# Import the necessary libraries
import pandas as pd # Importing pandas library to work with dataframes
from transformers import AutoTokenizer # Importing AutoTokenizer from transformers library to tokenize text data
from flax_bart import BartForSequenceClassification # Importing BartForSequenceClassification from flax_bart library to classify text data

# Load the dataset (in this case, a CSV file)
df = pd.read_csv('sentiment-data.csv') # Reading the CSV file and storing it in a dataframe called 'df'

# Define the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") # Initializing the tokenizer with the pre-trained model 'facebook/bart-large'
model = BartForSequenceClassification.from_pretrained("facebook/bart-large") # Initializing the model with the pre-trained weights from 'facebook/bart-large'

# Preprocess the text data (i.e., convert it into a list of tokenized sentences)
def preprocess(text):
    # Tokenize each sentence using the FlaxBartDecoderLayerCollection tokenizer
    tokens = tokenizer(text, return_tensors="np")['input_ids'] # tokenize the text using the FlaxBartDecoderLayerCollection tokenizer and store the input ids in a variable called tokens
    
    # Convert the input ids to a numpy array for faster processing
    input_ids = np.array(tokens["input_ids"]) # convert the input ids to a numpy array and store it in a variable called input_ids
    
    # Return the preprocessed data as a tuple (input_ids, labels)
    return (input_ids, df['sentiment'].iloc[i]) # return the preprocessed data as a tuple containing the input ids and the sentiment label for the corresponding text

# Train the model on the preprocessed data
def train(train_data):
    # Define the training loop (i.e., how many epochs to run and what loss function to use)
    def train_step(batch, params):
        # Unpack the batch data into input ids and labels
        input_ids = jnp.array(batch['input_ids']) # convert input ids to a jax array
        label = jnp.array(batch['labels']) # convert labels to a jax array
        
        # Run the model forward (i.e., feed it the input ids)
        output, _ = model.apply({'params': params}, input_ids=input_ids) # apply the model to the input ids
        
        # Calculate the loss function (i.e., how well our predictions match the true labels)
        loss = jnp.mean(jnp.square(output - label)) # calculate the mean squared error between the output and labels
        
        # Return the loss value and any other relevant information (in this case, just the input ids for debugging purposes)
        return (loss, {'input_ids': input_ids})
    
    # Define the training loop parameters (i.e., how many epochs to run and what learning rate to use)
    params = model.init(context=jax.devices()[0]) # initialize the model's parameters
    optimizer = jax.optimizers.adam(learning_rate=1e-5, eps=1e-8) # define the optimizer with a learning rate and epsilon
    
    # Run the training loop for 3 epochs (i.e., iterate over the data three times and update our model's parameters each time)
    for i in range(3):
        train_loss = jax.lax.scan(train_step, params, train_data['input_ids'], train_data['labels'])[0] # use jax.lax.scan to iterate over the data and update the model's parameters
        
        # Print the loss value after each epoch (i.e., how well our model is doing)
        print("Epoch {}: Loss = {:.4f}".format(i+1, jnp.mean(train_loss)))
        
        # Update the model's parameters using the optimizer (i.e., adjust them based on the loss value and any other relevant information)
        grads, _ = jax.jit(jax.grad(train_step))(params, train_data['input_ids'], train_data['labels']) # calculate the gradients of the loss function with respect to the model's parameters
        updates, _ = optimizer.update(grads, params) # use the optimizer to update the model's parameters
        
        # Apply the parameter updates to our model (i.e., update its internal state based on the new values of its parameters)
        params = jax.lax.map(lambda p, u: p + u, params, updates) # use jax.lax.map to update the model's parameters with the calculated updates