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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
This script tests to ensure that `accelerate` performs at the same level as raw `MS-AMP`.

This particular script verifies this for single GPU training.
"""

import evaluate
import msamp
import torch
from fp8_utils import evaluate_model, get_training_utilities

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.utils import FP8RecipeKwargs, get_grad_scaler, set_seed


MODEL_NAME = "bert-base-cased"
METRIC = evaluate.load("glue", "mrpc")


def train_baseline(opt_level="O2"):
    set_seed(42)
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)

    model, optimizer = msamp.initialize(model, optimizer, opt_level=opt_level)
    model.to("cuda")

    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
    model.train()
    scaler = get_grad_scaler()

    for batch in train_dataloader:
        batch = batch.to("cuda")
        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
            outputs = model(**batch)
        loss = outputs.loss
        loss = scaler.scale(loss)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        lr_scheduler.step()

    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)

    assert (
        trained_model_results["accuracy"] > base_model_results["accuracy"]
    ), f'Accuracy should be higher for the trained model: {trained_model_results["accuracy"]} > {base_model_results["accuracy"]}'
    assert (
        trained_model_results["f1"] > base_model_results["f1"]
    ), f'F1 score should be higher for the trained model: {trained_model_results["f1"]} > {base_model_results["f1"]}'

    return base_model_results, trained_model_results


def train_integration(opt_level="O2"):
    kwargs_handlers = [FP8RecipeKwargs(backend="msamp", opt_level=opt_level)]
    AcceleratorState()._reset_state(True)
    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
    set_seed(42)
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )

    model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
    model.train()

    for batch in train_dataloader:
        outputs = model(**batch)
        loss = outputs.loss
        accelerator.backward(loss)
        optimizer.step()
        optimizer.zero_grad()
        lr_scheduler.step()

    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)

    assert (
        trained_model_results["accuracy"] > base_model_results["accuracy"]
    ), f'Accuracy should be higher for the trained model: {trained_model_results["accuracy"]} > {base_model_results["accuracy"]}'
    assert (
        trained_model_results["f1"] > base_model_results["f1"]
    ), f'F1 score should be higher for the trained model: {trained_model_results["f1"]} > {base_model_results["f1"]}'

    return base_model_results, trained_model_results


if __name__ == "__main__":
    for opt_level in ["O1", "O2"]:
        baseline_not_trained, baseline_trained = train_baseline(opt_level)
        accelerator_not_trained, accelerator_trained = train_integration(opt_level)

        assert (
            baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"]
        ), f'Accuracy should be the same for the baseline and accelerator: {baseline_not_trained["accuracy"]} == {accelerator_not_trained["accuracy"]}'
        assert (
            baseline_not_trained["f1"] == accelerator_not_trained["f1"]
        ), f'F1 score should be the same for the baseline and accelerator: {baseline_not_trained["f1"]} == {accelerator_not_trained["f1"]}'
        assert (
            baseline_trained["accuracy"] == accelerator_trained["accuracy"]
        ), f'Accuracy should be the same for the baseline and accelerator: {baseline_trained["accuracy"]} == {accelerator_trained["accuracy"]}'
        assert (
            baseline_trained["f1"] == accelerator_trained["f1"]
        ), f'F1 score should be the same for the baseline and accelerator: {baseline_trained["f1"]} == {accelerator_trained["f1"]}'
