# Transformers installation
! pip install transformers datasets evaluate accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git

#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/1JvfrvZgi6c?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

from huggingface_hub import notebook_login

notebook_login()

from datasets import load_dataset

books = load_dataset("opus_books", "en-fr")

books = books["train"].train_test_split(test_size=0.2)

books["train"][0]

#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/XAR8jnZZuUs?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

from transformers import AutoTokenizer

checkpoint = "google-t5/t5-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

source_lang = "en"
target_lang = "fr"
prefix = "translate English to French: "

def preprocess_function(examples):
    inputs = [prefix + example[source_lang] for example in examples["translation"]]
    targets = [example[target_lang] for example in examples["translation"]]
    model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
    return model_inputs

tokenized_books = books.map(preprocess_function, batched=True)

from transformers import DataCollatorForSeq2Seq

data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)

from transformers import DataCollatorForSeq2Seq

data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf")

import evaluate

metric = evaluate.load("sacrebleu")

import numpy as np

def postprocess_text(preds, labels):
    preds = [pred.strip() for pred in preds]
    labels = [[label.strip()] for label in labels]

    return preds, labels

def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)

    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

    result = metric.compute(predictions=decoded_preds, references=decoded_labels)
    result = {"bleu": result["score"]}

    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
    result["gen_len"] = np.mean(prediction_lens)
    result = {k: round(v, 4) for k, v in result.items()}
    return result

from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer

model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

training_args = Seq2SeqTrainingArguments(
    output_dir="my_awesome_opus_books_model",
    eval_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=2,
    predict_with_generate=True,
    fp16=True, #change to bf16=True for XPU
    push_to_hub=True,
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_books["train"],
    eval_dataset=tokenized_books["test"],
    processing_class=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

trainer.train()

trainer.push_to_hub()

from transformers import AdamWeightDecay

optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

from transformers import TFAutoModelForSeq2SeqLM

model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)

tf_train_set = model.prepare_tf_dataset(
    tokenized_books["train"],
    shuffle=True,
    batch_size=16,
    collate_fn=data_collator,
)

tf_test_set = model.prepare_tf_dataset(
    tokenized_books["test"],
    shuffle=False,
    batch_size=16,
    collate_fn=data_collator,
)

import tensorflow as tf

model.compile(optimizer=optimizer)  # No loss argument!

from transformers.keras_callbacks import KerasMetricCallback

metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_test_set)

from transformers.keras_callbacks import PushToHubCallback

push_to_hub_callback = PushToHubCallback(
    output_dir="my_awesome_opus_books_model",
    tokenizer=tokenizer,
)

callbacks = [metric_callback, push_to_hub_callback]

model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=callbacks)

text = "translate English to French: Legumes share resources with nitrogen-fixing bacteria."

from transformers import pipeline

translator = pipeline("translation_xx_to_yy", model="username/my_awesome_opus_books_model")
translator(text)

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_opus_books_model")
inputs = tokenizer(text, return_tensors="pt").input_ids

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("username/my_awesome_opus_books_model")
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)

tokenizer.decode(outputs[0], skip_special_tokens=True)

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_opus_books_model")
inputs = tokenizer(text, return_tensors="tf").input_ids

from transformers import TFAutoModelForSeq2SeqLM

model = TFAutoModelForSeq2SeqLM.from_pretrained("username/my_awesome_opus_books_model")
outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)

tokenizer.decode(outputs[0], skip_special_tokens=True)