import datetime
import uuid

import numpy as np
import pandas as pd
import tensorflow as tf
import os
import sys
import subprocess

from tensorflow.keras import datasets, layers, models
from sklearn.model_selection import train_test_split


! pip install -q tensorflow-cloud
import tensorflow_cloud as tfc

tf.version.VERSION

'2.6.0'

# Set Google Cloud Specific parameters

# TODO: Please set GCP_PROJECT_ID to your own Google Cloud project ID.
GCP_PROJECT_ID = 'YOUR_PROJECT_ID' 

# TODO: Change the Service Account Name to your own Service Account
SERVICE_ACCOUNT_NAME = 'YOUR_SERVICE_ACCOUNT_NAME'
SERVICE_ACCOUNT = f'{SERVICE_ACCOUNT_NAME}@{GCP_PROJECT_ID}.iam.gserviceaccount.com'

# TODO: set GCS_BUCKET to your own Google Cloud Storage (GCS) bucket.
GCS_BUCKET = 'YOUR_GCS_BUCKET_NAME'

# DO NOT CHANGE: Currently only the 'us-central1' region is supported.
REGION = 'us-central1'

# Set Tuning Specific parameters

# OPTIONAL: You can change the job name to any string.
JOB_NAME = 'cifar10'

# OPTIONAL:  Set Number of concurrent tuning jobs that you would like to run.
NUM_JOBS = 5

# TODO: Set the study ID for this run. Study_ID can be any unique string.
# Reusing the same Study_ID will cause the Tuner to continue tuning the
# Same Study parameters. This can be used to continue on a terminated job,
# or load stats from a previous study.
STUDY_NUMBER = '00001'
STUDY_ID = f'{GCP_PROJECT_ID}_{JOB_NAME}_{STUDY_NUMBER}'

# Setting location were training logs and checkpoints will be stored
GCS_BASE_PATH = f'gs://{GCS_BUCKET}/{JOB_NAME}/{STUDY_ID}'
TENSORBOARD_LOGS_DIR = os.path.join(GCS_BASE_PATH,"logs")

# Using tfc.remote() to ensure this code only runs in notebook
if not tfc.remote():

    # Authentication for Kaggle Notebooks
    if "kaggle_secrets" in sys.modules:
        from kaggle_secrets import UserSecretsClient
        UserSecretsClient().set_gcloud_credentials(project=GCP_PROJECT_ID)

    # Authentication for Colab Notebooks
    if "google.colab" in sys.modules:
        from google.colab import auth
        auth.authenticate_user()
        os.environ["GOOGLE_CLOUD_PROJECT"] = GCP_PROJECT_ID

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

# Setting input specific parameters
# The model expects input of dimensions (INPUT_IMG_SIZE, INPUT_IMG_SIZE, 3)
INPUT_IMG_SIZE = 32
NUM_CLASSES = 10

import kerastuner
from tensorflow.keras import layers

# Configure the search space
HPS = kerastuner.engine.hyperparameters.HyperParameters()

HPS.Int('conv_blocks', 3, 5, default=3)
for i in range(5):
    HPS.Int('filters_' + str(i), 32, 256, step=32)
    HPS.Choice('pooling_' + str(i), ['avg', 'max'])
HPS.Int('hidden_size', 30, 100, step=10, default=50)
HPS.Float('dropout', 0, 0.5, step=0.1, default=0.5)
HPS.Float('learning_rate', 1e-4, 1e-2, sampling='log')

def build_model(hp):
    inputs = tf.keras.Input(shape=(INPUT_IMG_SIZE, INPUT_IMG_SIZE, 3))
    x = inputs
    for i in range(hp.get('conv_blocks')):
        filters = hp.get('filters_'+ str(i))
        for _ in range(2):
            x = layers.Conv2D(
              filters, kernel_size=(3, 3), padding='same')(x)
            x = layers.BatchNormalization()(x)
            x = layers.ReLU()(x)
        if hp.get('pooling_' + str(i)) == 'max':
            x = layers.MaxPool2D()(x)
        else:
            x = layers.AvgPool2D()(x)
    x = layers.GlobalAvgPool2D()(x)
    x = layers.Dense(hp.get('hidden_size'),
      activation='relu')(x)
    x = layers.Dropout(hp.get('dropout'))(x)
    outputs = layers.Dense(NUM_CLASSES, activation='softmax')(x)

    model = tf.keras.Model(inputs, outputs)
    model.compile(
      optimizer=tf.keras.optimizers.Adam(
        hp.get('learning_rate')),
      loss='sparse_categorical_crossentropy',
      metrics=['accuracy'])
    return model

from tensorflow_cloud import CloudTuner

distribution_strategy = None
if not tfc.remote():
    # Using MirroredStrategy to use a single instance with multiple GPUs
    # during remote execution while using no strategy for local.
    distribution_strategy = tf.distribute.MirroredStrategy()

tuner = CloudTuner(
    build_model,
    project_id=GCP_PROJECT_ID,
    project_name= JOB_NAME,
    region=REGION,
    objective='accuracy',
    hyperparameters=HPS,
    max_trials=100,
    directory=GCS_BASE_PATH,
    study_id=STUDY_ID,
    overwrite=True,
    distribution_strategy=distribution_strategy)

# Configure Tensorboard logs
callbacks=[
    tf.keras.callbacks.TensorBoard(log_dir=TENSORBOARD_LOGS_DIR)]

# Setting to run tuning remotely, you can run tuner locally to validate it works first.
if tfc.remote():
    tuner.search(x=x_train, y=y_train, epochs=30, validation_split=0.2, callbacks=callbacks)

# You can uncomment the code below to run the tuner.search() locally to validate
# everything works before submitting the job to Cloud. Stop the job manually
# after one epoch.

# else:
#     tuner.search(x=x_train, y=y_train, epochs=1, validation_split=0.2, callbacks=callbacks)

# If you are using a custom image you can install modules via requirements txt file.
with open('requirements.txt','w') as f:
    f.write('pandas==1.1.5\n')
    f.write('numpy==1.18.5\n')
    f.write('tensorflow-cloud\n')
    f.write('keras-tuner\n')

# Optional: Some recommended base images. If you provide none the system will choose one for you.
TF_GPU_IMAGE= "tensorflow/tensorflow:latest-gpu"
TF_CPU_IMAGE= "tensorflow/tensorflow:latest"


tfc.run_cloudtuner(
    distribution_strategy='auto',
    requirements_txt='requirements.txt',
    docker_config=tfc.DockerConfig(
        parent_image=TF_GPU_IMAGE,
        image_build_bucket=GCS_BUCKET
        ),
    chief_config=tfc.COMMON_MACHINE_CONFIGS['K80_4X'],
    job_labels={'job': JOB_NAME},
    service_account=SERVICE_ACCOUNT,
    num_jobs=NUM_JOBS
)

%load_ext tensorboard
%tensorboard --logdir $TENSORBOARD_LOGS_DIR

if not tfc.remote():
    tuner.results_summary(1)
    best_model = tuner.get_best_models(1)[0]
    best_hyperparameters = tuner.get_best_hyperparameters(1)[0]

    # References to best trial assets
    best_trial_id = tuner.oracle.get_best_trials(1)[0].trial_id
    best_trial_dir = tuner.get_trial_dir(best_trial_id)