from google.colab import drive

drive.mount("/content/drive")

with h5py.File("dataset_top10/16069.hdf5", "r") as fread:
    keys = list(fread.keys())
    print(keys)
    group = fread[keys[0]]
    print(group.keys())
    feature = group["feature"][:]
    token = group["token"][:]
    print(feature.shape)
    print(token)

import copy
import json
import math
import os
import random
import sys
from functools import partial
from inspect import signature
from pathlib import Path
from typing import (
    Any,
    Dict,
    List
)

# Third party's modules
import numpy as np

import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import (
    DataLoader)

from torchvision.transforms import Compose

# Local modules
sys.path.append("modules_gislr")
from modules_gislr.dataset import (
    HDF5Dataset,
    merge_padded_batch)
from modules_gislr.defines import (
    get_fullbody_landmarks
)
from modules_gislr.layers import (
    Identity,
    GPoolRecognitionHead,
    TransformerEnISLR
)
from modules_gislr.train_functions import (
    test_loop,
    val_loop,
    train_loop
)
from modules_gislr.transforms import (
    PartsBasedNormalization,
    ReplaceNan,
    SelectLandmarksAndFeature,
    ToTensor
)

class RandomNoise():
    def __init__(self,
                 apply_ratio,
                 scale_range,
                 scale_unit="asis",
                 noise_type="uniform",
                 target_joints=None,
                 feature_dim=3,
                 include_conf=True):
        assert noise_type in ["uniform", "gauss"]
        self.apply_ratio = apply_ratio
        self.scale_range = np.array(scale_range)
        self.scale_unit = scale_unit
        self.noise_type = noise_type
        self.feature_dim = feature_dim
        self.include_conf = include_conf
        self.target_joints = target_joints

    def __call__(self,
                 data: Dict[str, Any]) -> Dict[str, Any]:

        if random.random() > self.apply_ratio:
            return data

        feature = data["feature"]
        # Remove confidence if it's included.
        if self.include_conf:
            confs = feature[-1:, :, :]
            feature = feature[:-1, :, :]
        else:
            confs = None

        # `[C, T, J]`
        target = feature[:, :, self.target_joints]
        # Generate mask to prevent adding noise to failed tracking.
        temp = target.reshape([target.shape[0], -1])
        bmask = (temp == 0).all(axis=0)
        bmask = np.bitwise_not(bmask)
        if bmask.any() == np.False_:
            return data
        # Mask for post-process.
        mask = bmask.reshape(
            [1, target.shape[1], target.shape[2]]).astype(target.dtype)

        # Calculate real scale.
        if self.scale_unit == "box_ratio":
            mins = temp[:, bmask].min(axis=1)
            maxes = temp[:, bmask].max(axis=1)
            size = np.linalg.norm(maxes - mins)
            scale_range = size * self.scale_range
        else:
            scale_range = self.scale_range

        # Apply augmentation.
        aug_scale = np.random.random() * (scale_range[1] - scale_range[0]) + scale_range[0]
        if self.noise_type == "uniform":
            target += np.random.uniform(low=-aug_scale, high=aug_scale, size=target.shape)
        elif self.noise_type == "gauss":
            target += np.random.normal(loc=0.0, scale=aug_scale, size=target.shape)

        # Filter out failed tracking to zero.
        target *= mask
        feature[:, :, self.target_joints] = target
        # Back confidence.
        if confs is not None:
            feature = np.concatenate([feature, confs], axis=0)

        data["feature"] = feature
        return data

    def __str__(self):
        return f"{self.__class__.__name__}:{self.__dict__}"

# Access check.
dataset_dir = Path("dataset_top10")
files = list(dataset_dir.iterdir())
dictionary = [fin for fin in files if ".json" in fin.name][0]
hdf5_files = [fin for fin in files if ".hdf5" in fin.name]

print(dictionary)
print(hdf5_files)

# Load dictionary.
with open(dictionary, "r") as fread:
    key2token = json.load(fread)

VOCAB = len(key2token)

_, use_landmarks = get_fullbody_landmarks()
use_features = ["x", "y"]
trans_select_feature = SelectLandmarksAndFeature(landmarks=use_landmarks, features=use_features)
trans_repnan = ReplaceNan()
trans_norm = PartsBasedNormalization(align_mode="framewise", scale_mode="unique")

apply_ratio = 0.5
scale_range = (1e-3, 1e-2)
scale_unit = "asis"
noise_type = "uniform"

pre_transforms = Compose([trans_select_feature,
                          trans_repnan,
                          trans_norm])

transforms_default = Compose([ToTensor()])

transforms_post_snoise_whole = Compose([
    RandomNoise(
        apply_ratio=apply_ratio,
        scale_range=scale_range,
        scale_unit=scale_unit,
        noise_type=noise_type,
        target_joints=np.arange(0, len(use_landmarks)),
        feature_dim=len(use_features),
        include_conf=False),
    ToTensor()])

batch_size = 2
feature_shape = (len(use_features), -1, len(use_landmarks))
token_shape = (1,)
merge_fn = partial(merge_padded_batch,
                   feature_shape=feature_shape,
                   token_shape=token_shape,
                   feature_padding_val=0.0,
                   token_padding_val=0)

dataset = HDF5Dataset(hdf5_files, pre_transforms=pre_transforms, transforms=transforms_post_snoise_whole)

dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=merge_fn)
try:
    data = next(iter(dataloader))
    feature_origin = data["feature"]

    print(feature_origin.shape)
except Exception as inst:
    print(inst)

# Define model.
# in_channels: J * C (130*2=260)
#   J: use_landmarks (130)
#   C: use_channels (2)
# out_channels: 10
in_channels = len(use_landmarks) * len(use_features)
inter_channels = 64
out_channels = VOCAB
activation = "relu"
tren_num_layers = 2
tren_num_heads = 2
tren_dim_ffw = 256
tren_dropout_pe = 0.1
tren_dropout = 0.1
tren_layer_norm_eps = 1e-5
tren_norm_first = True
tren_add_bias = True
tren_add_tailnorm = True

model = TransformerEnISLR(in_channels=in_channels,
                          inter_channels=inter_channels,
                          out_channels=out_channels,
                          activation=activation,
                          tren_num_layers=tren_num_layers,
                          tren_num_heads=tren_num_heads,
                          tren_dim_ffw=tren_dim_ffw,
                          tren_dropout_pe=tren_dropout_pe,
                          tren_dropout=tren_dropout,
                          tren_layer_norm_eps=tren_layer_norm_eps,
                          tren_norm_first=tren_norm_first,
                          tren_add_bias=tren_add_bias,
                          tren_add_tailnorm=tren_add_tailnorm)
print(model)

# Sanity check.
logit = model(feature_origin)
print(logit.shape)
attw0 = model.tr_encoder.layers[0].attw.detach().cpu().numpy()
attw1 = model.tr_encoder.layers[0].attw.detach().cpu().numpy()
print(attw0.shape, attw1.shape)

# Set common parameters.
batch_size = 32
load_into_ram = True
test_pid = 16069
num_workers = os.cpu_count()
print(f"Using {num_workers} cores for data loading.")
lr = 3e-4

epochs = 50
eval_every_n_epochs = 1
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} for computation.")

train_hdf5files = [fin for fin in hdf5_files if str(test_pid) not in fin.name]
val_hdf5files = [fin for fin in hdf5_files if str(test_pid) in fin.name]
test_hdf5files = [fin for fin in hdf5_files if str(test_pid) in fin.name]

_, use_landmarks = get_fullbody_landmarks()
use_features = ["x", "y"]

# Build dataloaders.
train_dataset = HDF5Dataset(train_hdf5files, pre_transforms=pre_transforms,
    transforms=transforms, load_into_ram=load_into_ram)
val_dataset = HDF5Dataset(val_hdf5files, pre_transforms=pre_transforms,
    transforms=transforms, load_into_ram=load_into_ram)
test_dataset = HDF5Dataset(test_hdf5files, pre_transforms=pre_transforms,
    transforms=transforms, load_into_ram=load_into_ram)

train_dataloader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=merge_fn, num_workers=num_workers, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, collate_fn=merge_fn, num_workers=num_workers, shuffle=False)
test_dataloader = DataLoader(test_dataset, batch_size=1, collate_fn=merge_fn, num_workers=num_workers, shuffle=False)

tren_norm_first = True
tren_add_tailnorm = True

model_default = TransformerEnISLR(
    in_channels=in_channels,
    inter_channels=inter_channels,
    out_channels=out_channels,
    activation=activation,
    tren_num_layers=tren_num_layers,
    tren_num_heads=tren_num_heads,
    tren_dim_ffw=tren_dim_ffw,
    tren_dropout_pe=tren_dropout_pe,
    tren_dropout=tren_dropout,
    tren_layer_norm_eps=tren_layer_norm_eps,
    tren_norm_first=tren_norm_first,
    tren_add_bias=tren_add_bias,
    tren_add_tailnorm=tren_add_tailnorm)
print(model_default)

loss_fn = nn.CrossEntropyLoss(reduction="mean")
optimizer = torch.optim.Adam(model_default.parameters(), lr=lr)

# Train, validation, and evaluation.
model_default.to(device)

train_losses = []
val_losses = []
test_accs = []
print("Start training.")
for epoch in range(epochs):
    print("-" * 80)
    print(f"Epoch {epoch+1}")

    train_losses = train_loop(train_dataloader, model_default, loss_fn, optimizer, device)
    val_loss = val_loop(val_dataloader, model_default, loss_fn, device)
    val_losses.append(val_loss)

    if (epoch+1) % eval_every_n_epochs == 0:
        acc = test_loop(test_dataloader, model_default, device)
        test_accs.append(acc)
train_losses_default = np.array(train_losses)
val_losses_default = np.array(val_losses)
test_accs_default = np.array(test_accs)

print(f"Minimum validation loss:{val_losses_default.min()} at {np.argmin(val_losses_default)+1} epoch.")
print(f"Maximum accuracy:{test_accs_default.max()} at {np.argmax(test_accs_default)*eval_every_n_epochs+1} epoch.")