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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 `torchao`.

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

from functools import partial

import evaluate
import torch
from fp8_utils import get_training_utilities
from torchao.float8 import convert_to_float8_training

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.utils import AORecipeKwargs, set_seed


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


def evaluate_model(model, dataloader, metric, accelerator=None):
    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
    model.eval()
    for step, batch in enumerate(dataloader):
        with torch.no_grad():
            outputs = model(**batch)
        predictions = outputs.logits.argmax(dim=-1)
        references = batch["labels"]
        if accelerator is not None and accelerator.num_processes > 1:
            predictions, references = accelerator.gather_for_metrics((predictions, references))
        metric.add_batch(predictions=predictions, references=references)
    return metric.compute()


def filter_linear_layers(module, fqn, first_layer_name=None, last_layer_name=None):
    if isinstance(module, torch.nn.Linear):
        if module.in_features % 16 != 0 or module.out_features % 16 != 0:
            return False
    # For stability reasons, we skip the first and last linear layers
    # Otherwise can lead to the model not training or converging properly
    if fqn in (first_layer_name, last_layer_name):
        return False
    return True


def train_baseline():
    set_seed(42)
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
    first_linear = None
    last_linear = None
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            if first_linear is None:
                first_linear = name
            last_linear = name

    func = partial(filter_linear_layers, first_layer_name=first_linear, last_layer_name=last_linear)
    model.to("cuda")
    convert_to_float8_training(model, module_filter_fn=func)
    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
    model.train()

    for batch in train_dataloader:
        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
            outputs = model(**batch)
            loss = outputs.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():
    set_seed(42)
    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=[AORecipeKwargs()])
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
        MODEL_NAME, accelerator=accelerator
    )
    model = accelerator.prepare(model)
    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
    model.train()

    for batch in train_dataloader:
        outputs = model(**batch)
        loss = outputs.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


if __name__ == "__main__":
    baseline_not_trained, baseline_trained = train_baseline()
    AcceleratorState._reset_state(True)
    accelerator_not_trained, accelerator_trained = train_integration()
    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']}"
    )