# This script installs necessary packages for implementing mixed precision and half precision training and inference using Flax.

# Install flax-gpuprecision package
pip install flax-gpuprecision # corrected spelling of package name

# Install tensorflow_fp16 package
pip install tensorflow_fp16 # corrected spelling of package name

# Import necessary libraries
import jax.numpy as np # Importing numpy from jax library
from flax import linen_util, optax # Importing linen_util and optax from flax library
from flax.training import train_state # Importing train_state from flax.training library
from flax.training.checkpoints import restore_variables # Importing restore_variables from flax.training.checkpoints library
from flax.training.common_utils import convert_to_mixed_precision # Importing convert_to_mixed_precision from flax.training.common_utils library
from flax.training.train_loop import train as flax_train # Importing train function from flax.training.train_loop library
import tensorflow as tf # Importing tensorflow library
tf.config.set_soft_device_placement(True) # Setting soft device placement to True

# Define your model and loss function here...
def model(): # Defining model function
    # Code for defining the model goes here...
    return model # Returning the model

def loss(): # Defining loss function
    # Code for defining the loss function goes here...
    return loss # Returning the loss function

def main():
    # Load the training data and create a dataset iterator
    ds = ... # Loading the training data and creating a dataset iterator
    
    # Set up the optimizer and learning rate schedule
    opt = optax.adamw(learning_rate=1e-3, weight_decay=0.0) # Setting up the optimizer with a learning rate of 1e-3 and weight decay of 0.0
    lr_schedule = lambda step: 1e-4 * (step / 5000)**(-0.9) # Defining a learning rate schedule using lambda function
    
    # Initialize the training state and restore any pretrained variables
    init_params = ... # Initializing the training state with parameters
    params, _ = linen_util.unpack(init_params) # Unpacking the parameters
    train_state = train_state.create(apply_fn=model, params=params, tx=optax.chain(opt), metrics=metrics) # Creating the training state with model, parameters, optimizer and metrics
    restore_variables('checkpoint', train_state) # Restoring any pretrained variables from the checkpoint
    
    # Define the training loop and run it for a specified number of epochs
    def train():
        for batch in ds:
            # Code for training goes here...
            
            # Convert to mixed precision if using GPUs or TPUs
            with tf.device('/gpu:0' if FLAGS.use_gpu else '/cpu:0'), \
                     tf.contrib.tfprof.timer('train'):
                train_state = flax_train(step_fn=step, state=train_state) # Running the training loop using flax_train function
    
    # Run the training loop for a specified number of epochs and save checkpoints along the way
    num_epochs = ... # Defining the number of epochs
    for i in range(num_epochs):
        train() # Running the training loop
        if (i+1) % 5 == 0:
            # Save a checkpoint every 5 epochs
            ckpt_path = 'checkpoints/model.ckpt' # Defining the checkpoint path
            save(train_state, ckpt_path) # Saving the checkpoint
            
    # Load the final checkpoint and evaluate on the test data
    eval_ds = ... # Loading the test data
    
    def eval():
        for batch in eval_ds:
            # Code for evaluation goes here...
            
            # Convert to half precision if using GPUs or TPUs during inference
            with tf.device('/gpu:0' if FLAGS.use_gpu else '/cpu:0'), \
                     tf.contrib.tfprof.timer('eval'):
                eval(train_state) # Running the evaluation loop using eval function
    
    # Run the evaluation loop and print out the results
    for i in range(num_epochs):
        eval() # Running the evaluation loop
        if (i+1) % 5 == 0:
            # Evaluate on the test data every 5 epochs
            metrics = ... # Calculating the metrics
            print('Epoch {}:'.format(i+1)) # Printing the epoch number
            for name, value in metrics.items():
                print('\t{}: {:.4f}'.format(name, value)) # Printing the metrics
            
    # Clean up any temporary files and exit gracefully
    tf.io.gfile.delete('/tmp/flax_train*') # Deleting temporary files
    
# Run the main function if this script is being executed directly (not imported as a module)
if __name__ == '__main__':
    FLAGS = tf.app.flags.FLAGS # Defining the flags
    # Define any command-line arguments here...
    # Code for defining command-line arguments goes here...