# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  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.

"""Pretrain GPT"""

import torch
import torch.nn.functional as F
from functools import partial
from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron.data.vit_dataset import build_train_valid_datasets
from megatron.data.gpt_dataset import build_train_valid_test_datasets, build_dataset_group
from megatron.enums import AttnMaskType
from megatron.model import GPTModel, GPTModelPipe
from megatron.model.vit_model import VitModel, VitModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids, get_prefix_indices
from megatron.utils import average_losses_across_data_parallel_group


import deepspeed
from deepspeed.runtime.utils import see_memory_usage
import os

try:
    from torch.distributed.elastic.multiprocessing.errors import record
except ImportError:
    # noop
    def record(fn):
        return fn

def model_provider(pre_process=True, post_process=True):
    """Build the model."""

    print_rank_0('building GPT model ...')
    see_memory_usage(f"Before Building Model", force=True)

    args = get_args()

    with deepspeed.zero.Init(data_parallel_group=mpu.get_data_parallel_group(),
                             remote_device=None if args.remote_device == 'none' else args.remote_device,
                             config_dict_or_path=args.deepspeed_config,
                             enabled=args.zero_stage == 3,
                             mpu=mpu):
        if args.deepspeed:
            args.pretrain_causal_attention = True
            model = VitModelPipe(num_classes=args.num_classes, finetune=False, attn_mask_type=AttnMaskType.causal)
            # This is a hack to give us a reference to get_batch_pipe from within training.py
            # We need to call model.set_batch_fn after deepspeed.initialize
            model._megatron_batch_fn = get_batch_pipe
        else:
            model = VitModel(num_classes=args.num_classes)
    see_memory_usage(f"After Building Model", force=True)
    return model


def get_batch(data_iterator):
    """Build the batch."""
    if data_iterator is not None:
        data = next(data_iterator)
        # only data parallelism; no need for broadcast
        images = data[0].cuda()
        labels = data[1].cuda()

        return images, labels

def get_batch_pipe(data):
    images = data[0].cuda()
    labels = data[1].cuda()
    return (images), (labels)

def forward_step(data_iterator, model):
    """Forward step."""
    timers = get_timers()


    # Get the batch.
    timers("batch-generator").start()
    (
        images,
        labels,
    ) = get_batch(data_iterator)
    timers("batch-generator").stop()

    # Forward model. lm_labels
    logits = model(images).contiguous().float()
    loss = F.cross_entropy(logits, labels)

    outputs = torch.argmax(logits, -1)
    correct = (outputs == labels).float()
    accuracy = torch.mean(correct)

    averaged_loss = average_losses_across_data_parallel_group([loss, accuracy])

    return loss, {"loss": averaged_loss[0], "accuracy": averaged_loss[1]}


def train_valid_test_datasets_provider(train_val_test_num_samples):
    """Build train, valid, and test datasets."""
    args = get_args()

    print_rank_0(
        "> building train, validation, and test datasets " "for VIT ..."
    )
    train_ds, valid_ds, test_ds = build_train_valid_datasets(data_path=args.data_path)
    print_rank_0("> finished creating VIT datasets ...")

    return train_ds, valid_ds, test_ds

@record
def main():
    pretrain(
        train_valid_test_datasets_provider,
        model_provider,
        forward_step,
        args_defaults={'dataloader_type': 'cyclic'}
    )

if __name__ == "__main__":
    main()


'''
def get_batch(data_iterator):
    """Generate a batch"""
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ['text']
    datatype = torch.int64

    # Broadcast data.
    if data_iterator is not None:
        data = next(data_iterator)
    else:
        data = None
    data_b = mpu.broadcast_data(keys, data, datatype)

    # Unpack.
    tokens_ = data_b['text'].long()
    labels = tokens_[:, 1:].contiguous()
    tokens = tokens_[:, :-1].contiguous()

    # Get the masks and postition ids.
    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
        tokens,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss,
        prefix_indices=None,
        loss_on_targets_only=args.loss_on_targets_only
    )

    return tokens, labels, loss_mask, attention_mask, position_ids


def get_batch_pipe(data):
    """Modification of `get_batch` to work on `next(data_iterator)` instead of `data_iterator`"""
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ['text']
    datatype = torch.int64

    # Broadcast data.
    data_b = mpu.broadcast_data(keys, data, datatype)

    # Unpack.
    tokens_ = data_b['text'].long()
    labels = tokens_[:, 1:].contiguous()
    tokens = tokens_[:, :-1].contiguous()

    # Get the masks and position ids.
    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
        tokens,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss,
        prefix_indices=None,
        loss_on_targets_only=args.loss_on_targets_only
    )
    if args.curriculum_learning and args.curriculum_seqlen < tokens.size()[1]:
        # seqlen-based curriculum learning
        # tokens, position_ids, labels, loss_mask have size [batch size, seqlen]
        tokens = tokens[:, :args.curriculum_seqlen].contiguous()
        position_ids = position_ids[:, :args.curriculum_seqlen].contiguous()
        labels = labels[:, :args.curriculum_seqlen].contiguous()
        loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()

    return (tokens, position_ids, attention_mask), (labels, loss_mask)


def loss_func(loss_mask, output_tensor):
    losses = output_tensor.float()
    loss_mask = loss_mask.view(-1).float()
    loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()

    # Reduce loss for logging.
    averaged_loss = average_losses_across_data_parallel_group([loss])

    return loss, {'lm loss': averaged_loss[0]}


def forward_step(data_iterator, model):
    """Forward step."""
    args = get_args()
    timers = get_timers()

    # Get the batch.
    timers('batch-generator').start()
    tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
        data_iterator)
    timers('batch-generator').stop()

    output_tensor = model(tokens, position_ids, attention_mask,
                          labels=labels)
    if args.curriculum_learning and args.curriculum_seqlen < args.seq_length:
        loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()

    return output_tensor, partial(loss_func, loss_mask)


def train_valid_test_datasets_provider(train_val_test_num_samples):
    """Build train, valid, and test datasets."""
    args = get_args()
    train_ds, valid_ds, test_ds = None, None, None

    print_rank_0('> building train, validation, and test datasets for GPT ...')
    # Option 1 of data loading using --data-path

    if args.data_path:
        train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
            data_prefix=args.data_path,
            data_impl=args.data_impl,
            splits_string=args.split,
            train_valid_test_num_samples=train_val_test_num_samples,
            seq_length=args.seq_length,
            seed=args.seed,
            skip_warmup=(not args.mmap_warmup))
    # Option 2 of data loading using --(train|valid|test)-weighted-split-paths
    elif args.train_weighted_split_paths:
        assigned_train_valid_test = []
        if args.train_weighted_split_paths is not None:
            train_ds = []
            assigned_train_valid_test.append("train")
        if args.valid_weighted_split_paths is not None:
            valid_ds = []
            assigned_train_valid_test.append("valid")
        if args.test_weighted_split_paths is not None:
            test_ds = []
            assigned_train_valid_test.append("test")

        for s in assigned_train_valid_test:
            data_groups = zip(eval(f"args.{s}_weighted_split_paths"),
                                eval(f"args.{s}_weighted_split_weights"),
                                eval(f"args.{s}_weighted_split_splits"),
                                eval(f"args.{s}_weighted_split_names"))
            for paths, weights, splits, name in data_groups:
                d = build_dataset_group(name, paths, weights, splits,
                                        args.data_impl,
                                        train_val_test_num_samples,
                                        args.seq_length, args.seed,
                                        (not args.mmap_warmup),
                                        train_valid_test=s)
                eval(f"{s}_ds").append(d)
    else:
        raise NotImplementedError("No dataloading argument passed")

    print_rank_0("> finished creating GPT datasets ...")
    return train_ds, valid_ds, test_ds

@record
def main():
    pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
             args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})

if __name__ == "__main__":
    main()
'''