() == 0:
            cur_step = epoch * (len(train_dataset) // train_batch_size)
            write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)

        # save checkpoint after each epoch and push checkpoint to the hub
        if jax.process_index() == 0:
            params = jax.device_get(unreplicate(state.params))
            model.save_pretrained(
                training_args.output_dir,
                params=params,
                push_to_hub=training_args.push_to_hub,
                commit_message=f"Saving weights and logs of epoch {epoch+1}",
            )

    #jax.profiler.stop_trace()

    return model, params


if __name__ == "__main__":
    model, params = main()
    model.save_pretrained("model", params=params)